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Brasher MS, Grotzinger AD, Friedman NP, Smolker HR, Evans LM. Disentangling differing relationships between internalizing disorders and alcohol use. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32975. [PMID: 38375614 PMCID: PMC11147714 DOI: 10.1002/ajmg.b.32975] [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: 07/17/2023] [Revised: 12/14/2023] [Accepted: 02/08/2024] [Indexed: 02/21/2024]
Abstract
Both internalizing disorders and alcohol use have dramatic, wide-spread implications for global health. Previous work has established common phenotypic comorbidity among these disorders, as well as shared genetic variation underlying them both. We used genomic structural equation modeling to investigate the shared genetics of internalizing, externalizing, and alcohol use traits, as well as to explore whether specific domains of internalizing symptoms mediate the contrasting relationships with problematic alcohol use compared to alcohol consumption. We also examined patterns of genetic correlations between similar traits within additional Finnish and East Asian ancestry groups. When the shared genetic influence of externalizing psychopathology was accounted for, the genetic effect of internalizing traits on alcohol use was reduced, suggesting the important role of common genetic factors underlying multiple psychiatric disorders and their genetic influences on comorbidity of internalizing and alcohol use traits. Individual internalizing domains had contrasting effects on frequency of alcohol consumption, which demonstrate the complex system of pleiotropy that exists, even within similar disorders, and can be missed when evaluating only relationships among formal diagnoses. Future work must consider the broad effects of shared psychopathology along with the fine-scale effects of heterogeneity within disorders to more fully understand the biology underlying complex traits.
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Affiliation(s)
- Maizy S Brasher
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Ecology and Evolutionary Biology, Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Andrew D Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Psychology and Neuroscience, Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Naomi P Friedman
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Psychology and Neuroscience, Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Harry R Smolker
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, Colorado, USA
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
- Department of Ecology and Evolutionary Biology, Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
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2
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Casanova F, Tian Q, Williamson DS, Qian Y, Zweibaum D, Ding J, Atkins JL, Melzer D, Ferrucci L, Pilling LC. MRI-derived brain iron, grey matter volume, and risk of dementia and Parkinson's disease: Observational and genetic analysis in the UK Biobank cohort. Neurobiol Dis 2024; 197:106539. [PMID: 38789058 DOI: 10.1016/j.nbd.2024.106539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/08/2024] [Accepted: 05/21/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Iron overload is observed in neurodegenerative diseases, especially Alzheimer's disease (AD) and Parkinson's disease (PD). Homozygotes for the iron-overload (haemochromatosis) causing HFE p.C282Y variant have increased risk of dementia and PD. Whether brain iron deposition is causal or secondary to the neurodegenerative processes in the general population is unclear. METHODS We analysed 39,533 UK Biobank participants of European genetic ancestry with brain MRI data. We studied brain iron estimated by R2* and quantitative susceptibility mapping (QSM) in 8 subcortical regions: accumbens, amygdala, caudate, hippocampus, pallidum, putamen, substantia nigra, and thalamus. We performed genome-wide associations studies (GWAS) and used Mendelian Randomization (MR) methods to estimate the causal effect of brain iron on grey matter volume, and risk of AD, non-AD and PD. We also used MR to test whether genetic liability to AD or PD causally increased brain iron (R2* and QSM). FINDINGS In GWAS of R2* and QSM we replicated 83% of previously reported genetic loci and identified 174 further loci across all eight brain regions. Higher genetically predicted brain iron, using both R2* and QSM, was associated with lower grey matter volumes in the caudate, putamen and thalamus (e.g., Beta-putamenQSM: -0.37, p = 2*10-46). Higher genetically predicted thalamus R2* was associated with increased risk of non-AD dementia (OR 1.36(1.16;1.60), p = 2*10-4) but not AD (p > 0.05). In males, genetically predicted putamen R2* increased non-AD dementia risk, but not in females. Higher genetically predicted iron in the caudate, putamen, and substantia nigra was associated with an increased risk of PD (Odds Ratio QSM ∼ substantia-nigra 1.21(1.07;1.37), p = 0.003). Genetic liability to AD or PD was not associated with R2* or QSM in the dementia or PD-associated regions. INTERPRETATION Our genetic analysis supports a causal effect of higher iron deposition in specific subcortical brain regions for Parkinson's disease, grey matter volume, and non-Alzheimer's dementia.
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Affiliation(s)
- Francesco Casanova
- Department of Clinical and Biomedical Sciences, University of Exeter, Magdalen Road, Exeter, Devon EX1 2LU, UK
| | - Qu Tian
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224, USA
| | - Daniel S Williamson
- Department of Clinical and Biomedical Sciences, University of Exeter, Magdalen Road, Exeter, Devon EX1 2LU, UK; Department of Medical Imaging, University of Exeter, Magdalen Road, Exeter, Devon EX1 2LU, UK
| | - Yong Qian
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224, USA
| | - David Zweibaum
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224, USA
| | - Jun Ding
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224, USA
| | - Janice L Atkins
- Department of Clinical and Biomedical Sciences, University of Exeter, Magdalen Road, Exeter, Devon EX1 2LU, UK
| | - David Melzer
- Department of Clinical and Biomedical Sciences, University of Exeter, Magdalen Road, Exeter, Devon EX1 2LU, UK
| | - Luigi Ferrucci
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224, USA.
| | - Luke C Pilling
- Department of Clinical and Biomedical Sciences, University of Exeter, Magdalen Road, Exeter, Devon EX1 2LU, UK.
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Strober BJ, Zhang MJ, Amariuta T, Rossen J, Price AL. Fine-mapping causal tissues and genes at disease-associated loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.01.23297909. [PMID: 37961337 PMCID: PMC10635248 DOI: 10.1101/2023.11.01.23297909] [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
Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO -thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2 -artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.
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McCaw ZR, Gao J, Lin X, Gronsbell J. Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks. Nat Genet 2024:10.1038/s41588-024-01793-9. [PMID: 38872030 DOI: 10.1038/s41588-024-01793-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 05/08/2024] [Indexed: 06/15/2024]
Abstract
Within population biobanks, incomplete measurement of certain traits limits the power for genetic discovery. Machine learning is increasingly used to impute the missing values from the available data. However, performing genome-wide association studies (GWAS) on imputed traits can introduce spurious associations, identifying genetic variants that are not associated with the original trait. Here we introduce a new method, synthetic surrogate (SynSurr) analysis, which makes GWAS on imputed phenotypes robust to imputation errors. Rather than replacing missing values, SynSurr jointly analyzes the original and imputed traits. We show that SynSurr estimates the same genetic effect as standard GWAS and improves power in proportion to the quality of the imputations. SynSurr requires a commonly made missing-at-random assumption but relaxes the requirements of existing imputation methods by not requiring correct model specification. We present extensive simulations and ablation analyses to validate SynSurr and apply it to empower the GWAS of dual-energy X-ray absorptiometry traits within the UK Biobank.
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Affiliation(s)
- Zachary R McCaw
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Jianhui Gao
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Jessica Gronsbell
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada.
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5
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Czech E, Millar TR, White T, Jeffery B, Miles A, Tallman S, Wojdyla R, Zabad S, Hammerbacher J, Kelleher J. Analysis-ready VCF at Biobank scale using Zarr. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.11.598241. [PMID: 38915693 PMCID: PMC11195102 DOI: 10.1101/2024.06.11.598241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Background Variant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasises efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. Biobank scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed. Results We present the VCF Zarr specification, an encoding of the VCF data model using Zarr which makes retrieving subsets of the data much more efficient. Zarr is a cloud-native format for storing multi-dimensional data, widely used in scientific computing. We show how this format is far more efficient than standard VCF based approaches, and competitive with specialised methods for storing genotype data in terms of compression ratios and calculation performance. We demonstrate the VCF Zarr format (and the vcf2zarr conversion utility) on a subset of the Genomics England aggV2 dataset comprising 78,195 samples and 59,880,903 variants, with a 5X reduction in storage and greater than 300X reduction in CPU usage in some representative benchmarks. Conclusions Large row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely-used, open-source technologies has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores.
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6
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Rodriguez-Algarra F, Evans DM, Rakyan VK. Ribosomal DNA copy number variation associates with hematological profiles and renal function in the UK Biobank. CELL GENOMICS 2024; 4:100562. [PMID: 38749448 DOI: 10.1016/j.xgen.2024.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/19/2023] [Accepted: 04/21/2024] [Indexed: 06/15/2024]
Abstract
The phenotypic impact of genetic variation of repetitive features in the human genome is currently understudied. One such feature is the multi-copy 47S ribosomal DNA (rDNA) that codes for rRNA components of the ribosome. Here, we present an analysis of rDNA copy number (CN) variation in the UK Biobank (UKB). From the first release of UKB whole-genome sequencing (WGS) data, a discovery analysis in White British individuals reveals that rDNA CN associates with altered counts of specific blood cell subtypes, such as neutrophils, and with the estimated glomerular filtration rate, a marker of kidney function. Similar trends are observed in other ancestries. A range of analyses argue against reverse causality or common confounder effects, and all core results replicate in the second UKB WGS release. Our work demonstrates that rDNA CN is a genetic influence on trait variance in humans.
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Affiliation(s)
| | - David M Evans
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia; Frazer Institute, The University of Queensland, Brisbane, QLD 4102, Australia; MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Vardhman K Rakyan
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.
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7
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Liu A, Genovese G, Zhao Y, Pirinen M, Zekavat SM, Kentistou KA, Yang Z, Yu K, Vlasschaert C, Liu X, Brown DW, Hudjashov G, Gorman BR, Dennis J, Zhou W, Momozawa Y, Pyarajan S, Tuzov V, Pajuste FD, Aavikko M, Sipilä TP, Ghazal A, Huang WY, Freedman ND, Song L, Gardner EJ, Sankaran VG, Palotie A, Ollila HM, Tukiainen T, Chanock SJ, Mägi R, Natarajan P, Daly MJ, Bick A, McCarroll SA, Terao C, Loh PR, Ganna A, Perry JRB, Machiela MJ. Genetic drivers and cellular selection of female mosaic X chromosome loss. Nature 2024:10.1038/s41586-024-07533-7. [PMID: 38867047 DOI: 10.1038/s41586-024-07533-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/07/2024] [Indexed: 06/14/2024]
Abstract
Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.
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Affiliation(s)
- Aoxing Liu
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Giulio Genovese
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Seyedeh M Zekavat
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | | | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Derek W Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Bryan R Gorman
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Booz Allen Hamilton, McLean, VA, USA
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Weiyin Zhou
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saiju Pyarajan
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Valdislav Tuzov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Mervi Aavikko
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Timo P Sipilä
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Awaisa Ghazal
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pradeep Natarajan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexander Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
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Kim B, Kim DS, Shin JG, Leem S, Cho M, Kim H, Gu KN, Seo JY, You SW, Martin AR, Park SG, Kim Y, Jeong C, Kang NG, Won HH. Mapping and annotating genomic loci to prioritize genes and implicate distinct polygenic adaptations for skin color. Nat Commun 2024; 15:4874. [PMID: 38849341 PMCID: PMC11161515 DOI: 10.1038/s41467-024-49031-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color evolution and genetics in East Asians are understudied. We quantified skin color in 48,433 East Asians using image analysis and identified associated genetic variants and potential causal genes for skin color as well as their polygenic interplay with sun exposure. This genome-wide association study (GWAS) identified 12 known and 11 previously unreported loci and SNP-based heritability was 23-24%. Potential causal genes were determined through the identification of nonsynonymous variants, colocalization with gene expression in skin tissues, and expression levels in melanocytes. Genomic loci associated with pigmentation in East Asians substantially diverged from European populations, and we detected signatures of polygenic adaptation. This large GWAS for objectively quantified skin color in an East Asian population improves understanding of the genetic architecture and polygenic adaptation of skin color and prioritizes potential causal genes.
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Affiliation(s)
- Beomsu Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Dan Say Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Joong-Gon Shin
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Sangseob Leem
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Minyoung Cho
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Hanji Kim
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Ki-Nam Gu
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Jung Yeon Seo
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Seung Won You
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, 02141, USA
| | - Sun Gyoo Park
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Yunkwan Kim
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea
| | - Choongwon Jeong
- School of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Nae Gyu Kang
- Research and Innovation Center, CTO, LG Household & Healthcare (LG H&H), Seoul, 07795, Republic of Korea.
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, 06351, Republic of Korea.
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9
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Kumasaka N. Genetic association mapping leveraging Gaussian processes. J Hum Genet 2024:10.1038/s10038-024-01259-0. [PMID: 38834722 DOI: 10.1038/s10038-024-01259-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/06/2024]
Abstract
Gaussian processes (GPs) are a powerful and useful approach for modelling nonlinear phenomena in various scientific fields, including genomics and genetics. This review focuses on the application of GPs in genetic association mapping. The aim is to identify genetic variants that alter gene regulation along continuous cellular states at the molecular level, as well as disease susceptibility over time and space at the population level. The challenges and opportunities in this field are also addressed.
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Affiliation(s)
- Natsuhiko Kumasaka
- Division of Digital Genomics, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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10
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Neale N, Lona-Durazo F, Ryten M, Gagliano Taliun SA. Leveraging sex-genetic interactions to understand brain disorders: recent advances and current gaps. Brain Commun 2024; 6:fcae192. [PMID: 38894947 PMCID: PMC11184352 DOI: 10.1093/braincomms/fcae192] [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: 04/03/2024] [Revised: 04/11/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024] Open
Abstract
It is established that there are sex differences in terms of prevalence, age of onset, clinical manifestations, and response to treatment for a variety of brain disorders, including neurodevelopmental, psychiatric, and neurodegenerative disorders. Cohorts of increasing sample sizes with diverse data types collected, including genetic, transcriptomic and/or phenotypic data, are providing the building blocks to permit analytical designs to test for sex-biased genetic variant-trait associations, and for sex-biased transcriptional regulation. Such molecular assessments can contribute to our understanding of the manifested phenotypic differences between the sexes for brain disorders, offering the future possibility of delivering personalized therapy for females and males. With the intention of raising the profile of this field as a research priority, this review aims to shed light on the importance of investigating sex-genetic interactions for brain disorders, focusing on two areas: (i) variant-trait associations and (ii) transcriptomics (i.e. gene expression, transcript usage and regulation). We specifically discuss recent advances in the field, current gaps and provide considerations for future studies.
