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El Sharkawy M, Felix JF, Grote V, Voortman T, Jaddoe VWV, Koletzko B, Küpers LK. Animal and plant protein intake during infancy and childhood DNA methylation: a meta-analysis in the NutriPROGRAM consortium. Epigenetics 2024; 19:2299045. [PMID: 38198623 PMCID: PMC10793674 DOI: 10.1080/15592294.2023.2299045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Higher early-life animal protein intake is associated with a higher childhood obesity risk compared to plant protein intake. Differential DNA methylation may represent an underlying mechanism. METHODS We analysed associations of infant animal and plant protein intakes with DNA methylation in early (2-6 years, N = 579) and late (7̄-12 years, N = 604) childhood in two studies. Study-specific robust linear regression models adjusted for relevant confounders were run, and then meta-analysed using a fixed-effects model. We also performed sex-stratified meta-analyses. Follow-up analyses included pathway analysis and eQTM look-up. RESULTS Infant animal protein intake was not associated with DNA methylation in early childhood, but was associated with late-childhood DNA methylation at cg21300373 (P = 4.27 × 10¯8, MARCHF1) and cg10633363 (P = 1.09 × 10¯7, HOXB9) after FDR correction. Infant plant protein intake was associated with early-childhood DNA methylation at cg25973293 (P = 2.26 × 10-7, C1orf159) and cg15407373 (P = 2.13 × 10-7, MBP) after FDR correction. There was no overlap between the findings from the animal and plant protein analyses. We did not find enriched functional pathways at either time point using CpGs associated with animal and plant protein. These CpGs were not previously associated with childhood gene expression. Sex-stratified meta-analyses showed sex-specific DNA methylation associations for both animal and plant protein intake. CONCLUSION Infant animal protein intake was associated with DNA methylation at two CpGs in late childhood. Infant plant protein intake was associated with DNA methylation in early childhood at two CpGs. A potential mediating role of DNA methylation at these CpGs between infant protein intake and health outcomes requires further investigation.
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Affiliation(s)
- Mohammed El Sharkawy
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. Von Hauner Children’s Hospital, LMU University Hospital Munich, Munich, Germany
- Munich Medical Research School, Faculty of Medicine, LMU - Ludwig-Maximilians Universität Munich, Munich, Germany
| | - Janine F. Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Veit Grote
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. Von Hauner Children’s Hospital, LMU University Hospital Munich, Munich, Germany
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Vincent W. V. Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Berthold Koletzko
- Division of Metabolic and Nutritional Medicine, Department of Pediatrics, Dr. Von Hauner Children’s Hospital, LMU University Hospital Munich, Munich, Germany
| | - Leanne K. Küpers
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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Herrera-Luis E, Martin-Almeida M, Pino-Yanes M. Asthma-Genomic Advances Toward Risk Prediction. Clin Chest Med 2024; 45:599-610. [PMID: 39069324 PMCID: PMC11284279 DOI: 10.1016/j.ccm.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe Street, Baltimore, MD 21205, USA.
| | - Mario Martin-Almeida
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid 28029, Spain; Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), San Cristóbal de La Laguna 38200, Tenerife, Spain
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3
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Kim S, Koppitch K, Parvez RK, Guo J, Achieng M, Schnell J, Lindström NO, McMahon AP. Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development. Dev Cell 2024:S1534-5807(24)00450-7. [PMID: 39121855 DOI: 10.1016/j.devcel.2024.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 04/02/2024] [Accepted: 07/12/2024] [Indexed: 08/12/2024]
Abstract
The mammalian kidney maintains fluid homeostasis through diverse epithelial cell types generated from nephron and ureteric progenitor cells. To extend a developmental understanding of the kidney's epithelial networks, we compared chromatin organization (single-nuclear assay for transposase-accessible chromatin sequencing [ATAC-seq]; 112,864 nuclei) and gene expression (single-cell/nuclear RNA sequencing [RNA-seq]; 109,477 cells/nuclei) in the developing human (10.6-17.6 weeks; n = 10) and mouse (post-natal day [P]0; n = 10) kidney, supplementing analysis with published mouse datasets from earlier stages. Single-cell/nuclear datasets were analyzed at a species level, and then nephron and ureteric cellular lineages were extracted and integrated into a common, cross-species, multimodal dataset. Comparative computational analyses identified conserved and divergent features of chromatin organization and linked gene activity, identifying species-specific and cell-type-specific regulatory programs. In situ validation of human-enriched gene activity points to human-specific signaling interactions in kidney development. Further, human-specific enhancer regions were linked to kidney diseases through genome-wide association studies (GWASs), highlighting the potential for clinical insight from developmental modeling.
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Affiliation(s)
- Sunghyun Kim
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Kari Koppitch
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Riana K Parvez
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Jinjin Guo
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - MaryAnne Achieng
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Jack Schnell
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Nils O Lindström
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrew P McMahon
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.
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4
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Gong T, Karlsson R, Yao S, Magnusson PKE, Ajnakina O, Steptoe A, Bhatta L, Brumpton B, Kumar A, Mélen E, Lin KH, Tian C, Fall T, Almqvist C. The genetic architecture of dog ownership: large-scale genome-wide association study in 97,552 European-ancestry individuals. G3 (BETHESDA, MD.) 2024; 14:jkae116. [PMID: 38820132 PMCID: PMC11304603 DOI: 10.1093/g3journal/jkae116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 12/10/2023] [Accepted: 04/16/2024] [Indexed: 06/02/2024]
Abstract
Dog ownership has been associated with several complex traits, and there is evidence of genetic influence. We performed a genome-wide association study of dog ownership through a meta-analysis of 31,566 Swedish twins in 5 discovery cohorts and an additional 65,986 European-ancestry individuals in 3 replication cohorts from Sweden, Norway, and the United Kingdom. Association tests with >7.4 million single-nucleotide polymorphisms were meta-analyzed using a fixed effect model after controlling for population structure and relatedness. We identified 2 suggestive loci using discovery cohorts, which did not reach genome-wide significance after meta-analysis with replication cohorts. Single-nucleotide polymorphism-based heritability of dog ownership using linkage disequilibrium score regression was estimated at 0.123 (CI 0.038-0.207) using the discovery cohorts and 0.018 (CI -0.002 to 0.039) when adding in replication cohorts. Negative genetic correlation with complex traits including type 2 diabetes, depression, neuroticism, and asthma was only found using discovery summary data. Furthermore, we did not identify any genes/gene-sets reaching even a suggestive level of significance. This genome-wide association study does not, by itself, provide clear evidence on common genetic variants that influence dog ownership among European-ancestry individuals.
