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Wen J, Sun Q, Huang L, Zhou L, Doyle MF, Ekunwe L, Durda P, Olson NC, Reiner AP, Li Y, Raffield LM. Gene expression and splicing QTL analysis of blood cells in African American participants from the Jackson Heart Study. Genetics 2024:iyae098. [PMID: 39056362 DOI: 10.1093/genetics/iyae098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 06/05/2024] [Indexed: 07/28/2024] Open
Abstract
Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analyses using TOPMed whole-genome sequencing-derived genotype data and RNA-sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate of 5%, we identified 17,630 unique eQTL credible sets covering 16,538 unique genes; and 24,525 unique sQTL credible sets covering 9,605 unique genes, with lead QTL at P < 5e-8. About 24% of independent eQTLs and independent sQTLs with a minor allele frequency > 1% in JHS were rare (minor allele frequency < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results.
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Affiliation(s)
- Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Le Huang
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Lingbo Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Margaret F Doyle
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Lynette Ekunwe
- Department of Medicine, University of MS Medical Center (UMMC), Jackson, MS 39213, USA
| | - Peter Durda
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Nels C Olson
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research, Seattle, WA 98109, USA
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
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Revathi Venkateswaran V, She R, Gui H, Luzum JA, Bryson TD, Malouf ZE, Williams LK, Sabbah HN, Gardell SJ, Lanfear DE. Genetic drivers of human plasma metabolites that determine mortality in heart failure patients with reduced ejection fraction. Front Cardiovasc Med 2024; 11:1409340. [PMID: 39045004 PMCID: PMC11263106 DOI: 10.3389/fcvm.2024.1409340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 06/20/2024] [Indexed: 07/25/2024] Open
Abstract
Background Heart failure with reduced ejection fraction (HFrEF) remains a significant public health issue, with the disease advancing despite neurohormonal antagonism. Energetic dysfunction is a likely contributor to residual disease progression, and we have previously reported a strong association of plasma metabolite profiles with survival among patients with HFrEF. However, the genetic and biologic mechanisms that underlie the metabolite-survival association in HFrEF were uncertain. Methods and results We performed genetic mapping of the key metabolite parameters, followed by mediation analyses of metabolites and genotypes on survival, and genetic pathway analyses. Patients with HFrEF (n = 1,003) in the Henry Ford Pharmacogenomic Registry (HFPGR; 500 self-reported Black/African race patients [AA], 503 self-reported White/European race patients [EA], and 249 deaths over a median of 2.7 years) with genome-wide genotyping and targeted metabolomic profiling of plasma were included. We tested genome-wide association (GWA) of single nucleotide polymorphisms (SNPs) with the prognostic metabolite profile (PMP) and its components; first stratified by race, and then combined via meta-analysis for the entire cohort. Seven independent loci were identified as GWA significant hits in AA patients (3 for PMP and 4 for individual metabolites), one of which was also significant in the entire cohort (rs944469). No genome wide significant hits were found in White/EA patients. Among these SNPs, only rs35792152, (a hit for 3.HBA) tended to be associated with mortality in standard survival analysis (HR = 1.436, p = 0.052). The mediation analyses indicated several significant associations between SNPs, metabolites, and mortality in AA patients. Functional annotation mapping (FUMA) implicated inflammation, DNA metabolic, and mRNA splicing processes. Conclusions GWAS of key metabolites and survival along with FUMA pathway analysis revealed new candidate genes which unveiled molecular pathways that contribute to HF disease progression via metabolic and energetic abnormalities.
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Affiliation(s)
| | - Ruicong She
- Department of Public Health Science, Henry Ford Health, Detroit, MI, United States
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, United States
| | - Jasmine A. Luzum
- Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, United States
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI, United States
| | - Timothy D. Bryson
- Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, United States
| | - Zack E. Malouf
- Cardiovascular Division, Department of Medicine, Henry Ford Hospital, Detroit, MI, United States
| | - L. Keoki Williams
- Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, United States
| | - Hani N. Sabbah
- Cardiovascular Division, Department of Medicine, Henry Ford Hospital, Detroit, MI, United States
| | - Stephen J. Gardell
- Translational Research Institute, Advent Health, Orlando, FL, United States
| | - David E. Lanfear
- Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, United States
- Cardiovascular Division, Department of Medicine, Henry Ford Hospital, Detroit, MI, United States
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Wang YZ, Zhao W, Moorjani P, Gross AL, Zhou X, Dey AB, Lee J, Smith JA, Kardia SLR. Effect of apolipoprotein E ε4 and its modification by sociodemographic characteristics on cognitive measures in South Asians from LASI-DAD. Alzheimers Dement 2024; 20:4854-4867. [PMID: 38889280 PMCID: PMC11247697 DOI: 10.1002/alz.14052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND We investigated the effects of apolipoprotein E (APOE) ε4 and its interactions with sociodemographic characteristics on cognitive measures in South Asians from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD). METHODS Linear regression was used to assess the association between APOE ε4 and global- and domain-specific cognitive function in 2563 participants (mean age 69.6 ± 7.3 years; 53% female). Effect modification by age, sex, and education were explored using interaction terms and subgroup analyses. RESULTS APOE ε4 was inversely associated with most cognitive measures (p < 0.05). This association was stronger with advancing age for the Hindi Mental State Examination (HMSE) score (βε4×age = -0.44, p = 0.03), orientation (βε4×age = -0.07, p = 0.01), and language/fluency (βε4×age = -0.07, p = 0.01), as well as in females for memory (βε4×male = 0.17, p = 0.02) and language/fluency (βε4×male = 0.12, p = 0.03). DISCUSSION APOE ε4 is associated with lower cognitive function in South Asians from India, with a more pronounced impact observed in females and older individuals. HIGHLIGHTS APOE ε4 carriers had lower global and domain-specific cognitive performance. Females and older individuals may be more susceptible to ε4 effects. For most cognitive measures, there was no interaction between ε4 and education.
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Affiliation(s)
- Yi Zhe Wang
- Department of EpidemiologySchool of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | - Wei Zhao
- Department of EpidemiologySchool of Public HealthUniversity of MichiganAnn ArborMichiganUSA
- Survey Research CenterInstitute for Social ResearchUniversity of MichiganAnn ArborMichiganUSA
| | - Priya Moorjani
- Department of Molecular and Cell BiologyUniversity of CaliforniaBerkeleyCaliforniaUSA
- Center for Computational BiologyUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Alden L. Gross
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Xiang Zhou
- Department of BiostatisticsSchool of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | - Aparajit B. Dey
- Department of Geriatric MedicineAll India Institute of Medical Sciences, Ansari NagarNew DelhiIndia
| | - Jinkook Lee
- Department of Economics and Center for Social ResearchUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Jennifer A. Smith
- Department of EpidemiologySchool of Public HealthUniversity of MichiganAnn ArborMichiganUSA
- Survey Research CenterInstitute for Social ResearchUniversity of MichiganAnn ArborMichiganUSA
| | - Sharon L. R. Kardia
- Department of EpidemiologySchool of Public HealthUniversity of MichiganAnn ArborMichiganUSA
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Legge SE, Pardiñas AF, Woolway G, Rees E, Cardno AG, Escott-Price V, Holmans P, Kirov G, Owen MJ, O’Donovan MC, Walters JTR. Genetic and Phenotypic Features of Schizophrenia in the UK Biobank. JAMA Psychiatry 2024; 81:681-690. [PMID: 38536179 PMCID: PMC10974692 DOI: 10.1001/jamapsychiatry.2024.0200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/07/2024] [Indexed: 04/04/2024]
Abstract
Importance Large-scale biobanks provide important opportunities for mental health research, but selection biases raise questions regarding the comparability of individuals with those in clinical research settings. Objective To compare the genetic liability to psychiatric disorders in individuals with schizophrenia in the UK Biobank with individuals in the Psychiatric Genomics Consortium (PGC) and to compare genetic liability and phenotypic features with participants recruited from clinical settings. Design, Setting, and Participants This cross-sectional study included participants from the population-based UK Biobank and schizophrenia samples recruited from clinical settings (CLOZUK, CardiffCOGS, Cardiff F-Series, and Cardiff Affected Sib-Pairs). Data were collected between January 1993 and July 2021. Data analysis was conducted between July 2021 and June 2023. Main Outcomes and Measures A genome-wide association study of UK Biobank schizophrenia case-control status was conducted, and the results were compared with those from the PGC via genetic correlations. To test for differences with the clinical samples, polygenic risk scores (PRS) were calculated for schizophrenia, bipolar disorder, depression, and intelligence using PRS-CS. PRS and phenotypic comparisons were conducted using pairwise logistic regressions. The proportions of individuals with copy number variants associated with schizophrenia were compared using Firth logistic regression. Results The sample of 517 375 participants included 1438 UK Biobank participants with schizophrenia (550 [38.2%] female; mean [SD] age, 54.7 [8.3] years), 499 475 UK Biobank controls (271 884 [54.4%] female; mean [SD] age, 56.5 [8.1] years), and 4 schizophrenia research samples (4758 [28.9%] female; mean [SD] age, 38.2 [21.0] years). Liability to schizophrenia in UK Biobank was highly correlated with the latest genome-wide association study from the PGC (genetic correlation, 0.98; SE, 0.18) and showed the expected patterns of correlations with other psychiatric disorders. The schizophrenia PRS explained 6.8% of the variance in liability for schizophrenia case status in UK Biobank. UK Biobank participants with schizophrenia had significantly lower schizophrenia PRS than 3 of the clinically ascertained samples and significantly lower rates of schizophrenia-associated copy number variants than the CLOZUK sample. UK Biobank participants with schizophrenia had higher educational attainment and employment rates than the clinically ascertained schizophrenia samples, lower rates of smoking, and a later age of onset of psychosis. Conclusions and Relevance Individuals with schizophrenia in the UK Biobank, and likely other volunteer-based biobanks, represent those less severely affected. Their inclusion in wider studies should enhance the representation of the full spectrum of illness severity.
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Affiliation(s)
- Sophie E. Legge
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Antonio F. Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Grace Woolway
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Elliott Rees
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Alastair G. Cardno
- Leeds Institute of Health Sciences, Division of Psychological and Social Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom
| | - Valentina Escott-Price
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Peter Holmans
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - George Kirov
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Michael J. Owen
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Michael C. O’Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - James T. R. Walters
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
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Dunca D, Chopade S, Gordillo-Marañón M, Hingorani AD, Kuchenbaecker K, Finan C, Schmidt AF. Comparing the effects of CETP in East Asian and European ancestries: a Mendelian randomization study. Nat Commun 2024; 15:5302. [PMID: 38906890 PMCID: PMC11192935 DOI: 10.1038/s41467-024-49109-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 05/24/2024] [Indexed: 06/23/2024] Open
Abstract
CETP inhibitors are a class of lipid-lowering drugs in development for treatment of coronary heart disease (CHD). Genetic studies in East Asian ancestry have interpreted the lack of CETP signal with low-density lipoprotein cholesterol (LDL-C) and lack of drug target Mendelian randomization (MR) effect on CHD as evidence that CETP inhibitors might not be effective in East Asian participants. Capitalizing on recent increases in sample size of East Asian genetic studies, we conducted a drug target MR analysis, scaled to a standard deviation increase in high-density lipoprotein cholesterol. Despite finding evidence for possible neutral effects of lower CETP levels on LDL-C, systolic blood pressure and pulse pressure in East Asians (interaction p-values < 1.6 × 10-3), effects on cardiovascular outcomes were similarly protective in both ancestry groups. In conclusion, on-target inhibition of CETP is anticipated to decrease cardiovascular disease in individuals of both European and East Asian ancestries.
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Affiliation(s)
- Diana Dunca
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom.
- UCL Genetics Institute, University College London, London, UK.
| | - Sandesh Chopade
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - María Gordillo-Marañón
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
- UCL British Heart Foundation Research Accelerator, London, UK
- Health Data Research UK, London, UK
| | - Karoline Kuchenbaecker
- UCL Genetics Institute, University College London, London, UK
- Division of Psychiatry, University College London, London, UK
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
- UCL British Heart Foundation Research Accelerator, London, UK
- Health Data Research UK, London, UK
| | - Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
- UCL British Heart Foundation Research Accelerator, London, UK
- Department of Cardiology, Amsterdam UMC Heart Center, Amsterdam, The Netherlands
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Li M, Song Z, Gurinovich A, Schork N, Sebastiani P, Monti S. yQTL Pipeline: A structured computational workflow for large scale quantitative trait loci discovery and downstream visualization. PLoS One 2024; 19:e0298501. [PMID: 38833463 PMCID: PMC11149833 DOI: 10.1371/journal.pone.0298501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 05/06/2024] [Indexed: 06/06/2024] Open
Abstract
Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline.
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Affiliation(s)
- Mengze Li
- Bioinformatics Program, Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, United States of America
- Section of Computational Biomedicine, School of Medicine, Boston University, Boston, Massachusetts, United States of America
| | - Zeyuan Song
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, United States of America
| | - Anastasia Gurinovich
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, United States of America
- Department of Medicine, School of Medicine, Tufts University, Boston, Massachusetts, United States of America
| | - Nicholas Schork
- Quantitative Medicine and Systems Biology, The Translational Genomics Research Institute, Phoenix, Arizona, United States of America
| | - Paola Sebastiani
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, United States of America
- Department of Medicine, School of Medicine, Tufts University, Boston, Massachusetts, United States of America
- Data Intensive Study Center, Tufts University, Boston, Massachusetts, United States of America
| | - Stefano Monti
- Bioinformatics Program, Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, United States of America
- Section of Computational Biomedicine, School of Medicine, Boston University, Boston, Massachusetts, United States of America
- Department of Biostatistics, School of Public Health, Boston University, Boston, Massachusetts, United States of America
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Smith DM, Parekh P, Kennedy J, Loughnan R, Frei O, Nichols TE, Andreassen OA, Jernigan TL, Dale AM. Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components. Cereb Cortex 2024; 34:bhae234. [PMID: 38850213 PMCID: PMC11161865 DOI: 10.1093/cercor/bhae234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 05/09/2024] [Accepted: 05/19/2024] [Indexed: 06/10/2024] Open
Abstract
The relative contributions of genetic variation and experience in shaping the morphology of the adolescent brain are not fully understood. Using longitudinal data from 11,665 subjects in the ABCD Study, we fit vertex-wise variance components including family effects, genetic effects, and subject-level effects using a computationally efficient framework. Variance in cortical thickness and surface area is largely attributable to genetic influence, whereas sulcal depth is primarily explained by subject-level effects. Our results identify areas with heterogeneous distributions of heritability estimates that have not been seen in previous work using data from cortical regions. We discuss the biological importance of subject-specific variance and its implications for environmental influences on cortical development and maturation.
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Affiliation(s)
- Diana M Smith
- Medical Scientist Training Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Neurosciences Graduate Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Problemveien 11, 0313 Oslo, Norway
| | - Joseph Kennedy
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Robert Loughnan
- Population Neuroscience and Genetics Lab, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Problemveien 11, 0313 Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Problemveien 11, 0313 Oslo, Norway
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7FZ, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Problemveien 11, 0313 Oslo, Norway
| | - Terry L Jernigan
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Cognitive Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Cognitive Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Neuroscience, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
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8
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Blackburn GS, Keeling CI, Prunier J, Keena MA, Béliveau C, Hamelin R, Havill NP, Hebert FO, Levesque RC, Cusson M, Porth I. Genetics of flight in spongy moths (Lymantria dispar ssp.): functionally integrated profiling of a complex invasive trait. BMC Genomics 2024; 25:541. [PMID: 38822259 PMCID: PMC11140922 DOI: 10.1186/s12864-023-09936-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 12/22/2023] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND Flight can drastically enhance dispersal capacity and is a key trait defining the potential of exotic insect species to spread and invade new habitats. The phytophagous European spongy moths (ESM, Lymantria dispar dispar) and Asian spongy moths (ASM; a multi-species group represented here by L. d. asiatica and L. d. japonica), are globally invasive species that vary in adult female flight capability-female ASM are typically flight capable, whereas female ESM are typically flightless. Genetic markers of flight capability would supply a powerful tool for flight profiling of these species at any intercepted life stage. To assess the functional complexity of spongy moth flight and to identify potential markers of flight capability, we used multiple genetic approaches aimed at capturing complementary signals of putative flight-relevant genetic divergence between ESM and ASM: reduced representation genome-wide association studies, whole genome sequence comparisons, and developmental transcriptomics. We then judged the candidacy of flight-associated genes through functional analyses aimed at addressing the proximate demands of flight and salient features of the ecological context of spongy moth flight evolution. RESULTS Candidate gene sets were typically non-overlapping across different genetic approaches, with only nine gene annotations shared between any pair of approaches. We detected an array of flight-relevant functional themes across gene sets that collectively suggest divergence in flight capability between European and Asian spongy moth lineages has coincided with evolutionary differentiation in multiple aspects of flight development, execution, and surrounding life history. Overall, our results indicate that spongy moth flight evolution has shaped or been influenced by a large and functionally broad network of traits. CONCLUSIONS Our study identified a suite of flight-associated genes in spongy moths suited to exploration of the genetic architecture and evolution of flight, or validation for flight profiling purposes. This work illustrates how complementary genetic approaches combined with phenotypically targeted functional analyses can help to characterize genetically complex traits.
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Affiliation(s)
- Gwylim S Blackburn
- Natural Resources Canada, Pacific Forestry Centre, Canadian Forest Service, 506 Burnside Road West, Victoria, BC, V8Z 1M5, Canada.
- Natural Resources Canada, Laurentian Forestry Centre, Canadian Forest Service, 1055 Rue du PEPS, Quebec City, Québec, G1V 4C7, Canada.