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Affiliation(s)
- Nikita Neale
- Faculty of Medicine, Université de Montréal, Québec, H3C 3J7 Canada
| | - Frida Lona-Durazo
- Faculty of Medicine, Université de Montréal, Québec, H3C 3J7 Canada
- Research Centre, Montreal Heart Institute, Québec, H1T 1C8 Canada
| | - Mina Ryten
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, WC1N 1EH London, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, 20815 MD, USA
- NIHR Great Ormond Street Hospital Biomedical Research Centre, Great Ormond Street Institute of Child Health, Bloomsbury, WC1N 1EH London, UK
| | - Sarah A Gagliano Taliun
- Research Centre, Montreal Heart Institute, Québec, H1T 1C8 Canada
- Department of Medicine & Department of Neurosciences, Faculty of Medicine, Université de Montréal, Québec, H3C 3J7 Canada
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11
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Rahman MS, Harrison E, Biggs H, Seikus C, Elliott P, Breen G, Kingston N, Bradley JR, Hill SM, Tom BDM, Chinnery PF. Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants. Nat Med 2024; 30:1739-1748. [PMID: 38745010 PMCID: PMC11186791 DOI: 10.1038/s41591-024-02960-5] [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: 11/21/2023] [Accepted: 03/28/2024] [Indexed: 05/16/2024]
Abstract
A leading explanation for translational failure in neurodegenerative disease is that new drugs are evaluated late in the disease course when clinical features have become irreversible. Here, to address this gap, we cognitively profiled 21,051 people aged 17-85 years as part of the Genes and Cognition cohort within the National Institute for Health and Care Research BioResource across England. We describe the cohort, present cognitive trajectories and show the potential utility. Surprisingly, when studied at scale, the APOE genotype had negligible impact on cognitive performance. Different cognitive domains had distinct genetic architectures, with one indicating brain region-specific activation of microglia and another with glycogen metabolism. Thus, the molecular and cellular mechanisms underpinning cognition are distinct from dementia risk loci, presenting different targets to slow down age-related cognitive decline. Participants can now be recalled stratified by genotype and cognitive phenotype for natural history and interventional studies of neurodegenerative and other disorders.
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Affiliation(s)
- Md Shafiqur Rahman
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Emma Harrison
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Heather Biggs
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Chloe Seikus
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health Research Biomedical Research Centre for Mental Health, South London and Maudsley Hospital, London, UK
| | - Nathalie Kingston
- National Institute for Health and Care Research BioResource, Cambridge, UK
- Dept of Haematology, Cambridge University, Cambridge, UK
| | - John R Bradley
- National Institute for Health and Care Research BioResource, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Steven M Hill
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
| | - Brian D M Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Patrick F Chinnery
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- National Institute for Health and Care Research BioResource, Cambridge, UK.
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK.
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12
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Socrates AJ, Mullins N, Gur RC, Gur RE, Stahl E, O'Reilly PF, Reichenberg A, Jones H, Zammit S, Velthorst E. Polygenic risk of social isolation behavior and its influence on psychopathology and personality. Mol Psychiatry 2024:10.1038/s41380-024-02617-2. [PMID: 38811692 DOI: 10.1038/s41380-024-02617-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/02/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024]
Abstract
Social isolation has been linked to a range of psychiatric issues, but the behavioral component that drives it is not well understood. Here, a genome-wide associations study (GWAS) was carried out to identify genetic variants that contribute specifically to social isolation behavior (SIB) in up to 449,609 participants from the UK Biobank. 17 loci were identified at genome-wide significance, contributing to a 4% SNP-based heritability estimate. Using the SIB GWAS, polygenic risk scores (PRS) were derived in ALSPAC, an independent, developmental cohort, and used to test for association with self-reported friendship scores, comprising items related to friendship quality and quantity, at age 12 and 18 to determine whether genetic predisposition manifests during childhood development. At age 18, friendship scores were associated with the SIB PRS, demonstrating that the genetic factors can predict related social traits in late adolescence. Linkage disequilibrium (LD) score correlation using the SIB GWAS demonstrated genetic correlations with autism spectrum disorder (ASD), schizophrenia, major depressive disorder (MDD), educational attainment, extraversion, and loneliness. However, no evidence of causality was found using a conservative Mendelian randomization approach between SIB and any of the traits in either direction. Genomic Structural Equation Modeling (SEM) revealed a common factor contributing to SIB, neuroticism, loneliness, MDD, and ASD, weakly correlated with a second common factor that contributes to psychiatric and psychotic traits. Our results show that SIB contributes a small heritable component, which is associated genetically with other social traits such as friendship as well as psychiatric disorders.
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Affiliation(s)
- Adam J Socrates
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA.
| | - Niamh Mullins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine and the Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, 3400 Spruce, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine and the Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, 3400 Spruce, Philadelphia, PA, 19104, USA
| | - Eli Stahl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
- Regeneron Genetics Centre, Tarrytown, NY, USA
| | - Paul F O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
| | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
| | - Hannah Jones
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2PR, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PR, UK
| | - Stanley Zammit
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PR, UK
- Centre for Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, BS8 2PR, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Eva Velthorst
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY, 10029, USA
- Department of Research, Mental Health Organization "GGZ Noord-Holland-Noord,", Heerhugowaard, The Netherlands
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13
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Wang JT, Chang XY, Zhao Q, Zhang YM. FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study. Brief Bioinform 2024; 25:bbae290. [PMID: 38888457 PMCID: PMC11184901 DOI: 10.1093/bib/bbae290] [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: 01/18/2024] [Revised: 05/19/2024] [Accepted: 06/09/2024] [Indexed: 06/20/2024] Open
Abstract
Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.
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Affiliation(s)
| | | | | | - Yuan-Ming Zhang
- Corresponding author. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China. Tel.: +086-13505161564; E-mail:
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14
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Rodriguez A, Kim Y, Nandi TN, Keat K, Kumar R, Bhukar R, Conery M, Liu M, Hessington J, Maheshwari K, Schmidt D, Begoli E, Tourassi G, Muralidhar S, Natarajan P, Voight BF, Cho K, Gaziano JM, Damrauer SM, Liao KP, Zhou W, Huffman JE, Verma A, Madduri RK. Accelerating Genome- and Phenome-Wide Association Studies using GPUs - A case study using data from the Million Veteran Program. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594583. [PMID: 38826407 PMCID: PMC11142062 DOI: 10.1101/2024.05.17.594583] [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 expansion of biobanks has significantly propelled genomic discoveries yet the sheer scale of data within these repositories poses formidable computational hurdles, particularly in handling extensive matrix operations required by prevailing statistical frameworks. In this work, we introduce computational optimizations to the SAIGE (Scalable and Accurate Implementation of Generalized Mixed Model) algorithm, notably employing a GPU-based distributed computing approach to tackle these challenges. We applied these optimizations to conduct a large-scale genome-wide association study (GWAS) across 2,068 phenotypes derived from electronic health records of 635,969 diverse participants from the Veterans Affairs (VA) Million Veteran Program (MVP). Our strategies enabled scaling up the analysis to over 6,000 nodes on the Department of Energy (DOE) Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC), resulting in a 20-fold acceleration compared to the baseline model. We also provide a Docker container with our optimizations that was successfully used on multiple cloud infrastructures on UK Biobank and All of Us datasets where we showed significant time and cost benefits over the baseline SAIGE model.
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Affiliation(s)
- Alex Rodriguez
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Youngdae Kim
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Tarak Nath Nandi
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Karl Keat
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Rachit Kumar
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Rohan Bhukar
- Program in Medical and Population Genetics, Cambridge, MA, 02142, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Mitchell Conery
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Molei Liu
- Department of Biostatistics, Columbia University's Mailman School of Public Health, New York, NY, 10032, USA
| | - John Hessington
- Information systems, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Drew Schmidt
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Georgia Tourassi
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Kelly Cho
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - J Michael Gaziano
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Scott M Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Cardiovascular Institute, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Katherine P Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Wei Zhou
- Program in Medical and Population Genetics, Cambridge, MA, 02142, USA
- Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- Stanley Center for Psychiatric Research, Cambridge, MA, 02142, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Anurag Verma
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Ravi K Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
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15
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Pirruccello JP, Di Achille P, Choi SH, Rämö JT, Khurshid S, Nekoui M, Jurgens SJ, Nauffal V, Kany S, Ng K, Friedman SF, Batra P, Lunetta KL, Palotie A, Philippakis AA, Ho JE, Lubitz SA, Ellinor PT. Deep learning of left atrial structure and function provides link to atrial fibrillation risk. Nat Commun 2024; 15:4304. [PMID: 38773065 PMCID: PMC11109224 DOI: 10.1038/s41467-024-48229-w] [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: 09/03/2021] [Accepted: 04/24/2024] [Indexed: 05/23/2024] Open
Abstract
Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.
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Affiliation(s)
- James P Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.
- Cardiovascular Genetics Center, University of California San Francisco, San Francisco, CA, USA.
| | - Paolo Di Achille
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Joel T Rämö
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Mahan Nekoui
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sean J Jurgens
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, NL, Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, University of Amsterdam, Amsterdam, NL, Netherlands
| | - Victor Nauffal
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Shinwan Kany
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Samuel F Friedman
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
| | | | - Jennifer E Ho
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- CardioVascular Institute, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Steven A Lubitz
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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16
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van der Plaat DA, Lenoir A, Dharmage S, Potts J, Gómez Real F, Shaheen SO, Jarvis D, Minelli C, Leynaert B. Effects of testosterone and sex hormone binding globulin on lung function in males and females: a multivariable Mendelian Randomisation study. Thorax 2024; 79:564-572. [PMID: 38418196 DOI: 10.1136/thorax-2023-220374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 01/12/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND Observational studies suggest that total testosterone (TT) and sex hormone-binding globulin (SHBG) may have beneficial effects on lung function, but these findings might be spurious due to confounding and reverse causation. We addressed these limitations by using multivariable Mendelian randomisation (MVMR) to investigate the independent causal effects of TT and SHBG on lung function. METHODS We first identified genetic instruments by performing genome-wide association analyses of TT and SHBG in the large UK Biobank, separately in males and females. We then assessed the independent effects of TT and SHBG on forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC using one-sample MVMR. We addressed pleiotropy, which could bias MVMR, using several methods that account for it. We performed subgroup MVMR analyses by obesity, physical activity and menopausal status, and assessed associations between TT and SHBG with lung function decline. Finally, we compared the MVMR results with those of observational analyses in the UK Biobank. FINDINGS In the MVMR analyses, there was evidence of pleiotropy, but results were consistent when accounting for it. We found a strong beneficial effect of TT on FVC and FEV1 in both males and females, but a moderate detrimental effect of SHBG on FEV1 and FEV1/FVC in males only. Subgroup analyses suggested stronger effects of TT among obese and older males. The observational analyses, in line with previous studies, agreed with MRMV for TT, but not for SHBG. INTERPRETATION These findings suggest that testosterone improves lung function in males and females, while SHBG has an opposite independent effect in males.
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Affiliation(s)
| | - Alexandra Lenoir
- Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Gesundheitsamt Fürstenfeldbruck, Fürstenfeldbruck, Switzerland
| | - Shyamali Dharmage
- Allergy and Lung Health Unit, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - James Potts
- National Heart and Lung Institute (NHLI), Imperial College London, London, UK
| | - Francisco Gómez Real
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway
| | - Seif O Shaheen
- Wolfson Institute of Population Health, Queen Mary University of London, Barts and The London School of Medicine and Dentistry, London, London, UK
| | - Debbie Jarvis
- National Heart and Lung Institute (NHLI), Imperial College London, London, UK
| | - Cosetta Minelli
- National Heart and Lung Institute (NHLI), Imperial College London, London, UK
| | - Bénédicte Leynaert
- Université Paris-Saclay, UVSQ, Université Paris-Sud, Inserm, Équipe d'Épidémiologie Respiratoire Intégrative, CESP, INSERM, Villejuif, France
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17
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Zhou W, Cuomo ASE, Xue A, Kanai M, Chau G, Krishna C, Xavier RJ, MacArthur DG, Powell JE, Daly MJ, Neale BM. Efficient and accurate mixed model association tool for single-cell eQTL analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.15.24307317. [PMID: 38798318 PMCID: PMC11118640 DOI: 10.1101/2024.05.15.24307317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.
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18
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Akdeniz BC, Frei O, Hagen E, Filiz TT, Karthikeyan S, Pasman J, Jangmo A, Bergstedt J, Shorter JR, Zetterberg R, Meijsen J, Sønderby IE, Buil A, Tesli M, Lu Y, Sullivan P, Andreassen OA, Hovig E. COSGAP: COntainerized Statistical Genetics Analysis Pipelines. BIOINFORMATICS ADVANCES 2024; 4:vbae067. [PMID: 38808072 PMCID: PMC11132817 DOI: 10.1093/bioadv/vbae067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/30/2024] [Accepted: 05/08/2024] [Indexed: 05/30/2024]
Abstract
Summary The collection and analysis of sensitive data in large-scale consortia for statistical genetics is hampered by multiple challenges, due to their non-shareable nature. Time-consuming issues in installing software frequently arise due to different operating systems, software dependencies, and limited internet access. For federated analysis across sites, it can be challenging to resolve different problems, including format requirements, data wrangling, setting up analysis on high-performance computing (HPC) facilities, etc. Easier, more standardized, automated protocols and pipelines can be solutions to overcome these issues. We have developed one such solution for statistical genetic data analysis using software container technologies. This solution, named COSGAP: "COntainerized Statistical Genetics Analysis Pipelines," consists of already established software tools placed into Singularity containers, alongside corresponding code and instructions on how to perform statistical genetic analyses, such as genome-wide association studies, polygenic scoring, LD score regression, Gaussian Mixture Models, and gene-set analysis. Using provided helper scripts written in Python, users can obtain auto-generated scripts to conduct the desired analysis either on HPC facilities or on a personal computer. COSGAP is actively being applied by users from different countries and projects to conduct genetic data analyses without spending much effort on software installation, converting data formats, and other technical requirements. Availability and implementation COSGAP is freely available on GitHub (https://github.com/comorment/containers) under the GPLv3 license.