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Affiliation(s)
- Tong Gong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Olesya Ajnakina
- Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London WC1E 7HBUK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Andrew Steptoe
- Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London WC1E 7HBUK
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim NO-7491, Norway
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim NO-7491, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim NO-7030, Norway
| | - Ashish Kumar
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm SE-118 83, Sweden
| | - Erik Mélen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm SE-118 83, Sweden
- Sachs' Children's Hospital, South General Hospital, Stockholm SE-118 61, Sweden
| | | | - Chao Tian
- 23andMe, Inc., Sunnyvale, CA 94086, USA
| | - Tove Fall
- Molecular Epidemiology, Department of Medical Sciences, and Science for Life Laboratory, Uppsala University, Uppsala SE-751 85, Sweden
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Pediatric Allergy and Pulmonology Unit at Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm SE-141 86, Sweden
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5
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Jeter KM, Srinivasan S, Tryggestad JB. Polygenic risk for obesity and body dissatisfaction: beyond BMI. Pediatr Res 2024:10.1038/s41390-024-03442-7. [PMID: 39103630 DOI: 10.1038/s41390-024-03442-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 07/12/2024] [Indexed: 08/07/2024]
Affiliation(s)
- Kathryn M Jeter
- Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Shylaja Srinivasan
- Department of Pediatrics, University of California, San Francisco, CA, USA
| | - Jeanie B Tryggestad
- Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
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6
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Kullo IJ. Promoting equity in polygenic risk assessment through global collaboration. Nat Genet 2024:10.1038/s41588-024-01843-2. [PMID: 39103647 DOI: 10.1038/s41588-024-01843-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 06/24/2024] [Indexed: 08/07/2024]
Abstract
The long delay before genomic technologies become available in low- and middle-income countries is a concern from both scientific and ethical standpoints. Polygenic risk scores (PRSs), a relatively recent advance in genomics, could have a substantial impact on promoting health by improving disease risk prediction and guiding preventive strategies. However, clinical use of PRSs in their current forms might widen global health disparities, as their portability to diverse groups is limited. This Perspective highlights the need for global collaboration to develop and implement PRSs that perform equitably across the world. Such collaboration requires capacity building and the generation of new data in low-resource settings, the sharing of harmonized genotype and phenotype data securely across borders, novel population genetics and statistical methods to improve PRS performance, and thoughtful clinical implementation in diverse settings. All this needs to occur while considering the ethical, legal and social implications, with support from regulatory and funding agencies and policymakers.
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Affiliation(s)
- Iftikhar J Kullo
- Department of Cardiovascular Medicine and the Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.
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7
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Choucair I, Mallela DP, Hilser JR, Hartiala JA, Nemet I, Gogonea V, Li L, Lusis AJ, Fischbach MA, Tang WW, Allayee H, Hazen SL. Comprehensive Clinical and Genetic Analyses of Circulating Bile Acids and Their Associations With Diabetes and Its Indices. Diabetes 2024; 73:1215-1228. [PMID: 38701355 PMCID: PMC11262044 DOI: 10.2337/db23-0676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 04/24/2024] [Indexed: 05/05/2024]
Abstract
Bile acids (BAs) are cholesterol-derived compounds that regulate glucose, lipid, and energy metabolism. Despite their significance in glucose homeostasis, the association between specific BA molecular species and their synthetic pathways with diabetes is unclear. Here, we used a recently validated, stable-isotope dilution, high-performance liquid chromatography with tandem mass spectrometry method to quantify a panel of BAs in fasting plasma from 2,145 study participants and explored structural and genetic determinants of BAs linked to diabetes, insulin resistance, and obesity. Multiple 12α-hydroxylated BAs were associated with diabetes (adjusted odds ratio [aOR] range, 1.3-1.9; P < 0.05 for all) and insulin resistance (aOR range, 1.3-2.2; P < 0.05 for all). Conversely, multiple 6α-hydroxylated BAs and isolithocholic acid (iso-LCA) were inversely associated with diabetes and obesity (aOR range, 0.3-0.9; P < 0.05 for all). Genome-wide association studies revealed multiple genome-wide significant loci linked with 9 of the 14 diabetes-associated BAs, including a locus for iso-LCA (rs11866815). Mendelian randomization analyses showed genetically elevated deoxycholic acid levels were causally associated with higher BMI, and iso-LCA levels were causally associated with reduced BMI and diabetes risk. In conclusion, comprehensive, large-scale, quantitative mass spectrometry and genetics analyses show circulating levels of multiple structurally specific BAs, especially DCA and iso-LCA, are clinically associated with and genetically linked to obesity and diabetes. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Ibrahim Choucair
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
- Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH
| | - Deepthi P. Mallela
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
- Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH
| | - James R. Hilser
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Jaana A. Hartiala
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Ina Nemet
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
- Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH
| | - Valentin Gogonea
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
- Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH
- Department of Chemistry, Cleveland State University, Cleveland, OH
| | - Lin Li
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
- Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH
| | - Aldons J. Lusis
- Division of Cardiology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA
| | | | - W.H. Wilson Tang
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
- Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH
- Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH
| | - Hooman Allayee
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Stanley L. Hazen
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
- Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH
- Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH
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Don J, Schork AJ, Glusman G, Rappaport N, Cummings SR, Duggan D, Raju A, Hellberg KLG, Gunn S, Monti S, Perls T, Lapidus J, Goetz LH, Sebastiani P, Schork NJ. The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank. GeroScience 2024; 46:3911-3927. [PMID: 38451433 PMCID: PMC11226417 DOI: 10.1007/s11357-024-01107-1] [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: 10/30/2023] [Accepted: 02/21/2024] [Indexed: 03/08/2024] Open
Abstract
Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.
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Affiliation(s)
- Janith Don
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Andrew J Schork
- The Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- GLOBE Institute, Copenhagen University, Copenhagen, Denmark
| | | | | | - Steve R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - David Duggan
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Anish Raju
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Kajsa-Lotta Georgii Hellberg
- The Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- GLOBE Institute, Copenhagen University, Copenhagen, Denmark
| | - Sophia Gunn
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Stefano Monti
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Thomas Perls
- Department of Medicine, Section of Geriatrics, Boston University, Boston, MA, USA
| | - Jodi Lapidus
- Department of Biostatistics, Oregon Health & Science University, Portland, OR, USA
| | - Laura H Goetz
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Veterans Affairs Loma Linda Health Care, Loma Linda, CA, USA
| | - Paola Sebastiani
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Tufts University School of Medicine and Data Intensive Study Center, Boston, MA, USA
| | - Nicholas J Schork
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA.
- The City of Hope National Medical Center, Duarte, CA, USA.