- Department of Wood and Forest Sciences, Laval University, 1030 Avenue de La Médecine, Québec, QC, G1V 0A6, Canada.
| | - Christopher I Keeling
- Natural Resources Canada, Laurentian Forestry Centre, Canadian Forest Service, 1055 Rue du PEPS, Quebec City, Québec, G1V 4C7, Canada
- Department of Biochemistry, Microbiology, and Bioinformatics, Laval University, Québec, QC, G1V 0A6, Canada
| | - Julien Prunier
- Department of Wood and Forest Sciences, Laval University, 1030 Avenue de La Médecine, Québec, QC, G1V 0A6, Canada
- Institute of Integrative Biology and Systems, Laval University, Québec, QC, Canada
| | - Melody A Keena
- United States Department of Agriculture, Northern Research Station, Forest Service, 51 Mill Pond Road, Hamden, CT, 06514, USA
| | - Catherine Béliveau
- Natural Resources Canada, Laurentian Forestry Centre, Canadian Forest Service, 1055 Rue du PEPS, Quebec City, Québec, G1V 4C7, Canada
| | - Richard Hamelin
- Forest Sciences Centre, University of British Columbia, 2424 Main Mall, Vancouver, BC, 3032V6T 1Z4, Canada
| | - Nathan P Havill
- United States Department of Agriculture, Northern Research Station, Forest Service, 51 Mill Pond Road, Hamden, CT, 06514, USA
| | | | - Roger C Levesque
- Institute of Integrative Biology and Systems, Laval University, Québec, QC, Canada
| | - Michel Cusson
- Natural Resources Canada, Laurentian Forestry Centre, Canadian Forest Service, 1055 Rue du PEPS, Quebec City, Québec, G1V 4C7, Canada
- Department of Biochemistry, Microbiology, and Bioinformatics, Laval University, Québec, QC, G1V 0A6, Canada
| | - Ilga Porth
- Department of Wood and Forest Sciences, Laval University, 1030 Avenue de La Médecine, Québec, QC, G1V 0A6, Canada
- Institute of Integrative Biology and Systems, Laval University, Québec, QC, Canada
- Centre for Forest Research, Laval University, 2405 Rue de La Terrasse, Québec, QC, G1V 0A6, Canada
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9
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Yan Q, Blue NR, Truong B, Zhang Y, Guerrero RF, Liu N, Honigberg MC, Parry S, McNeil RB, Simhan HN, Chung J, Mercer BM, Grobman WA, Silver R, Greenland P, Saade GR, Reddy UM, Wapner RJ, Haas DM. Genetic Associations with Placental Proteins in Maternal Serum Identify Biomarkers for Hypertension in Pregnancy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.05.25.23290460. [PMID: 37398343 PMCID: PMC10312829 DOI: 10.1101/2023.05.25.23290460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Background Preeclampsia is a complex syndrome that accounts for considerable maternal and perinatal morbidity and mortality. Despite its prevalence, no effective disease-modifying therapies are available. Maternal serum placenta-derived proteins have been in longstanding use as markers of risk for aneuploidy and placental dysfunction, but whether they have a causal contribution to preeclampsia is unknown. Objective We aimed to investigate the genetic regulation of serum placental proteins in early pregnancy and their potential causal links with preeclampsia and gestational hypertension. Study design This study used a nested case-control design with nulliparous women enrolled in the nuMoM2b study from eight clinical sites across the United States between 2010 and 2013. The first- and second-trimester serum samples were collected, and nine proteins were measured, including vascular endothelial growth factor (VEGF), placental growth factor, endoglin, soluble fms-like tyrosine kinase-1 (sFlt-1), a disintegrin and metalloproteinase domain-containing protein 12 (ADAM-12), pregnancy-associated plasma protein A, free beta-human chorionic gonadotropin, inhibin A, and alpha-fetoprotein. This study used genome-wide association studies to discern genetic influences on these protein levels, treating proteins as outcomes. Furthermore, Mendelian randomization was used to evaluate the causal effects of these proteins on preeclampsia and gestational hypertension, and their further causal relationship with long-term hypertension, treating proteins as exposures. Results A total of 2,352 participants were analyzed. We discovered significant associations between the pregnancy zone protein locus and concentrations of ADAM-12 (rs6487735, P= 3.03×10 -22 ), as well as between the vascular endothelial growth factor A locus and concentrations of both VEGF (rs6921438, P= 7.94×10 -30 ) and sFlt-1 (rs4349809, P= 2.89×10 -12 ). Our Mendelian randomization analyses suggested a potential causal association between first-trimester ADAM-12 levels and gestational hypertension (odds ratio=0.78, P= 8.6×10 -4 ). We also found evidence for a potential causal effect of preeclampsia (odds ratio=1.75, P =8.3×10 -3 ) and gestational hypertension (odds ratio=1.84, P =4.7×10 -3 ) during the index pregnancy on the onset of hypertension 2-7 years later. The additional mediation analysis indicated that the impact of ADAM-12 on postpartum hypertension could be explained in part by its indirect effect through gestational hypertension (mediated effect=-0.15, P= 0.03). Conclusions Our study discovered significant genetic associations with placental proteins ADAM-12, VEGF, and sFlt-1, offering insights into their regulation during pregnancy. Mendelian randomization analyses demonstrated evidence of potential causal relationships between the serum levels of placental proteins, particularly ADAM-12, and gestational hypertension, potentially informing future prevention and treatment investigations.
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10
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchell BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJF, Kardia SLR, Rich SS, Redline S, Kelly T, O'Connor T, Zhao W, Kim W, Guo X, Ida Chen YD, Sofer T. Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Sci Rep 2024; 14:12436. [PMID: 38816422 PMCID: PMC11139858 DOI: 10.1038/s41598-024-62945-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024] Open
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael Elgart
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alanna C Morrison
- Department of Epidemiology, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Byron C Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Matthew Moll
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, West Roxbury, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - Myriam Fornage
- Department of Epidemiology, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O'Connor
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Health Equity and Population Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tamar Sofer
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Center for Life Sciences CLS-934, 3 Blackfan St., Boston, MA, 02115, USA.
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11
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Yan D, Hu B, Darst BF, Mukherjee S, Kunkle BW, Deming Y, Dumitrescu L, Wang Y, Naj A, Kuzma A, Zhao Y, Kang H, Johnson SC, Carlos C, Hohman TJ, Crane PK, Engelman CD, Lu Q. Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes. eLife 2024; 12:RP91360. [PMID: 38787369 PMCID: PMC11126309 DOI: 10.7554/elife.91360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024] Open
Abstract
Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.
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Affiliation(s)
- Donghui Yan
- University of Wisconsin-MadisonMadisonUnited States
| | - Bowen Hu
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
| | - Burcu F Darst
- Department of Population Health Sciences, University of Wisconsin-MadisonMadisonUnited States
| | - Shubhabrata Mukherjee
- Division of General Internal Medicine, Department of Medicine, University of WashingtonSeattleUnited States
| | - Brian W Kunkle
- University of Miami Miller School of MedicineMiamiUnited States
| | - Yuetiva Deming
- Department of Population Health Sciences, University of Wisconsin-MadisonMadisonUnited States
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of MedicineNashvilleUnited States
| | - Yunling Wang
- University of Wisconsin-MadisonMadisonUnited States
| | - Adam Naj
- School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Amanda Kuzma
- School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Yi Zhao
- School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Hyunseung Kang
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA HospitalMadisonUnited States
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
| | - Cruchaga Carlos
- Department of Psychiatry, Washington University in St. LouisSt. LouisUnited States
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of MedicineNashvilleUnited States
| | - Paul K Crane
- Division of General Internal Medicine, Department of Medicine, University of WashingtonSeattleUnited States
| | - Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin-MadisonMadisonUnited States
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public HealthMadisonUnited States
| | | | - Qiongshi Lu
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
- Department of Biostatistics and Medical Informatics, University of Wisconsin-MadisonMadisonUnited States
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12
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Joo YY, Lee E, Kim BG, Kim G, Seo J, Cha J. Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595444. [PMID: 38826224 PMCID: PMC11142157 DOI: 10.1101/2024.05.22.595444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.
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Affiliation(s)
- Yoonjung Yoonie Joo
- Department of Psychology, Seoul National University
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Eunji Lee
- Department of Psychology, Seoul National University
| | - Bo-Gyeom Kim
- Department of Psychology, Seoul National University
| | - Gakyung Kim
- Department of Brain and Cognitive Sciences, Seoul National University
| | - Jungwoo Seo
- Department of Brain and Cognitive Sciences, Seoul National University
| | - Jiook Cha
- Department of Psychology, Seoul National University
- Department of Brain and Cognitive Sciences, Seoul National University
- Institute of Psychological Science, Seoul National University, Seoul, South Korea
- Graduate School of Artificial Intelligence, Seoul National University, Seoul, South Korea
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13
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de Vries PS, Reventun P, Brown MR, Heath AS, Huffman JE, Le NQ, Bebo A, Brody JA, Temprano-Sagrera G, Raffield LM, Ozel AB, Thibord F, Jain D, Lewis JP, Rodriguez BAT, Pankratz N, Taylor KD, Polasek O, Chen MH, Yanek LR, Carrasquilla GD, Marioni RE, Kleber ME, Trégouët DA, Yao J, Li-Gao R, Joshi PK, Trompet S, Martinez-Perez A, Ghanbari M, Howard TE, Reiner AP, Arvanitis M, Ryan KA, Bartz TM, Rudan I, Faraday N, Linneberg A, Ekunwe L, Davies G, Delgado GE, Suchon P, Guo X, Rosendaal FR, Klaric L, Noordam R, van Rooij F, Curran JE, Wheeler MM, Osburn WO, O'Connell JR, Boerwinkle E, Beswick A, Psaty BM, Kolcic I, Souto JC, Becker LC, Hansen T, Doyle MF, Harris SE, Moissl AP, Deleuze JF, Rich SS, van Hylckama Vlieg A, Campbell H, Stott DJ, Soria JM, de Maat MPM, Almasy L, Brody LC, Auer PL, Mitchell BD, Ben-Shlomo Y, Fornage M, Hayward C, Mathias RA, Kilpeläinen TO, Lange LA, Cox SR, März W, Morange PE, Rotter JI, Mook-Kanamori DO, Wilson JF, van der Harst P, Jukema JW, Ikram MA, Blangero J, Kooperberg C, Desch KC, Johnson AD, Sabater-Lleal M, Lowenstein CJ, Smith NL, Morrison AC. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels. Blood 2024; 143:1845-1855. [PMID: 38320121 DOI: 10.1182/blood.2023021452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 02/08/2024] Open
Abstract
ABSTRACT Coagulation factor VIII (FVIII) and its carrier protein von Willebrand factor (VWF) are critical to coagulation and platelet aggregation. We leveraged whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program along with TOPMed-based imputation of genotypes in additional samples to identify genetic associations with circulating FVIII and VWF levels in a single-variant meta-analysis, including up to 45 289 participants. Gene-based aggregate tests were implemented in TOPMed. We identified 3 candidate causal genes and tested their functional effect on FVIII release from human liver endothelial cells (HLECs) and VWF release from human umbilical vein endothelial cells. Mendelian randomization was also performed to provide evidence for causal associations of FVIII and VWF with thrombotic outcomes. We identified associations (P < 5 × 10-9) at 7 new loci for FVIII (ST3GAL4, CLEC4M, B3GNT2, ASGR1, F12, KNG1, and TREM1/NCR2) and 1 for VWF (B3GNT2). VWF, ABO, and STAB2 were associated with FVIII and VWF in gene-based analyses. Multiphenotype analysis of FVIII and VWF identified another 3 new loci, including PDIA3. Silencing of B3GNT2 and the previously reported CD36 gene decreased release of FVIII by HLECs, whereas silencing of B3GNT2, CD36, and PDIA3 decreased release of VWF by HVECs. Mendelian randomization supports causal association of higher FVIII and VWF with increased risk of thrombotic outcomes. Seven new loci were identified for FVIII and 1 for VWF, with evidence supporting causal associations of FVIII and VWF with thrombotic outcomes. B3GNT2, CD36, and PDIA3 modulate the release of FVIII and/or VWF in vitro.
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Affiliation(s)
- Paul S de Vries
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Paula Reventun
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Michael R Brown
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Adam S Heath
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA
| | - Ngoc-Quynh Le
- Unit of Genomics of Complex Disease, Institut de Recerca Sant Pau, Barcelona, Spain
| | - Allison Bebo
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | | | - Laura M Raffield
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Ayse Bilge Ozel
- Department of Human Genetics, University of Michigan, Ann Arbor, MI
| | - Florian Thibord
- Division of Intramural Research, Population Sciences Branch, National Heart, Lung, and Blood Institute, Framingham Heart Study, Framingham, MA
| | - Deepti Jain
- Department of Biostatistics, Genetic Analysis Center, School of Public Health, University of Washington, Seattle, WA
| | - Joshua P Lewis
- Department of Medicine, University of Maryland, Baltimore, MD
| | - Benjamin A T Rodriguez
- Division of Intramural Research, Population Sciences Branch, National Heart, Lung, and Blood Institute, Framingham Heart Study, Framingham, MA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Kent D Taylor
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
| | - Ming-Huei Chen
- Division of Intramural Research, Population Sciences Branch, National Heart, Lung, and Blood Institute, Framingham Heart Study, Framingham, MA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - German D Carrasquilla
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Marcus E Kleber
- SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
- Fifth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Jie Yao
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Angel Martinez-Perez
- Unit of Genomics of Complex Disease, Institut de Recerca Sant Pau, Barcelona, Spain
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Tom E Howard
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX
| | - Alex P Reiner
- Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
| | - Marios Arvanitis
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kathleen A Ryan
- Department of Medicine, University of Maryland, Baltimore, MD
| | - Traci M Bartz
- Departments of Biostatistics and Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lynette Ekunwe
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Gail Davies
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, Scotland
| | - Graciela E Delgado
- Fifth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Pierre Suchon
- C2VN, INSERM, INRAE, Aix Marseille University, Marseille, France
- Laboratory of Haematology, La Timone Hospital, Marseille, France
| | - Xiuqing Guo
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Frank van Rooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX
| | - Marsha M Wheeler
- Department of Genome Sciences, University of Washington, Seattle, WA
| | - William O Osburn
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | | | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Andrew Beswick
- Translational Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Departments of Epidemiology and Health Systems and Population Health, Seattle, WA
| | - Ivana Kolcic
- Faculty of Medicine, University of Split, Split, Croatia
| | - Juan Carlos Souto
- Unit of Genomics of Complex Disease, Institut de Recerca Sant Pau, Barcelona, Spain
- Unit of Thrombosis and Hemostasis, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Lewis C Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Margaret F Doyle
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Colchester, VT
| | - Sarah E Harris
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, Scotland
| | - Angela P Moissl
- Institute of Nutritional Sciences, Friedrich-Schiller-University Jena, Jena, Germany
- Competence Cluster for Nutrition and Cardiovascular Health Halle-Jena-Leipzig, Jena, Germany
| | - Jean-François Deleuze
- Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, CEA, Evry, France
- Centre d'Etude du Polymorphisme Humain, Fondation Jean Dausset, Paris, France
| | - Stephen S Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA
| | | | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, Scotland
| | - Jose Manuel Soria
- Unit of Genomics of Complex Disease, Institut de Recerca Sant Pau, Barcelona, Spain
| | - Moniek P M de Maat
- Department of Hematology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Laura Almasy
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Lawrence C Brody
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Paul L Auer
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, WI
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland, Baltimore, MD
- Geriatric Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD
| | - Yoav Ben-Shlomo
- Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Myriam Fornage
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Simon R Cox
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, Scotland
| | - Winfried März
- Fifth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
| | - Pierre-Emmanuel Morange
- C2VN, INSERM, INRAE, Aix Marseille University, Marseille, France
- Laboratory of Haematology, La Timone Hospital, Marseille, France
| | - Jerome I Rotter
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Pim van der Harst
- Division of Heart and Lungs, Department of Cardiology, Utrecht University, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX
| | | | - Karl C Desch
- Department of Pediatrics, University of Michigan, C.S. Mott Children's Hospital, Ann Arbor, MI
| | - Andrew D Johnson
- Division of Intramural Research, Population Sciences Branch, National Heart, Lung, and Blood Institute, Framingham Heart Study, Framingham, MA
| | - Maria Sabater-Lleal
- Unit of Genomics of Complex Disease, Institut de Recerca Sant Pau, Barcelona, Spain
- Department of Medicine, Cardiovascular Medicine Unit, Karolinska Institutet, Center for Molecular Medicine, Stockholm, Sweden
| | - Charles J Lowenstein
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Nicholas L Smith
- Department of Epidemiology, University of Washington, Seattle, WA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
- Department of Veterans Affairs Office of Research and Development, Seattle Epidemiologic and Information Center, Seattle, WA
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
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Grinde KE, Browning BL, Reiner AP, Thornton TA, Browning SR. Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587682. [PMID: 38617337 PMCID: PMC11014513 DOI: 10.1101/2024.04.02.587682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/24/2024]
Abstract
Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.
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Affiliation(s)
- Kelsey E. Grinde
- Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, Minnesota, 55105, USA
| | - Brian L. Browning
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, 98195, USA
| | - Alexander P. Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, 98195, USA
| | - Timothy A. Thornton
- Regeneron Genetics Center, Tarrytown, New York, 10591, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, 98195, USA
| | - Sharon R. Browning
- Department of Biostatistics, University of Washington, Seattle, Washington, 98195, USA
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15
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Han SH, Camp SY, Chu H, Collins R, Gillani R, Park J, Bakouny Z, Ricker CA, Reardon B, Moore N, Kofman E, Labaki C, Braun D, Choueiri TK, AlDubayan SH, Van Allen EM. Integrative Analysis of Germline Rare Variants in Clear and Non-clear Cell Renal Cell Carcinoma. EUR UROL SUPPL 2024; 62:107-122. [PMID: 38496821 PMCID: PMC10940785 DOI: 10.1016/j.euros.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/12/2024] [Indexed: 03/19/2024] Open
Abstract
Background and objective Previous germline studies on renal cell carcinoma (RCC) have usually pooled clear and non-clear cell RCCs and have not adequately accounted for population stratification, which might have led to an inaccurate estimation of genetic risk. Here, we aim to analyze the major germline drivers of RCC risk and clinically relevant but underexplored germline variant types. Methods We first characterized germline pathogenic variants (PVs), cryptic splice variants, and copy number variants (CNVs) in 1436 unselected RCC patients. To evaluate the enrichment of PVs in RCC, we conducted a case-control study of 1356 RCC patients ancestry matched with 16 512 cancer-free controls using approaches accounting for population stratification and histological subtypes, followed by characterization of secondary somatic events. Key findings and limitations Clear cell RCC patients (n = 976) exhibited a significant burden of PVs in VHL compared with controls (odds ratio [OR]: 39.1, p = 4.95e-05). Non-clear cell RCC patients (n = 380) carried enrichment of PVs in FH (OR: 77.9, p = 1.55e-08) and MET (OR: 1.98e11, p = 2.07e-05). In a CHEK2-focused analysis with European participants, clear cell RCC (n = 906) harbored nominal enrichment of low-penetrance CHEK2 variants-p.Ile157Thr (OR: 1.84, p = 0.049) and p.Ser428Phe (OR: 5.20, p = 0.045), while non-clear cell RCC (n = 295) exhibited nominal enrichment of CHEK2 loss of function PVs (OR: 3.51, p = 0.033). Patients with germline PVs in FH, MET, and VHL exhibited significantly earlier age of cancer onset than patients without germline PVs (mean: 46.0 vs 60.2 yr, p < 0.0001), and more than half had secondary somatic events affecting the same gene (n = 10/15, 66.7%). Conversely, CHEK2 PV carriers exhibited a similar age of onset to patients without germline PVs (mean: 60.1 vs 60.2 yr, p = 0.99), and only 30.4% carried somatic events in CHEK2 (n = 7/23). Finally, pathogenic germline cryptic splice variants were identified in SDHA and TSC1, and pathogenic germline CNVs were found in 18 patients, including CNVs in FH, SDHA, and VHL. Conclusions and clinical implications This analysis supports the existing link between several RCC risk genes and RCC risk manifesting in earlier age of onset. It calls for caution when assessing the role of CHEK2 due to the burden of founder variants with varying population frequency. It also broadens the definition of the RCC germline landscape of pathogenicity to incorporate previously understudied types of germline variants. Patient summary In this study, we carefully compared the frequency of rare inherited mutations with a focus on patients' genetic ancestry. We discovered that subtle variations in genetic background may confound a case-control analysis, especially in evaluating the cancer risk associated with specific genes, such as CHEK2. We also identified previously less explored forms of rare inherited mutations, which could potentially increase the risk of kidney cancer.