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Affiliation(s)
- Bayram Cevdet Akdeniz
- Department of Informatics, Centre for Bioinformatics, University of Oslo, Oslo 0373, Norway
- Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo 0450, Norway
| | - Oleksandr Frei
- Department of Informatics, Centre for Bioinformatics, University of Oslo, Oslo 0373, Norway
- Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo 0450, Norway
| | - Espen Hagen
- Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo 0450, Norway
| | - Tahir Tekin Filiz
- Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo 0450, Norway
| | - Sandeep Karthikeyan
- Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo 0450, Norway
| | - Joëlle Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
| | - Andreas Jangmo
- Department of Mental Health and Suicide, Norwegian Institute of Public Health, Oslo 0213, Norway
| | - Jacob Bergstedt
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | - John R Shorter
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde 4000, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen 8210, Denmark
| | - Richard Zetterberg
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde 4000, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen 8210, Denmark
| | - Joeri Meijsen
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde 4000, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen 8210, Denmark
| | - Ida Elken Sønderby
- Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo 0450, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo 0424, Norway
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde 4000, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen 8210, Denmark
| | - Martin Tesli
- Department of Mental Health and Suicide, Norwegian Institute of Public Health, Oslo 0213, Norway
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
| | - Patrick Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
| | - Ole A Andreassen
- Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo 0450, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo 4956, Norway
| | - Eivind Hovig
- Department of Informatics, Centre for Bioinformatics, University of Oslo, Oslo 0373, Norway
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo 0424, Norway
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19
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Siraj L, Castro RI, Dewey H, Kales S, Nguyen TTL, Kanai M, Berenzy D, Mouri K, Wang QS, McCaw ZR, Gosai SJ, Aguet F, Cui R, Vockley CM, Lareau CA, Okada Y, Gusev A, Jones TR, Lander ES, Sabeti PC, Finucane HK, Reilly SK, Ulirsch JC, Tewhey R. Functional dissection of complex and molecular trait variants at single nucleotide resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.05.592437. [PMID: 38766054 PMCID: PMC11100724 DOI: 10.1101/2024.05.05.592437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.
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Affiliation(s)
- Layla Siraj
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biophysics, Harvard Graduate School of Arts and Sciences, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology MD/PhD Program, Harvard Medical School, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | | | | | | | | | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Qingbo S. Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | | | - Sager J. Gosai
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - François Aguet
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ran Cui
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Caleb A. Lareau
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - Thouis R. Jones
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eric S. Lander
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Pardis C. Sabeti
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Hilary K. Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jacob C. Ulirsch
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ryan Tewhey
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
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20
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Wang Q, Tang TM, Youlton N, Weldy CS, Kenney AM, Ronen O, Weston Hughes J, Chin ET, Sutton SC, Agarwal A, Li X, Behr M, Kumbier K, Moravec CS, Wilson Tang WH, Margulies KB, Cappola TP, Butte AJ, Arnaout R, Brown JB, Priest JR, Parikh VN, Yu B, Ashley EA. Epistasis regulates genetic control of cardiac hypertrophy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.06.23297858. [PMID: 37987017 PMCID: PMC10659487 DOI: 10.1101/2023.11.06.23297858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141 , IGF1R , TTN , and TNKS. Several loci where variants were deemed insignificant in univariate genome-wide association analyses are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we found strong gene co-expression correlations between these statistical epistasis contributors in healthy hearts and a significant connectivity decrease in failing hearts. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R . Our results expand the scope of genetic regulation of cardiac structure to epistasis.
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21
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Nievergelt CM, Maihofer AX, Atkinson EG, Chen CY, Choi KW, Coleman JRI, Daskalakis NP, Duncan LE, Polimanti R, Aaronson C, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegoviç E, Babić D, Bacanu SA, Baker DG, Batzler A, Beckham JC, Belangero S, Benjet C, Bergner C, Bierer LM, Biernacka JM, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Brandolino A, Breen G, Bressan RA, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Bækvad-Hansen M, Børglum AD, Børte S, Cahn L, Calabrese JR, Caldas-de-Almeida JM, Chatzinakos C, Cheema S, Clouston SAP, Colodro-Conde L, Coombes BJ, Cruz-Fuentes CS, Dale AM, Dalvie S, Davis LK, Deckert J, Delahanty DL, Dennis MF, Desarnaud F, DiPietro CP, Disner SG, Docherty AR, Domschke K, Dyb G, Kulenović AD, Edenberg HJ, Evans A, Fabbri C, Fani N, Farrer LA, Feder A, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Gelernter J, Geuze E, Gillespie CF, Goleva SB, Gordon SD, Goçi A, Grasser LR, Guindalini C, Haas M, Hagenaars S, Hauser MA, Heath AC, Hemmings SMJ, Hesselbrock V, Hickie IB, Hogan K, Hougaard DM, Huang H, Huckins LM, Hveem K, Jakovljević M, Javanbakht A, Jenkins GD, Johnson J, Jones I, Jovanovic T, Karstoft KI, Kaufman ML, Kennedy JL, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kotov R, Kranzler HR, Krebs K, Kremen WS, Kuan PF, Lawford BR, Lebois LAM, Lehto K, Levey DF, Lewis C, Liberzon I, Linnstaedt SD, Logue MW, Lori A, Lu Y, Luft BJ, Lupton MK, Luykx JJ, Makotkine I, Maples-Keller JL, Marchese S, Marmar C, Martin NG, Martínez-Levy GA, McAloney K, McFarlane A, McLaughlin KA, McLean SA, Medland SE, Mehta D, Meyers J, Michopoulos V, Mikita EA, Milani L, Milberg W, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Mufford MS, Nelson EC, Nordentoft M, Norman SB, Nugent NR, O'Donnell M, Orcutt HK, Pan PM, Panizzon MS, Pathak GA, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Porjesz B, Powers A, Qin XJ, Ratanatharathorn A, Risbrough VB, Roberts AL, Rothbaum AO, Rothbaum BO, Roy-Byrne P, Ruggiero KJ, Rung A, Runz H, Rutten BPF, de Viteri SS, Salum GA, Sampson L, Sanchez SE, Santoro M, Seah C, Seedat S, Seng JS, Shabalin A, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stensland S, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Tiwari AK, Trapido E, Uddin M, Ursano RJ, Valdimarsdóttir U, Van Hooff M, Vermetten E, Vinkers CH, Voisey J, Wang Y, Wang Z, Waszczuk M, Weber H, Wendt FR, Werge T, Williams MA, Williamson DE, Winsvold BS, Winternitz S, Wolf C, Wolf EJ, Xia Y, Xiong Y, Yehuda R, Young KA, Young RM, Zai CC, Zai GC, Zervas M, Zhao H, Zoellner LA, Zwart JA, deRoon-Cassini T, van Rooij SJH, van den Heuvel LL, Stein MB, Ressler KJ, Koenen KC. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder. Nat Genet 2024; 56:792-808. [PMID: 38637617 DOI: 10.1038/s41588-024-01707-9] [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: 05/24/2023] [Accepted: 03/05/2024] [Indexed: 04/20/2024]
Abstract
Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.
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Affiliation(s)
- Caroline M Nievergelt
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA.
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA.
| | - Adam X Maihofer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Chia-Yen Chen
- Biogen Inc.,Translational Sciences, Cambridge, MA, USA
| | - Karmel W Choi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jonathan R I Coleman
- King's College London, National Institute for Health and Care Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Nikolaos P Daskalakis
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Center of Excellence in Depression and Anxiety Disorders, Belmont, MA, USA
| | - Laramie E Duncan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Renato Polimanti
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Cindy Aaronson
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ananda B Amstadter
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Soren B Andersen
- The Danish Veteran Centre, Research and Knowledge Centre, Ringsted, Denmark
| | - Ole A Andreassen
- Oslo University Hospital, Division of Mental Health and Addiction, Oslo, Norway
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
| | - Paul A Arbisi
- Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | | | - S Bryn Austin
- Boston Children's Hospital, Division of Adolescent and Young Adult Medicine, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Esmina Avdibegoviç
- Department of Psychiatry, University Clinical Center of Tuzla, Tuzla, Bosnia and Herzegovina
| | - Dragan Babić
- Department of Psychiatry, University Clinical Center of Mostar, Mostar, Bosnia and Herzegovina
| | - Silviu-Alin Bacanu
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Dewleen G Baker
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Jean C Beckham
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA
| | - Sintia Belangero
- Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, Brazil
- Department of Psychiatry, Universidade Federal de São Paulo, Laboratory of Integrative Neuroscience, São Paulo, Brazil
| | - Corina Benjet
- Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Center for Global Mental Health, Mexico City, Mexico
| | - Carisa Bergner
- Medical College of Wisconsin, Comprehensive Injury Center, Milwaukee, WI, USA
| | - Linda M Bierer
- Department of Psychiatry, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Jonathan I Bisson
- Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Marco P Boks
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht, The Netherlands
| | - Elizabeth A Bolger
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Amber Brandolino
- Department of Surgery, Division of Trauma & Acute Care Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Gerome Breen
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- King's College London, NIHR Maudsley BRC, London, UK
| | - Rodrigo Affonseca Bressan
- Department of Psychiatry, Universidade Federal de São Paulo, Laboratory of Integrative Neuroscience, São Paulo, Brazil
- Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Richard A Bryant
- University of New South Wales, School of Psychology, Sydney, New South Wales, Australia
| | - Angela C Bustamante
- Department of Internal Medicine, University of Michigan Medical School, Division of Pulmonary and Critical Care Medicine, Ann Arbor, MI, USA
| | - Jonas Bybjerg-Grauholm
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Marie Bækvad-Hansen
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Aarhus University, Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark
- Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark
| | - Sigrid Børte
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
| | - Leah Cahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Joseph R Calabrese
- Case Western Reserve University, School of Medicine, Cleveland, OH, USA
- Department of Psychiatry, University Hospitals, Cleveland, OH, USA
| | | | - Chris Chatzinakos
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Division of Depression and Anxiety Disorders, Belmont, MA, USA
| | - Sheraz Cheema
- University of Toronto, CanPath National Coordinating Center, Toronto, Ontario, Canada
| | - Sean A P Clouston
- Stony Brook University, Family, Population, and Preventive Medicine, Stony Brook, NY, USA
- Stony Brook University, Public Health, Stony Brook, NY, USA
| | - Lucía Colodro-Conde
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Carlos S Cruz-Fuentes
- Department of Genetics, Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Mexico City, Mexico
| | - Anders M Dale
- Department of Radiology, Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Shareefa Dalvie
- Department of Pathology, University of Cape Town, Division of Human Genetics, Cape Town, South Africa
| | - Lea K Davis
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA
| | - Jürgen Deckert
- University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Denmark
| | | | - Michelle F Dennis
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA
| | - Frank Desarnaud
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Christopher P DiPietro
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- McLean Hospital, Division of Depression and Anxiety Disorders, Belmont, MA, USA
| | - Seth G Disner
- Minneapolis VA Health Care System, Research Service Line, Minneapolis, MN, USA
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Anna R Docherty
- Huntsman Mental Health Institute, Salt Lake City, UT, USA
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Katharina Domschke
- University of Freiburg, Faculty of Medicine, Centre for Basics in Neuromodulation, Freiburg, Denmark
- Department of Psychiatry and Psychotherapy, University of Freiburg, Faculty of Medicine, Freiburg, Denmark
| | - Grete Dyb
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
- Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway
| | - Alma Džubur Kulenović
- Department of Psychiatry, University Clinical Center of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Howard J Edenberg
- Indiana University School of Medicine, Biochemistry and Molecular Biology, Indianapolis, IN, USA
- Indiana University School of Medicine, Medical and Molecular Genetics, Indianapolis, IN, USA
| | - Alexandra Evans
- Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Chiara Fabbri
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, 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
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Adriana Feder
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Norah C Feeny
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Janine D Flory
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - David Forbes
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Sandro Galea
- Boston University School of Public Health, Boston, MA, USA
| | - Melanie E Garrett
- Duke University, Duke Molecular Physiology Institute, Durham, NC, USA
| | - Bizu Gelaye
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Joel Gelernter
- VA Connecticut Healthcare Center, Psychiatry Service, West Haven, CT, USA
- Department of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Elbert Geuze
- Netherlands Ministry of Defence, Brain Research and Innovation Centre, Utrecht, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Charles F Gillespie
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Slavina B Goleva
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA
- National Institutes of Health, National Human Genome Research Institute, Bethesda, MD, USA
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Aferdita Goçi
- Department of Psychiatry, University Clinical Centre of Kosovo, Prishtina, Kosovo
| | - Lana Ruvolo Grasser
- Wayne State University School of Medicine, Psychiatry and Behavioral Neurosciencess, Detroit, MI, USA
| | - Camila Guindalini
- Gallipoli Medical Research Foundation, Greenslopes Private Hospital, Greenslopes, Queensland, Australia
| | - Magali Haas
- Cohen Veterans Bioscience, New York City, NY, USA
| | - Saskia Hagenaars
- King's College London, National Institute for Health and Care Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Michael A Hauser
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Andrew C Heath
- Department of Genetics, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Sian M J Hemmings
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- SAMRC Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
| | - Victor Hesselbrock
- University of Connecticut School of Medicine, Psychiatry, Farmington, CT, USA
| | - Ian B Hickie
- University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia
| | - Kelleigh Hogan
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - David Michael Hougaard
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Hailiang Huang
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
| | - Laura M Huckins
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Kristian Hveem
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
| | - Miro Jakovljević
- Department of Psychiatry, University Hospital Center of Zagreb, Zagreb, Croatia
| | - Arash Javanbakht
- Wayne State University School of Medicine, Psychiatry and Behavioral Neurosciencess, Detroit, MI, USA
| | - Gregory D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Jessica Johnson
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ian Jones
- Cardiff University, National Centre for Mental Health, Cardiff University Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Karen-Inge Karstoft
- The Danish Veteran Centre, Research and Knowledge Centre, Ringsted, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Milissa L Kaufman
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - James L Kennedy
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Alaptagin Khan
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Nathan A Kimbrel
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA
- Durham VA Health Care System, Mental Health Service Line, Durham, NC, USA
| | - Anthony P King
- The Ohio State University, College of Medicine, Institute for Behavioral Medicine Research, Columbus, OH, USA
| | - Nastassja Koen
- University of Cape Town, Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kristi Krebs
- University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Bruce R Lawford
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
| | - Lauren A M Lebois