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Huang T, Lu F. Genetically predicted circulating concentrations of micronutrients and risk of hypertensive disorders of pregnancy: a Mendelian randomization study. Arch Gynecol Obstet 2024; 310:1019-1025. [PMID: 38194093 DOI: 10.1007/s00404-023-07331-y] [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: 08/26/2023] [Accepted: 12/01/2023] [Indexed: 01/10/2024]
Abstract
PURPOSE Epidemiological studies examining the association between circulating micronutrients and the risk of hypertensive disorders during pregnancy (HDP) have produced inconsistent results. Therefore, we conducted a Mendelian randomization (MR) analysis to evaluate the potential causal relationship between micronutrients and HDP. METHODS Nine micronutrients (beta-carotene, vitamin B6, vitamin B12, calcium, zinc, selenium, copper, folate, and phosphorus) were selected as the exposure factors. Summary data for gestational hypertension (14,727 cases and 196,143 controls) and preeclampsia/eclampsia (7212 cases and 174,266 controls) were extracted from the FinnGen consortium. The MR analysis employed the inverse variance weighted method and conducted a range of sensitivity analyses. RESULTS The inverse variance weighted method indicated no significant causal relationship between nine genetically predicted micronutrient concentrations and gestational hypertension, as well as preeclampsia/eclampsia. Sensitivity analyses suggested the absence of pleiotropy. CONCLUSION There is no strong evidence to support the causation between circulating micronutrients and hypertensive disorder during pregnancy.
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Affiliation(s)
- Ting Huang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China
| | - Fan Lu
- Department of Emergency, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.
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10
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024; 40:642-667. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [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: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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Wang Y, Ye Z, Lou X, Xu J, Jing D, Zhou C, Qin Y, Chen J, Xu X, Yu X, Ji S. Comparison among different preclinical models derived from the same patient with a non-functional pancreatic neuroendocrine tumor. Hum Cell 2024:10.1007/s13577-024-01107-5. [PMID: 39078546 DOI: 10.1007/s13577-024-01107-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 07/17/2024] [Indexed: 07/31/2024]
Abstract
Pancreatic neuroendocrine tumors are the second most common tumors of the pancreas, and approximately half of patients are diagnosed with liver metastases. Currently, the improvement in the efficacy of relevant treatment methods is still limited. Therefore, there is an urgent need for in-depth research on the molecular biological mechanism of pancreatic neuroendocrine tumors. However, due to their relatively inert biology, preclinical models are extremely scarce. Here, the patient-derived organoid, and patient-derived xenograft were successfully constructed. These two models and the previously constructed cell line named SPNE1 all derived from the same patient with a grade 3 non-functional pancreatic neuroendocrine tumor, providing new tumor modeling platforms, and characterized using immunohistochemistry, whole-exome sequencing, and single-cell transcriptome sequencing. Combined with a tumor formation experiment in immunodeficient mice, we selected the model that most closely recapitulated the parental tumor. Overall, the patient-derived xenograft model most closely resembled human tumor tissue.
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Affiliation(s)
- Yan Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China
| | - Zeng Ye
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China
| | - Xin Lou
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China
| | - Junfeng Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China
| | - Desheng Jing
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China
| | - Chenjie Zhou
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China
| | - Yi Qin
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China
| | - Jie Chen
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Head and Neck and Neuroendocrine Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Xiaowu Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China.
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China.
| | - Shunrong Ji
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- Shanghai Pancreatic Cancer Institute, Shanghai, 200032, China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, China.
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12
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Abel ED, Gloyn AL, Evans-Molina C, Joseph JJ, Misra S, Pajvani UB, Simcox J, Susztak K, Drucker DJ. Diabetes mellitus-Progress and opportunities in the evolving epidemic. Cell 2024; 187:3789-3820. [PMID: 39059357 PMCID: PMC11299851 DOI: 10.1016/j.cell.2024.06.029] [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: 03/07/2024] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/28/2024]
Abstract
Diabetes, a complex multisystem metabolic disorder characterized by hyperglycemia, leads to complications that reduce quality of life and increase mortality. Diabetes pathophysiology includes dysfunction of beta cells, adipose tissue, skeletal muscle, and liver. Type 1 diabetes (T1D) results from immune-mediated beta cell destruction. The more prevalent type 2 diabetes (T2D) is a heterogeneous disorder characterized by varying degrees of beta cell dysfunction in concert with insulin resistance. The strong association between obesity and T2D involves pathways regulated by the central nervous system governing food intake and energy expenditure, integrating inputs from peripheral organs and the environment. The risk of developing diabetes or its complications represents interactions between genetic susceptibility and environmental factors, including the availability of nutritious food and other social determinants of health. This perspective reviews recent advances in understanding the pathophysiology and treatment of diabetes and its complications, which could alter the course of this prevalent disorder.
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Affiliation(s)
- E Dale Abel
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Anna L Gloyn
- Department of Pediatrics, Division of Endocrinology & Diabetes, Department of Genetics, Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Carmella Evans-Molina
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Joshua J Joseph
- Division of Endocrinology, Diabetes and Metabolism, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Shivani Misra
- Department of Metabolism, Digestion and Reproduction, Imperial College London, and Imperial College NHS Trust, London, UK
| | - Utpal B Pajvani
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Judith Simcox
- Howard Hughes Medical Institute, Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Daniel J Drucker
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada; Department of Medicine, University of Toronto, Toronto, ON, Canada
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13
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A genome-wide association meta-analysis of all-cause and vascular dementia. Alzheimers Dement 2024. [PMID: 39046104 DOI: 10.1002/alz.14115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 04/30/2024] [Accepted: 05/20/2024] [Indexed: 07/25/2024]
Abstract
INTRODUCTION Dementia is a multifactorial disease with Alzheimer's disease (AD) and vascular dementia (VaD) pathologies making the largest contributions. Yet, most genome-wide association studies (GWAS) focus on AD. METHODS We conducted a GWAS of all-cause dementia (ACD) and examined the genetic overlap with VaD. Our dataset includes 800,597 individuals, with 46,902 and 8702 cases of ACD and VaD, respectively. Known AD loci for ACD and VaD were replicated. Bioinformatic analyses prioritized genes that are likely functionally relevant and shared with closely related traits and risk factors. RESULTS For ACD, novel loci identified were associated with energy transport (SEMA4D), neuronal excitability (ANO3), amyloid deposition in the brain (RBFOX1), and magnetic resonance imaging markers of small vessel disease (SVD; HBEGF). Novel VaD loci were associated with hypertension, diabetes, and neuron maintenance (SPRY2, FOXA2, AJAP1, and PSMA3). DISCUSSION Our study identified genetic risks underlying ACD, demonstrating overlap with neurodegenerative processes, vascular risk factors, and cerebral SVD. HIGHLIGHTS We conducted the largest genome-wide association study of all-cause dementia (ACD) and vascular dementia (VaD). Known genetic variants associated with AD were replicated for ACD and VaD. Functional analyses identified novel loci for ACD and VaD. Genetic risks of ACD overlapped with neurodegeneration, vascular risk factors, and cerebral small vessel disease.