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Affiliation(s)
- Seung Hun Han
- Ph.D. Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sabrina Y. Camp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hoyin Chu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ryan Collins
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Riaz Gillani
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Boston Children’s Hospital, Boston, MA, USA
| | - Jihye Park
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ziad Bakouny
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cora A. Ricker
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brendan Reardon
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas Moore
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
| | - Eric Kofman
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Chris Labaki
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - David Braun
- Center of Molecular and Cellular Oncology, Yale School of Medicine, New Haven, CT, USA
| | - Toni K. Choueiri
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | - Saud H. AlDubayan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Boston, MA, USA
- College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Eliezer M. Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
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16
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Stonebraker JR, Pace RG, Gallins PJ, Dang H, Aksit MA, Faino AV, Gordon WW, MacParland S, Bamshad MJ, Gibson RL, Cutting GR, Durie PR, Wright FA, Zhou YH, Blackman SM, O'Neal WK, Ling SC, Knowles MR. Genetic variation in severe cystic fibrosis liver disease is associated with novel mechanisms for disease pathogenesis. Hepatology 2024:01515467-990000000-00819. [PMID: 38536042 DOI: 10.1097/hep.0000000000000863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/11/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND AND AIMS It is not known why severe cystic fibrosis (CF) liver disease (CFLD) with portal hypertension occurs in only ~7% of people with CF. We aimed to identify genetic modifiers for severe CFLD to improve understanding of disease mechanisms. APPROACH AND RESULTS Whole-genome sequencing was available in 4082 people with CF with pancreatic insufficiency (n = 516 with severe CFLD; n = 3566 without CFLD). We tested ~15.9 million single nucleotide polymorphisms (SNPs) for association with severe CFLD versus no-CFLD, using pre-modulator clinical phenotypes including (1) genetic variant ( SERPINA1 ; Z allele) previously associated with severe CFLD; (2) candidate SNPs (n = 205) associated with non-CF liver diseases; (3) genome-wide association study of common/rare SNPs; (4) transcriptome-wide association; and (5) gene-level and pathway analyses. The Z allele was significantly associated with severe CFLD ( p = 1.1 × 10 -4 ). No significant candidate SNPs were identified. A genome-wide association study identified genome-wide significant SNPs in 2 loci and 2 suggestive loci. These 4 loci contained genes [significant, PKD1 ( p = 8.05 × 10 -10 ) and FNBP1 ( p = 4.74 × 10 -9 ); suggestive, DUSP6 ( p = 1.51 × 10 -7 ) and ANKUB1 ( p = 4.69 × 10 -7 )] relevant to severe CFLD pathophysiology. The transcriptome-wide association identified 3 genes [ CXCR1 ( p = 1.01 × 10 -6 ) , AAMP ( p = 1.07 × 10 -6 ), and TRBV24 ( p = 1.23 × 10 -5 )] involved in hepatic inflammation and innate immunity. Gene-ranked analyses identified pathways enriched in genes linked to multiple liver pathologies. CONCLUSION These results identify loci/genes associated with severe CFLD that point to disease mechanisms involving hepatic fibrosis, inflammation, innate immune function, vascular pathology, intracellular signaling, actin cytoskeleton and tight junction integrity and mechanisms of hepatic steatosis and insulin resistance. These discoveries will facilitate mechanistic studies and the development of therapeutics for severe CFLD.
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Affiliation(s)
- Jaclyn R Stonebraker
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Rhonda G Pace
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Paul J Gallins
- Bioinformatics Research Center, Departments of Statistics and Biological Science, North Carolina State University, Raleigh, North Carolina, USA
| | - Hong Dang
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Melis A Aksit
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Anna V Faino
- Children's Core for Biostatistics, Epidemiology and Analytics in Research, Seattle Children's Research Institute, Seattle, Washington, USA
| | - William W Gordon
- Department of Pediatrics, Division of Genetic Medicine, University of Washington, Seattle, Washington, USA
| | - Sonya MacParland
- Ajmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Michael J Bamshad
- Department of Pediatrics, Division of Genetic Medicine, University of Washington, Seattle, Washington, USA
- Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Ronald L Gibson
- Department of Pediatrics, Division of Pulmonary & Respiratory Diseases, Center for Respiratory Biology and Therapeutics, Seattle Children's Research Institute, Seattle, Washington, USA
| | - Garry R Cutting
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Fred A Wright
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Yi-Hui Zhou
- Bioinformatics Research Center, Departments of Statistics and Biological Science, North Carolina State University, Raleigh, North Carolina, USA
- Departments of Statistics and Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | - Scott M Blackman
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Wanda K O'Neal
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Simon C Ling
- Division of Gastroenterology, Hepatology, and Nutrition, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Michael R Knowles
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Park J, Lee E, Cho G, Hwang H, Kim BG, Kim G, Joo YY, Cha J. Gene-environment pathways to cognitive intelligence and psychotic-like experiences in children. eLife 2024; 12:RP88117. [PMID: 38441539 PMCID: PMC10942586 DOI: 10.7554/elife.88117] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024] Open
Abstract
In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.
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Affiliation(s)
- Junghoon Park
- Interdisciplinary Program in Artificial Intelligence, College of Engineering, Seoul National UniversitySeoulRepublic of Korea
| | - Eunji Lee
- Department of Psychology, College of Social Sciences, Seoul National UniversitySeoulRepublic of Korea
| | - Gyeongcheol Cho
- Department of Psychology, College of Arts and Sciences, The Ohio State UniversityColumbusUnited States
| | - Heungsun Hwang
- Department of Psychology, McGill UniversityMontréalCanada
| | - Bo-Gyeom Kim
- Department of Psychology, College of Social Sciences, Seoul National UniversitySeoulRepublic of Korea
| | - Gakyung Kim
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National UniversitySeoulRepublic of Korea
| | - Yoonjung Yoonie Joo
- Department of Psychology, College of Social Sciences, Seoul National UniversitySeoulRepublic of Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan UniversitySeoulRepublic of Korea
- Samsung Medical CenterSeoulRepublic of Korea
| | - Jiook Cha
- Interdisciplinary Program in Artificial Intelligence, College of Engineering, Seoul National UniversitySeoulRepublic of Korea
- Department of Psychology, College of Social Sciences, Seoul National UniversitySeoulRepublic of Korea
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National UniversitySeoulRepublic of Korea
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18
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Capawana MR, Vuijk PJ, Martin J, Pollastri AR, Forchelli GA, Woscoboinik GG, Tremblay SL, Wolfe LE, Braaten EB, Doyle AE. Polygenic Variation Underlying Educational Attainment and Attention-Deficit/Hyperactivity Disorder Indexes Behavior Ratings of Executive Functions in Child Psychiatry Outpatients. J Atten Disord 2024; 28:861-871. [PMID: 38281105 DOI: 10.1177/10870547231219763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2024]
Abstract
OBJECTIVE We leveraged common genetic variation underlying ADHD, educational attainment (EA) and cognition (COG) to understand the nature of the Behavior Rating Inventory for Executive Functions (BRIEF) and its relationship to academic functioning. METHOD Participants were 991 youth, ages 7 to 17, consecutively referred for neuropsychiatric evaluation. Polygenic scores (PGS) for ADHD, EA, and COG were related to the BRIEF using regression analyses. Structural equation models were used to examine the associations between the PGS, BRIEF and academic outcomes (math, reading, and special education services [EDPLAN]). RESULTS After modeling the PGS together, only the EA and ADHD PGS significantly associated with the BRIEF. The BRIEF partially mediated the relationships between EA PGS with math and EDPLAN and fully mediated the relationship between ADHD PGS and EDPLAN. CONCLUSION Genetic data extend evidence that the BRIEF measures a construct relevant to educational success that differs from what is indexed by cognitive testing.
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Affiliation(s)
- Michael R Capawana
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | | | - Alisha R Pollastri
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Gina A Forchelli
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | | | | | - Ellen B Braaten
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Alysa E Doyle
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, MGH, Boston, MA, USA
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19
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Qafoud F, Elshrif M, Kunji K, Althani A, Salam A, Al Suwaidi J, Asaad N, Darbar D, Saad M. Genetic Susceptibility to Arrhythmia Phenotypes in a Middle Eastern Cohort of 14,259 Whole-Genome Sequenced Individuals. J Clin Med 2024; 13:1102. [PMID: 38398418 PMCID: PMC10888535 DOI: 10.3390/jcm13041102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Background: The current study explores the genetic underpinnings of cardiac arrhythmia phenotypes within Middle Eastern populations, which are under-represented in genomic medicine research. Methods: Whole-genome sequencing data from 14,259 individuals from the Qatar Biobank were used and contained 47.8% of Arab ancestry, 18.4% of South Asian ancestry, and 4.6% of African ancestry. The frequency of rare functional variants within a set of 410 candidate genes for cardiac arrhythmias was assessed. Polygenic risk score (PRS) performance for atrial fibrillation (AF) prediction was evaluated. Results: This study identified 1196 rare functional variants, including 162 previously linked to arrhythmia phenotypes, with varying frequencies across Arab, South Asian, and African ancestries. Of these, 137 variants met the pathogenic or likely pathogenic (P/LP) criteria according to ACMG guidelines. Of these, 91 were in ACMG actionable genes and were present in 1030 individuals (~7%). Ten P/LP variants showed significant associations with atrial fibrillation p < 2.4 × 10-10. Five out of ten existing PRSs were significantly associated with AF (e.g., PGS000727, p = 0.03, OR = 1.43 [1.03, 1.97]). Conclusions: Our study is the largest to study the genetic predisposition to arrhythmia phenotypes in the Middle East using whole-genome sequence data. It underscores the importance of including diverse populations in genomic investigations to elucidate the genetic landscape of cardiac arrhythmias and mitigate health disparities in genomic medicine.
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Affiliation(s)
- Fatima Qafoud
- College of Health Sciences, Qatar University, Doha P.O. Box 2713, Qatar; (F.Q.); (A.A.)
| | - Mohamed Elshrif
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar; (M.E.); (K.K.)
| | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar; (M.E.); (K.K.)
| | - Asma Althani
- College of Health Sciences, Qatar University, Doha P.O. Box 2713, Qatar; (F.Q.); (A.A.)
| | - Amar Salam
- Department of Cardiology, Al-Khor Hospital, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar;
| | - Jassim Al Suwaidi
- Heart Hospital, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar; (J.A.S.); (N.A.)
| | - Nidal Asaad
- Heart Hospital, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar; (J.A.S.); (N.A.)
| | - Dawood Darbar
- Division of Cardiology, Department of Medicine, University of Illinois Chicago, Chicago, IL 60612, USA;
| | - Mohamad Saad
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar; (M.E.); (K.K.)
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20
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Kim SJ, Miller B, Hartel NG, Ramirez R, Braniff RG, Leelaprachakul N, Huang A, Wang Y, Arpawong TE, Crimmins EM, Wang P, Sun X, Liu C, Levy D, Yen K, Petzinger GM, Graham NA, Jakowec MW, Cohen P. A naturally occurring variant of SHLP2 is a protective factor in Parkinson's disease. Mol Psychiatry 2024; 29:505-517. [PMID: 38167865 PMCID: PMC11116102 DOI: 10.1038/s41380-023-02344-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024]
Abstract
Mitochondrial DNA single nucleotide polymorphisms (mtSNPs) have been associated with a reduced risk of developing Parkinson's disease (PD), yet the underlying mechanisms remain elusive. In this study, we investigate the functional role of a PD-associated mtSNP that impacts the mitochondrial-derived peptide (MDP) Small Humanin-like Peptide 2 (SHLP2). We identify m.2158 T > C, a mtSNP associated with reduced PD risk, within the small open reading frame encoding SHLP2. This mtSNP results in an alternative form of SHLP2 (lysine 4 replaced with arginine; K4R). Using targeted mass spectrometry, we detect specific tryptic fragments of SHLP2 in neuronal cells and demonstrate its binding to mitochondrial complex 1. Notably, we observe that the K4R variant, associated with reduced PD risk, exhibits increased stability compared to WT SHLP2. Additionally, both WT and K4R SHLP2 show enhanced protection against mitochondrial dysfunction in in vitro experiments and confer protection against a PD-inducing toxin, a mitochondrial complex 1 inhibitor, in a mouse model. This study sheds light on the functional consequences of the m.2158 T > C mtSNP on SHLP2 and provides insights into the potential mechanisms by which this mtSNP may reduce the risk of PD.
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Affiliation(s)
- Su-Jeong Kim
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Brendan Miller
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Nicolas G Hartel
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, USA
| | - Ricardo Ramirez
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Regina Gonzalez Braniff
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Naphada Leelaprachakul
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Environmental Toxicology Program, Chulabhorn Graduate Institute, Bangkok, 10210, Thailand
| | - Amy Huang
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Yuzhu Wang
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Thalida Em Arpawong
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Eileen M Crimmins
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Penglong Wang
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xianbang Sun
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Chunyu Liu
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Daniel Levy
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Kelvin Yen
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Giselle M Petzinger
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Nicholas A Graham
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Michael W Jakowec
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
- Department of Biokinesiology and Physical Therapy, The George and MaryLou Boone Center for Parkinson's Disease Research, University of Southern California, Los Angeles, CA, USA
| | - Pinchas Cohen
- The Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
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21
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Li M, Song Z, Gurinovich A, Schork N, Sebastiani P, Monti S. yQTL Pipeline: a structured computational workflow for large scale quantitative trait loci discovery and downstream visualization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577518. [PMID: 38370654 PMCID: PMC10874520 DOI: 10.1101/2024.01.26.577518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. In order to facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to genome-wide association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. Upon uploading the result file, it can generate Manhattan plots of user-selected phenotype traits and trait-QTL connection networks based on user-specified p-value thresholds. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs cumulatively associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTL-Pipeline.
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Affiliation(s)
- Mengze Li
- Bioinformatics Program, Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, School of Medicine, Boston University, Boston, MA, USA
| | - Zeyuan Song
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Anastasia Gurinovich
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Department of Medicine, School of Medicine, Tufts University, Boston, MA, USA
| | - Nicholas Schork
- Quantitative Medicine and Systems Biology, The Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Paola Sebastiani
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Department of Medicine, School of Medicine, Tufts University, Boston, MA, USA
- Data Intensive Study Center, Tufts University, Boston, MA, USA
| | - Stefano Monti
- Bioinformatics Program, Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, School of Medicine, Boston University, Boston, MA, USA
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
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22
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Wang H, Chang TS, Dombroski BA, Cheng PL, Patil V, Valiente-Banuet L, Farrell K, Mclean C, Molina-Porcel L, Rajput A, De Deyn PP, Bastard NL, Gearing M, Kaat LD, Swieten JCV, Dopper E, Ghetti BF, Newell KL, Troakes C, de Yébenes JG, Rábano-Gutierrez A, Meller T, Oertel WH, Respondek G, Stamelou M, Arzberger T, Roeber S, Müller U, Hopfner F, Pastor P, Brice A, Durr A, Ber IL, Beach TG, Serrano GE, Hazrati LN, Litvan I, Rademakers R, Ross OA, Galasko D, Boxer AL, Miller BL, Seeley WW, Deerlin VMV, Lee EB, White CL, Morris H, de Silva R, Crary JF, Goate AM, Friedman JS, Leung YY, Coppola G, Naj AC, Wang LS, Dickson DW, Höglinger GU, Schellenberg GD, Geschwind DH, Lee WP. Whole-Genome Sequencing Analysis Reveals New Susceptibility Loci and Structural Variants Associated with Progressive Supranuclear Palsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.12.28.23300612. [PMID: 38234807 PMCID: PMC10793533 DOI: 10.1101/2023.12.28.23300612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Background Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease characterized by the accumulation of aggregated tau proteins in astrocytes, neurons, and oligodendrocytes. Previous genome-wide association studies for PSP were based on genotype array, therefore, were inadequate for the analysis of rare variants as well as larger mutations, such as small insertions/deletions (indels) and structural variants (SVs). Method In this study, we performed whole genome sequencing (WGS) and conducted association analysis for single nucleotide variants (SNVs), indels, and SVs, in a cohort of 1,718 cases and 2,944 controls of European ancestry. Of the 1,718 PSP individuals, 1,441 were autopsy-confirmed and 277 were clinically diagnosed. Results Our analysis of common SNVs and indels confirmed known genetic loci at MAPT, MOBP, STX6, SLCO1A2, DUSP10, and SP1, and further uncovered novel signals in APOE, FCHO1/MAP1S, KIF13A, TRIM24, TNXB, and ELOVL1. Notably, in contrast to Alzheimer's disease (AD), we observed the APOE ε2 allele to be the risk allele in PSP. Analysis of rare SNVs and indels identified significant association in ZNF592 and further gene network analysis identified a module of neuronal genes dysregulated in PSP. Moreover, seven common SVs associated with PSP were observed in the H1/H2 haplotype region (17q21.31) and other loci, including IGH, PCMT1, CYP2A13, and SMCP. In the H1/H2 haplotype region, there is a burden of rare deletions and duplications (P = 6.73×10-3) in PSP. Conclusions Through WGS, we significantly enhanced our understanding of the genetic basis of PSP, providing new targets for exploring disease mechanisms and therapeutic interventions.