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Center of Excellence in Depression and Anxiety Disorders, Belmont, MA, USA
| | - Kelli Lehto
- University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia
| | - Daniel F Levey
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Catrin Lewis
- Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Israel Liberzon
- Department of Psychiatry and Behavioral Sciences, Texas A&M University College of Medicine, Bryan, TX, USA
| | - Sarah D Linnstaedt
- Department of Anesthesiology, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA
| | - Mark W Logue
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Boston University School of Medicine, Psychiatry, Biomedical Genetics, Boston, MA, USA
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
| | - Adriana Lori
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Benjamin J Luft
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Michelle K Lupton
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Jurjen J Luykx
- Department of Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands
- Department of Translational Neuroscience, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Iouri Makotkine
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | | | - Shelby Marchese
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Marmar
- New York University, Grossman School of Medicine, New York City, NY, USA
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Genetics, Brisbane, Queensland, Australia
| | - Gabriela A Martínez-Levy
- Department of Genetics, Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Mexico City, Mexico
| | - Kerrie McAloney
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Alexander McFarlane
- University of Adelaide, Discipline of Psychiatry, Adelaide, South Australia, Australia
| | | | - Samuel A McLean
- Department of Anesthesiology, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA
- Department of Emergency Medicine, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Divya Mehta
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
- Queensland University of Technology, Centre for Genomics and Personalised Health, Kelvin Grove, Queensland, Australia
| | - Jacquelyn Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Vasiliki Michopoulos
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Elizabeth A Mikita
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Lili Milani
- University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia
| | | | - Mark W Miller
- Boston University School of Medicine, Psychiatry, Biomedical Genetics, Boston, MA, USA
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
| | - Rajendra A Morey
- Duke University School of Medicine, Duke Brain Imaging and Analysis Center, Durham, NC, USA
| | - Charles Phillip Morris
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Aarhus University Hospital-Psychiatry, Psychosis Research Unit, Aarhus, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Aarhus University, Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark
- Aarhus University, Centre for Integrated Register-Based Research, Aarhus, Denmark
- Aarhus University, National Centre for Register-Based Research, Aarhus, Denmark
| | - Mary S Mufford
- Department of Pathology, University of Cape Town, Division of Human Genetics, Cape Town, South Africa
| | - Elliot C Nelson
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- University of Copenhagen, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Sonya B Norman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- National Center for Post Traumatic Stress Disorder, Executive Division, White River Junction, VT, USA
| | - Nicole R Nugent
- Department of Emergency Medicine, Alpert Brown Medical School, Providence, RI, USA
- Department of Pediatrics, Alpert Brown Medical School, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Brown Medical School, Providence, RI, USA
| | - Meaghan O'Donnell
- Department of Psychiatry, University of Melbourne, Phoenix Australia, Melbourne, Victoria, Australia
| | - Holly K Orcutt
- Department of Psychology, Northern Illinois University, DeKalb, IL, USA
| | - Pedro M Pan
- Universidade Federal de São Paulo, Psychiatry, São Paulo, Brazil
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Gita A Pathak
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Edward S Peters
- University of Nebraska Medical Center, College of Public Health, Omaha, NE, USA
| | - Alan L Peterson
- South Texas Veterans Health Care System, Research and Development Service, San Antonio, TX, USA
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Matthew Peverill
- Department of Psychology, University of Washington, Seattle, WA, USA
| | - Robert H Pietrzak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, West Haven, CT, USA
| | - Melissa A Polusny
- Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA
- Center for Care Delivery and Outcomes Research (CCDOR), Minneapolis, MN, USA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Abigail Powers
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Xue-Jun Qin
- Duke University, Duke Molecular Physiology Institute, Durham, NC, USA
| | - Andrew Ratanatharathorn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Columbia University Mailmain School of Public Health, New York City, NY, USA
| | - Victoria B Risbrough
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Andrea L Roberts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alex O Rothbaum
- Department of Psychological Sciences, Emory University, Atlanta, GA, USA
- Department of Research and Outcomes, Skyland Trail, Atlanta, GA, USA
| | - Barbara O Rothbaum
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Peter Roy-Byrne
- Department of Psychiatry, University of Washington, Seattle, WA, USA
| | - Kenneth J Ruggiero
- Department of Nursing, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA
| | - Ariane Rung
- Department of Epidemiology, Louisiana State University Health Sciences Center, School of Public Health, New Orleans, LA, USA
| | - Heiko Runz
- Biogen Inc., Research & Development, Cambridge, MA, USA
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, Maastricht Universitair Medisch Centrum, School for Mental Health and Neuroscience, Maastricht, The Netherlands
| | | | - Giovanni Abrahão Salum
- Child Mind Institute, New York City, NY, USA
- Instituto Nacional de Psiquiatria de Desenvolvimento, São Paulo, Brazil
| | - Laura Sampson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Sixto E Sanchez
- Department of Medicine, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
| | - Marcos Santoro
- Universidade Federal de São Paulo, Departamento de Bioquímica-Disciplina de Biologia Molecular, São Paulo, Brazil
| | - Carina Seah
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Soraya Seedat
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Stellenbosch University, SAMRC Extramural Genomics of Brain Disorders Research Unit, Cape Town, South Africa
| | - Julia S Seng
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA
- Department of Women's and Gender Studies, University of Michigan, Ann Arbor, MI, USA
- University of Michigan, Institute for Research on Women and Gender, Ann Arbor, MI, USA
- University of Michigan, School of Nursing, Ann Arbor, MI, USA
| | - Andrey Shabalin
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Christina M Sheerin
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Derrick Silove
- Department of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Alicia K Smith
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Department of Gynecology and Obstetrics, Department of Psychiatry and Behavioral Sciences, Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Dan J Stein
- University of Cape Town, Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Synne Stensland
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
- Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Jennifer A Sumner
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Martin H Teicher
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Developmental Biopsychiatry Research Program, Belmont, MA, USA
| | - Wesley K Thompson
- Mental Health Centre Sct. Hans, Institute of Biological Psychiatry, Roskilde, Denmark
- University of California San Diego, Herbert Wertheim School of Public Health and Human Longevity Science, La Jolla, CA, USA
| | - Arun K Tiwari
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Edward Trapido
- Department of Epidemiology, Louisiana State University Health Sciences Center, School of Public Health, New Orleans, LA, USA
| | - Monica Uddin
- University of South Florida College of Public Health, Genomics Program, Tampa, FL, USA
| | - Robert J Ursano
- Department of Psychiatry, Uniformed Services University, Bethesda, MD, USA
| | - Unnur Valdimarsdóttir
- Karolinska Institutet, Unit of Integrative Epidemiology, Institute of Environmental Medicine, Stockholm, Sweden
- University of Iceland, Faculty of Medicine, Center of Public Health Sciences, School of Health Sciences, Reykjavik, Iceland
| | - Miranda Van Hooff
- University of Adelaide, Adelaide Medical School, Adelaide, South Australia, Australia
| | - Eric Vermetten
- ARQ Nationaal Psychotrauma Centrum, Psychotrauma Research Expert Group, Diemen, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
- Department of Psychiatry, New York University School of Medicine, New York City, NY, USA
| | - Christiaan H Vinkers
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Joanne Voisey
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
- Queensland University of Technology, Centre for Genomics and Personalised Health, Kelvin Grove, Queensland, Australia
| | - Yunpeng Wang
- Department of Psychology, University of Oslo, Lifespan Changes in Brain and Cognition (LCBC), Oslo, Norway
| | - Zhewu Wang
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
- Department of Mental Health, Ralph H Johnson VA Medical Center, Charleston, SC, USA
| | - Monika Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Heike Weber
- University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Denmark
| | - Frank R Wendt
- Department of Anthropology, University of Toronto, Dalla Lana School of Public Health, Toronto, Ontario, Canada
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Copenhagen University Hospital, Institute of Biological Psychiatry, Mental Health Services, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- University of Copenhagen, The Globe Institute, Lundbeck Foundation Center for Geogenetics, Copenhagen, Denmark
| | - Michelle A Williams
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Douglas E Williamson
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
| | - Bendik S Winsvold
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Sherry Winternitz
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Christiane Wolf
- University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Denmark
| | - Erika J Wolf
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Yan Xia
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
| | - Ying Xiong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Rachel Yehuda
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Mental Health, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Keith A Young
- Central Texas Veterans Health Care System, Research Service, Temple, TX, USA
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, Bryan, TX, USA
| | - Ross McD Young
- Queensland University of Technology, School of Clinical Sciences, Kelvin Grove, Queensland, Australia
- University of the Sunshine Coast, The Chancellory, Sippy Downs, Queensland, Australia
| | - Clement C Zai
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathology, University of Toronto, Toronto, Ontario, Canada
| | - Gwyneth C Zai
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, General Adult Psychiatry and Health Systems Division, Toronto, Ontario, Canada
| | - Mark Zervas
- Cohen Veterans Bioscience, New York City, NY, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Lori A Zoellner
- Department of Psychology, University of Washington, Seattle, WA, USA
| | - John-Anker Zwart
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
| | - Terri deRoon-Cassini
- Department of Surgery, Division of Trauma & Acute Care Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Leigh L van den Heuvel
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- SAMRC Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- University of California San Diego, School of Public Health, La Jolla, CA, USA
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA
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Xie Z, Zhang T, Kim S, Lu J, Zhang W, Lin CH, Wu MR, Davis A, Channa R, Giancardo L, Chen H, Wang S, Chen R, Zhi D. iGWAS: Image-based genome-wide association of self-supervised deep phenotyping of retina fundus images. PLoS Genet 2024; 20:e1011273. [PMID: 38728357 PMCID: PMC11111076 DOI: 10.1371/journal.pgen.1011273] [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: 12/08/2023] [Revised: 05/22/2024] [Accepted: 04/25/2024] [Indexed: 05/12/2024] Open
Abstract
Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes and identified 14 loci with genome-wide significance (p<5×10-8 and intersection of hits from left and right eyes). We also did GWAS on the retina color, the average color of the center region of the retinal fundus photos. The GWAS of retina colors identified 34 loci, 7 are overlapping with GWAS of raw image phenotype. Our results establish the feasibility of this new framework of genomic study based on self-supervised phenotyping of medical images.
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Affiliation(s)
- Ziqian Xie
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Tao Zhang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Sangbae Kim
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Jiaxiong Lu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Wanheng Zhang
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Cheng-Hui Lin
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Man-Ru Wu
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Alexander Davis
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Luca Giancardo
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Han Chen
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- 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, Texas, United States of America
| | - Sui Wang
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Degui Zhi
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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23
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Cho C, Kim B, Kim DS, Hwang MY, Shim I, Song M, Lee YC, Jung SH, Cho SK, Park WY, Myung W, Kim BJ, Do R, Choi HK, Merriman TR, Kim YJ, Won HH. Large-scale cross-ancestry genome-wide meta-analysis of serum urate. Nat Commun 2024; 15:3441. [PMID: 38658550 PMCID: PMC11043400 DOI: 10.1038/s41467-024-47805-4] [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: 08/08/2023] [Accepted: 04/10/2024] [Indexed: 04/26/2024] Open
Abstract
Hyperuricemia is an essential causal risk factor for gout and is associated with cardiometabolic diseases. Given the limited contribution of East Asian ancestry to genome-wide association studies of serum urate, the genetic architecture of serum urate requires exploration. A large-scale cross-ancestry genome-wide association meta-analysis of 1,029,323 individuals and ancestry-specific meta-analysis identifies a total of 351 loci, including 17 previously unreported loci. The genetic architecture of serum urate control is similar between European and East Asian populations. A transcriptome-wide association study, enrichment analysis, and colocalization analysis in relevant tissues identify candidate serum urate-associated genes, including CTBP1, SKIV2L, and WWP2. A phenome-wide association study using polygenic risk scores identifies serum urate-correlated diseases including heart failure and hypertension. Mendelian randomization and mediation analyses show that serum urate-associated genes might have a causal relationship with serum urate-correlated diseases via mediation effects. This study elucidates our understanding of the genetic architecture of serum urate control.
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Affiliation(s)
- Chamlee Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Beomsu Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Dan Say Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Minku Song
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Yeong Chan Lee
- Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Kweon Cho
- Department of Pharmacology, Ajou University School of Medicine (AUSOM), Suwon, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - 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
| | - Hyon K Choi
- Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tony R Merriman
- Biochemistry Department, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea.
| | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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24
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Benning JW, Carlson J, Shaw RG, Harpak A. Confounding Fuels Hereditarian Fallacies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565061. [PMID: 37961599 PMCID: PMC10635045 DOI: 10.1101/2023.11.01.565061] [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
Scientific literature has seen a resurgence of interest in genetic influences on socioeconomic outcomes. Such investigations are often limited by confounding between signals of genetic and non-genetic influences. An illustrative example is Clark (2023), which considers the similarity in socioeconomic status between relatives, drawing on genealogical records spanning four centuries in England. Based on the fit of a quantitative genetics model, it suggests that social status is largely determined by one's DNA; and that, for that reason, contemporary English people "remain correlated in outcomes with their lineage relatives in exactly the same way as in preindustrial England." These conclusions are based on a conflation of genetic and non-genetic transmission (e.g., of wealth) within families. We demonstrate that additional errors and statistical artifacts influenced inferences in Clark (2023). In reality, Clark (2023) provides no information about the relative contribution of genetic and non-genetic factors to social status. We discuss how lessons learned from the failure to account for confounding generalize to contemporary studies that claim to establish genetic underpinnings to social outcomes.