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14
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Stefansson OA, Sigurpalsdottir BD, Rognvaldsson S, Halldorsson GH, Juliusson K, Sveinbjornsson G, Gunnarsson B, Beyter D, Jonsson H, Gudjonsson SA, Olafsdottir TA, Saevarsdottir S, Magnusson MK, Lund SH, Tragante V, Oddsson A, Hardarson MT, Eggertsson HP, Gudmundsson RL, Sverrisson S, Frigge ML, Zink F, Holm H, Stefansson H, Rafnar T, Jonsdottir I, Sulem P, Helgason A, Gudbjartsson DF, Halldorsson BV, Thorsteinsdottir U, Stefansson K. The correlation between CpG methylation and gene expression is driven by sequence variants. Nat Genet 2024:10.1038/s41588-024-01851-2. [PMID: 39048797 DOI: 10.1038/s41588-024-01851-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 06/27/2024] [Indexed: 07/27/2024]
Abstract
Gene promoter and enhancer sequences are bound by transcription factors and are depleted of methylated CpG sites (cytosines preceding guanines in DNA). The absence of methylated CpGs in these sequences typically correlates with increased gene expression, indicating a regulatory role for methylation. We used nanopore sequencing to determine haplotype-specific methylation rates of 15.3 million CpG units in 7,179 whole-blood genomes. We identified 189,178 methylation depleted sequences where three or more proximal CpGs were unmethylated on at least one haplotype. A total of 77,789 methylation depleted sequences (~41%) associated with 80,503 cis-acting sequence variants, which we termed allele-specific methylation quantitative trait loci (ASM-QTLs). RNA sequencing of 896 samples from the same blood draws used to perform nanopore sequencing showed that the ASM-QTL, that is, DNA sequence variability, drives most of the correlation found between gene expression and CpG methylation. ASM-QTLs were enriched 40.2-fold (95% confidence interval 32.2, 49.9) among sequence variants associating with hematological traits, demonstrating that ASM-QTLs are important functional units in the noncoding genome.
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Affiliation(s)
| | - Brynja Dogg Sigurpalsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | | | - Gisli Hreinn Halldorsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | | | | | | | - Thorunn Asta Olafsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Saedis Saevarsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Magnus Karl Magnusson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Sigrun Helga Lund
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | - Marteinn Thor Hardarson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | | | | | | | | | | | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | | | | | - Ingileif Jonsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Agnar Helgason
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Department of Anthropology, University of Iceland, Reykjavik, Iceland
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Bjarni V Halldorsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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15
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Pozarickij A, Gan W, Lin K, Clarke R, Fairhurst-Hunter Z, Koido M, Kanai M, Okada Y, Kamatani Y, Bennett D, Du H, Chen Y, Yang L, Avery D, Guo Y, Yu M, Yu C, Schmidt Valle D, Lv J, Chen J, Peto R, Collins R, Li L, Chen Z, Millwood IY, Walters RG. Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian randomisation in 100,000 Chinese adults. Nat Commun 2024; 15:6265. [PMID: 39048560 PMCID: PMC11269703 DOI: 10.1038/s41467-024-50297-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: 01/27/2023] [Accepted: 07/04/2024] [Indexed: 07/27/2024] Open
Abstract
Elevated blood pressure (BP) is major risk factor for cardiovascular diseases (CVD). Genome-wide association studies (GWAS) conducted predominantly in populations of European ancestry have identified >2,000 BP-associated loci, but other ancestries have been less well-studied. We conducted GWAS of systolic, diastolic, pulse, and mean arterial BP in 100,453 Chinese adults. We identified 128 non-overlapping loci associated with one or more BP traits, including 74 newly-reported associations. Despite strong genetic correlations between populations, we identified appreciably higher heritability and larger variant effect sizes in Chinese compared with European or Japanese ancestry populations. Using instruments derived from these GWAS, multivariable Mendelian randomisation demonstrated that BP traits contribute differently to the causal associations of BP with CVD. In particular, only pulse pressure was independently causally associated with carotid plaque. These findings reinforce the need for studies in diverse populations to understand the genetic determinants of BP traits and their roles in disease risk.
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Affiliation(s)
- Alfred Pozarickij
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Wei Gan
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Innovation Building, Old Road Campus, Oxford, UK
| | - Kuang Lin
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zammy Fairhurst-Hunter
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Masaru Koido
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, 113-0033, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, 230- 0045, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Japan
| | - Yoichiro Kamatani
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Derrick Bennett
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yu Guo
- National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, 100037, Beijing, China
| | - Min Yu
- Zhejiang CDC, Zhejiang, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, 100191, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 100191, Beijing, China
| | - Dan Schmidt Valle
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, 100191, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 100191, Beijing, China
| | - Junshi Chen
- China National Center For Food Safety Risk Assessment, Beijing, China
| | - Richard Peto
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, 100191, Beijing, China.
- Peking University Center for Public Health and Epidemic Preparedness and Response, 100191, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 100191, Beijing, China.
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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16
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Wu CHW, Tomaszewski A, Stark L, Scaglia F, Elenberg E, Schumaker FR. Perspectives from cystinosis: access to healthcare may be a confounding factor for variant classification. Front Genet 2024; 15:1402667. [PMID: 39113682 PMCID: PMC11303213 DOI: 10.3389/fgene.2024.1402667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/19/2024] [Indexed: 08/10/2024] Open
Abstract
Genetic variability persists across diverse populations, and it may impact the characterization of heritable diseases in different ancestral groups. Cystinosis is a metabolic disease caused by pathogenic variants in the CTNS gene causing the cellular accumulation of cystine. We attempted to assess the currently poorly characterized prevalence of cystinosis by employing a population genetics methodology. However, we encountered a significant challenge due to genetic variations across different populations, and the consideration of potential disparities in access to healthcare made our results inconclusive. Pathogenic CTNS variants were identified in a representative global population cohort using The Human Gene Mutation Database (HGMD) and the 1000 Genomes (1 KG) database. The c.124G>A (p.Val42Ile) variant was reported to be pathogenic based on an observation in the white population presenting with atypical phenotypes, but it would be reclassified as benign in the African ancestral group if applying the ACMG allele frequency guideline due to its high allele frequency specifically in this population. Inclusion or exclusion of this c.124G>A (p.Val42Ile) variant results in a significant change in estimated disease prevalence, which can impact the diagnosis and treatment of affected patients with a broad range of phenotypic presentations. This observation led us to postulate that pathogenic manifestations of the disease may be underdiagnosed due to variable expressivity and systemic inequities in access to care, specifically in the African subpopulation. We call for a more cautious and inclusive approach to achieve more equitable care across diverse populations.