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Affiliation(s)
- Hui Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy S Chang
- Movement Disorders Programs, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Beth A Dombroski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Po-Liang Cheng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vishakha Patil
- Movement Disorders Programs, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Leopoldo Valiente-Banuet
- Movement Disorders Programs, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kurt Farrell
- Department of Pathology, Department of Artificial Intelligence & Human Health, Nash Family, Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain, Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Catriona Mclean
- Victorian Brain Bank, The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Laura Molina-Porcel
- Alzheimer's disease and other cognitive disorders unit. Neurology Service, Hospital Clínic, Fundació Recerca Clínic Barcelona (FRCB). Institut d'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
- Neurological Tissue Bank of the Biobanc-Hospital Clínic-IDIBAPS, Barcelona, Spain
| | - Alex Rajput
- Movement Disorders Program, Division of Neurology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Peter Paul De Deyn
- Laboratory of Neurochemistry and Behavior, Experimental Neurobiology Unit, University of Antwerp, Wilrijk (Antwerp), Belgium
- Department of Neurology, University Medical Center Groningen, NL-9713 AV Groningen, Netherlands
| | | | - Marla Gearing
- Department of Pathology and Laboratory Medicine and Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | | | | | - Elise Dopper
- Netherlands Brain Bank and Erasmus University, Netherlands
| | - Bernardino F Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kathy L Newell
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Claire Troakes
- London Neurodegenerative Diseases Brain Bank, King's College London, London, UK
| | | | - Alberto Rábano-Gutierrez
- Fundación CIEN (Centro de Investigación de Enfermedades Neurológicas) - Centro Alzheimer Fundación Reina Sofía, Madrid, Spain
| | - Tina Meller
- Department of Neurology, Philipps-Universität, Marburg, Germany
| | | | - Gesine Respondek
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Maria Stamelou
- Parkinson's disease and Movement Disorders Department, HYGEIA Hospital, Athens, Greece
- European University of Cyprus, Nicosia, Cyprus
| | - Thomas Arzberger
- Department of Psychiatry and Psychotherapy, University Hospital Munich, Ludwig-Maximilians-University Munich, Germany
- Center for Neuropathology and Prion Research, Ludwig-Maximilians-University Munich, Germany
| | | | | | - Franziska Hopfner
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pau Pastor
- Unit of Neurodegenerative diseases, Department of Neurology, University Hospital Germans Trias i Pujol, Badalona, Barcelona, Spain
- Neurosciences, The Germans Trias i Pujol Research Institute (IGTP) Badalona, Badalona, Spain
| | - Alexis Brice
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, APHP - Hôpital Pitié-Salpêtrière, Paris, France
| | - Alexandra Durr
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, APHP - Hôpital Pitié-Salpêtrière, Paris, France
| | - Isabelle Le Ber
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, APHP - Hôpital Pitié-Salpêtrière, Paris, France
| | | | | | | | - Irene Litvan
- Department of Neuroscience, University of California, San Diego, CA, USA
| | - Rosa Rademakers
- VIB Center for Molecular Neurology, University of Antwerp, Belgium
- Department of Neuroscience, Mayo Clinic Jacksonville, FL, USA
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic Jacksonville, FL, USA
| | - Douglas Galasko
- Department of Neuroscience, University of California, San Diego, CA, USA
| | - Adam L Boxer
- Memory and Aging Center, University of California, San Francisco, CA, USA
| | - Bruce L Miller
- Memory and Aging Center, University of California, San Francisco, CA, USA
| | - Willian W Seeley
- Memory and Aging Center, University of California, San Francisco, CA, USA
| | - Vivanna M Van Deerlin
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Charles L White
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Huw Morris
- Departmento of Clinical and Movement Neuroscience, University College of London, London, UK
| | - Rohan de Silva
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK
| | - John F Crary
- Department of Pathology, Department of Artificial Intelligence & Human Health, Nash Family, Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain, Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alison M Goate
- Department of Genetics and Genomic Sciences, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeffrey S Friedman
- Friedman Bioventure, Inc., Del Mar, CA, USA; Department of Genetics and Genomic Sciences, New York, NY, USA
| | - Yuk Yee Leung
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Giovanni Coppola
- Movement Disorders Programs, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Adam C Naj
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Günter U Höglinger
- Department of Neurology, LMU University Hospital, Ludwig-Maximilians-Universität (LMU) München; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel H Geschwind
- Movement Disorders Programs, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Wan-Ping Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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23
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Wang HH, Moon SY, Kim H, Kim G, Ahn WY, Joo YY, Cha J. Early life stress modulates the genetic influence on brain structure and cognitive function in children. Heliyon 2024; 10:e23345. [PMID: 38187352 PMCID: PMC10770463 DOI: 10.1016/j.heliyon.2023.e23345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/03/2023] [Accepted: 12/01/2023] [Indexed: 01/09/2024] Open
Abstract
The enduring influence of early life stress (ELS) on brain and cognitive development has been widely acknowledged, yet the precise mechanisms underlying this association remain elusive. We hypothesize that ELS might disrupt the genome-wide influence on brain morphology and connectivity development, consequently exerting a detrimental impact on children's cognitive ability. We analyzed the multimodal data of DNA genotypes, brain imaging (structural and diffusion MRI), and neurocognitive battery (NIH Toolbox) of 4276 children (ages 9-10 years, European ancestry) from the Adolescent Brain Cognitive Development (ABCD) study. The genome-wide influence on cognitive function was estimated using the polygenic score (GPS). By using brain morphometry and tractography, we identified the brain correlates of the cognition GPSs. Statistical analyses revealed relationships for the gene-brain-cognition pathway. The brain structural variance significantly mediated the genetic influence on cognition (indirect effect = 0.016, PFDR < 0.001). Of note, this gene-brain relationship was significantly modulated by abuse, resulting in diminished cognitive capacity (Index of Moderated Mediation = -0.007; 95 % CI = -0.012 ∼ -0.002). Our results support a novel gene-brain-cognition model likely elucidating the long-lasting negative impact of ELS on children's cognitive development.
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Affiliation(s)
- Hee-Hwan Wang
- Department of Brain Cognitive and Science, Seoul National University, Seoul, 08825, South Korea
| | - Seo-Yoon Moon
- College of Liberal Studies, Seoul National University, Seoul, 08825, South Korea
| | - Hyeonjin Kim
- Department of Psychology, Seoul National University, Seoul, 08825, South Korea
| | - Gakyung Kim
- Department of Brain Cognitive and Science, Seoul National University, Seoul, 08825, South Korea
| | - Woo-Young Ahn
- Department of Psychology, Seoul National University, Seoul, 08825, South Korea
| | - Yoonjung Yoonie Joo
- Department of Psychology, Seoul National University, Seoul, 08825, South Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, 06355, South Korea
- Research Center for Future Medicine, Samsung Medical Center, Seoul, 06335, South Korea
| | - Jiook Cha
- Department of Brain Cognitive and Science, Seoul National University, Seoul, 08825, South Korea
- Department of Psychology, Seoul National University, Seoul, 08825, South Korea
- AI Institute, Seoul National University, Seoul, 08825, South Korea
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24
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Wiley LK, Shortt JA, Roberts ER, Lowery J, Kudron E, Lin M, Mayer D, Wilson M, Brunetti TM, Chavan S, Phang TL, Pozdeyev N, Lesny J, Wicks SJ, Moore ET, Morgenstern JL, Roff AN, Shalowitz EL, Stewart A, Williams C, Edelmann MN, Hull M, Patton JT, Axell L, Ku L, Lee YM, Jirikowic J, Tanaka A, Todd E, White S, Peterson B, Hearst E, Zane R, Greene CS, Mathias R, Coors M, Taylor M, Ghosh D, Kahn MG, Brooks IM, Aquilante CL, Kao D, Rafaels N, Crooks KR, Hess S, Barnes KC, Gignoux CR. Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine. Am J Hum Genet 2024; 111:11-23. [PMID: 38181729 PMCID: PMC10806731 DOI: 10.1016/j.ajhg.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/07/2024] Open
Abstract
Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.
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Affiliation(s)
- Laura K Wiley
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jonathan A Shortt
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Emily R Roberts
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jan Lowery
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Community and Behavioral Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Elizabeth Kudron
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Meng Lin
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - David Mayer
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Melissa Wilson
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Tonya M Brunetti
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Sameer Chavan
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Tzu L Phang
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nikita Pozdeyev
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Joseph Lesny
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Stephen J Wicks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Ethan T Moore
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Joshua L Morgenstern
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Alanna N Roff
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Elise L Shalowitz
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Adrian Stewart
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Cole Williams
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michelle N Edelmann
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Madelyne Hull
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - J Tacker Patton
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Lisen Axell
- CU Cancer Center, Hereditary Cancer Clinic, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Lisa Ku
- CU Cancer Center, Hereditary Cancer Clinic, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Yee Ming Lee
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | | | - Emily Todd
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; UCHealth, Aurora, CO 80045, USA
| | | | - Brett Peterson
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Richard Zane
- UCHealth, Aurora, CO 80045, USA; University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Casey S Greene
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Rasika Mathias
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Marilyn Coors
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Matthew Taylor
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Division of Cardiology, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA
| | - Michael G Kahn
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Ian M Brooks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christina L Aquilante
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Pharmaceutical Sciences, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - David Kao
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Division of Cardiology, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; CARE Innovation Center, UCHealth, Aurora, CO 80045, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kristy R Crooks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Pathology, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Kathleen C Barnes
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
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25
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchel BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJ, Kardia SLR, Rich SS, Redline S, Kelly T, O’Connor T, Zhao W, Kim W, Guo X, Der Ida Chen Y, Sofer T. Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299909. [PMID: 38168328 PMCID: PMC10760279 DOI: 10.1101/2023.12.13.23299909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Michael Elgart
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D. Mitchel
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bruce M. Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Byron C. Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Matthew Moll
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- VA Boston Healthcare System, West Roxbury, MA, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Ramon Casanova
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Denmark, DK
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O’Connor
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Tamar Sofer
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
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26
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Armstrong ND, Srinivasasainagendra V, Ammous F, Assimes TL, Beitelshees AL, Brody J, Cade BE, Ida Chen YD, Chen H, de Vries PS, Floyd JS, Franceschini N, Guo X, Hellwege JN, House JS, Hwu CM, Kardia SLR, Lange EM, Lange LA, McDonough CW, Montasser ME, O’Connell JR, Shuey MM, Sun X, Tanner RM, Wang Z, Zhao W, Carson AP, Edwards TL, Kelly TN, Kenny EE, Kooperberg C, Loos RJF, Morrison AC, Motsinger-Reif A, Psaty BM, Rao DC, Redline S, Rich SS, Rotter JI, Smith JA, Smith AV, Irvin MR, Arnett DK. Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program. Front Genet 2023; 14:1278215. [PMID: 38162683 PMCID: PMC10755672 DOI: 10.3389/fgene.2023.1278215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction: Apparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Methods: Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP (n = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg (n = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg (n = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). Results: One variant in the known HTN locus, KCNK3, was a top finding in the multi-ethnic analysis (p = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center's DNA repository data. Aggregate gene-based signals included the genes AGTPBP, MYL4, PDCD4, BBS9, ERG, and IER3. Discussion: Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.
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Affiliation(s)
- Nicole D. Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Farah Ammous
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - Themistocles L. Assimes
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Amber L. Beitelshees
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Jennifer Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Brian E. Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - James S. Floyd
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Jacklyn N. Hellwege
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - John S. House
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Ethan M. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - May E. Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | | | - Megan M. Shuey
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Rikki M. Tanner
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, United States
| | - Todd L. Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Tanika N. Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, United States
| | - Eimear E. Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Alison Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Dabeeru C. Rao
- Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Stephen S. Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - Albert V. Smith
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Donna K. Arnett
- Office of the Provost, University of South Carolina, Columbia, SC, United States
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27
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de Vries PS, Conomos MP, Singh K, Nicholson CJ, Jain D, Hasbani NR, Jiang W, Lee S, Lino Cardenas CL, Lutz SM, Wong D, Guo X, Yao J, Young EP, Tcheandjieu C, Hilliard AT, Bis JC, Bielak LF, Brown MR, Musharoff S, Clarke SL, Terry JG, Palmer ND, Yanek LR, Xu H, Heard-Costa N, Wessel J, Selvaraj MS, Li RH, Sun X, Turner AW, Stilp AM, Khan A, Newman AB, Rasheed A, Freedman BI, Kral BG, McHugh CP, Hodonsky C, Saleheen D, Herrington DM, Jacobs DR, Nickerson DA, Boerwinkle E, Wang FF, Heiss G, Jun G, Kinney GL, Sigurslid HH, Doddapaneni H, Hall IM, Bensenor IM, Broome J, Crapo JD, Wilson JG, Smith JA, Blangero J, Vargas JD, Mosquera JV, Smith JD, Viaud-Martinez KA, Ryan KA, Young KA, Taylor KD, Lange LA, Emery LS, Bittencourt MS, Budoff MJ, Montasser ME, Yu M, Mahaney MC, Mahamdeh MS, Fornage M, Franceschini N, Lotufo PA, Natarajan P, Wong Q, Mathias RA, Gibbs RA, Do R, Mehran R, Tracy RP, Kim RW, Nelson SC, Damrauer SM, Kardia SL, Rich SS, Fuster V, Napolioni V, Zhao W, Tian W, Yin X, Min YI, Manning AK, Peloso G, Kelly TN, O’Donnell CJ, Morrison AC, Curran JE, Zapol WM, Bowden DW, Becker LC, Correa A, Mitchell BD, Psaty BM, Carr JJ, Pereira AC, Assimes TL, Stitziel NO, Hokanson JE, Laurie CA, Rotter JI, Vasan RS, Post WS, Peyser PA, Miller CL, Malhotra R. Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification. NATURE CARDIOVASCULAR RESEARCH 2023; 2:1159-1172. [PMID: 38817323 PMCID: PMC11138106 DOI: 10.1038/s44161-023-00375-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/25/2023] [Indexed: 06/01/2024]
Abstract
Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.
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Affiliation(s)
- Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX, USA
| | - Matthew P. Conomos
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Kuldeep Singh
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
| | - Christopher J. Nicholson
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
| | - Deepti Jain
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Natalie R. Hasbani
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX, USA
| | - Wanlin Jiang
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
| | - Sujin Lee
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
| | - Christian L Lino Cardenas
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
| | - Sharon M. Lutz
- PRecisiOn Medicine Translational Research (PROMoTeR)
Center, Department of Population Medicine, Harvard Medical School and Harvard
Pilgrim Health Care Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of
Public Health, Boston, MA, USA
| | - Doris Wong
- Center for Public Health Genomics, University of Virginia
School of Medicine, Charlottesville, VA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population
Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical
Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population
Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical
Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Erica P. Young
- Cardiovascular Division, Department of Internal Medicine,
Washington University School of Medicine, St. Louis, MO, USA
| | - Catherine Tcheandjieu
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of
Medicine, Stanford, CA, USA
| | - Austin T. Hilliard
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Palo Alto Veterans Institute for Research, Palo Alto, CA,
USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of
Medicine, University of Washington, Seattle, WA, USA
| | - Lawrence F. Bielak
- School of Public Health, Department of Epidemiology,
University of Michigan, Ann Arbor, MI, USA
| | - Michael R. Brown
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX, USA
| | - Shaila Musharoff
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Genetics, Stanford University School of
Medicine, Stanford, CA, USA
| | - Shoa L. Clarke
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of
Medicine, Stanford, CA, USA
| | - James G. Terry
- Department of Radiology, Vanderbilt Translational and
Clinical Cardiovascular Research Center, Vanderbilt University Medical Center,
Nashville, TN, USA
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest School of
Medicine, Winston-Salem, NC, USA
| | - Lisa R. Yanek
- Division of General Internal Medicine, Department of
Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition,
Department of Medicine, University of Maryland School of Medicine, Baltimore, MD,
USA
| | - Nancy Heard-Costa
- Boston University School of Medicine, Boston, MA,
USA
- Boston University and National Heart, Lung, and Blood
Institute’s Framingham Heart Study, Framingham, MA, USA
| | - Jennifer Wessel
- Department of Epidemiology, Fairbanks School of Public
Health, Indiana University, Indianapolis, IN, USA
- Diabetes Translational Research Center, Indiana
University, Indianapolis, IN, USA
| | - Margaret Sunitha Selvaraj
- Cardiovascular Research Center and Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad
Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston,
MA, USA
| | - Rebecca H. Li
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
| | - Xiao Sun
- School of Public Health and Tropical Medicine, Department
of Epidemiology, Tulane University, New Orleans, LA, USA
- College of Medicine, Department of Medicine, Division of
Nephrology, University of Illinois Chicago, Chicago, IL, USA
| | - Adam W. Turner
- Center for Public Health Genomics, University of Virginia
School of Medicine, Charlottesville, VA, USA
| | - Adrienne M. Stilp
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Alyna Khan
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Anne B. Newman
- Department of Epidemiology, Graduate School of Public
Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Asif Rasheed
- Center For Non-Communicable Diseases, Karachi,
Pakistan
| | - Barry I Freedman
- Section on Nephrology, Department of Internal Medicine,
Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Brian G. Kral
- Division of Cardiology, Department of Medicine, Johns
Hopkins University School of Medicine, Baltimore, MD, USA
| | - Caitlin P. McHugh
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Chani Hodonsky
- Center for Public Health Genomics, University of Virginia
School of Medicine, Charlottesville, VA, USA
| | - Danish Saleheen
- Center For Non-Communicable Diseases, Karachi,
Pakistan
- Department of Medicine, Columbia University Irving
Medical Center, New York, NY, USA
- Department of Cardiology, Columbia University Irving
Medical Center, New York, NY, USA
| | - David M. Herrington
- Department of Internal Medicine, Section of
Cardiovascular Medicine, Wake Forest School of Medicine, Winston-Salem, NC,
USA
| | - David R. Jacobs
- Division of Epidemiology and Community Health, University
of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Deborah A. Nickerson
- Department of Genome Sciences, University of Washington,
Seattle, WA, USA
- Northwest Genomics Center, University of Washington,
Seattle, WA, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of
Medicine, Houston, TX, USA
| | - Fei Fei Wang
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Gerardo Heiss
- Department of Epidemiology, Gillings School of Global
Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Goo Jun
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX, USA
| | - Greg L. Kinney
- Department of Epidemiology, Colorado School of Public
Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Haakon H. Sigurslid
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
| | | | - Ira M. Hall
- Yale Center for Genomic Health, Yale School of Medicine,
New Haven, CT, USA
| | - Isabela M. Bensenor
- Center for Clinical and Epidemiological Research,
University Hospital, University of Sao Paulo Medical School, São Paulo, Brazil
| | - Jai Broome
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - James D. Crapo
- Department of Medicine, National Jewish Health, Denver,
CO, USA
| | - James G. Wilson
- Division of Cardiology, Beth Israel Deaconess Medical
Center, Boston, MA, USA
| | - Jennifer A. Smith
- School of Public Health, Department of Epidemiology,
University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research,
University of Michigan, Ann Arbor, MI, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio
Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of
Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Jose D. Vargas
- Medstar Heart and Vascular Institute, Medstar Georgetown
University Hospital, Washington, DC, USA
| | - Jose Verdezoto Mosquera
- Center for Public Health Genomics, University of Virginia
School of Medicine, Charlottesville, VA, USA
| | - Joshua D. Smith
- Department of Genome Sciences, University of Washington,
Seattle, WA, USA
- Northwest Genomics Center, University of Washington,
Seattle, WA, USA
| | | | - Kathleen A. Ryan
- Division of Endocrinology, Diabetes and Nutrition,
Department of Medicine, University of Maryland School of Medicine, Baltimore, MD,
USA
| | - Kendra A. Young
- Department of Epidemiology, Colorado School of Public
Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population
Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical
Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Leslie A. Lange
- Department of Medicine, University of Colorado Denver,
Anschutz Medical Campus, Aurora, CO, USA
| | - Leslie S. Emery
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Marcio S. Bittencourt
- Center for Clinical and Epidemiological Research,
University Hospital, University of Sao Paulo Medical School, São Paulo, Brazil
| | - Matthew J. Budoff
- Department of Medicine, The Lundquist Institute for
Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - May E. Montasser
- Division of Endocrinology, Diabetes and Nutrition,
Department of Medicine, University of Maryland School of Medicine, Baltimore, MD,
USA
| | - Miao Yu
- School of Public Health, Department of Epidemiology,
University of Michigan, Ann Arbor, MI, USA
| | - Michael C. Mahaney
- Department of Human Genetics, University of Texas Rio
Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of
Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Mohammed S Mahamdeh
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX, USA
- Institute of Molecular Medicine, McGovern Medical School,
The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global
Public health, University of North Carolina, Chapel Hill, NC, USA
| | - Paulo A. Lotufo
- Center for Clinical and Epidemiological Research,
University Hospital, University of Sao Paulo Medical School, São Paulo, Brazil
| | - Pradeep Natarajan
- Cardiovascular Research Center and Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad
Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston,
MA, USA
| | - Quenna Wong
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Rasika A. Mathias
- Division of General Internal Medicine, Department of
Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Allergy and Clinical Immunology, Department
of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Richard A. Gibbs
- Human Genome Sequencing Center, Baylor College of
Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor
College of Medicine, Houston, TX, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine,
Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School
of Medicine at Mount Sinai, New York, NY, USA
| | - Roxana Mehran
- Icahn School of Medicine at Mount Sinai, New York, NY,
USA
| | - Russell P. Tracy
- Department of Pathology and Laboratory Medicine, Robert
Larner, M.D. College of Medicine, University of Vermont, Burlington, VT, USA
| | | | - Sarah C. Nelson
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Scott M. Damrauer
- Corporal Michael J. Crescenz VA Medical Center,
Philadelphia, PA, USA
- Department of Surgery, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon L.R. Kardia
- School of Public Health, Department of Epidemiology,
University of Michigan, Ann Arbor, MI, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia
School of Medicine, Charlottesville, VA, USA
| | - Valentin Fuster
- Centro Nacional de Investigaciones Cardiovasculares
Carlos III, Madrid, Spain
- Mount Sinai Heart Center, New York, NY, USA
| | - Valerio Napolioni
- Genomic And Molecular Epidemiology (GAME) Lab, School of
Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
| | - Wei Zhao
- School of Public Health, Department of Epidemiology,
University of Michigan, Ann Arbor, MI, USA
| | - Wenjie Tian
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
| | - Xianyong Yin
- Department of Biostatistics and Center for Statistical
Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Yuan-I Min
- Jackson Heart Study, Department of Medicine, University
of Mississippi Medical Center, Jackson, MS, USA
| | - Alisa K. Manning
- Clinical and Translation Epidemiology Unit, Department of
Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical and Population
Genetics, Broad Institute, Cambridge, MA, USA
| | - Gina Peloso
- Department of Biostatistics, Boston University School of
Public Health, Boston, MA, USA
| | - Tanika N. Kelly
- College of Medicine, Department of Medicine, Division of
Nephrology, University of Illinois Chicago, Chicago, IL, USA
| | - Christopher J. O’Donnell
- VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital,
Boston, MA, USA
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX, USA
| | - Joanne E. Curran
- Department of Human Genetics, University of Texas Rio
Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of
Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Warren M. Zapol
- Department of Anesthesia, Critical Care and Pain Medicine
at Massachusetts General Hospital, Boston, MA, USA
| | - Donald W. Bowden
- Department of Biochemistry, Wake Forest School of
Medicine, Winston-Salem, NC, USA
| | - Lewis C. Becker
- Division of Cardiology, Department of Medicine, Johns
Hopkins University School of Medicine, Baltimore, MD, USA
| | - Adolfo Correa
- Jackson Heart Study, Department of Medicine, University
of Mississippi Medical Center, Jackson, MS, USA
- Department of Population Health Science, University of
Mississippi Medical Center, Jackson, MS, USA
| | - Braxton D. Mitchell
- Division of Endocrinology, Diabetes and Nutrition,
Department of Medicine, University of Maryland School of Medicine, Baltimore, MD,
USA
- Geriatrics Research and Education Clinical Center,
Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of
Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington,
Seattle, WA, USA
- Department of Health Services, University of Washington,
Seattle, WA, USA
| | - John Jeffrey Carr
- Department of Radiology, Vanderbilt Translational and
Clinical Cardiovascular Research Center, Vanderbilt University Medical Center,
Nashville, TN, USA
| | - Alexandre C. Pereira
- Department of Genetics, Harvard Medical School, Boston,
MA, USA
- Laboratory of Genetics and Molecular Cardiology, Heart
Institute, University of São Paulo, São Paulo, Brazil
| | - Themistocles L. Assimes
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of
Medicine, Stanford, CA, USA
| | - Nathan O. Stitziel
- Cardiovascular Division, Department of Internal Medicine,
Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of
Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School
of Medicine, St. Louis, MO, USA
| | - John E. Hokanson
- Department of Epidemiology, Colorado School of Public
Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Cecelia A. Laurie
- Genetic Analysis Center, Department of Biostatistics,
School of Public Health, University of Washington, Seattle, WA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population
Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical
Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ramachandran S. Vasan
- Boston University and National Heart, Lung, and Blood
Institute’s Framingham Heart Study, Framingham, MA, USA
- Department of Medicine, Boston University School of
Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of
Public Health, Boston, MA, USA
| | - Wendy S. Post
- Division of Cardiology, Department of Medicine, Johns
Hopkins University School of Medicine, Baltimore, MD, USA
| | - Patricia A. Peyser
- School of Public Health, Department of Epidemiology,
University of Michigan, Ann Arbor, MI, USA
| | - Clint L. Miller
- Center for Public Health Genomics, University of Virginia
School of Medicine, Charlottesville, VA, USA
| | - Rajeev Malhotra
- Cardiovascular Research Center, Division of Cardiology,
Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, MA, USA
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28
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Phongpreecha T, Godrich D, Berson E, Espinosa C, Kim Y, Cholerton B, Chang AL, Mataraso S, Bukhari SA, Perna A, Yakabi K, Montine KS, Poston KL, Mormino E, White L, Beecham G, Aghaeepour N, Montine TJ. Quantitative estimate of cognitive resilience and its medical and genetic associations. Alzheimers Res Ther 2023; 15:192. [PMID: 37926851 PMCID: PMC10626669 DOI: 10.1186/s13195-023-01329-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined. METHODS This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR's relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer's Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR. RESULTS CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele (P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms. CONCLUSIONS Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals.