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Affiliation(s)
- John W Benning
- Department of Botany, University of Wyoming, Laramie, WY, USA
| | - Jedidiah Carlson
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
- Department of Population Health, University of Texas at Austin, Austin, TX, USA
| | - Ruth G Shaw
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN, USA
| | - Arbel Harpak
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
- Department of Population Health, University of Texas at Austin, Austin, TX, USA
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25
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Guo R, Feng R, Yang J, Xiao Y, Yin C. Genetic correlation and Mendelian randomization analyses support causal relationships between dietary habits and age at menarche. Sci Rep 2024; 14:8425. [PMID: 38600095 PMCID: PMC11006932 DOI: 10.1038/s41598-024-58999-4] [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: 06/12/2023] [Accepted: 04/05/2024] [Indexed: 04/12/2024] Open
Abstract
Dietary habits are essential in the mean age at menarche (AAM). However, the causal relationship between these factors remains unclear. Therefore, this study aimed to elucidate the genetic relationship between dietary habits and AAM. Genetic summary statistics for dietary habits were obtained from the UK Biobank. GWAS summary data for AAM was obtained from the ReproGen Consortium. Linkage disequilibrium score regression was used to test genetic correlations between dietary habits and AAM. The Mendelian randomization (MR) analyses used the inverse-variance weighted method. Genetic correlations with AAM were identified for 29 candi-date dietary habits, such as milk type (skimmed, semi-skimmed, full cream; coefficient = 0.2704, Pldsc = 1.13 × 10-14). MR evaluations revealed that 19 dietary habits were associated with AAM, including bread type (white vs. any other; OR 1.71, 95% CI 1.28-2.29, Pmr = 3.20 × 10-4), tablespoons of cooked vegetables (OR 0.437, 95% CI 0.29-0.67; Pmr = 1.30 × 10-4), and cups of coffee per day (OR 0.72, 95% CI 0.57-0.92, Pmr = 8.31 × 10-3). These results were observed to be stable under the sensitivity analysis. Our study provides potential insights into the genetic mechanisms underlying AAM and evidence that dietary habits are associated with AAM.
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Affiliation(s)
- Ruilong Guo
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, 710054, Shanxi, China
| | - Ruoyang Feng
- Department of Joint Surgery, Xi'an Jiaotong University Hong Hui Hospital, Xi'an, 710054, Shanxi, China
| | - Jiong Yang
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, 710054, Shanxi, China
| | - Yanfeng Xiao
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, 710054, Shanxi, China.
| | - Chunyan Yin
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, 710054, Shanxi, China.
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26
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Moksnes MR, Hansen AF, Wolford BN, Thomas LF, Rasheed H, Simić A, Bhatta L, Brantsæter AL, Surakka I, Zhou W, Magnus P, Njølstad PR, Andreassen OA, Syversen T, Zheng J, Fritsche LG, Evans DM, Warrington NM, Nøst TH, Åsvold BO, Flaten TP, Willer CJ, Hveem K, Brumpton BM. A genome-wide association study provides insights into the genetic etiology of 57 essential and non-essential trace elements in humans. Commun Biol 2024; 7:432. [PMID: 38594418 PMCID: PMC11004147 DOI: 10.1038/s42003-024-06101-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: 05/09/2023] [Accepted: 03/22/2024] [Indexed: 04/11/2024] Open
Abstract
Trace elements are important for human health but may exert toxic or adverse effects. Mechanisms of uptake, distribution, metabolism, and excretion are partly under genetic control but have not yet been extensively mapped. Here we report a comprehensive multi-element genome-wide association study of 57 essential and non-essential trace elements. We perform genome-wide association meta-analyses of 14 trace elements in up to 6564 Scandinavian whole blood samples, and genome-wide association studies of 43 trace elements in up to 2819 samples measured only in the Trøndelag Health Study (HUNT). We identify 11 novel genetic loci associated with blood concentrations of arsenic, cadmium, manganese, selenium, and zinc in genome-wide association meta-analyses. In HUNT, several genome-wide significant loci are also indicated for other trace elements. Using two-sample Mendelian randomization, we find several indications of weak to moderate effects on health outcomes, the most precise being a weak harmful effect of increased zinc on prostate cancer. However, independent validation is needed. Our current understanding of trace element-associated genetic variants may help establish consequences of trace elements on human health.
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Affiliation(s)
- Marta R Moksnes
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
| | - Ailin F Hansen
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Brooke N Wolford
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Laurent F Thomas
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- BioCore-Bioinformatics Core Facility, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Humaira Rasheed
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
| | - Anica Simić
- Department of Chemistry, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Laxmi Bhatta
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Anne Lise Brantsæter
- Department of Food Safety, Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Pål R Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tore Syversen
- Department of Neuroscience, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Lars G Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - David M Evans
- Institute for Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, The University of Queensland, Woolloongabba, QLD, Australia
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Nicole M Warrington
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Institute for Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, The University of Queensland, Woolloongabba, QLD, Australia
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Therese H Nøst
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Bjørn Olav Åsvold
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Trond Peder Flaten
- Department of Chemistry, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Cristen J Willer
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Kristian Hveem
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Levanger, Norway
| | - Ben M Brumpton
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
- HUNT Research Centre, Department of Public Health and Nursing, NTNU-Norwegian University of Science and Technology, Levanger, Norway.
- Clinic of Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
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27
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Kentistou KA, Lim BEM, Kaisinger LR, Steinthorsdottir V, Sharp LN, Patel KA, Tragante V, Hawkes G, Gardner EJ, Olafsdottir T, Wood AR, Zhao Y, Thorleifsson G, Day FR, Ozanne SE, Hattersley AT, O'Rahilly S, Stefansson K, Ong KK, Beaumont RN, Perry JRB, Freathy RM. Rare variant associations with birth weight identify genes involved in adipose tissue regulation, placental function and insulin-like growth factor signalling. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.03.24305248. [PMID: 38633783 PMCID: PMC11023655 DOI: 10.1101/2024.04.03.24305248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Investigating the genetic factors influencing human birth weight may lead to biological insights into fetal growth and long-term health. Genome-wide association studies of birth weight have highlighted associated variants in more than 200 regions of the genome, but the causal genes are mostly unknown. Rare genetic variants with robust evidence of association are more likely to point to causal genes, but to date, only a few rare variants are known to influence birth weight. We aimed to identify genes that harbour rare variants that impact birth weight when carried by either the fetus or the mother, by analysing whole exome sequence data in UK Biobank participants. We annotated rare (minor allele frequency <0.1%) protein-truncating or high impact missense variants on whole exome sequence data in up to 234,675 participants with data on their own birth weight (fetal variants), and up to 181,883 mothers who reported the birth weight of their first child (maternal variants). Variants within each gene were collapsed to perform gene burden tests and for each associated gene, we compared the observed fetal and maternal effects. We identified 8 genes with evidence of rare fetal variant effects on birth weight, of which 2 also showed maternal effects. One additional gene showed evidence of maternal effects only. We observed 10/11 directionally concordant associations in an independent sample of up to 45,622 individuals (sign test P=0.01). Of the genes identified, IGF1R and PAPPA2 (fetal and maternal-acting) have known roles in insulin-like growth factor bioavailability and signalling. PPARG, INHBE and ACVR1C (all fetal-acting) have known roles in adipose tissue regulation and rare variants in the latter two also showed associations with favourable adiposity patterns in adults. We highlight the dual role of PPARG in both adipocyte differentiation and placental angiogenesis. NOS3, NRK, and ADAMTS8 (fetal and maternal-acting) have been implicated in both placental function and hypertension. Analysis of rare coding variants has identified regulators of fetal adipose tissue and fetoplacental angiogenesis as determinants of birth weight, as well as further evidence for the role of insulin-like growth factors.
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Affiliation(s)
- Katherine A Kentistou
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Brandon E M Lim
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Lena R Kaisinger
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | | | - Luke N Sharp
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Kashyap A Patel
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | | | - Gareth Hawkes
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | | | - Andrew R Wood
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Yajie Zhao
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | | | - Felix R Day
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Susan E Ozanne
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Stephen O'Rahilly
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc., 102 Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Ken K Ong
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
- Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Robin N Beaumont
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - John R B Perry
- MRC Epidemiology Unit, Box 285 Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Rachel M Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
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28
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Hujoel MLA, Handsaker RE, Sherman MA, Kamitaki N, Barton AR, Mukamel RE, Terao C, McCarroll SA, Loh PR. Protein-altering variants at copy number-variable regions influence diverse human phenotypes. Nat Genet 2024; 56:569-578. [PMID: 38548989 PMCID: PMC11018521 DOI: 10.1038/s41588-024-01684-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: 06/23/2023] [Accepted: 02/08/2024] [Indexed: 04/09/2024]
Abstract
Copy number variants (CNVs) are among the largest genetic variants, yet CNVs have not been effectively ascertained in most genetic association studies. Here we ascertained protein-altering CNVs from UK Biobank whole-exome sequencing data (n = 468,570) using haplotype-informed methods capable of detecting subexonic CNVs and variation within segmental duplications. Incorporating CNVs into analyses of rare variants predicted to cause gene loss of function (LOF) identified 100 associations of predicted LOF variants with 41 quantitative traits. A low-frequency partial deletion of RGL3 exon 6 conferred one of the strongest protective effects of gene LOF on hypertension risk (odds ratio = 0.86 (0.82-0.90)). Protein-coding variation in rapidly evolving gene families within segmental duplications-previously invisible to most analysis methods-generated some of the human genome's largest contributions to variation in type 2 diabetes risk, chronotype and blood cell traits. These results illustrate the potential for new genetic insights from genomic variation that has escaped large-scale analysis to date.
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Affiliation(s)
- Margaux L A Hujoel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Robert E Handsaker
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Maxwell A Sherman
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Serinus Biosciences Inc., New York, NY, USA
| | - Nolan Kamitaki
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alison R Barton
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Ronen E Mukamel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Zhao Y, Chukanova M, Kentistou KA, Fairhurst-Hunter Z, Siegert AM, Jia RY, Dowsett GKC, Gardner EJ, Lawler K, Day FR, Kaisinger LR, Tung YCL, Lam BYH, Chen HJC, Wang Q, Berumen-Campos J, Kuri-Morales P, Tapia-Conyer R, Alegre-Diaz J, Barroso I, Emberson J, Torres JM, Collins R, Saleheen D, Smith KR, Paul DS, Merkle F, Farooqi IS, Wareham NJ, Petrovski S, O'Rahilly S, Ong KK, Yeo GSH, Perry JRB. Protein-truncating variants in BSN are associated with severe adult-onset obesity, type 2 diabetes and fatty liver disease. Nat Genet 2024; 56:579-584. [PMID: 38575728 PMCID: PMC11018524 DOI: 10.1038/s41588-024-01694-x] [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: 06/05/2023] [Accepted: 02/21/2024] [Indexed: 04/06/2024]
Abstract
Obesity is a major risk factor for many common diseases and has a substantial heritable component. To identify new genetic determinants, we performed exome-sequence analyses for adult body mass index (BMI) in up to 587,027 individuals. We identified rare loss-of-function variants in two genes (BSN and APBA1) with effects substantially larger than those of well-established obesity genes such as MC4R. In contrast to most other obesity-related genes, rare variants in BSN and APBA1 were not associated with normal variation in childhood adiposity. Furthermore, BSN protein-truncating variants (PTVs) magnified the influence of common genetic variants associated with BMI, with a common variant polygenic score exhibiting an effect twice as large in BSN PTV carriers than in noncarriers. Finally, we explored the plasma proteomic signatures of BSN PTV carriers as well as the functional consequences of BSN deletion in human induced pluripotent stem cell-derived hypothalamic neurons. Collectively, our findings implicate degenerative processes in synaptic function in the etiology of adult-onset obesity.
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Affiliation(s)
- Yajie Zhao
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Maria Chukanova
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Katherine A Kentistou
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Zammy Fairhurst-Hunter
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Anna Maria Siegert
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Raina Y Jia
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Georgina K C Dowsett
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Katherine Lawler
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Felix R Day
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Lena R Kaisinger
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Yi-Chun Loraine Tung
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Brian Yee Hong Lam
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Hsiao-Jou Cortina Chen
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Jaime Berumen-Campos
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
| | - Pablo Kuri-Morales
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
- Instituto Tecnológico de Estudios Superiores de Monterrey, Tecnológico, Monterrey, Mexico
| | - Roberto Tapia-Conyer
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
| | - Jesus Alegre-Diaz
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Jonathan Emberson
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jason M Torres
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dirk S Paul
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Florian Merkle
- Institute of Metabolic Science and Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - I Sadaf Farooqi
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Nick J Wareham
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Stephen O'Rahilly
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Giles S H Yeo
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
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30
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Qi M, Zhang H, Xiu X, He D, Cooper DN, Yang Y, Zhao H. Genetic evidence for T-wave area from 12-lead electrocardiograms to monitor cardiovascular diseases in patients taking diabetes medications. Hum Genet 2024:10.1007/s00439-024-02661-6. [PMID: 38507016 DOI: 10.1007/s00439-024-02661-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/15/2024] [Indexed: 03/22/2024]
Abstract
Aims Many studies indicated use of diabetes medications can influence the electrocardiogram (ECG), which remains the simplest and fastest tool for assessing cardiac functions. However, few studies have explored the role of genetic factors in determining the relationship between the use of diabetes medications and ECG trace characteristics (ETC). Methods Genome-wide association studies (GWAS) were performed for 168 ETCs extracted from the 12-lead ECGs of 42,340 Europeans in the UK Biobank. The genetic correlations, causal relationships, and phenotypic relationships of these ETCs with medication usage, as well as the risk of cardiovascular diseases (CVDs), were estimated by linkage disequilibrium score regression (LDSC), Mendelian randomization (MR), and regression model, respectively. Results The GWAS identified 124 independent single nucleotide polymorphisms (SNPs) that were study-wise and genome-wide significantly associated with at least one ETC. Regression model and LDSC identified significant phenotypic and genetic correlations of T-wave area in lead aVR (aVR_T-area) with usage of diabetes medications (ATC code: A10 drugs, and metformin), and the risks of ischemic heart disease (IHD) and coronary atherosclerosis (CA). MR analyses support a putative causal effect of the use of diabetes medications on decreasing aVR_T-area, and on increasing risk of IHD and CA. ConclusionPatients taking diabetes medications are prone to have decreased aVR_T-area and an increased risk of IHD and CA. The aVR_T-area is therefore a potential ECG marker for pre-clinical prediction of IHD and CA in patients taking diabetes medications.