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Affiliation(s)
- Chen-Han Wilfred Wu
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine and University Hospitals, Cleveland, OH, United States
- Department of Urology, Case Western Reserve University School of Medicine and University Hospitals, Cleveland, OH, United States
| | - Alicja Tomaszewski
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine and University Hospitals, Cleveland, OH, United States
- Department of Urology, Case Western Reserve University School of Medicine and University Hospitals, Cleveland, OH, United States
| | - Louisa Stark
- Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Fernando Scaglia
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | - Ewa Elenberg
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | - Fredrick R. Schumaker
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, United States
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17
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Reales G, Amos CI, Benveniste O, Chinoy H, De Bleecker J, De Paepe B, Doria A, Gregersen PK, Lamb JA, Limaye V, Lundberg IE, Machado PM, Maurer B, Miller FW, Molberg Ø, Pachman LM, Padyukov L, Radstake TR, Reed AM, Rider LG, Rothwell S, Selva-O'Callaghan A, Vencovský J, Wedderburn LR, Wallace C. Discovery of new myositis genetic associations through leveraging other immune-mediated diseases. HGG ADVANCES 2024; 5:100336. [PMID: 39044428 DOI: 10.1016/j.xhgg.2024.100336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 07/16/2024] [Accepted: 07/16/2024] [Indexed: 07/25/2024] Open
Abstract
Genome-wide association studies (GWASs) have been successful at finding associations between genetic variants and human traits, including the immune-mediated diseases (IMDs). However, the requirement of large sample sizes for discovery poses a challenge for learning about less common diseases, where increasing volunteer numbers might not be feasible. An example of this is myositis (or idiopathic inflammatory myopathies [IIM]s), a group of rare, heterogeneous autoimmune diseases affecting skeletal muscle and other organs, severely impairing life quality. Here, we applied a feature engineering method to borrow information from larger IMD GWASs to find new genetic associations with IIM and its subgroups. Combining this approach with two clustering methods, we found 17 IMDs genetically close to IIM, including some common comorbid conditions, such as systemic sclerosis and Sjögren's syndrome, as well as hypo- and hyperthyroidism. All IIM subtypes were genetically similar within this framework. Next, we colocalized IIM signals that overlapped IMD signals, and found seven potentially novel myositis associations mapped to immune-related genes, including BLK, IRF5/TNPO3, and ITK/HAVCR2, implicating a role for both B and T cells in IIM. This work proposes a new paradigm of genetic discovery in rarer diseases by leveraging information from more common IMD, and can be expanded to other conditions and traits beyond IMD.
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Affiliation(s)
- Guillermo Reales
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK.
| | | | - Olivier Benveniste
- Department of Internal Medicine and Clinical Immunology, Pitié-Salpêtrière Hospital, Paris, France
| | - Hector Chinoy
- Department of Rheumatology, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Science Centre, Salford, UK; Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Jan De Bleecker
- Department of Neurology, Ghent University, Ghent, Belgium; Neuromuscular Reference Center, Ghent University Hospital, Ghent, Belgium
| | - Boel De Paepe
- Department of Neurology, Ghent University, Ghent, Belgium; Neuromuscular Reference Center, Ghent University Hospital, Ghent, Belgium
| | - Andrea Doria
- Rheumatology Unit, Department of Medicine, University of Padova, Padova, Italy
| | - Peter K Gregersen
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute, Manhasset, NY, USA
| | - Janine A Lamb
- Epidemiology and Public Health Group, Division of Population Health, Health Services Research & Primary Care, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Vidya Limaye
- Rheumatology Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia; Discipline of Medicine, Adelaide University, Adelaide, South Australia, Australia
| | - Ingrid E Lundberg
- Division of Rheumatology, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Pedro M Machado
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology. London, UK; Centre for Rheumatology, UCL Division of Medicine, University College London, London, UK
| | - Britta Maurer
- Department of Rheumatology and Immunology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Frederick W Miller
- Environmental Autoimmunity Group, National Institute of Environmental Health Sciences, NIH, Bethesda, MD, USA
| | - Øyvind Molberg
- Department of Rheumatology, Oslo University Hospital, Oslo, Norway
| | - Lauren M Pachman
- Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Leonid Padyukov
- Division of Rheumatology, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Timothy R Radstake
- Department of Rheumatology and Clinical Immunology, University Medical Center, Utrecht, the Netherlands
| | - Ann M Reed
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Lisa G Rider
- Environmental Autoimmunity Group, National Institute of Environmental Health Sciences, NIH, Bethesda, MD, USA
| | - Simon Rothwell
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Albert Selva-O'Callaghan
- Internal Medicine Department, Vall d'Hebron General Hospital, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Jiri Vencovský
- Institute of Rheumatology and Department of Rheumatology, First Medical Faculty, Charles University, Prague, Czech Republic
| | - Lucy R Wedderburn
- Centre for Adolescent Rheumatology Versus Arthritis, UCL Great Ormond Street Institute of Child Health, University College London, London, UK; NIHR Biomedical Research Centre at Great Ormond Street Hospital, London, UK
| | - Chris Wallace
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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18
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Salido E, de Medeiros Vieira C, Mosquera JV, Zade R, Miller CL, Lo Sardo V. The 9p21.3 coronary artery disease risk locus drives vascular smooth muscle cells to an osteochondrogenic state. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.25.595888. [PMID: 38853913 PMCID: PMC11160673 DOI: 10.1101/2024.05.25.595888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Genome-wide association studies have identified common genetic variants at ~400 human genomic loci linked to coronary artery disease (CAD) susceptibility. Among these genomic regions, the most impactful is the 9p21.3 CAD risk locus, which spans a 60 kb gene desert and encompasses ~80 SNPs in high linkage disequilibrium. Despite nearly two decades since its discovery, the functional mechanism of this genomic region remains incompletely resolved. To investigate the transcriptional gene programs mediated by 9p21.3 risk locus, we applied a model of induced pluripotent stem cells (iPSCs) from risk and non-risk donors at 9p21.3, as well as isogenic lines with a full haplotype deletion. Upon differentiation to vascular smooth muscle cells (VSMC), single-cell transcriptomic profiling demonstrated iPSC-VSMC phenotypes resembling those from native human coronary arteries, confirming the robustness of this model. Remarkably, our analyses revealed that VSMCs harboring the 9p21.3 risk haplotype preferentially adopt an osteochondrogenic state. Importantly, we identified LIMCH1 and CRABP1 as signature genes critical for defining this transcriptional program. Our study provides new insights into the mechanism at the 9p21.3 risk locus and defines its role in driving a disease-prone transcriptional state in VSMCs.