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Affiliation(s)
- Thanaphong Phongpreecha
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, 300 Pasteur Dr Rm L216, Stanford, CA, 94305, USA
| | - Dana Godrich
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, USA
| | - Eloise Berson
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, 300 Pasteur Dr Rm L216, Stanford, CA, 94305, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, 300 Pasteur Dr Rm L216, Stanford, CA, 94305, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Yeasul Kim
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, 300 Pasteur Dr Rm L216, Stanford, CA, 94305, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Alan L Chang
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, 300 Pasteur Dr Rm L216, Stanford, CA, 94305, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Samson Mataraso
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, 300 Pasteur Dr Rm L216, Stanford, CA, 94305, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Syed A Bukhari
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Amalia Perna
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Koya Yakabi
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Kathleen L Poston
- Department of Neurology Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Elizabeth Mormino
- Department of Neurology Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Lon White
- Pacific Health Research and Education Institute, Honolulu, HI, USA
| | - Gary Beecham
- Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, 300 Pasteur Dr Rm L216, Stanford, CA, 94305, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University, Stanford, CA, USA.
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29
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Li X, Chen H, Selvaraj MS, Van Buren E, Zhou H, Wang Y, Sun R, McCaw ZR, Yu Z, Arnett DK, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Brody JA, Cade BE, Carson AP, Carlson JC, Chami N, Chen YDI, Curran JE, de Vries PS, Fornage M, Franceschini N, Freedman BI, Gu C, Heard-Costa NL, He J, Hou L, Hung YJ, Irvin MR, Kaplan RC, Kardia SL, Kelly T, Konigsberg I, Kooperberg C, Kral BG, Li C, Loos RJ, Mahaney MC, Martin LW, Mathias RA, Minster RL, Mitchell BD, Montasser ME, Morrison AC, Palmer ND, Peyser PA, Psaty BM, Raffield LM, Redline S, Reiner AP, Rich SS, Sitlani CM, Smith JA, Taylor KD, Tiwari H, Vasan RS, Wang Z, Yanek LR, Yu B, Rice KM, Rotter JI, Peloso GM, Natarajan P, Li Z, Liu Z, Lin X. A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564764. [PMID: 37961350 PMCID: PMC10634938 DOI: 10.1101/2023.10.30.564764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.
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Affiliation(s)
- Xihao Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Margaret Sunitha Selvaraj
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Eric Van Buren
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yuxuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ryan Sun
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zachary R. McCaw
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhi Yu
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Donna K. Arnett
- Provost Office, University of South Carolina, Columbia, SC, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Donald W. Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E. Cade
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jenna C. Carlson
- Department of Human Genetics and Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Joanne E. Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Barry I. Freedman
- Department of Internal Medicine, Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Charles Gu
- Division of Biology & Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Nancy L. Heard-Costa
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
- Tulane University Translational Science Institute, New Orleans, LA, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Yi-Jen Hung
- Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Marguerite R. Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert C. Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Sharon L.R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Tanika Kelly
- Department of Medicine, Division of Nephrology, University of Illinois Chicago, Chicago, IL, USA
| | - Iain Konigsberg
- Department of Biomedical Informatics, University of Colorado, Aurora, CO, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Brian G. Kral
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
- Tulane University Translational Science Institute, New Orleans, LA, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael C. Mahaney
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Lisa W. Martin
- George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Rasika A. Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ryan L. Minster
- Department of Human Genetics and Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - May E. Montasser
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Departments of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Alexander P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Departments of Epidemiology, University of Washington, Seattle, WA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Colleen M. Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hemant Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ramachandran S. Vasan
- Framingham Heart Study, Framingham, MA, USA
- Department of Quantitative and Qualitative Health Sciences, UT Health San Antonio School of Public Health, San Antonia, TX, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lisa R. Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Kenneth M. Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Pradeep Natarajan
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zhonghua Liu
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Xihong Lin
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
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30
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Liu Z, Turkmen AS, Lin S. Population stratification correction using Bayesian shrinkage priors for genetic association studies. Ann Hum Genet 2023; 87:302-315. [PMID: 37771252 DOI: 10.1111/ahg.12527] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/20/2023] [Accepted: 08/24/2023] [Indexed: 09/30/2023]
Abstract
INTRODUCTION Population stratification (PS) is a major source of confounding in population-based genetic association studies of quantitative traits. Principal component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for PS in association studies. Previous studies have shown that LMM can be interpreted as including all principal components (PCs) as random-effect covariates. However, including all PCs in LMM may dilute the influence of relevant PCs in some scenarios, while including only a few preselected PCs in PCR may fail to fully capture the genetic diversity. MATERIALS AND METHODS To address these shortcomings, we introduce Bayestrat-a method to detect associated variants with PS correction under the Bayesian LASSO framework. To adjust for PS, Bayestrat accommodates a large number of PCs and utilizes appropriate shrinkage priors to shrink the effects of nonassociated PCs. RESULTS Simulation results show that Bayestrat consistently controls type I error rates and achieves higher power compared to its non-shrinkage counterparts, especially when the number of PCs included in the model is large. As a demonstration of the utility of Bayestrat, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA). Variants and genes associated with serum triglyceride or HDL cholesterol are identified in our analyses. DISCUSSION The automatic and self-selection features of Bayestrat make it particularly suited in situations with complex underlying PS scenarios, where it is unknown a priori which PCs are potential confounders, yet the number that needs to be considered could be large in order to fully account for PS.
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Affiliation(s)
- Zilu Liu
- Department of Statistics, The Ohio State University, Columbus, Ohio, USA
| | - Asuman S Turkmen
- Department of Statistics, The Ohio State University, Columbus, Ohio, USA
| | - Shili Lin
- Department of Statistics, The Ohio State University, Columbus, Ohio, USA
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31
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Harris RV, Oliver KL, Perucca P, Striano P, Labate A, Riva A, Grinton BE, Reid J, Hutton J, Todaro M, O'Brien TJ, Kwan P, Sadleir LG, Mullen SA, Dazzo E, Crompton DE, Scheffer IE, Bahlo M, Nobile C, Gambardella A, Berkovic SF. Familial Mesial Temporal Lobe Epilepsy: Clinical Spectrum and Genetic Evidence for a Polygenic Architecture. Ann Neurol 2023; 94:825-835. [PMID: 37597255 PMCID: PMC10952415 DOI: 10.1002/ana.26765] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/12/2023] [Accepted: 08/05/2023] [Indexed: 08/21/2023]
Abstract
OBJECTIVE Familial mesial temporal lobe epilepsy (FMTLE) is an important focal epilepsy syndrome; its molecular genetic basis is unknown. Clinical descriptions of FMTLE vary between a mild syndrome with prominent déjà vu to a more severe phenotype with febrile seizures and hippocampal sclerosis. We aimed to refine the phenotype of FMTLE by analyzing a large cohort of patients and asked whether common risk variants for focal epilepsy and/or febrile seizures, measured by polygenic risk scores (PRS), are enriched in individuals with FMTLE. METHODS We studied 134 families with ≥ 2 first or second-degree relatives with temporal lobe epilepsy, with clear mesial ictal semiology required in at least one individual. PRS were calculated for 227 FMTLE cases, 124 unaffected relatives, and 16,077 population controls. RESULTS The age of patients with FMTLE onset ranged from 2.5 to 70 years (median = 18, interquartile range = 13-28 years). The most common focal seizure symptom was déjà vu (62% of cases), followed by epigastric rising sensation (34%), and fear or anxiety (22%). The clinical spectrum included rare cases with drug-resistance and/or hippocampal sclerosis. FMTLE cases had a higher mean focal epilepsy PRS than population controls (odds ratio = 1.24, 95% confidence interval = 1.06, 1.46, p = 0.007); in contrast, no enrichment for the febrile seizure PRS was observed. INTERPRETATION FMTLE is a generally mild drug-responsive syndrome with déjà vu being the commonest symptom. In contrast to dominant monogenic focal epilepsy syndromes, our molecular data support a polygenic basis for FMTLE. Furthermore, the PRS data suggest that sub-genome-wide significant focal epilepsy genome-wide association study single nucleotide polymorphisms are important risk variants for FMTLE. ANN NEUROL 2023;94:825-835.
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Affiliation(s)
- Rebekah V. Harris
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
| | - Karen L. Oliver
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
- Population Health and Immunity DivisionWalter and Eliza Hall Institute of Medical ResearchParkvilleVictoriaAustralia
- Department of Medical BiologyThe University of MelbourneParkvilleVictoriaAustralia
| | - Piero Perucca
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
- Bladin‐Berkovic Comprehensive Epilepsy Program, Department of NeurologyAustin HealthHeidelbergVictoriaAustralia
- Departments of Medicine and Neurology, Royal Melbourne HospitalThe University of MelbourneMelbourneVictoriaAustralia
- Department of NeurologyAlfred HealthMelbourneVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - Pasquale Striano
- IRCCS Istituto Giannina Gaslini, Member of ERN‐EpicareGenoaItaly
- Departments of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal, and Child HealthUniversity of GenoaGenoaItaly
| | - Angelo Labate
- Neurophysiopatology and Movement Disorders ClinicUniversity of MessinaMessinaItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Graecia University of CatanzaroCatanzaroItaly
| | - Antonella Riva
- IRCCS Istituto Giannina Gaslini, Member of ERN‐EpicareGenoaItaly
- Departments of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal, and Child HealthUniversity of GenoaGenoaItaly
| | - Bronwyn E. Grinton
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
| | - Joshua Reid
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
| | - Jessica Hutton
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
- Departments of Medicine and Neurology, Royal Melbourne HospitalThe University of MelbourneMelbourneVictoriaAustralia
- Department of NeurologyAlfred HealthMelbourneVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - Marian Todaro
- Departments of Medicine and Neurology, Royal Melbourne HospitalThe University of MelbourneMelbourneVictoriaAustralia
- Department of NeurologyAlfred HealthMelbourneVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - Terence J. O'Brien
- Departments of Medicine and Neurology, Royal Melbourne HospitalThe University of MelbourneMelbourneVictoriaAustralia
- Department of NeurologyAlfred HealthMelbourneVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - Patrick Kwan
- Departments of Medicine and Neurology, Royal Melbourne HospitalThe University of MelbourneMelbourneVictoriaAustralia
- Department of NeurologyAlfred HealthMelbourneVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - Lynette G. Sadleir
- Department of Paediatrics and Child HealthUniversity of OtagoWellingtonNew Zealand
| | - Saul A. Mullen
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
- Bladin‐Berkovic Comprehensive Epilepsy Program, Department of NeurologyAustin HealthHeidelbergVictoriaAustralia
| | - Emanuela Dazzo
- The CNR Institute of Neuroscience (CNR‐IN), National Research Council of ItalyPadovaItaly
| | - Douglas E. Crompton
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
- Department of NeurologyNorthern HealthEppingVictoriaAustralia
| | - Ingrid E. Scheffer
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
- Bladin‐Berkovic Comprehensive Epilepsy Program, Department of NeurologyAustin HealthHeidelbergVictoriaAustralia
- Florey Institute of Neuroscience and Mental HealthMelbourneVictoriaAustralia
- Murdoch Children's Research Institute and Department of PaediatricsUniversity of Melbourne, Royal Children's HospitalMelbourneVictoriaAustralia
| | - Melanie Bahlo
- Population Health and Immunity DivisionWalter and Eliza Hall Institute of Medical ResearchParkvilleVictoriaAustralia
- Department of Medical BiologyThe University of MelbourneParkvilleVictoriaAustralia
| | - Carlo Nobile
- Department of Paediatrics and Child HealthUniversity of OtagoWellingtonNew Zealand
| | - Antonio Gambardella
- Neurophysiopatology and Movement Disorders ClinicUniversity of MessinaMessinaItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Graecia University of CatanzaroCatanzaroItaly
| | - Samuel F. Berkovic
- Epilepsy Research Centre, Department of Medicine (Austin Health)The University of MelbourneHeidelbergVictoriaAustralia
- Bladin‐Berkovic Comprehensive Epilepsy Program, Department of NeurologyAustin HealthHeidelbergVictoriaAustralia
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Jakubek YA, Zhou Y, Stilp A, Bacon J, Wong JW, Ozcan Z, Arnett D, Barnes K, Bis JC, Boerwinkle E, Brody JA, Carson AP, Chasman DI, Chen J, Cho M, Conomos MP, Cox N, Doyle MF, Fornage M, Guo X, Kardia SLR, Lewis JP, Loos RJF, Ma X, Machiela MJ, Mack TM, Mathias RA, Mitchell BD, Mychaleckyj JC, North K, Pankratz N, Peyser PA, Preuss MH, Psaty B, Raffield LM, Vasan RS, Redline S, Rich SS, Rotter JI, Silverman EK, Smith JA, Smith AP, Taub M, Taylor KD, Yun J, Li Y, Desai P, Bick AG, Reiner AP, Scheet P, Auer PL. Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing. Nat Genet 2023; 55:1912-1919. [PMID: 37904051 PMCID: PMC10632132 DOI: 10.1038/s41588-023-01553-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 09/27/2023] [Indexed: 11/01/2023]
Abstract
Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.
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Affiliation(s)
- Yasminka A Jakubek
- Department of Internal Medicine, University of Kentucky, Lexington, KY, USA
| | - Ying Zhou
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Adrienne Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jason Bacon
- Department of Computer Science, Department of Biological Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Justin W Wong
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Zuhal Ozcan
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | | | - Kathleen Barnes
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington Seattle, Seattle, WA, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Nancy Cox
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Margaret F Doyle
- Department of Pathology and Laboratory Medicine, The University of Vermont Larner College of Medicine, Colchester, VT, USA
| | - Myriam Fornage
- University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Joshua P Lewis
- Department of Medicine, University of Maryland Baltimore, Baltimore, MD, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Xiaolong Ma
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Taralynn M Mack
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rasika A Mathias
- Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MA, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland Baltimore, Baltimore, MD, USA
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina Chapel-Hill, Chapel Hill, NC, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Department of Epidemiology, Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Susan Redline
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, USA
| | - Aaron P Smith
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA
| | - Margaret Taub
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MA, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jeong Yun
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Yun Li
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina Chapel-Hill, Chapel Hill, NC, USA
| | - Pinkal Desai
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alexander G Bick
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Paul Scheet
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA.
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Mbatchou J, Abney M, McPeek MS. BRASS: Permutation methods for binary traits in genetic association studies with structured samples. PLoS Genet 2023; 19:e1011020. [PMID: 37934792 PMCID: PMC10656004 DOI: 10.1371/journal.pgen.1011020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 11/17/2023] [Accepted: 10/16/2023] [Indexed: 11/09/2023] Open
Abstract
In genetic association analysis of complex traits, permutation testing can be a valuable tool for assessing significance when the distribution of the test statistic is unknown or not well-approximated. This commonly arises, e.g, in tests of gene-set, pathway or genome-wide significance, or when the statistic is formed by machine learning or data adaptive methods. Existing applications include eQTL mapping, association testing with rare variants, inclusion of admixed individuals in genetic association analysis, and epistasis detection among many others. For genetic association testing in samples with population structure and/or relatedness, use of naive permutation can lead to inflated type 1 error. To address this in quantitative traits, the MVNpermute method was developed. However, for association mapping of a binary trait, the relationship between the mean and variance makes both naive permutation and the MVNpermute method invalid. We propose BRASS, a permutation method for binary traits, for use in association mapping in structured samples. In addition to modeling structure in the sample, BRASS allows for covariates, ascertainment and simultaneous testing of multiple markers, and it accommodates a wide range of test statistics. In simulation studies, we compare BRASS to other permutation and resampling-based methods in a range of scenarios that include population structure, familial relatedness, ascertainment and phenotype model misspecification. In these settings, we demonstrate the superior control of type 1 error by BRASS compared to the other 6 methods considered. We apply BRASS to assess genome-wide significance for association analyses in domestic dog for elbow dysplasia (ED) and idiopathic epilepsy (IE). For both traits we detect previously identified associations, and in addition, for ED, we detect significant association with a SNP on chromosome 35 that was not detected by previous analyses, demonstrating the potential of the method.