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Affiliation(s)
- Mengling Qi
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Haoyang Zhang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Xuehao Xiu
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Dan He
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Yuanhao Yang
- Mater Research Institute, Translational Research Institute, Brisbane, QLD, Australia
| | - Huiying Zhao
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China.
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31
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Venkatesh SS, Wittemans LBL, Palmer DS, Baya NA, Ferreira T, Hill B, Lassen FH, Parker MJ, Reibe S, Elhakeem A, Banasik K, Bruun MT, Erikstrup C, Jensen BA, Juul A, Mikkelsen C, Nielsen HS, Ostrowski SR, Pedersen OB, Rohde PD, Sorensen E, Ullum H, Westergaard D, Haraldsson A, Holm H, Jonsdottir I, Olafsson I, Steingrimsdottir T, Steinthorsdottir V, Thorleifsson G, Figueredo J, Karjalainen MK, Pasanen A, Jacobs BM, Hubers N, Lippincott M, Fraser A, Lawlor DA, Timpson NJ, Nyegaard M, Stefansson K, Magi R, Laivuori H, van Heel DA, Boomsma DI, Balasubramanian R, Seminara SB, Chan YM, Laisk T, Lindgren CM. Genome-wide analyses identify 21 infertility loci and over 400 reproductive hormone loci across the allele frequency spectrum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304530. [PMID: 38562841 PMCID: PMC10984039 DOI: 10.1101/2024.03.19.24304530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Genome-wide association studies (GWASs) may help inform treatments for infertility, whose causes remain unknown in many cases. Here we present GWAS meta-analyses across six cohorts for male and female infertility in up to 41,200 cases and 687,005 controls. We identified 21 genetic risk loci for infertility (P≤5E-08), of which 12 have not been reported for any reproductive condition. We found positive genetic correlations between endometriosis and all-cause female infertility (rg=0.585, P=8.98E-14), and between polycystic ovary syndrome and anovulatory infertility (rg=0.403, P=2.16E-03). The evolutionary persistence of female infertility-risk alleles in EBAG9 may be explained by recent directional selection. We additionally identified up to 269 genetic loci associated with follicle-stimulating hormone (FSH), luteinising hormone, oestradiol, and testosterone through sex-specific GWAS meta-analyses (N=6,095-246,862). While hormone-associated variants near FSHB and ARL14EP colocalised with signals for anovulatory infertility, we found no rg between female infertility and reproductive hormones (P>0.05). Exome sequencing analyses in the UK Biobank (N=197,340) revealed that women carrying testosterone-lowering rare variants in GPC2 were at higher risk of infertility (OR=2.63, P=1.25E-03). Taken together, our results suggest that while individual genes associated with hormone regulation may be relevant for fertility, there is limited genetic evidence for correlation between reproductive hormones and infertility at the population level. We provide the first comprehensive view of the genetic architecture of infertility across multiple diagnostic criteria in men and women, and characterise its relationship to other health conditions.
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Affiliation(s)
- Samvida S Venkatesh
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Nikolas A Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Barney Hill
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Frederik Heymann Lassen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Melody J Parker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Saskia Reibe
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Mie T Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - Bitten A Jensen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Anders Juul
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen; Copenhagen, Denmark
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Christina Mikkelsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, Copenhagen University, Copenhagen, Denmark
| | - Henriette S Nielsen
- Department of Obstetrics and Gynecology, The Fertility Clinic, Hvidovre University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Kge, Denmark
| | - Palle D Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Erik Sorensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Asgeir Haraldsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Children's Hospital Iceland, Landspitali University Hospital, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Ingileif Jonsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Thora Steingrimsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Obstetrics and Gynecology, Landspitali University Hospital, Reykjavik, Iceland
| | | | | | - Jessica Figueredo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Minna K Karjalainen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Finland
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Anu Pasanen
- Research Unit of Clinical Medicine, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Benjamin M Jacobs
- Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University London, London, EC1M 6BQ, United Kingdom
| | - Nikki Hubers
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Margaret Lippincott
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Mette Nyegaard
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Reedik Magi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University Hospital, Finland
- Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Finland
| | - David A van Heel
- Blizard Institute, Queen Mary University London, London, E1 2AT, United Kingdom
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Ravikumar Balasubramanian
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stephanie B Seminara
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yee-Ming Chan
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Endocrinology, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, United States of America
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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32
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Constantinescu AE, Hughes DA, Bull CJ, Fleming K, Mitchell RE, Zheng J, Kar S, Timpson NJ, Amulic B, Vincent EE. A genome-wide association study of neutrophil count in individuals associated to an African continental ancestry group facilitates studies of malaria pathogenesis. Hum Genomics 2024; 18:26. [PMID: 38491524 PMCID: PMC10941368 DOI: 10.1186/s40246-024-00585-w] [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: 10/19/2023] [Accepted: 02/12/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND 'Benign ethnic neutropenia' (BEN) is a heritable condition characterized by lower neutrophil counts, predominantly observed in individuals of African ancestry, and the genetic basis of BEN remains a subject of extensive research. In this study, we aimed to dissect the genetic architecture underlying neutrophil count variation through a linear-mixed model genome-wide association study (GWAS) in a population of African ancestry (N = 5976). Malaria caused by P. falciparum imposes a tremendous public health burden on people living in sub-Saharan Africa. Individuals living in malaria endemic regions often have a reduced circulating neutrophil count due to BEN, raising the possibility that reduced neutrophil counts modulate severity of malaria in susceptible populations. As a follow-up, we tested this hypothesis by conducting a Mendelian randomization (MR) analysis of neutrophil counts on severe malaria (MalariaGEN, N = 17,056). RESULTS We carried out a GWAS of neutrophil count in individuals associated to an African continental ancestry group within UK Biobank, identifying 73 loci (r2 = 0.1) and 10 index SNPs (GCTA-COJO loci) associated with neutrophil count, including previously unknown rare loci regulating neutrophil count in a non-European population. BOLT-LMM was reliable when conducted in a non-European population, and additional covariates added to the model did not largely alter the results of the top loci or index SNPs. The two-sample bi-directional MR analysis between neutrophil count and severe malaria showed the greatest evidence for an effect between neutrophil count and severe anaemia, although the confidence intervals crossed the null. CONCLUSION Our GWAS of neutrophil count revealed unique loci present in individuals of African ancestry. We note that a small sample-size reduced our power to identify variants with low allele frequencies and/or low effect sizes in our GWAS. Our work highlights the need for conducting large-scale biobank studies in Africa and for further exploring the link between neutrophils and severe malaria.
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Affiliation(s)
- Andrei-Emil Constantinescu
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Louisiana State University, Louisiana, USA
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
- Health Data Research UK, London, UK
| | - Kathryn Fleming
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Ruth E Mitchell
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases, National Health Commission, Shanghai, People's Republic of China
- Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Early Cancer Insitute, University of Cambridge, Cambridge, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Borko Amulic
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK.
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.
- School of Translational Health Sciences, University of Bristol, Bristol, UK.
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33
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Xue A, Zhu Z, Wang H, Jiang L, Visscher PM, Zeng J, Yang J. Unravelling the complex causal effects of substance use behaviours on common diseases. COMMUNICATIONS MEDICINE 2024; 4:43. [PMID: 38472333 DOI: 10.1038/s43856-024-00473-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Substance use behaviours (SUB) including smoking, alcohol consumption, and coffee intake are associated with many health outcomes. However, whether the health effects of SUB are causal remains controversial, especially for alcohol consumption and coffee intake. METHODS In this study, we assess 11 commonly used Mendelian Randomization (MR) methods by simulation and apply them to investigate the causal relationship between 7 SUB traits and health outcomes. We also combine stratified regression, genetic correlation, and MR analyses to investigate the dosage-dependent effects. RESULTS We show that smoking initiation has widespread risk effects on common diseases such as asthma, type 2 diabetes, and peripheral vascular disease. Alcohol consumption shows risk effects specifically on cardiovascular diseases, dyslipidemia, and hypertensive diseases. We find evidence of dosage-dependent effects of coffee and tea intake on common diseases (e.g., cardiovascular disease and osteoarthritis). We observe that the minor allele effect of rs4410790 (the top signal for tea intake level) is negative on heavy tea intake ( b ̂ G W A S = - 0.091 , s . e . = 0.007 , P = 4.90 × 10 - 35 ) but positive on moderate tea intake ( b ̂ G W A S = 0.034 , s . e . = 0.006 , P = 3.40 × 10 - 8 ) , compared to the non-tea-drinkers. CONCLUSION Our study reveals the complexity of the health effects of SUB and informs design for future studies aiming to dissect the causal relationships between behavioural traits and complex diseases.
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Affiliation(s)
- Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- National Centre for Register-Based Research, Aarhus University, Aarhus V, 8210, Denmark
| | - Huanwei Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, 310024, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China.
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34
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Zhu Y, Zhuang Z, Lv J, Sun D, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Liu F, Stevens R, Chen J, Chen Z, Li L, Yu C. A genome-wide association study based on the China Kadoorie Biobank identifies genetic associations between snoring and cardiometabolic traits. Commun Biol 2024; 7:305. [PMID: 38461358 PMCID: PMC10924953 DOI: 10.1038/s42003-024-05978-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 02/27/2024] [Indexed: 03/11/2024] Open
Abstract
Despite the high prevalence of snoring in Asia, little is known about the genetic etiology of snoring and its causal relationships with cardiometabolic traits. Based on 100,626 Chinese individuals, a genome-wide association study on snoring was conducted. Four novel loci were identified for snoring traits mapped on SLC25A21, the intergenic region of WDR11 and FGFR, NAA25, ALDH2, and VTI1A, respectively. The novel loci highlighted the roles of structural abnormality of the upper airway and craniofacial region and dysfunction of metabolic and transport systems in the development of snoring. In the two-sample bi-directional Mendelian randomization analysis, higher body mass index, weight, and elevated blood pressure were causal for snoring, and a reverse causal effect was observed between snoring and diastolic blood pressure. Altogether, our results revealed the possible etiology of snoring in China and indicated that managing cardiometabolic health was essential to snoring prevention, and hypertension should be considered among snorers.
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Affiliation(s)
- Yunqing Zhu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Zhenhuang Zhuang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Fang Liu
- Suzhou Centers for Disease Control, NO.72 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China.
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Wong THT, Mo JMY, Zhou M, Zhao JV, Schooling CM, He B, Luo S, Au Yeung SL. A two-sample Mendelian randomization study explores metabolic profiling of different glycemic traits. Commun Biol 2024; 7:293. [PMID: 38459184 PMCID: PMC10923832 DOI: 10.1038/s42003-024-05977-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
We assessed the causal relation of four glycemic traits and type 2 diabetes liability with 167 metabolites using Mendelian randomization with various sensitivity analyses and a reverse Mendelian randomization analysis. We extracted instruments for fasting glucose, 2-h glucose, fasting insulin, and glycated hemoglobin from the Meta-Analyses of Glucose and Insulin-related traits Consortium (n = 200,622), and those for type 2 diabetes liability from a meta-analysis of multiple cohorts (148,726 cases, 965,732 controls) in Europeans. Outcome data were from summary statistics of 167 metabolites from the UK Biobank (n = 115,078). Fasting glucose and 2-h glucose were not associated with any metabolite. Higher glycated hemoglobin was associated with higher free cholesterol in small low-density lipoprotein. Type 2 diabetes liability and fasting insulin were inversely associated with apolipoprotein A1, total cholines, lipoprotein subfractions in high-density-lipoprotein and intermediate-density lipoproteins, and positively associated with aromatic amino acids. These findings indicate hyperglycemia-independent patterns and highlight the role of insulin in type 2 diabetes development. Further studies should evaluate these glycemic traits in type 2 diabetes diagnosis and clinical management.
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Affiliation(s)
- Tommy H T Wong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jacky M Y Mo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mingqi Zhou
- Department of Biological Chemistry, School of Medicine, University of California Irvine, Irvine, CA, USA
- Center for Epigenetics and Metabolism, University of California Irvine, Irvine, CA, USA
| | - Jie V Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Baoting He
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shan Luo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - Shiu Lun Au Yeung
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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36
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Schraiber JG, Edge MD, Pennell M. Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.10.579721. [PMID: 38496530 PMCID: PMC10942266 DOI: 10.1101/2024.02.10.579721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.
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37
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Lu HF, Chou CH, Lin YJ, Uchiyama S, Terao C, Wang YW, Yang JS, Liu TY, Wong HSC, Chen SCC, Tsai FJ. The genome-wide association study of serum IgE levels demonstrated a shared genetic background in allergic diseases. Clin Immunol 2024; 260:109897. [PMID: 38199299 DOI: 10.1016/j.clim.2024.109897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/12/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
Immunoglobulin E (IgE) synthessis is highly related to a variety of atopic diseases, and several genome-wide association studies (GWASs) have demonstrated the association between genes and IgE level. In this study, we conducted the largest genome-wide association study of IgE involving a Taiwanese Han population. Eight independent variants exhibited genome-wide significance. Among them, an intronic SNP of CD28, rs1181388, and an intergenic SNP, rs1002957030, on 11q23.2 were identified as novel signals for IgE. Seven of the loci were replicated successfully in a meta-analysis using data on Japanese population. Among all the human leukocyte antigen (HLA) regions, HLA-DQA1*03:02 - HLA-DQB1*03:03 was the most significant haplotype (OR = 1.25, SE = 0.02, FDR = 1.6 × 10-14), corresponding to HLA-DQA1 Asp160 and HLA-DQB1 Leu87 amino acid residues. The genetic correlation showed significance between IgE and allergic diseases including asthma, atopic dermatitis, and pollinosis. IgE PRS was significantly correlated with total IgE levels. Furthermore, the top decile IgE polygenic risk score (PRS) group had the highest risk of asthma for the Taiwan Biobank and Biobank Japan cohorts. IgE PRS may be used to aid in predicting the occurrence of allergic reactions before symptoms occur and biomarkers are detectable. Our study provided a more comprehensive understanding of the impact of genomic variants, including complex HLA alleles, on serum IgE levels.