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Affiliation(s)
- Elsa Salido
- Department of Cell and Regenerative Biology; University of Wisconsin-Madison; Madison, WI 53705 USA
| | | | - José Verdezoto Mosquera
- Center for Public Health Genomics, Department of Public Health Sciences, Department of Biochemistry and Molecular Genetics; University of Virginia; Charlottesville, VA 22908 USA
| | - Rohan Zade
- Department of Cell and Regenerative Biology; University of Wisconsin-Madison; Madison, WI 53705 USA
| | - Clint L. Miller
- Center for Public Health Genomics, Department of Public Health Sciences, Department of Biochemistry and Molecular Genetics; University of Virginia; Charlottesville, VA 22908 USA
| | - Valentina Lo Sardo
- Department of Cell and Regenerative Biology; University of Wisconsin-Madison; Madison, WI 53705 USA
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19
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics. Nat Commun 2024; 15:6064. [PMID: 39025851 PMCID: PMC11258354 DOI: 10.1038/s41467-024-50309-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: 06/23/2023] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA.
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yilin Yang
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT, 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, Philadelphia, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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20
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Liang Q, Abraham A, Capra JA, Kostka D. Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility. HGG ADVANCES 2024; 5:100310. [PMID: 38773771 PMCID: PMC11259938 DOI: 10.1016/j.xhgg.2024.100310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 05/24/2024] Open
Abstract
Non-protein-coding genetic variants are a major driver of the genetic risk for human disease; however, identifying which non-coding variants contribute to diseases and their mechanisms remains challenging. In silico variant prioritization methods quantify a variant's severity, but for most methods, the specific phenotype and disease context of the prediction remain poorly defined. For example, many commonly used methods provide a single, organism-wide score for each variant, while other methods summarize a variant's impact in certain tissues and/or cell types. Here, we propose a complementary disease-specific variant prioritization scheme, which is motivated by the observation that variants contributing to disease often operate through specific biological mechanisms. We combine tissue/cell-type-specific variant scores (e.g., GenoSkyline, FitCons2, DNA accessibility) into disease-specific scores with a logistic regression approach and apply it to ∼25,000 non-coding variants spanning 111 diseases. We show that this disease-specific aggregation significantly improves the association of common non-coding genetic variants with disease (average precision: 0.151, baseline = 0.09), compared with organism-wide scores (GenoCanyon, LINSIGHT, GWAVA, Eigen, CADD; average precision: 0.129, baseline = 0.09). Further on, disease similarities based on data-driven aggregation weights highlight meaningful disease groups, and it provides information about tissues and cell types that drive these similarities. We also show that so-learned similarities are complementary to genetic similarities as quantified by genetic correlation. Overall, our approach demonstrates the strengths of disease-specific variant prioritization, leads to improvement in non-coding variant prioritization, and enables interpretable models that link variants to disease via specific tissues and/or cell types.
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Affiliation(s)
- Qianqian Liang
- Department of Computational & Systems Biology and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Human Genetics, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA
| | - Abin Abraham
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - John A Capra
- Department of Epidemiology & Biostatistics and Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Dennis Kostka
- Department of Computational & Systems Biology and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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21
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability. HGG ADVANCES 2024; 5:100319. [PMID: 38872309 PMCID: PMC11260573 DOI: 10.1016/j.xhgg.2024.100319] [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: 01/03/2024] [Revised: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France; Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France.
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22
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Tkachenko AA, Changalidis AI, Maksiutenko EM, Nasykhova YA, Barbitoff YA, Glotov AS. Replication of Known and Identification of Novel Associations in Biobank-Scale Datasets: A Survey Using UK Biobank and FinnGen. Genes (Basel) 2024; 15:931. [PMID: 39062709 PMCID: PMC11275374 DOI: 10.3390/genes15070931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 07/03/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024] Open
Abstract
Over the last two decades, numerous genome-wide association studies (GWAS) have been performed to unveil the genetic architecture of human complex traits. Despite multiple efforts aimed at the trans-biobank integration of GWAS results, no systematic analysis of the variant-level properties affecting the replication of known associations (or identifying novel ones) in genome-wide meta-analysis has yet been performed using biobank-scale data. To address this issue, we performed a systematic comparison of GWAS summary statistics for 679 complex traits in the UK Biobank (UKB) and FinnGen (FG) cohorts. We identified 37,148 index variants with genome-wide associations with at least one trait in either cohort or in the meta-analysis, only 3528 (9.5%) of which were shared between UKB and FG. Nearly twice as many variants (6577) were replicated in another dataset at the significance level adjusted for the number of variants selected for replication. However, as many as 9230 loci failed to be replicated. Moreover, as many as 5813 loci were observed as significant associations only in meta-analysis results, highlighting the importance of trans-biobank meta-analysis efforts. We showed that variants that failed to replicate in UKB or FG tend to correspond to rare, less pleiotropic variants with lower effect sizes and lower LD score values. Genome-wide associations specific to meta-analysis were also enriched in low-effect variants; however, such variants tended to be more common and have more consistent frequencies between populations. Taken together, our results show a relatively high rate of non-replication of genome-wide associations in the studied cohorts and highlight both widely appreciated and less acknowledged properties of the associations affecting their identification and replication.
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Affiliation(s)
| | | | | | | | - Yury A. Barbitoff
- Department of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, 199034 St. Petersburg, Russia; (A.A.T.); (A.I.C.); (E.M.M.); (Y.A.N.); (A.S.G.)
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23
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Sakellaropoulos T, Do C, Jiang G, Cova G, Meyn P, Dimartino D, Ramaswami S, Heguy A, Tsirigos A, Skok JA. MethNet: a robust approach to identify regulatory hubs and their distal targets from cancer data. Nat Commun 2024; 15:6027. [PMID: 39025865 PMCID: PMC11258126 DOI: 10.1038/s41467-024-50380-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 07/09/2024] [Indexed: 07/20/2024] Open
Abstract
Aberrations in the capacity of DNA/chromatin modifiers and transcription factors to bind non-coding regions can lead to changes in gene regulation and impact disease phenotypes. However, identifying distal regulatory elements and connecting them with their target genes remains challenging. Here, we present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover cis regulatory elements (CREs) in a 1 Mb region around every promoter in the genome. MethNet identifies clusters of highly ranked CREs, referred to as 'hubs', which contribute to the regulation of multiple genes and significantly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPR interference based single-cell RNA Perturb-seq validated the functional impact of CREs. Thus, MethNet-identified CREs represent a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots.