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Affiliation(s)
- Joelle Mbatchou
- Regeneron Genetics Center, Tarrytown, New York, United States of America
- Department of Statistics, The University of Chicago, Chicago, Illinois, United States of America
| | - Mark Abney
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, Illinois, United States of America
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
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34
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Zhou J, Indik CE, Kuipers TB, Li C, Nivard MG, Ryan CP, Tucker-Drob EM, Taeubert MJ, Wang S, Wang T, Conley D, Heijmans BT, Lumey LH, Belsky DW. Genetic analysis of selection bias in a natural experiment: Investigating in-utero famine effects on elevated body mass index in the Dutch Hunger Winter Families Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.23.23297381. [PMID: 37961592 PMCID: PMC10635168 DOI: 10.1101/2023.10.23.23297381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Natural-experiment designs that compare survivors of in-utero famine exposure to unaffected controls suggest that in-utero undernutrition predisposes to development of obesity. However, birth rates drop dramatically during famines. Selection bias could arise if factors that contribute to obesity also protect fertility and/or fetal survival under famine conditions. We investigated this hypothesis using genetic analysis of a famine-exposed birth cohort. We genotyped participants in the Dutch Hunger Winter Families Study (DHWFS, N=950; 45% male), of whom 51% were exposed to the 1944-1945 Dutch Famine during gestation and 49% were their unexposed same-sex siblings or "time controls" born before or after the famine in the same hospitals. We computed body-mass index (BMI) polygenic indices (PGIs) in DHWFS participants and compared BMI PGIs between famine-exposed and control groups. Participants with higher polygenic risk had higher BMIs (Pearson r=0.42, p<0.001). However, differences between BMI PGIs of famine-exposed participants and controls were small and not statistically different from zero across specifications (Cohen's d=0.10, p>0.092). Our findings did not indicate selection bias, supporting the validity of the natural-experiment design within DHWFS. In summary, our study outlines a novel approach to explore the presence of selection bias in famine and other natural experiment studies.
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Araujo DS, Nguyen C, Hu X, Mikhaylova AV, Gignoux C, Ardlie K, Taylor KD, Durda P, Liu Y, Papanicolaou G, Cho MH, Rich SS, Rotter JI, Im HK, Manichaikul A, Wheeler HE. Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations. HGG ADVANCES 2023; 4:100216. [PMID: 37869564 PMCID: PMC10589725 DOI: 10.1016/j.xhgg.2023.100216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 06/27/2023] [Indexed: 10/24/2023] Open
Abstract
Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.
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Affiliation(s)
- Daniel S. Araujo
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
| | - Chris Nguyen
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
| | - Xiaowei Hu
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - George Papanicolaou
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - NHLBI TOPMed Consortium
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Heather E. Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
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Sohail M, Palma-Martínez MJ, Chong AY, Quinto-Cortés CD, Barberena-Jonas C, Medina-Muñoz SG, Ragsdale A, Delgado-Sánchez G, Cruz-Hervert LP, Ferreyra-Reyes L, Ferreira-Guerrero E, Mongua-Rodríguez N, Canizales-Quintero S, Jimenez-Kaufmann A, Moreno-Macías H, Aguilar-Salinas CA, Auckland K, Cortés A, Acuña-Alonzo V, Gignoux CR, Wojcik GL, Ioannidis AG, Fernández-Valverde SL, Hill AVS, Tusié-Luna MT, Mentzer AJ, Novembre J, García-García L, Moreno-Estrada A. Mexican Biobank advances population and medical genomics of diverse ancestries. Nature 2023; 622:775-783. [PMID: 37821706 PMCID: PMC10600006 DOI: 10.1038/s41586-023-06560-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 08/22/2023] [Indexed: 10/13/2023]
Abstract
Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories and complex trait architectures remain hidden owing to insufficient data1. To fill this gap, the Mexican Biobank project genotyped 6,057 individuals from 898 rural and urban localities across all 32 states in Mexico at a resolution of 1.8 million genome-wide markers with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Here, using ancestry deconvolution and inference of identity-by-descent segments, we inferred ancestral population sizes across Mesoamerican regions over time, unravelling Indigenous, colonial and postcolonial demographic dynamics2-6. We observed variation in runs of homozygosity among genomic regions with different ancestries reflecting distinct demographic histories and, in turn, different distributions of rare deleterious variants. We conducted genome-wide association studies (GWAS) for 22 complex traits and found that several traits are better predicted using the Mexican Biobank GWAS compared to the UK Biobank GWAS7,8. We identified genetic and environmental factors associating with trait variation, such as the length of the genome in runs of homozygosity as a predictor for body mass index, triglycerides, glucose and height. This study provides insights into the genetic histories of individuals in Mexico and dissects their complex trait architectures, both crucial for making precision and preventive medicine initiatives accessible worldwide.
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Affiliation(s)
- Mashaal Sohail
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico.
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Centro de Ciencias Genómicas (CCG), Universidad Nacional Autónoma de México (UNAM), Cuernavaca, Mexico.
| | - María J Palma-Martínez
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Amanda Y Chong
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Consuelo D Quinto-Cortés
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Carmina Barberena-Jonas
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Santiago G Medina-Muñoz
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Aaron Ragsdale
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Luis Pablo Cruz-Hervert
- Instituto Nacional de Salud Pública (INSP), Cuernavaca, Mexico
- División de Estudios de Posgrado e Investigación, Facultad de Odontología, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | | | | | | | | | - Andrés Jimenez-Kaufmann
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
| | - Hortensia Moreno-Macías
- Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas UNAM/Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Universidad Autónoma Metropolitana, Mexico City, Mexico
| | - Carlos A Aguilar-Salinas
- Division de Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Kathryn Auckland
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Adrián Cortés
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Selene L Fernández-Valverde
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico
- School of Biotechnology and Biomolecular Sciences and the RNA Institute, The University of New South Wales, Sydney, New South Wales, Australia
| | - Adrian V S Hill
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- The Jenner Institute, University of Oxford, Oxford, UK
| | - María Teresa Tusié-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas UNAM/Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Alexander J Mentzer
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - John Novembre
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | | | - Andrés Moreno-Estrada
- Unidad de Genómica Avanzada (UGA-LANGEBIO), Centro de Investigación y Estudios Avanzados del IPN (Cinvestav), Irapuato, Mexico.
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Austin-Zimmerman I, Levey DF, Giannakopoulou O, Deak JD, Galimberti M, Adhikari K, Zhou H, Denaxas S, Irizar H, Kuchenbaecker K, McQuillin A, Concato J, Buysse DJ, Gaziano JM, Gottlieb DJ, Polimanti R, Stein MB, Bramon E, Gelernter J. Genome-wide association studies and cross-population meta-analyses investigating short and long sleep duration. Nat Commun 2023; 14:6059. [PMID: 37770476 PMCID: PMC10539313 DOI: 10.1038/s41467-023-41249-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 08/28/2023] [Indexed: 09/30/2023] Open
Abstract
Sleep duration has been linked to a wide range of negative health outcomes and to reduced life expectancy. We present genome-wide association studies of short ( ≤ 5 h) and long ( ≥ 10 h) sleep duration in adults of European (N = 445,966), African (N = 27,785), East Asian (N = 3141), and admixed-American (N = 16,250) ancestry from UK Biobank and the Million Veteran Programme. In a cross-population meta-analysis, we identify 84 independent loci for short sleep and 1 for long sleep. We estimate SNP-based heritability for both sleep traits in each ancestry based on population derived linkage disequilibrium (LD) scores using cov-LDSC. We identify positive genetic correlation between short and long sleep traits (rg = 0.16 ± 0.04; p = 0.0002), as well as similar patterns of genetic correlation with other psychiatric and cardiometabolic phenotypes. Mendelian randomisation reveals a directional causal relationship between short sleep and depression, and a bidirectional causal relationship between long sleep and depression.
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Affiliation(s)
- Isabelle Austin-Zimmerman
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, W1T 7BN, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Olga Giannakopoulou
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, W1T 7BN, UK
- UCL Genetics Institute, Division of Biosciences, University College London, London, WC1E 6BT, UK
| | - Joseph D Deak
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Marco Galimberti
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Keyrun Adhikari
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Hang Zhou
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Spiros Denaxas
- Health Data Research UK, Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Haritz Irizar
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, W1T 7BN, UK
- Department of Genetics & Genomic Sciences and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Karoline Kuchenbaecker
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, W1T 7BN, UK
- UCL Genetics Institute, Division of Biosciences, University College London, London, WC1E 6BT, UK
| | - Andrew McQuillin
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, W1T 7BN, UK
| | - John Concato
- School of Medicine, Yale University, New Haven, CT, 06511, USA
- Office of Medical Policy, Center for Drug Evaluation and Research, Food and Drug Administration (FDA), Silver Spring, MD, USA
| | - Daniel J Buysse
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Daniel J Gottlieb
- VA Boston Healthcare System, 1400 VFW Parkway (111PI), West Roxbury, MA, 02132, USA
- Division of Sleep and Circadian Disorders, Brigham & Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Renato Polimanti
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Murray B Stein
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
- Departments of Psychiatry and Herbert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Elvira Bramon
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, W1T 7BN, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA.
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38
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Roshandel D, Sanders EJ, Shakeshaft A, Panjwani N, Lin F, Collingwood A, Hall A, Keenan K, Deneubourg C, Mirabella F, Topp S, Zarubova J, Thomas RH, Talvik I, Syvertsen M, Striano P, Smith AB, Selmer KK, Rubboli G, Orsini A, Ng CC, Møller RS, Lim KS, Hamandi K, Greenberg DA, Gesche J, Gardella E, Fong CY, Beier CP, Andrade DM, Jungbluth H, Richardson MP, Pastore A, Fanto M, Pal DK, Strug LJ. SLCO5A1 and synaptic assembly genes contribute to impulsivity in juvenile myoclonic epilepsy. NPJ Genom Med 2023; 8:28. [PMID: 37770509 PMCID: PMC10539321 DOI: 10.1038/s41525-023-00370-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/29/2023] [Indexed: 09/30/2023] Open
Abstract
Elevated impulsivity is a key component of attention-deficit hyperactivity disorder (ADHD), bipolar disorder and juvenile myoclonic epilepsy (JME). We performed a genome-wide association, colocalization, polygenic risk score, and pathway analysis of impulsivity in JME (n = 381). Results were followed up with functional characterisation using a drosophila model. We identified genome-wide associated SNPs at 8q13.3 (P = 7.5 × 10-9) and 10p11.21 (P = 3.6 × 10-8). The 8q13.3 locus colocalizes with SLCO5A1 expression quantitative trait loci in cerebral cortex (P = 9.5 × 10-3). SLCO5A1 codes for an organic anion transporter and upregulates synapse assembly/organisation genes. Pathway analysis demonstrates 12.7-fold enrichment for presynaptic membrane assembly genes (P = 0.0005) and 14.3-fold enrichment for presynaptic organisation genes (P = 0.0005) including NLGN1 and PTPRD. RNAi knockdown of Oatp30B, the Drosophila polypeptide with the highest homology to SLCO5A1, causes over-reactive startling behaviour (P = 8.7 × 10-3) and increased seizure-like events (P = 6.8 × 10-7). Polygenic risk score for ADHD genetically correlates with impulsivity scores in JME (P = 1.60 × 10-3). SLCO5A1 loss-of-function represents an impulsivity and seizure mechanism. Synaptic assembly genes may inform the aetiology of impulsivity in health and disease.
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Affiliation(s)
- Delnaz Roshandel
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Eric J Sanders
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, The University of Toronto, Toronto, Canada
| | - Amy Shakeshaft
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Naim Panjwani
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Fan Lin
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Amber Collingwood
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Anna Hall
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Katherine Keenan
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Celine Deneubourg
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Filippo Mirabella
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Simon Topp
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jana Zarubova
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Rhys H Thomas
- Newcastle upon Tyne NHS Foundation Trust, Newcastle, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | | | - Marte Syvertsen
- Department of Neurology, Drammen Hospital, Vestre Viken Health Trust, Oslo, Norway
| | - Pasquale Striano
- IRCCS Istituto 'G. Gaslini', Genova, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Anna B Smith
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Kaja K Selmer
- Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- National Centre for Epilepsy, Oslo University Hospital, Oslo, Norway
| | - Guido Rubboli
- Danish Epilepsy Centre, Dianalund, Denmark
- University of Copenhagen, Copenhagen, Denmark
| | - Alessandro Orsini
- Pediatric Neurology, Azienda Ospedaliero-Universitaria Pisana, Pisa University Hospital, Pisa, Italy
| | - Ching Ching Ng
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Rikke S Møller
- Danish Epilepsy Centre, Dianalund, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Kheng Seang Lim
- Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Khalid Hamandi
- The Welsh Epilepsy Unit, Department of Neurology Cardiff & Vale University Health Board, Cardiff, UK
- Department of Psychological Medicine and Clinical Neuroscience, Cardiff University, Cardiff, UK
| | | | | | - Elena Gardella
- Danish Epilepsy Centre, Dianalund, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Choong Yi Fong
- Division of Paediatric Neurology, Department of Pediatrics, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Danielle M Andrade
- Adult Epilepsy Genetics Program, Krembil Research Institute, University of Toronto, Toronto, Canada
| | - Heinz Jungbluth
- Randall Centre for Cell and Molecular Biophysics, Muscle Signalling Section, Faculty of Life Sciences and Medicine, King's College London, London, UK
- Department of Paediatric Neurology, Neuromuscular Service, Evelina's Children Hospital, Guy's & St. Thomas' Hospital NHS Foundation Trust, London, UK
| | - Mark P Richardson
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- King's College Hospital, London, UK
| | - Annalisa Pastore
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Manolis Fanto
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Deb K Pal
- Department of Basic & Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK.
- King's College Hospital, London, UK.
| | - Lisa J Strug
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.
- Division of Biostatistics, Dalla Lana School of Public Health, The University of Toronto, Toronto, Canada.
- Departments of Statistical Sciences and Computer Science, The University of Toronto, Toronto, Canada.
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada.
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39
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Jiang MZ, Gaynor SM, Li X, Van Buren E, Stilp A, Buth E, Wang FF, Manansala R, Gogarten SM, Li Z, Polfus LM, Salimi S, Bis JC, Pankratz N, Yanek LR, Durda P, Tracy RP, Rich SS, Rotter JI, Mitchell BD, Lewis JP, Psaty BM, Pratte KA, Silverman EK, Kaplan RC, Avery C, North K, Mathias RA, Faraday N, Lin H, Wang B, Carson AP, Norwood AF, Gibbs RA, Kooperberg C, Lundin J, Peters U, Dupuis J, Hou L, Fornage M, Benjamin EJ, Reiner AP, Bowler RP, Lin X, Auer PL, Raffield LM. Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.10.555215. [PMID: 37745480 PMCID: PMC10515765 DOI: 10.1101/2023.09.10.555215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.
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Affiliation(s)
- Min-Zhi Jiang
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA
| | - Sheila M. Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
- Regeneron Genetics Center, Tarrytown, NY, 10591, USA
| | - Xihao Li
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Eric Van Buren
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
| | - Adrienne Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Erin Buth
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Fei Fei Wang
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Regina Manansala
- Centre for Health Economics Research & Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO) WHO Collaborating Centre, University of Antwerp, Antwerp, BE
| | | | - Zilin Li
- School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Linda M. Polfus
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, 90033, USA
| | - Shabnam Salimi
- Department of Epidemiology and Public Health, Division of Gerontology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA, 98195, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Lisa R. Yanek
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, 1830 E Monument St Rm 8024, Baltimore, MD, 21287, USA
| | - Peter Durda
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT, 05446, USA
| | - Russell P. Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT, 05446, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, 200 Jeanette Lancaster Way, Charlottesville, VA, 22903, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA, 90502, USA
| | - Braxton D. Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD, 21201, USA
| | - Joshua P. Lewis
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD, 21201, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA, 98195, USA
- Departments of Epidemiology and Health Systems and Population Health, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98101, USA
| | - Katherine A. Pratte
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Edwin K. Silverman
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA
| | - Robert C. Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Christy Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rasika A. Mathias
- Department of Medicine, Allergy and Clinical Immunology, Johns Hopkins University School of Medicine, 5501 Hopkins Bayview Cir JHAAC Room 3B53, Baltimore, MD, 21287, USA
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA, 01655, USA
| | - Biqi Wang
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA, 01655, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS, 39213, USA
| | - Arnita F. Norwood
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS, 39213, USA
| | - Richard A. Gibbs
- Department of Molecular and Human Genetics, Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jessica Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Josée Dupuis
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, H3A 1G1, Canada
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Emelia J. Benjamin
- Department of Medicine, Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, 02118, USA
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA, 01702, USA
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98105, USA
| | - Russell P. Bowler
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
| | - Paul L. Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA
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Gratton J, Humphries SE, Futema M. Prevalence of FH-Causing Variants and Impact on LDL-C Concentration in European, South Asian, and African Ancestry Groups of the UK Biobank-Brief Report. Arterioscler Thromb Vasc Biol 2023; 43:1737-1742. [PMID: 37409534 PMCID: PMC10443626 DOI: 10.1161/atvbaha.123.319438] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Familial hypercholesterolemia (FH) is a monogenic disease that causes high low-density lipoprotein cholesterol (LDL-C) and higher risk of premature coronary heart disease. The prevalence of FH-causing variants and their association with LDL-C in non-European populations remains largely unknown. Using DNA diagnosis in a population-based cohort, we aimed to estimate the prevalence of FH across 3 major ancestry groups in the United Kingdom. METHODS Principal component analysis was used to distinguish genetic ancestry in UK Biobank participants. Whole exome sequencing data were analyzed to provide a genetic diagnosis of FH. LDL-C concentrations were adjusted for statin use. RESULTS Principal component analysis distinguished 140 439 European, 4067 South Asian, and 3906 African participants with lipid and whole exome sequencing data. There were significant differences between the 3 groups, including total and LDL-C concentrations, and prevalence and incidence of coronary heart disease. We identified 488, 18, and 15 participants of European, South Asian, and African ancestry carrying a likely pathogenic or pathogenic FH-variant. No statistical difference in the prevalence of an FH-causing variant was observed: 1 out of 288 (95% CI, 1/316-1/264) in European, 1 out of 260 (95% CI, 1/526-1/173) in African, and 1 out of 226 (95% CI, 1/419-1/155) in South Asian. Carriers of an FH-causing variant had significantly higher LDL-C concentration than noncarriers in every ancestry group. There was no difference in median (statin-use adjusted) LDL-C concentration in FH-variant carriers depending on their ancestry background. Self-reported statin use was nonsignificantly highest in FH-variant carriers of South Asian ancestry (55.6%), followed by African (40.0%) and European (33.8%; P=0.15). CONCLUSIONS The prevalence of FH-causing variants in the UK Biobank is similar across the ancestry groups analyzed. Despite overall differences in lipid concentrations, FH-variant carriers across the 3 ancestry groups had similar LDL-C levels. In all ancestry groups, the proportion of FH-variant carriers treated with lipid-lowering therapy should be improved to reduce future risk of premature coronary heart disease.