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Affiliation(s)
- Hsing-Fang Lu
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan; Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chen-Hsing Chou
- PhD Program for Health Science and Industry, College of Health Care, China Medical University, Taichung, Taiwan; Department of Health Services Administration, China Medical University, Taichung, Taiwan
| | - Ying-Ju Lin
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan; School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Shunsuke Uchiyama
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan; The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Yu-Wen Wang
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Jai-Sing Yang
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, 404327, Taiwan
| | - Ting-Yuan Liu
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Henry Sung-Ching Wong
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Sean Chun-Chang Chen
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Fuu-Jen Tsai
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan; School of Chinese Medicine, China Medical University, Taichung, Taiwan; Department of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan.
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38
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Yang ML, Xu C, Gupte T, Hoffmann TJ, Iribarren C, Zhou X, Ganesh SK. Sex-specific genetic architecture of blood pressure. Nat Med 2024; 30:818-828. [PMID: 38459180 DOI: 10.1038/s41591-024-02858-2] [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: 01/17/2023] [Accepted: 02/05/2024] [Indexed: 03/10/2024]
Abstract
The genetic and genomic basis of sex differences in blood pressure (BP) traits remain unstudied at scale. Here, we conducted sex-stratified and combined-sex genome-wide association studies of BP traits using the UK Biobank resource, identifying 1,346 previously reported and 29 new BP trait-associated loci. Among associated loci, 412 were female-specific (Pfemale ≤ 5 × 10-8; Pmale > 5 × 10-8) and 142 were male-specific (Pmale ≤ 5 × 10-8; Pfemale > 5 × 10-8); these sex-specific loci were enriched for hormone-related transcription factors, in particular, estrogen receptor 1. Analyses of gene-by-sex interactions and sexually dimorphic effects identified four genomic regions, showing female-specific associations with diastolic BP or pulse pressure, including the chromosome 13q34-COL4A1/COL4A2 locus. Notably, female-specific pulse pressure-associated loci exhibited enriched acetylated histone H3 Lys27 modifications in arterial tissues and a female-specific association with fibromuscular dysplasia, a female-biased vascular disease; colocalization signals included Chr13q34: COL4A1/COL4A2, Chr9p21: CDKN2B-AS1 and Chr4q32.1: MAP9 regions. Sex-specific and sex-biased polygenic associations of BP traits were associated with multiple cardiovascular traits. These findings suggest potentially clinically significant and BP sex-specific pleiotropic effects on cardiovascular diseases.
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Affiliation(s)
- Min-Lee Yang
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chang Xu
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Trisha Gupte
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Thomas J Hoffmann
- Department of Epidemiology & Biostatistics, and Institute for Human Genetics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | | | - Xiang Zhou
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Santhi K Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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Yang Q, Yang Q, Wu X, Zheng R, Lin H, Wang S, Joseph J, Sun YV, Li M, Wang T, Zhao Z, Xu M, Lu J, Chen Y, Ning G, Wang W, Bi Y, Zheng J, Xu Y. Sex-stratified genome-wide association and transcriptome-wide Mendelian randomization studies reveal drug targets of heart failure. Cell Rep Med 2024; 5:101382. [PMID: 38237596 PMCID: PMC10897518 DOI: 10.1016/j.xcrm.2023.101382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/31/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
The prevalence of heart failure (HF) subtypes, which are classified by left ventricular ejection fraction (LVEF), demonstrate significant sex differences. Here, we perform sex-stratified genome-wide association studies (GWASs) on LVEF and transcriptome-wide Mendelian randomization (MR) on LVEF, all-cause HF, HF with reduced ejection fraction (HFrEF), and HF with preserved ejection fraction (HFpEF). The sex-stratified GWASs of LVEF identified three sex-specific loci that were exclusively detected in the sex-stratified GWASs. Three drug target genes show sex-differential effects on HF/HFrEF via influencing LVEF, with NPR2 as the target gene for the HF drug Cenderitide under phase 2 clinical trial. Our study highlights the importance of considering sex-differential genetic effects in sex-balanced diseases such as HF and emphasizes the value of sex-stratified GWASs and MR in identifying putative genetic variants, causal genes, and candidate drug targets for HF, which is not identifiable using a sex-combined strategy.
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Affiliation(s)
- Qianqian Yang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qian Yang
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Xueyan Wu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ruizhi Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hong Lin
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Shuangyuan Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jacob Joseph
- Cardiology Section, VA Providence Healthcare System, 830 Chalkstone Avenue, Providence, RI 02908, USA; Department of Medicine, Warren Alpert Medical School of Brown University, 222 Richmond Street, Providence, RI 02903, USA
| | - Yan V Sun
- Emory University Rollins School of Public Health, Atlanta, GA, USA; Atlanta VA Health Care System, Decatur, GA, USA
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Tiange Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhiyun Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yuhong Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
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40
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Pathan N, Deng WQ, Di Scipio M, Khan M, Mao S, Morton RW, Lali R, Pigeyre M, Chong MR, Paré G. A method to estimate the contribution of rare coding variants to complex trait heritability. Nat Commun 2024; 15:1245. [PMID: 38336875 PMCID: PMC10858280 DOI: 10.1038/s41467-024-45407-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
It has been postulated that rare coding variants (RVs; MAF < 0.01) contribute to the "missing" heritability of complex traits. We developed a framework, the Rare variant heritability (RARity) estimator, to assess RV heritability (h2RV) without assuming a particular genetic architecture. We applied RARity to 31 complex traits in the UK Biobank (n = 167,348) and showed that gene-level RV aggregation suffers from 79% (95% CI: 68-93%) loss of h2RV. Using unaggregated variants, 27 traits had h2RV > 5%, with height having the highest h2RV at 21.9% (95% CI: 19.0-24.8%). The total heritability, including common and rare variants, recovered pedigree-based estimates for 11 traits. RARity can estimate gene-level h2RV, enabling the assessment of gene-level characteristics and revealing 11, previously unreported, gene-phenotype relationships. Finally, we demonstrated that in silico pathogenicity prediction (variant-level) and gene-level annotations do not generally enrich for RVs that over-contribute to complex trait variance, and thus, innovative methods are needed to predict RV functionality.
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Affiliation(s)
- Nazia Pathan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
| | - Wei Q Deng
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Canada
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
| | - Matteo Di Scipio
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Mohammad Khan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Shihong Mao
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
| | - Robert W Morton
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
| | - Ricky Lali
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Marie Pigeyre
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Michael R Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada.
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada.
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Clayton GL, Borges MC, Lawlor DA. The impact of reproductive factors on the metabolic profile of females from menarche to menopause. Nat Commun 2024; 15:1103. [PMID: 38320991 PMCID: PMC10847109 DOI: 10.1038/s41467-023-44459-6] [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: 06/02/2022] [Accepted: 12/14/2023] [Indexed: 02/08/2024] Open
Abstract
We explore the relation between age at menarche, parity and age at natural menopause with 249 metabolic traits in over 65,000 UK Biobank women using multivariable regression, Mendelian randomization and negative control (parity only). Older age of menarche is related to a less atherogenic metabolic profile in multivariable regression and Mendelian randomization, which is largely attenuated when accounting for adult body mass index. In multivariable regression, higher parity relates to more particles and lipids in VLDL, which are not observed in male negative controls. In multivariable regression and Mendelian randomization, older age at natural menopause is related to lower concentrations of inflammation markers, but we observe inconsistent results for LDL-related traits due to chronological age-specific effects. For example, older age at menopause is related to lower LDL-cholesterol in younger women but slightly higher in older women. Our findings support a role of reproductive traits on later life metabolic profile and provide insights into identifying novel markers for the prevention of adverse cardiometabolic outcomes in women.
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Affiliation(s)
- Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
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42
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Hu Z, Xu Z, Yue Q, Pan X, Shi P, Zhang D, Zhang J, Deng R, Lin Z. The role of blood metabolites in oral cancer: insights from a Mendelian randomization approach. Front Oncol 2024; 14:1305684. [PMID: 38375154 PMCID: PMC10876297 DOI: 10.3389/fonc.2024.1305684] [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: 10/09/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024] Open
Abstract
Aim This research aimed to explore the causal impact of blood metabolites on oral cancer using a two-sample Mendelian randomization (MR) analysis. The study endeavored to identify potential biomarkers for oral cancer's clinical management. Materials and methods Based on the large individual-level datasets from UK Biobank as well as GWAS summary datasets, we first constructed genetic risk scores (GRSs) of 486 human blood metabolites and evaluated the effect on oral cancer. Various statistical methods, including inverse variance weighted (IVW), MR-Egger, and weighted median, among others, were employed to analyze the potential causal relationship between blood metabolites and oral cancer. The sensitivity analyses were conducted using Cochran's Q tests, funnel plots, leave-one-out analyses, and MR-Egger intercept tests. Results 29 metabolites met the stringent selection criteria. Out of these, 14 metabolites demonstrated a positive association with oral cancer risk, while 15 metabolites indicated a protective effect against oral cancer. The IVW-derived estimates were significant, and the results were consistent across different statistical methodologies. Both the Cochran Q test and the MR-Egger intercept test indicated no heterogeneity and pleiotropy. Conclusion This MR study offers evidence of the role specific blood metabolites play in oral cancer, pinpointing several with potential risk or protective effects. These findings could be helpful for new diagnostic tools and treatments for oral cancer. While the results are promising, additional research is necessary to fully validate and refine these conclusions. This study serves as a foundational step towards more comprehensive understandings in the future.
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Affiliation(s)
- Ziyang Hu
- Shenzhen Longhua District Central Hospital, Department of Stomatology, Shenzhen, China
| | - Zhe Xu
- Shenzhen Longhua District Central Hospital, Department of Stomatology, Shenzhen, China
| | - Qu Yue
- Shenzhen Longhua District Central Hospital, Department of Stomatology, Shenzhen, China
| | - Xuhong Pan
- Shenzhen Longhua District Central Hospital, Department of Stomatology, Shenzhen, China
| | - Ping Shi
- Shenzhen Longhua District Central Hospital, Department of Stomatology, Shenzhen, China
| | - Dandan Zhang
- Shenzhen Longhua District Central Hospital, Department of Stomatology, Shenzhen, China
| | - Jiexia Zhang
- Shenzhen Longhua District Central Hospital, Department of Stomatology, Shenzhen, China
| | - Runzhi Deng
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China
| | - Zitong Lin
- Department of Dentomaxillofacial Radiology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China
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43
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Liao K, Zöllner S. A Stacking Framework for Polygenic Risk Prediction in Admixed Individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.31.24302103. [PMID: 38434717 PMCID: PMC10907988 DOI: 10.1101/2024.01.31.24302103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Polygenic risk scores (PRS) are summaries of an individual's personalized genetic risk for a trait or disease. However, PRS often perform poorly for phenotype prediction when the ancestry of the target population does not match the population in which GWAS effect sizes were estimated. For many populations this can be addressed by performing GWAS in the target population. However, admixed individuals (whose genomes can be traced to multiple ancestral populations) lie on an ancestry continuum and are not easily represented as a discrete population. Here, we propose slaPRS (stacking local ancestry PRS), which incorporates multiple ancestry GWAS to alleviate the ancestry dependence of PRS in admixed samples. slaPRS uses ensemble learning (stacking) to combine local population specific PRS in regions across the genome. We compare slaPRS to single population PRS and a method that combines single population PRS globally. In simulations, slaPRS outperformed existing approaches and reduced the ancestry dependence of PRS in African Americans. In lipid traits from African British individuals (UK Biobank), slaPRS again improved on single population PRS while performing comparably to the globally combined PRS. slaPRS provides a data-driven and flexible framework to incorporate multiple population-specific GWAS and local ancestry in samples of admixed ancestry.
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Affiliation(s)
- Kevin Liao
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Sebastian Zöllner
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
- University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA
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44
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Sun J, Noss S, Banerjee D, Das M, Girirajan S. Strategies for dissecting the complexity of neurodevelopmental disorders. Trends Genet 2024; 40:187-202. [PMID: 37949722 PMCID: PMC10872993 DOI: 10.1016/j.tig.2023.10.009] [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: 05/27/2023] [Revised: 09/20/2023] [Accepted: 10/16/2023] [Indexed: 11/12/2023]
Abstract
Neurodevelopmental disorders (NDDs) are associated with a wide range of clinical features, affecting multiple pathways involved in brain development and function. Recent advances in high-throughput sequencing have unveiled numerous genetic variants associated with NDDs, which further contribute to disease complexity and make it challenging to infer disease causation and underlying mechanisms. Herein, we review current strategies for dissecting the complexity of NDDs using model organisms, induced pluripotent stem cells, single-cell sequencing technologies, and massively parallel reporter assays. We further highlight single-cell CRISPR-based screening techniques that allow genomic investigation of cellular transcriptomes with high efficiency, accuracy, and throughput. Overall, we provide an integrated review of experimental approaches that can be applicable for investigating a broad range of complex disorders.