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Affiliation(s)
- Theodore Sakellaropoulos
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Catherine Do
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Guimei Jiang
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Giulia Cova
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Peter Meyn
- Genome Technology Center, NYU Grossman School of Medicine, New York, NY, USA
| | - Dacia Dimartino
- Genome Technology Center, NYU Grossman School of Medicine, New York, NY, USA
| | - Sitharam Ramaswami
- Genome Technology Center, NYU Grossman School of Medicine, New York, NY, USA
| | - Adriana Heguy
- Genome Technology Center, NYU Grossman School of Medicine, New York, NY, USA
| | - Aristotelis Tsirigos
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA.
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA.
- Applied Bioinformatics Laboratories, Office of Science & Research, NYU Grossman School of Medicine, New York, NY, USA.
| | - Jane A Skok
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA.
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA.
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24
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Levy D, Kirmani S, Huan T, Van Amburg J, Joehanes R, Uddin MM, Nguyen NQ, Yu B, Brody J, Fornage M, Bressler J, Sotoodehnia N, Ong D, Puddu F, Floyd J, Ballantyne C, Psaty B, Raffield L, Natarajan P, Conneely K, Carson A, Lange L, Ferrier K, Heard-Costa N, Murabito J, Bick A. Epigenome-wide DNA Methylation Association Study of CHIP Provides Insight into Perturbed Gene Regulation. RESEARCH SQUARE 2024:rs.3.rs-4656898. [PMID: 39070619 PMCID: PMC11276001 DOI: 10.21203/rs.3.rs-4656898/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
With age, hematopoietic stem cells can acquire somatic mutations in leukemogenic genes that confer a proliferative advantage in a phenomenon termed "clonal hematopoiesis of indeterminate potential" (CHIP). How these mutations confer a proliferative advantage and result in increased risk for numerous age-related diseases remains poorly understood. We conducted a multiracial meta-analysis of epigenome-wide association studies (EWAS) of CHIP and its subtypes in four cohorts (N=8196) to elucidate the molecular mechanisms underlying CHIP and illuminate how these changes influence cardiovascular disease risk. The EWAS findings were functionally validated using human hematopoietic stem cell (HSC) models of CHIP. A total of 9615 CpGs were associated with any CHIP, 5990 with DNMT3A CHIP, 5633 with TET2 CHIP, and 6078 with ASXL1 CHIP (P <1×10-7). CpGs associated with CHIP subtypes overlapped moderately, and the genome-wide DNA methylation directions of effect were opposite for TET2 and DNMT3A CHIP, consistent with their opposing effects on global DNA methylation. There was high directional concordance between the CpGs shared from the meta-EWAS and human edited CHIP HSCs. Expression quantitative trait methylation analysis further identified transcriptomic changes associated with CHIP-associated CpGs. Causal inference analyses revealed 261 CHIP-associated CpGs associated with cardiovascular traits and all-cause mortality (FDR adjusted p-value <0.05). Taken together, our study sheds light on the epigenetic changes impacted by CHIP and their associations with age-related disease outcomes. The novel genes and pathways linked to the epigenetic features of CHIP may serve as therapeutic targets for preventing or treating CHIP-mediated diseases.
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Affiliation(s)
- Daniel Levy
- Framingham Heart Study, Framingham, MA, 01702, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health
| | - Sara Kirmani
- Framingham Heart Study, Framingham, MA, 01702, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda
| | | | - Joseph Van Amburg
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center
| | | | | | | | - Bing Yu
- University of Texas Health Science Center at Houston
| | | | - Myriam Fornage
- 1. Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center 2. Human Genetics Center, Department of Epidemiology, School of Public Health
| | - Jan Bressler
- School of Public Health, University of Texas Health Science Center at Houston
| | | | - David Ong
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | | | | | | | | | | | - Pradeep Natarajan
- Broad Institute of Harvard and Massachusetts Institute of Technology
| | | | | | - Leslie Lange
- Division of Biomedical Informatics and Personalized Medicine
| | | | | | - Joanne Murabito
- Section of General Internal Medicine, Boston University Chobanian & Avedisian School of Medicine
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25
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Shibata M, Terada A, Kawaguchi T, Kamatani Y, Okada D, Nagashima K, Ohmura K, Matsuda F, Kawaguchi S, Sese J, Yamada R. Identification of epistatic SNP combinations in rheumatoid arthritis using LAMPLINK and Japanese cohorts. J Hum Genet 2024:10.1038/s10038-024-01269-y. [PMID: 39014190 DOI: 10.1038/s10038-024-01269-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 06/16/2024] [Accepted: 06/20/2024] [Indexed: 07/18/2024]
Abstract
Genome-wide association studies have enabled the identification of important genetic factors in many trait studies. However, only a fraction of the heritability can be explained by known genetic factors, even in the most common diseases. Genetic loci combinations, or epistatic contributions expressed by combinations of single nucleotide polymorphisms (SNPs), have been argued to be one of the critical factors explaining some of the missing heritability, especially in oligogenic/polygenic diseases. Rheumatoid arthritis (RA) is a complex disease with more than 100 reported SNP associations, as well as various HLA haplotypes and amino acids; however, many associations between RA and inter-chromosomal SNP combinations are unknown. To discover novel associations of epistatic interactions with high odds ratios in RA, we applied the LAMPLINK method, a systematic enumerative procedure for identifying high-order SNP combinations, to a Japanese RA cohort (discovery cohort; 4024 patients with RA and 7731 controls). We validated the identified associations in a different Japanese cohort (validation cohort; 810 RA patients and 6303 controls). In this study, we identified 90 significant genetic associations in the discovery cohort. Among these, 74 (82.2%) associations were replicated in the validation cohort, and eight combinations were inter-chromosomal, all of which comprised rs7765379 or rs35265698 located in the HLA region. These two SNPs exhibited strong correlations with valine at amino acid position 11 in HLA-DRB1 (HLA-DRB1-11-Val). Finally, we discovered that rs9624 showed an association with RA through an epistatic interaction with HLA-DRB1-11-Val. Overall, LAMPLINK showed high reliability for identifying epistatic genetic contributions hidden in complex traits.
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Affiliation(s)
- Mio Shibata
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Takahisa Kawaguchi
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kazuhisa Nagashima
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shuji Kawaguchi
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Jun Sese
- Humanome Lab. Inc., Tokyo, Japan.