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Affiliation(s)
- Jasmine Gratton
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, United Kingdom (J.G., S.E.H., M.F.)
| | - Steve E. Humphries
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, United Kingdom (J.G., S.E.H., M.F.)
| | - Marta Futema
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, United Kingdom (J.G., S.E.H., M.F.)
- Cardiology Research Centre, Molecular and Clinical Sciences Research Institute, St George’s University of London, United Kingdom (M.F.)
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Arbeev KG, Ukraintseva S, Bagley O, Duan H, Wu D, Akushevich I, Stallard E, Kulminski A, Christensen K, Feitosa MF, O’Connell JR, Parker D, Whitson H, Yashin AI. Interactions between genes involved in physiological dysregulation and axon guidance: role in Alzheimer's disease. Front Genet 2023; 14:1236509. [PMID: 37719713 PMCID: PMC10500346 DOI: 10.3389/fgene.2023.1236509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/17/2023] [Indexed: 09/19/2023] Open
Abstract
Dysregulation of physiological processes may contribute to Alzheimer's disease (AD) development. We previously found that an increase in the level of physiological dysregulation (PD) in the aging body is associated with declining resilience and robustness to major diseases. Also, our genome-wide association study found that genes associated with the age-related increase in PD frequently represented pathways implicated in axon guidance and synaptic function, which in turn were linked to AD and related traits (e.g., amyloid, tau, neurodegeneration) in the literature. Here, we tested the hypothesis that genes involved in PD and axon guidance/synapse function may jointly influence onset of AD. We assessed the impact of interactions between SNPs in such genes on AD onset in the Long Life Family Study and sought to replicate the findings in the Health and Retirement Study. We found significant interactions between SNPs in the UNC5C and CNTN6, and PLXNA4 and EPHB2 genes that influenced AD onset in both datasets. Associations with individual SNPs were not statistically significant. Our findings, thus, support a major role of genetic interactions in the heterogeneity of AD and suggest the joint contribution of genes involved in PD and axon guidance/synapse function (essential for the maintenance of complex neural networks) to AD development.
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Affiliation(s)
- Konstantin G. Arbeev
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States
| | - Svetlana Ukraintseva
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States
| | - Olivia Bagley
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States
| | - Hongzhe Duan
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States
| | - Deqing Wu
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States
| | - Igor Akushevich
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States
| | - Eric Stallard
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States
| | - Alexander Kulminski
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States
| | - Kaare Christensen
- Danish Aging Research Center, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Mary F. Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
| | - Jeffrey R. O’Connell
- Division of Endocrinology, Diabetes and Nutrition and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Daniel Parker
- Duke Center for the Study of Aging and Human Development, Duke University, Durham, NC, United States
| | - Heather Whitson
- Duke Center for the Study of Aging and Human Development, Duke University, Durham, NC, United States
- Durham VA Geriatrics Research Education and Clinical Center, Durham, NC, United States
| | - Anatoliy I. Yashin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States
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Zhang X, Brody JA, Graff M, Highland HM, Chami N, Xu H, Wang Z, Ferrier K, Chittoor G, Josyula NS, Li X, Li Z, Allison MA, Becker DM, Bielak LF, Bis JC, Boorgula MP, Bowden DW, Broome JG, Buth EJ, Carlson CS, Chang KM, Chavan S, Chiu YF, Chuang LM, Conomos MP, DeMeo DL, Du M, Duggirala R, Eng C, Fohner AE, Freedman BI, Garrett ME, Guo X, Haiman C, Heavner BD, Hidalgo B, Hixson JE, Ho YL, Hobbs BD, Hu D, Hui Q, Hwu CM, Jackson RD, Jain D, Kalyani RR, Kardia SL, Kelly TN, Lange EM, LeNoir M, Li C, Marchand LL, McDonald MLN, McHugh CP, Morrison AC, Naseri T, O’Connell J, O’Donnell CJ, Palmer ND, Pankow JS, Perry JA, Peters U, Preuss MH, Rao D, Regan EA, Reupena SM, Roden DM, Rodriguez-Santana J, Sitlani CM, Smith JA, Tiwari HK, Vasan RS, Wang Z, Weeks DE, Wessel J, Wiggins KL, Wilkens LR, Wilson PW, Yanek LR, Yoneda ZT, Zhao W, Zöllner S, Arnett DK, Ashley-Koch AE, Barnes KC, Blangero J, Boerwinkle E, Burchard EG, Carson AP, Chasman DI, Chen YDI, Curran JE, Fornage M, Gordeuk VR, He J, Heckbert SR, Hou L, Irvin MR, Kooperberg C, Minster RL, Mitchell BD, Nouraie M, Psaty BM, Raffield LM, Reiner AP, Rich SS, Rotter JI, Shoemaker MB, Smith NL, Taylor KD, Telen MJ, Weiss ST, Zhang Y, Heard-Costa N, Sun YV, Lin X, Adrienne Cupples L, Lange LA, Liu CT, Loos RJ, North KE, Justice AE. WHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.21.23293271. [PMID: 37662265 PMCID: PMC10473809 DOI: 10.1101/2023.08.21.23293271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.
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Affiliation(s)
- Xinruo Zhang
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Heather M. Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hanfei Xu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kendra Ferrier
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | | | | | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zilin Li
- Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Matthew A. Allison
- Department of Family Medicine, Division of Preventive Medicine, The University of California San Diego, La Jolla, CA, USA
| | - Diane M. Becker
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lawrence F. Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | | | - Donald W. Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jai G. Broome
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | - Erin J. Buth
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Christopher S. Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kyong-Mi Chang
- The Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sameer Chavan
- Department of Medicine, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Yen-Feng Chiu
- Institute of Population Health Sciences, National Health Research Institutes, Taipei, Taiwan
| | - Lee-Ming Chuang
- Department of Internal Medicine, Division of Metabolism/Endocrinology, National Taiwan University Hospital, Taipei, Taiwan
| | - Matthew P. Conomos
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Dawn L. DeMeo
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Margaret Du
- Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ravindranath Duggirala
- Life Sciences, College of Arts and Sciences, Texas A&M University-San Antonio, San Antonio, TX, USA
| | - Celeste Eng
- Department of Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, CA, USA
| | - Alison E. Fohner
- Epidemiology, Institute of Public Health Genetics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Barry I. Freedman
- Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Melanie E. Garrett
- Department of Medicine, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Xiuqing Guo
- Department of Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Chris Haiman
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Benjamin D. Heavner
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Bertha Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - James E. Hixson
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yuk-Lam Ho
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Brian D. Hobbs
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Donglei Hu
- Department of Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, CA, USA
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Chii-Min Hwu
- Department of Medicine, Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan, Taiwan
| | | | - Deepti Jain
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Rita R. Kalyani
- Department of Medicine, Endocrinology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sharon L.R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Tanika N. Kelly
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Ethan M. Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Michael LeNoir
- Department of Pediatrics, Bay Area Pediatrics, Oakland, CA, USA
| | - Changwei Li
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Loic Le. Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Merry-Lynn N. McDonald
- Department of Medicine, Pulmonary, Allergy and Critical Care, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Caitlin P. McHugh
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Alanna C. Morrison
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Take Naseri
- Ministry of Health, Government of Samoa, Apia, Samoa
| | | | - Jeffrey O’Connell
- Department of Medicine, Program for Personalized and Genomic Medicine, University of Maryland, Baltimore, MD, USA
| | - Christopher J. O’Donnell
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James A. Perry
- Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Michael H. Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - D.C. Rao
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Elizabeth A. Regan
- Department of Medicine, Rheumatology, National Jewish Health, Denver, CO, USA
| | | | - Dan M. Roden
- Medicine, Pharmacology, and Biomedical Informatics, Clinical Pharmacology and Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Colleen M. Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Hemant K. Tiwari
- Department of Biostatistics, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | | | - Zeyuan Wang
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Daniel E. Weeks
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jennifer Wessel
- Department of Epidemiology, Indiana University, Indianapolis, IN, USA
- Department of Medicine, Indiana University, Indianapolis, IN, USA
- Diabaetes Translational Research Center, Indiana University, Indianapolis, IN, USA
| | - Kerri L. Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Lynne R. Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Peter W.F. Wilson
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Lisa R. Yanek
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zachary T. Yoneda
- Department of Medicine, Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Sebastian Zöllner
- Department of Biostatistics, Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Donna K. Arnett
- Department of Epidemiology, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Allison E. Ashley-Koch
- Department of Medicine, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Kathleen C. Barnes
- Department of Medicine, School of Medicine, University of Colorado, Aurora, CO, USA
| | - John Blangero
- Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Esteban G. Burchard
- Bioengineering and Therapeutic Sciences and Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, CA, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi, Jackson, MI, USA
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yii-Der Ida Chen
- Department of Medical Genetics, Genomic Outcomes, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Joanne E. Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Myriam Fornage
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Victor R. Gordeuk
- Department of Medicine, School of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Jiang He
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Lifang Hou
- Northwestern University, Chicago, IL, USA
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ryan L. Minster
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Braxton D. Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland, Baltimore, MD, USA
| | - Mehdi Nouraie
- Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Stephen S. Rich
- Public Health Science, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I. Rotter
- Department of Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - M. Benjamin Shoemaker
- Department of Medicine, Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicholas L. Smith
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
- Seattle Epidemiologic Research and Information Center, Office of Research and Development, Department of Veterans Affairs, Seattle, WA, USA
| | - Kent D. Taylor
- Department of Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marilyn J. Telen
- Department of Medicine, Hematology, Duke University Medical Center, Durham, NC, USA
| | - Scott T. Weiss
- Department of Medicine, Channing Division of Network Medicine, Harvard Medical School, Boston, MA, USA
| | - Yingze Zhang
- Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nancy Heard-Costa
- Framingham Heart Study, School of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Yan V. Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Statistics, Harvard University, Boston, MA, USA
| | - L. Adrienne Cupples
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Ching-Ti Liu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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43
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Venkataraghavan S, Pankow JS, Boerwinkle E, Fornage M, Selvin E, Ray D. Epigenome-wide association study of incident type 2 diabetes in Black and White participants from the Atherosclerosis Risk in Communities Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.09.23293896. [PMID: 37609313 PMCID: PMC10441493 DOI: 10.1101/2023.08.09.23293896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
DNA methylation studies of incident type 2 diabetes in US populations are limited, and to our knowledge none included individuals of African descent living in the US. We performed an epigenome-wide association analysis of blood-based methylation levels at CpG sites with incident type 2 diabetes using Cox regression in 2,091 Black and 1,029 White individuals from the Atherosclerosis Risk in Communities study. At an epigenome-wide significance threshold of 10-7, we detected 7 novel diabetes-associated CpG sites in C1orf151 (cg05380846: HR= 0.89, p = 8.4 × 10-12), ZNF2 (cg01585592: HR= 0.88, p = 1.6 × 10-9), JPH3 (cg16696007: HR= 0.87, p = 7.8 × 10-9), GPX6 (cg02793507: HR= 0.85, p = 2.7 × 10-8 and cg00647063: HR= 1.20, p = 2.5 × 10-8), chr17q25 (cg16865890: HR= 0.8, p = 6.9 × 10-8), and chr11p15 (cg13738793: HR= 1.11, p = 7.7 × 10-8). The CpG sites at C1orf151, ZNF2, JPH3 and GPX6, were identified in Black adults, chr17q25 was identified in White adults, and chr11p15 was identified upon meta-analyzing the two groups. The CpG sites at JPH3 and GPX6 were likely associated with incident type 2 diabetes independent of BMI. All the CpG sites, except at JPH3, were likely consequences of elevated glucose at baseline. We additionally replicated known type 2 diabetes-associated CpG sites including cg19693031 at TXNIP, cg00574958 at CPT1A, cg16567056 at PLBC2, cg11024682 at SREBF1, cg08857797 at VPS25, and cg06500161 at ABCG1, 3 of which were replicated in Black adults at the epigenome-wide threshold. We observed modest increase in type 2 diabetes variance explained upon addition of the significantly associated CpG sites to a Cox model that included traditional type 2 diabetes risk factors and fasting glucose (increase from 26.2% to 30.5% in Black adults; increase from 36.9% to 39.4% in White adults). We examined if groups of proximal CpG sites were associated with incident type 2 diabetes using a gene-region specific and a gene-region agnostic differentially methylated region (DMR) analysis. Our DMR analyses revealed several clusters of significant CpG sites, including a DMR consisting of a previously discovered CpG site at ADCY7 and promoter regions of TP63 which were differentially methylated across all race groups. This study illustrates improved discovery of CpG sites/regions by leveraging both individual CpG site and DMR analyses in an unexplored population. Our findings include genes linked to diabetes in experimental studies (e.g., GPX6, JPH3, and TP63), and future gene-specific methylation studies could elucidate the link between genes, environment, and methylation in the pathogenesis of type 2 diabetes.
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Affiliation(s)
- Sowmya Venkataraghavan
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of American
| | - Eric Boerwinkle
- The UTHealth School of Public Health, Houston, Texas, United States of America
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, Texas, United States of America
| | - Elizabeth Selvin
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
- Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
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Tan T, Atkinson EG. Strategies for the Genomic Analysis of Admixed Populations. Annu Rev Biomed Data Sci 2023; 6:105-127. [PMID: 37127050 PMCID: PMC10871708 DOI: 10.1146/annurev-biodatasci-020722-014310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations-the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.
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Affiliation(s)
- Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
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Pham C, Andrzejczyk K, Jurgens SJ, Lekanne Deprez R, Palm KC, Vermeer AM, Nijman J, Christiaans I, Barge-Schaapveld DQ, van Dessel PF, Beekman L, Choi SH, Lubitz SA, Skoric-Milosavljevic D, van den Bersselaar L, Jansen PR, Copier JS, Ellinor PT, Wilde AA, Bezzina CR, Lodder EM. Genetic Burden of TNNI3K in Diagnostic Testing of Patients With Dilated Cardiomyopathy and Supraventricular Arrhythmias. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:328-336. [PMID: 37199186 PMCID: PMC10426786 DOI: 10.1161/circgen.122.003975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 04/10/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Genetic variants in TNNI3K (troponin-I interacting kinase) have previously been associated with dilated cardiomyopathy (DCM), cardiac conduction disease, and supraventricular tachycardias. However, the link between TNNI3K variants and these cardiac phenotypes shows a lack of consensus concerning phenotype and protein function. METHODS We describe a systematic retrospective study of a cohort of patients undergoing genetic testing for cardiac arrhythmias and cardiomyopathy including TNNI3K. We further performed burden testing of TNNI3K in the UK Biobank. For 2 novel TNNI3K variants, we tested cosegregation. TNNI3K kinase function was estimated by TNNI3K autophosphorylation assays. RESULTS We demonstrate enrichment of rare coding TNNI3K variants in DCM patients in the Amsterdam cohort. In the UK Biobank, we observed an association between TNNI3K missense (but not loss-of-function) variants and DCM and atrial fibrillation. Furthermore, we demonstrate genetic segregation for 2 rare variants, TNNI3K-p.Ile512Thr and TNNI3K-p.His592Tyr, with phenotypes consisting of DCM, cardiac conduction disease, and supraventricular tachycardia, together with increased autophosphorylation. In contrast, TNNI3K-p.Arg556_Asn590del, a likely benign variant, demonstrated depleted autophosphorylation. CONCLUSIONS Our findings demonstrate an increased burden of rare coding TNNI3K variants in cardiac patients with DCM. Furthermore, we present 2 novel likely pathogenic TNNI3K variants with increased autophosphorylation, suggesting that enhanced autophosphorylation is likely to drive pathogenicity.
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Affiliation(s)
- Caroline Pham
- Department of Experimental Cardiology (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., C.R.B., E.M.L.), Heart Center, Amsterdam UMC location University of Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, the Netherlands (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., A.A.M.W., C.R.B., E.M.L.)
| | - Karolina Andrzejczyk
- Department of Experimental Cardiology (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., C.R.B., E.M.L.), Heart Center, Amsterdam UMC location University of Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, the Netherlands (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., A.A.M.W., C.R.B., E.M.L.)
| | - Sean J. Jurgens
- Department of Experimental Cardiology (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., C.R.B., E.M.L.), Heart Center, Amsterdam UMC location University of Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, the Netherlands (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., A.A.M.W., C.R.B., E.M.L.)
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.J.J., S.A.L., P.T.E.)
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (S.J.J., S.A.L., P.T.E.)
| | - Ronald Lekanne Deprez
- Department of Human Genetics, Amsterdam UMC location University of Amsterdam, the Netherlands (R.L.D., A.M.C.V., J.N., D.S.-M., P.R.J., E.M.L.)
| | - Kaylin C.A. Palm
- Department of Experimental Cardiology (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., C.R.B., E.M.L.), Heart Center, Amsterdam UMC location University of Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, the Netherlands (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., A.A.M.W., C.R.B., E.M.L.)
| | - Alexa M.C. Vermeer
- Department of Human Genetics, Amsterdam UMC location University of Amsterdam, the Netherlands (R.L.D., A.M.C.V., J.N., D.S.-M., P.R.J., E.M.L.)
| | - Janneke Nijman
- Department of Human Genetics, Amsterdam UMC location University of Amsterdam, the Netherlands (R.L.D., A.M.C.V., J.N., D.S.-M., P.R.J., E.M.L.)
| | - Imke Christiaans
- Department of Genetics, University Medical Center Groningen, University of Groningen, the Netherlands (I.C.)
| | | | - Pascal F.H.M. van Dessel
- Department of Cardiology, Thorax Center Twente, Medisch Spectrum Twente (MST), Enschede, the Netherlands (P.F.H.M.v.D.)
| | - Leander Beekman
- Department of Experimental Cardiology (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., C.R.B., E.M.L.), Heart Center, Amsterdam UMC location University of Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, the Netherlands (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., A.A.M.W., C.R.B., E.M.L.)
| | | | - Steven A. Lubitz
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.J.J., S.A.L., P.T.E.)
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (S.J.J., S.A.L., P.T.E.)
| | - Doris Skoric-Milosavljevic
- Department of Human Genetics, Amsterdam UMC location University of Amsterdam, the Netherlands (R.L.D., A.M.C.V., J.N., D.S.-M., P.R.J., E.M.L.)
| | - Lisa van den Bersselaar
- Department of Clinical Genetics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (L.v.d.B.)
| | - Philip R. Jansen
- Department of Human Genetics, Amsterdam UMC location University of Amsterdam, the Netherlands (R.L.D., A.M.C.V., J.N., D.S.-M., P.R.J., E.M.L.)
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Complex Trait Genetics, the Netherlands (P.R.J.)
| | - Jaël S. Copier
- Department of Experimental Cardiology (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., C.R.B., E.M.L.), Heart Center, Amsterdam UMC location University of Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, the Netherlands (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., A.A.M.W., C.R.B., E.M.L.)
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.J.J., S.A.L., P.T.E.)