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Affiliation(s)
- Jiawan Sun
- Molecular, Cellular, and Integrative Biosciences Graduate Program, The Huck Institutes of Life Sciences, University Park, PA 16802, USA
| | - Serena Noss
- Molecular, Cellular, and Integrative Biosciences Graduate Program, The Huck Institutes of Life Sciences, University Park, PA 16802, USA
| | - Deepro Banerjee
- Bioinformatics and Genomics Graduate Program, The Huck Institutes of Life Sciences, University Park, PA 16802, USA
| | - Maitreya Das
- Molecular, Cellular, and Integrative Biosciences Graduate Program, The Huck Institutes of Life Sciences, University Park, PA 16802, USA
| | - Santhosh Girirajan
- Molecular, Cellular, and Integrative Biosciences Graduate Program, The Huck Institutes of Life Sciences, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate Program, The Huck Institutes of Life Sciences, University Park, PA 16802, USA; Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Department of Anthropology, Pennsylvania State University, University Park, PA 16802, USA.
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45
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Khouja JN, Sanderson E, Wootton RE, Taylor AE, Church BA, Richmond RC, Munafò MR. Estimating the health impact of nicotine exposure by dissecting the effects of nicotine versus non-nicotine constituents of tobacco smoke: A multivariable Mendelian randomisation study. PLoS Genet 2024; 20:e1011157. [PMID: 38335242 PMCID: PMC10883537 DOI: 10.1371/journal.pgen.1011157] [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: 08/09/2023] [Revised: 02/22/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
The detrimental health effects of smoking are well-known, but the impact of regular nicotine use without exposure to the other constituents of tobacco is less clear. Given the increasing daily use of alternative nicotine delivery systems, such as e-cigarettes, it is increasingly important to understand and separate the effects of nicotine use from the impact of tobacco smoke exposure. Using a multivariable Mendelian randomisation framework, we explored the direct effects of nicotine compared with the non-nicotine constituents of tobacco smoke on health outcomes (lung cancer, chronic obstructive pulmonary disease [COPD], forced expiratory volume in one second [FEV-1], forced vital capacity [FVC], coronary heart disease [CHD], and heart rate [HR]). We used Genome-Wide Association Study (GWAS) summary statistics from Buchwald and colleagues, the GWAS and Sequencing Consortium of Alcohol and Nicotine, the International Lung Cancer Consortium, and UK Biobank. Increased nicotine metabolism increased the risk of COPD, lung cancer, and lung function in the univariable analysis. However, when accounting for smoking heaviness in the multivariable analysis, we found that increased nicotine metabolite ratio (indicative of decreased nicotine exposure per cigarette smoked) decreases heart rate (b = -0.30, 95% CI -0.50 to -0.10) and lung function (b = -33.33, 95% CI -41.76 to -24.90). There was no clear evidence of an effect on the remaining outcomes. The results suggest that these smoking-related outcomes are not due to nicotine exposure but are caused by the other components of tobacco smoke; however, there are multiple potential sources of bias, and the results should be triangulated using evidence from a range of methodologies.
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Affiliation(s)
- Jasmine N. Khouja
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Robyn E. Wootton
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Nic Waals Institute, Lovisenberg diakonale sykehus, Oslo, Norway
| | - Amy E. Taylor
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Billy A. Church
- School of Psychology and Vision Sciences, University of Leicester, United Kingdom
| | - Rebecca C. Richmond
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Marcus R. Munafò
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Bristol, United Kingdom
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46
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Chong AY, Brenner N, Jimenez-Kaufmann A, Cortes A, Hill M, Littlejohns TJ, Gilchrist JJ, Fairfax BP, Knight JC, Hodel F, Fellay J, McVean G, Moreno-Estrada A, Waterboer T, Hill AVS, Mentzer AJ. A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy. Am J Hum Genet 2024; 111:295-308. [PMID: 38232728 PMCID: PMC10870136 DOI: 10.1016/j.ajhg.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 12/07/2023] [Accepted: 12/10/2023] [Indexed: 01/19/2024] Open
Abstract
Infectious agents contribute significantly to the global burden of diseases through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens, including those from the herpes, retro-, and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant and could play a key role in regulation of the immune response. Using 121 infection- and inflammation-related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates hematopoietic pathways and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation.
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Affiliation(s)
- Amanda Y Chong
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
| | - Nicole Brenner
- Division of Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andres Jimenez-Kaufmann
- Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Mexico
| | - Adrian Cortes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Michael Hill
- MRC-Population Health Research Unit, University of Oxford, Oxford, UK
| | | | - James J Gilchrist
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK; Department of Paediatrics, University of Oxford, Oxford, UK
| | | | - Julian C Knight
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Flavia Hodel
- Global Health Institute, School of Life Sciences, EPFL, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jacques Fellay
- Global Health Institute, School of Life Sciences, EPFL, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland; Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Andres Moreno-Estrada
- Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Mexico
| | - Tim Waterboer
- Division of Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Adrian V S Hill
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK; The Jenner Institute, University of Oxford, Oxford, UK
| | - 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.
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47
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Thorolfsdottir RB, Jonsdottir AB, Sveinbjornsson G, Aegisdottir HM, Oddsson A, Stefansson OA, Halldorsson GH, Saevarsdottir S, Thorleifsson G, Stefansdottir L, Pedersen OB, Sørensen E, Ghouse J, Raja AA, Zheng C, Silajdzija E, Rand SA, Erikstrup C, Ullum H, Mikkelsen C, Banasik K, Brunak S, Ivarsdottir EV, Sigurdsson A, Beyter D, Sturluson A, Einarsson H, Tragante V, Helgason H, Lund SH, Halldorsson BV, Sigurpalsdottir BD, Olafsson I, Arnar DO, Thorgeirsson G, Knowlton KU, Nadauld LD, Gretarsdottir S, Helgadottir A, Ostrowski SR, Gudbjartssson DF, Jonsdottir I, Bundgaard H, Holm H, Sulem P, Stefansson K. Variants at the Interleukin 1 Gene Locus and Pericarditis. JAMA Cardiol 2024; 9:165-172. [PMID: 38150231 PMCID: PMC10753444 DOI: 10.1001/jamacardio.2023.4820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/14/2023] [Indexed: 12/28/2023]
Abstract
Importance Recurrent pericarditis is a treatment challenge and often a debilitating condition. Drugs inhibiting interleukin 1 cytokines are a promising new treatment option, but their use is based on scarce biological evidence and clinical trials of modest sizes, and the contributions of innate and adaptive immune processes to the pathophysiology are incompletely understood. Objective To use human genomics, transcriptomics, and proteomics to shed light on the pathogenesis of pericarditis. Design, Setting, and Participants This was a meta-analysis of genome-wide association studies of pericarditis from 5 countries. Associations were examined between the pericarditis-associated variants and pericarditis subtypes (including recurrent pericarditis) and secondary phenotypes. To explore mechanisms, associations with messenger RNA expression (cis-eQTL), plasma protein levels (pQTL), and CpG methylation of DNA (ASM-QTL) were assessed. Data from Iceland (deCODE genetics, 1983-2020), Denmark (Copenhagen Hospital Biobank/Danish Blood Donor Study, 1977-2022), the UK (UK Biobank, 1953-2021), the US (Intermountain, 1996-2022), and Finland (FinnGen, 1970-2022) were included. Data were analyzed from September 2022 to August 2023. Exposure Genotype. Main Outcomes and Measures Pericarditis. Results In this genome-wide association study of 4894 individuals with pericarditis (mean [SD] age at diagnosis, 51.4 [17.9] years, 2734 [67.6%] male, excluding the FinnGen cohort), associations were identified with 2 independent common intergenic variants at the interleukin 1 locus on chromosome 2q14. The lead variant was rs12992780 (T) (effect allele frequency [EAF], 31%-40%; odds ratio [OR], 0.83; 95% CI, 0.79-0.87; P = 6.67 × 10-16), downstream of IL1B and the secondary variant rs7575402 (A or T) (EAF, 45%-55%; adjusted OR, 0.89; 95% CI, 0.85-0.93; adjusted P = 9.6 × 10-8). The lead variant rs12992780 had a smaller odds ratio for recurrent pericarditis (0.76) than the acute form (0.86) (P for heterogeneity = .03) and rs7575402 was associated with CpG methylation overlapping binding sites of 4 transcription factors known to regulate interleukin 1 production: PU.1 (encoded by SPI1), STAT1, STAT3, and CCAAT/enhancer-binding protein β (encoded by CEBPB). Conclusions and Relevance This study found an association between pericarditis and 2 independent sequence variants at the interleukin 1 gene locus. This finding has the potential to contribute to development of more targeted and personalized therapy of pericarditis with interleukin 1-blocking drugs.
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Affiliation(s)
| | | | | | | | | | | | - Gisli H. Halldorsson
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Saedis Saevarsdottir
- deCODE genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Medicine, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | | | | | - Ole B. Pedersen
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Jonas Ghouse
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Anna Axelsson Raja
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Chaoqun Zheng
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Elvira Silajdzija
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Søren Albertsen Rand
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | | | - Christina Mikkelsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | | | | | | | - Hafsteinn Einarsson
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Hannes Helgason
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Bjarni V. Halldorsson
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Brynja D. Sigurpalsdottir
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali, National University Hospital of Iceland, Reykjavik, Iceland
| | - David O. Arnar
- deCODE genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Medicine, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Kirk U. Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, Utah
- School of Medicine, University of Utah, Salt Lake City
| | - Lincoln D. Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, Utah
- School of Medicine, Stanford University, Stanford, California
| | | | | | - Sisse R. Ostrowski
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Daniel F. Gudbjartssson
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Ingileif Jonsdottir
- deCODE genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Immunology, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | - Henning Bundgaard
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Hilma Holm
- deCODE genetics, Amgen, Reykjavik, Iceland
| | | | - Kari Stefansson
- deCODE genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
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Kar A, Alvarez M, Garske KM, Huang H, Lee SHT, Deal M, Das SS, Koka A, Jamal Z, Mohlke KL, Laakso M, Heinonen S, Pietiläinen KH, Pajukanta P. Age-dependent genes in adipose stem and precursor cells affect regulation of fat cell differentiation and link aging to obesity via cellular and genetic interactions. Genome Med 2024; 16:19. [PMID: 38297378 PMCID: PMC10829214 DOI: 10.1186/s13073-024-01291-x] [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: 09/11/2023] [Accepted: 01/19/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner. METHODS We integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI). RESULTS We found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level. CONCLUSIONS Overall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.
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Affiliation(s)
- Asha Kar
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Huiling Huang
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA
| | - Seung Hyuk T Lee
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Milena Deal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Sankha Subhra Das
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Amogha Koka
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Zoeb Jamal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Sini Heinonen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HealthyWeightHub, Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA.
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA.
- Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, USA.
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Carvalho NRG, He Y, Smadbeck P, Flannick J, Mercader JM, Udler M, Manrai AK, Moreno J, Patel CJ. Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.30.24302019. [PMID: 38352440 PMCID: PMC10863013 DOI: 10.1101/2024.01.30.24302019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
While genetic factors, behavior, and environmental exposures form a complex web of interrelated associations in type 2 diabetes (T2D), their interaction is poorly understood. Here, using data from ~500K participants of the UK Biobank, we identify the genetic determinants of a "polyexposure risk score" (PXS) a new risk factor that consists of an accumulation of 25 associated individual-level behaviors and environmental risk factors that predict longitudinal T2D incidence. PXS-T2D had a non-zero heritability (h2 = 0.18) extensive shared genetic architecture with established clinical and biological determinants of T2D, most prominently with body mass index (genetic correlation [rg] = 0.57) and Homeostatic Model Assessment for Insulin Resistance (rg = 0.51). Genetic loci associated with PXS-T2D were enriched for expression in the brain. Biobank scale data with genetic information illuminates how complex and cumulative exposures and behaviors as a whole impact T2D risk but whose biology have been elusive in genome-wide studies of T2D.
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Affiliation(s)
- Nuno R. G. Carvalho
- School of Biological Sciences; Georgia Institute of Technology; Atlanta, GA, 30332, USA
| | - Yixuan He
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Patrick Smadbeck
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Jason Flannick
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | - Josep M. Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Miriam Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Jordi Moreno
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Chirag J. Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
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50
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Lee A, Seo J, Park S, Cho Y, Kim G, Li J, Liang L, Park T, Chung W. Type 2 diabetes and its genetic susceptibility are associated with increased severity and mortality of COVID-19 in UK Biobank. Commun Biol 2024; 7:122. [PMID: 38267566 PMCID: PMC10808197 DOI: 10.1038/s42003-024-05799-1] [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: 08/25/2023] [Accepted: 01/09/2024] [Indexed: 01/26/2024] Open
Abstract
Type 2 diabetes (T2D) is known as one of the important risk factors for the severity and mortality of COVID-19. Here, we evaluate the impact of T2D and its genetic susceptibility on the severity and mortality of COVID-19, using 459,119 individuals in UK Biobank. Utilizing the polygenic risk scores (PRS) for T2D, we identified a significant association between T2D or T2D PRS, and COVID-19 severity. We further discovered the efficacy of vaccination and the pivotal role of T2D-related genetics in the pathogenesis of severe COVID-19. Moreover, we found that individuals with T2D or those in the high T2D PRS group had a significantly increased mortality rate. We also observed that the mortality rate for SARS-CoV-2-infected patients was approximately 2 to 7 times higher than for those not infected, depending on the time of infection. These findings emphasize the potential of T2D PRS in estimating the severity and mortality of COVID-19.
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Affiliation(s)
- Aeyeon Lee
- Department of Statistics and Actuarial Science, Soongsil University, Seoul, 06978, Korea
| | - Jieun Seo
- Department of Statistics and Actuarial Science, Soongsil University, Seoul, 06978, Korea
| | - Seunghwan Park
- Department of Statistics and Actuarial Science, Soongsil University, Seoul, 06978, Korea
- Institute of Genetic Epidemiology, Basgenbio Co. Ltd., Seoul, 04167, Korea
| | - Youngkwang Cho
- Department of Statistics and Actuarial Science, Soongsil University, Seoul, 06978, Korea
| | - Gaeun Kim
- Department of Statistics and Actuarial Science, Soongsil University, Seoul, 06978, Korea
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Liming Liang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, 08826, Korea.
| | - Wonil Chung
- Department of Statistics and Actuarial Science, Soongsil University, Seoul, 06978, Korea.
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
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