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (S.J.J., S.A.L., P.T.E.)
| | - Arthur A.M. Wilde
- Department of Cardiology (A.A.M.W.), Heart Center, Amsterdam UMC location University of Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, the Netherlands (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., A.A.M.W., C.R.B., E.M.L.)
| | - Connie R. Bezzina
- Department of Experimental Cardiology (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., C.R.B., E.M.L.), Heart Center, Amsterdam UMC location University of Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, the Netherlands (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., A.A.M.W., C.R.B., E.M.L.)
| | - Elisabeth M. Lodder
- Department of Experimental Cardiology (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., C.R.B., E.M.L.), Heart Center, Amsterdam UMC location University of Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, the Netherlands (C.P., K.A., S.J.J., K.C.A.P., L.B., J.S.C., A.A.M.W., C.R.B., E.M.L.)
- Department of Human Genetics, Amsterdam UMC location University of Amsterdam, the Netherlands (R.L.D., A.M.C.V., J.N., D.S.-M., P.R.J., E.M.L.)
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Beck JJ, Ahmed T, Finnicum CT, Zwinderman K, Ehli EA, Boomsma DI, Hottenga JJ. Genetic Ancestry Estimates within Dutch Family Units and Across Genotyping Arrays: Insights from Empirical Analysis Using Two Estimation Methods. Genes (Basel) 2023; 14:1497. [PMID: 37510400 PMCID: PMC10379078 DOI: 10.3390/genes14071497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Accurate inference of genetic ancestry is crucial for population-based association studies, accounting for population heterogeneity and structure. This study analyzes genome-wide SNP data from the Netherlands Twin Register to compare genetic ancestry estimates. The focus is on the comparison of ancestry estimates between family members and individuals genotyped on multiple arrays (Affymetrix 6.0, Affymetrix Axiom, and Illumina GSA). Two conventional methods, principal component analysis and ADMIXTURE, were implemented to estimate ancestry, each serving its specific purpose, rather than for direct comparison. The results reveal that as the degree of genetic relatedness decreases, the Euclidean distances of genetic ancestry estimates between family members significantly increase (empirical p < 0.001), regardless of the estimation method and genotyping array. Ancestry estimates among individuals genotyped on multiple arrays also show statistically significant differences (empirical p < 0.001). Additionally, this study investigates the relationship between the ancestry estimates of non-identical twin offspring with ancestrally diverse parents and those with ancestrally similar parents. The results indicate a statistically significant weak correlation between the variation in ancestry estimates among offspring and differences in ancestry estimates among parents (Spearman's rho: 0.07, p = 0.005). This study highlights the utility of current methods in inferring genetic ancestry, emphasizing the importance of reference population composition in determining ancestry estimates.
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Affiliation(s)
- Jeffrey J Beck
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
| | - Talitha Ahmed
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Casey T Finnicum
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
| | - Koos Zwinderman
- Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Erik A Ehli
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
| | - Dorret I Boomsma
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
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Horimoto AR, Boyken LA, Blue EE, Grinde KE, Nafikov RA, Sohi HK, Nato AQ, Bis JC, Brusco LI, Morelli L, Ramirez A, Dalmasso MC, Temple S, Satizabal C, Browning SR, Seshadri S, Wijsman EM, Thornton TA. Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations. HGG ADVANCES 2023; 4:100207. [PMID: 37333771 PMCID: PMC10276158 DOI: 10.1016/j.xhgg.2023.100207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/16/2023] [Indexed: 06/20/2023] Open
Abstract
Alzheimer disease (AD) is the most common form of senile dementia, with high incidence late in life in many populations including Caribbean Hispanic (CH) populations. Such admixed populations, descended from more than one ancestral population, can present challenges for genetic studies, including limited sample sizes and unique analytical constraints. Therefore, CH populations and other admixed populations have not been well represented in studies of AD, and much of the genetic variation contributing to AD risk in these populations remains unknown. Here, we conduct genome-wide analysis of AD in multiplex CH families from the Alzheimer Disease Sequencing Project (ADSP). We developed, validated, and applied an implementation of a logistic mixed model for admixture mapping with binary traits that leverages genetic ancestry to identify ancestry-of-origin loci contributing to AD. We identified three loci on chromosome 13q33.3 associated with reduced risk of AD, where associations were driven by Native American (NAM) ancestry. This AD admixture mapping signal spans the FAM155A, ABHD13, TNFSF13B, LIG4, and MYO16 genes and was supported by evidence for association in an independent sample from the Alzheimer's Genetics in Argentina-Alzheimer Argentina consortium (AGA-ALZAR) study with considerable NAM ancestry. We also provide evidence of NAM haplotypes and key variants within 13q33.3 that segregate with AD in the ADSP whole-genome sequencing data. Interestingly, the widely used genome-wide association study approach failed to identify associations in this region. Our findings underscore the potential of leveraging genetic ancestry diversity in recently admixed populations to improve genetic mapping, in this case for AD-relevant loci.
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Affiliation(s)
| | - Lisa A. Boyken
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Elizabeth E. Blue
- Division of Medical Genetics/Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA 98195, USA
| | - Kelsey E. Grinde
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Department of Mathematics, Statistics and Computer Science, Macalester College, Saint Paul, MN 55105, USA
| | - Rafael A. Nafikov
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Division of Medical Genetics/Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Harkirat K. Sohi
- Division of Medical Genetics/Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Biomedical and Health Informatics Program, University of Washington, Seattle, WA 98195, USA
| | - Alejandro Q. Nato
- Division of Medical Genetics/Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV 25755, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Luis I. Brusco
- CENECON - Center of Behavioural Neurology and Neuropsychiatry, School of Medicine, University of Buenos Aires, C1121A6B Buenos Aires, Argentina
| | - Laura Morelli
- Laboratory of Brain Aging and Neurodegeneration-Fundación Instituto Leloir-IIBBA- National Scientific and Technical Research Council (CONICET), C1405BWE Ciudad Autónoma de Buenos Aires, Argentina
| | - Alfredo Ramirez
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, 50937 Cologne, Germany
- Department of Neurodegeneration and Gerontopsychiatry, University of Bonn, 53127 Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD) University of Cologne, 50674 Cologne, Germany
- Department of Psychiatry, UT Health San Antonio, San Antonio, TX 78229, USA
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX 78229, USA
| | - Maria Carolina Dalmasso
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, 50937 Cologne, Germany
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hospital El Cruce, National University A. Jauretche (UNAJ), B1888AAE Florencio Varela, Argentina
| | - Seth Temple
- Department of Statistics, University of Washington, Seattle, WA 98195, USA
| | - Claudia Satizabal
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas, San Antonio, TX 78229, USA
- Department of Neurology, University of Texas, San Antonio, TX 78229, USA
| | - Sharon R. Browning
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Sudha Seshadri
- Department of Neurology, University of Texas, San Antonio, TX 78229, USA
| | - Ellen M. Wijsman
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Division of Medical Genetics/Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Department of Statistics, University of Washington, Seattle, WA 98195, USA
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Kang M, Ang TFA, Devine SA, Sherva R, Mukherjee S, Trittschuh EH, Gibbons LE, Scollard P, Lee M, Choi SE, Klinedinst B, Nakano C, Dumitrescu LC, Durant A, Hohman TJ, Cuccaro ML, Saykin AJ, Kukull WA, Bennett DA, Wang LS, Mayeux RP, Haines JL, Pericak-Vance MA, Schellenberg GD, Crane PK, Au R, Lunetta KL, Mez JB, Farrer LA. A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores. Mol Neurodegener 2023; 18:40. [PMID: 37349795 PMCID: PMC10286470 DOI: 10.1186/s13024-023-00633-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 06/06/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND More than 75 common variant loci account for only a portion of the heritability for Alzheimer's disease (AD). A more complete understanding of the genetic basis of AD can be deduced by exploring associations with AD-related endophenotypes. METHODS We conducted genome-wide scans for cognitive domain performance using harmonized and co-calibrated scores derived by confirmatory factor analyses for executive function, language, and memory. We analyzed 103,796 longitudinal observations from 23,066 members of community-based (FHS, ACT, and ROSMAP) and clinic-based (ADRCs and ADNI) cohorts using generalized linear mixed models including terms for SNP, age, SNP × age interaction, sex, education, and five ancestry principal components. Significance was determined based on a joint test of the SNP's main effect and interaction with age. Results across datasets were combined using inverse-variance meta-analysis. Genome-wide tests of pleiotropy for each domain pair as the outcome were performed using PLACO software. RESULTS Individual domain and pleiotropy analyses revealed genome-wide significant (GWS) associations with five established loci for AD and AD-related disorders (BIN1, CR1, GRN, MS4A6A, and APOE) and eight novel loci. ULK2 was associated with executive function in the community-based cohorts (rs157405, P = 2.19 × 10-9). GWS associations for language were identified with CDK14 in the clinic-based cohorts (rs705353, P = 1.73 × 10-8) and LINC02712 in the total sample (rs145012974, P = 3.66 × 10-8). GRN (rs5848, P = 4.21 × 10-8) and PURG (rs117523305, P = 1.73 × 10-8) were associated with memory in the total and community-based cohorts, respectively. GWS pleiotropy was observed for language and memory with LOC107984373 (rs73005629, P = 3.12 × 10-8) in the clinic-based cohorts, and with NCALD (rs56162098, P = 1.23 × 10-9) and PTPRD (rs145989094, P = 8.34 × 10-9) in the community-based cohorts. GWS pleiotropy was also found for executive function and memory with OSGIN1 (rs12447050, P = 4.09 × 10-8) and PTPRD (rs145989094, P = 3.85 × 10-8) in the community-based cohorts. Functional studies have previously linked AD to ULK2, NCALD, and PTPRD. CONCLUSION Our results provide some insight into biological pathways underlying processes leading to domain-specific cognitive impairment and AD, as well as a conduit toward a syndrome-specific precision medicine approach to AD. Increasing the number of participants with harmonized cognitive domain scores will enhance the discovery of additional genetic factors of cognitive decline leading to AD and related dementias.
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Affiliation(s)
- Moonil Kang
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street E200, Boston, MA 02118 USA
| | - Ting Fang Alvin Ang
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
| | - Sherral A. Devine
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
| | - Richard Sherva
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street E200, Boston, MA 02118 USA
| | - Shubhabrata Mukherjee
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Emily H. Trittschuh
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA USA
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Laura E. Gibbons
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Phoebe Scollard
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Michael Lee
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Seo-Eun Choi
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Brandon Klinedinst
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Connie Nakano
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Logan C. Dumitrescu
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | - Alaina Durant
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | - Timothy J. Hohman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | - Michael L. Cuccaro
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, Miami, FL USA
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, WA USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL USA
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
| | - Richard P. Mayeux
- Department of Neurology, Columbia University School of Medicine, New York, NY USA
| | - Jonathan L. Haines
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH USA
| | | | - Gerard D. Schellenberg
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
| | - Paul K. Crane
- Department of Medicine, University of Washington School of Medicine, Seattle, WA USA
| | - Rhoda Au
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA USA
| | - Kathryn L. Lunetta
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA USA
| | - Jesse B. Mez
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street E200, Boston, MA 02118 USA
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
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Rajabli F, Tosto G, Hamilton-Nelson KL, Kunkle BW, Vardarajan BN, Naj A, Whitehead PG, Gardner OK, Bush WS, Sariya S, Mayeux RP, Farrer LA, Cuccaro ML, Vance JM, Griswold AJ, Schellenberg GD, Haines JL, Byrd GS, Reitz C, Beecham GW, Pericak-Vance MA, Martin ER. Admixture mapping identifies novel Alzheimer's disease risk regions in African Americans. Alzheimers Dement 2023; 19:2538-2548. [PMID: 36539198 PMCID: PMC10272044 DOI: 10.1002/alz.12865] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 08/09/2022] [Accepted: 08/17/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND This study used admixture mapping to prioritize the genetic regions associated with Alzheimer's disease (AD) in African American (AA) individuals, followed by ancestry-aware regression analysis to fine-map the prioritized regions. METHODS We analyzed 10,271 individuals from 17 different AA datasets. We performed admixture mapping and meta-analyzed the results. We then used regression analysis, adjusting for local ancestry main effects and interactions with genotype, to refine the regions identified from admixture mapping. Finally, we leveraged in silico annotation and differential gene expression data to prioritize AD-related variants and genes. RESULTS Admixture mapping identified two genome-wide significant loci on chromosomes 17p13.2 (p = 2.2 × 10-5 ) and 18q21.33 (p = 1.2 × 10-5 ). Our fine mapping of the chromosome 17p13.2 and 18q21.33 regions revealed several interesting genes such as the MINK1, KIF1C, and BCL2. DISCUSSION Our ancestry-aware regression approach showed that AA individuals have a lower risk of AD if they inherited African ancestry admixture block at the 17p13.2 locus. HIGHLIGHTS We identified two genome-wide significant admixture mapping signals: on chromosomes 17p13.2 and 18q21.33, which are novel in African American (AA) populations. Our ancestry-aware regression approach showed that AA individuals have a lower risk of Alzheimer's disease (AD) if they inherited African ancestry admixture block at the 17p13.2 locus. We found that the overall proportion of African ancestry does not differ between the cases and controls that suggest African genetic ancestry alone is not likely to explain the AD prevalence difference between AA and non-Hispanic White populations.
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Affiliation(s)
- Farid Rajabli
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Giuseppe Tosto
- Gertrude H. Sergievsky Center, Taub Institute for Research on the Aging Brain, Departments of Neurology, Psychiatry, and Epidemiology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Kara L. Hamilton-Nelson
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Brian W. Kunkle
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Badri N. Vardarajan
- Gertrude H. Sergievsky Center, Taub Institute for Research on the Aging Brain, Departments of Neurology, Psychiatry, and Epidemiology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Adam Naj
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PE, USA
| | - Patrice G. Whitehead
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Olivia K. Gardner
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - William S. Bush
- Department of Population & Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sanjeev Sariya
- Gertrude H. Sergievsky Center, Taub Institute for Research on the Aging Brain, Departments of Neurology, Psychiatry, and Epidemiology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Richard P. Mayeux
- Gertrude H. Sergievsky Center, Taub Institute for Research on the Aging Brain, Departments of Neurology, Psychiatry, and Epidemiology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Lindsay A. Farrer
- Departments of Medicine (Biomedical Genetics), Neurology, Ophthalmology, Epidemiology, and Biostatistics, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | - Michael L. Cuccaro
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
- Dr. John T. MacDonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Jeffrey M. Vance
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
- Dr. John T. MacDonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Anthony J. Griswold
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
- Dr. John T. MacDonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Gerard D. Schellenberg
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PE, USA
| | - Jonathan L. Haines
- Department of Population & Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Goldie S. Byrd
- Maya Angelou Center for Health Equity, Wake Forest University, Winston-Salem, NC, USA
| | - Christiane Reitz
- Gertrude H. Sergievsky Center, Taub Institute for Research on the Aging Brain, Departments of Neurology, Psychiatry, and Epidemiology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Gary W. Beecham
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
- Dr. John T. MacDonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Margaret A. Pericak-Vance
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
- Dr. John T. MacDonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Eden R. Martin
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
- Dr. John T. MacDonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
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50
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Zidane M, Haber M, Truong T, Rachédi F, Ory C, Chevillard S, Blanché H, Olaso R, Boland A, Conte É, Karimi M, Ren Y, Xhaard C, Souchard V, Gardon J, Taquet M, Bouville A, Deleuze JF, Drozdovitch V, de Vathaire F, Cazier JB. Genetic factors for differentiated thyroid cancer in French Polynesia: new candidate loci. PRECISION CLINICAL MEDICINE 2023; 6:pbad015. [PMID: 37383672 PMCID: PMC10294640 DOI: 10.1093/pcmedi/pbad015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 06/09/2023] [Indexed: 06/30/2023] Open
Abstract
Background Populations of French Polynesia (FP), where France performed atmospheric tests between 1966 and 1974, experience a high incidence of differentiated thyroid cancer (DTC). However, up to now, no sufficiently large study of DTC genetic factors in this population has been performed to reach definitive conclusion. This research aimed to analyze the genetic factors of DTC risk among the native FP populations. Methods We analyzed more than 300 000 single nucleotide polymorphisms (SNPs) genotyped in 283 DTC cases and 418 matched controls born in FP, most being younger than 15 years old at the time of the first nuclear tests. We analyzed the genetic profile of our cohort to identify population subgroups. We then completed a genome-wide analysis study on the whole population. Results We identified a specific genetic structure in the FP population reflecting admixture from Asian and European populations. We identified three regions associated with increased DTC risk at 6q24.3, 10p12.2, and 17q21.32. The lead SNPs at these loci showed respective p-values of 1.66 × 10-7, 2.39 × 10-7, and 7.19 × 10-7 and corresponding odds ratios of 2.02, 1.89, and 2.37. Conclusion Our study results suggest a role of the loci 6q24.3, 10p12.2 and 17q21.32 in DTC risk. However, a whole genome sequencing approach would be better suited to characterize these factors than genotyping with microarray chip designed for the Caucasian population. Moreover, the functional impact of these three new loci needs to be further explored and validated.
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Affiliation(s)
- Monia Zidane
- University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team "Radiations Epidemiology", Villejuif 94805, France
| | - Marc Haber
- Centre for Computational Biology, University of Birmingham, Birmingham B152TT, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B152TT, UK
| | - Thérèse Truong
- University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team "Exposome and Heredity", Villejuif 94805, France
| | - Frédérique Rachédi
- Endocrinology Unit, Territorial Hospital Taaone, F-98713, Papeete, Tahiti 98713, French Polynesia
| | - Catherine Ory
- CEA, Laboratoire de Cancérologie Fondamentale, Institut de Biologie François Jacob, iRCM, SREIT, Laboratoire de Cancérologie Expérimentale (LCE), Université Paris-Saclay, Fontenay aux Roses 92265, France
| | - Sylvie Chevillard
- CEA, Laboratoire de Cancérologie Fondamentale, Institut de Biologie François Jacob, iRCM, SREIT, Laboratoire de Cancérologie Expérimentale (LCE), Université Paris-Saclay, Fontenay aux Roses 92265, France
| | - Hélène Blanché
- Fondation Jean Dausset-Centre d'Etude du Polymorphisme Humain, Paris 75010, France
| | - Robert Olaso
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry 91057, France
| | - Anne Boland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry 91057, France
| | - Éric Conte
- U.S.R. 2003 (CNRS / UPF), Faa'a, Tahiti 98702, France
| | - Mojgan Karimi
- University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team "Exposome and Heredity", Villejuif 94805, France
| | - Yan Ren
- University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team "Radiations Epidemiology", Villejuif 94805, France
| | - Constance Xhaard
- University of Lorraine, INSERM CIC 1433, Nancy CHRU, INSERM U1116, Nancy 54500, France
| | - Vincent Souchard
- University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team "Radiations Epidemiology", Villejuif 94805, France
| | - Jacques Gardon
- Hydrosciences Montpellier, Research Institute for Development, CNRS, University of Montpellier, Montpellier 62307, France
| | - Marc Taquet
- Research Institute for Development, Center IRD on Tahiti, Arue, Tahiti 98713, French Polynesia
| | - André Bouville
- National Cancer Institute (retired), Bethesda, MD 20892, USA
| | - Jean-François Deleuze
- Fondation Jean Dausset-Centre d'Etude du Polymorphisme Humain, Paris 75010, France
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry 91057, France
| | - Vladimir Drozdovitch
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Bethesda, MD 20892, USA
| | | | - Jean-Baptiste Cazier
- Centre for Computational Biology, University of Birmingham, Birmingham B152TT, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B152TT, UK
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