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.
| | - Ryo Yamada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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26
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Bjornson KJ, Vanderplow AM, Bhasker AI, Cahill ME. Increased regional activity of a pro-autophagy pathway in schizophrenia as a contributor to sex differences in the disease pathology. Cell Rep Med 2024; 5:101652. [PMID: 39019008 PMCID: PMC11293356 DOI: 10.1016/j.xcrm.2024.101652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/14/2024] [Accepted: 06/19/2024] [Indexed: 07/19/2024]
Abstract
Based on recent genome-wide association studies, it is theorized that altered regulation of autophagy contributes to the pathophysiology of schizophrenia and bipolar disorder. As activity of autophagy-regulatory pathways is controlled by discrete phosphorylation sites on the relevant proteins, phospho-protein profiling is one of the few approaches available for enabling a quantitative assessment of autophagic activity in the brain. Despite this, a comprehensive phospho-protein assessment in the brains of schizophrenia and bipolar disorder subjects is currently lacking. Using this direction, our broad screening identifies an increase in AMP-activated protein kinase (AMPK)-mediated phospho-activation of the pro-autophagy protein beclin-1 solely in the prefrontal cortex of female, but not male, schizophrenia subjects. Using a reverse translational approach, we surprisingly find that this increase in beclin-1 activity facilitates synapse formation and enhances cognition. These findings are interpreted in the context of human studies demonstrating that female schizophrenia subjects have a lower susceptibility to cognitive dysfunction than males.
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Affiliation(s)
- Kathryn J Bjornson
- Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Amanda M Vanderplow
- Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Aishwarya I Bhasker
- Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Michael E Cahill
- Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI 53706, USA.
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Urzúa-Traslaviña CG, van Lieshout T, Boulogne F, Domanegg K, Zidan M, Bakker OB, Claringbould A, de Ridder J, Zwart W, Westra HJ, Deelen P, Franke L. Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes. BMC Med Genomics 2024; 17:186. [PMID: 39010058 PMCID: PMC11247850 DOI: 10.1186/s12920-024-01941-4] [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: 05/01/2024] [Accepted: 06/18/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. RESULTS We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. CONCLUSION We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.
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Affiliation(s)
- Carlos G Urzúa-Traslaviña
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Tijs van Lieshout
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Floranne Boulogne
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Kevin Domanegg
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - Mahmoud Zidan
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - Olivier B Bakker
- Wellcome Sanger Institute, Human Genetics, Hinxton, UK
- Open Targets, Hinxton, UK
| | - Annique Claringbould
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- EMBL Heidelberg, Structural and Computational Biology Unit, Heidelberg, Germany
| | - Jeroen de Ridder
- Oncode Institute, Utrecht, The Netherlands
- University Medical Center Utrecht, Utrecht, The Netherlands
| | - Wilbert Zwart
- Oncode Institute, Utrecht, The Netherlands
- Division of Oncogenomics, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Harm-Jan Westra
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
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Olfson E, Farhat LC, Liu W, Vitulano LA, Zai G, Lima MO, Parent J, Polanczyk GV, Cappi C, Kennedy JL, Fernandez TV. Rare de novo damaging DNA variants are enriched in attention-deficit/hyperactivity disorder and implicate risk genes. Nat Commun 2024; 15:5870. [PMID: 38997333 PMCID: PMC11245598 DOI: 10.1038/s41467-024-50247-7] [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: 08/18/2023] [Accepted: 06/29/2024] [Indexed: 07/14/2024] Open
Abstract
Research demonstrates the important role of genetic factors in attention-deficit/hyperactivity disorder (ADHD). DNA sequencing of families provides a powerful approach for identifying de novo (spontaneous) variants, leading to the discovery of hundreds of clinically informative risk genes for other childhood neurodevelopmental disorders. This approach has yet to be extensively leveraged in ADHD. We conduct whole-exome DNA sequencing in 152 families, comprising a child with ADHD and both biological parents, and demonstrate a significant enrichment of rare and ultra-rare de novo gene-damaging mutations in ADHD cases compared to unaffected controls. Combining these results with a large independent case-control DNA sequencing cohort (3206 ADHD cases and 5002 controls), we identify lysine demethylase 5B (KDM5B) as a high-confidence risk gene for ADHD and estimate that 1057 genes contribute to ADHD risk. Using our list of genes harboring ultra-rare de novo damaging variants, we show that these genes overlap with previously reported risk genes for other neuropsychiatric conditions and are enriched in several canonical biological pathways, suggesting early neurodevelopmental underpinnings of ADHD. This work provides insight into the biology of ADHD and demonstrates the discovery potential of DNA sequencing in larger parent-child trio cohorts.
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Affiliation(s)
- Emily Olfson
- Child Study Center, Yale University, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
| | - Luis C Farhat
- Child Study Center, Yale University, New Haven, CT, USA
- Division of Child & Adolescent Psychiatry, Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Wenzhong Liu
- Child Study Center, Yale University, New Haven, CT, USA
| | | | - Gwyneth Zai
- Tanenbaum Centre, Molecular Brain Sciences Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science and Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Monicke O Lima
- Division of Child & Adolescent Psychiatry, Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Justin Parent
- University of Rhode Island, Kingston, RI, USA
- Bradley/Hasbro Children's Research Center, E.P. Bradley Hospital, Providence, RI, USA
- Alpert Medical School of Brown University, Providence, RI, USA
| | - Guilherme V Polanczyk
- Division of Child & Adolescent Psychiatry, Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Carolina Cappi
- Department of Psychiatry at Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - James L Kennedy
- Tanenbaum Centre, Molecular Brain Sciences Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science and Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Thomas V Fernandez
- Child Study Center, Yale University, New Haven, CT, USA.
- Department of Psychiatry, Yale University, New Haven, CT, USA.
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Ostridge HJ, Fontsere C, Lizano E, Soto DC, Schmidt JM, Saxena V, Alvarez-Estape M, Barratt CD, Gratton P, Bocksberger G, Lester JD, Dieguez P, Agbor A, Angedakin S, Assumang AK, Bailey E, Barubiyo D, Bessone M, Brazzola G, Chancellor R, Cohen H, Coupland C, Danquah E, Deschner T, Dotras L, Dupain J, Egbe VE, Granjon AC, Head J, Hedwig D, Hermans V, Hernandez-Aguilar RA, Jeffery KJ, Jones S, Junker J, Kadam P, Kaiser M, Kalan AK, Kambere M, Kienast I, Kujirakwinja D, Langergraber KE, Lapuente J, Larson B, Laudisoit A, Lee KC, Llana M, Maretti G, Martín R, Meier A, Morgan D, Neil E, Nicholl S, Nixon S, Normand E, Orbell C, Ormsby LJ, Orume R, Pacheco L, Preece J, Regnaut S, Robbins MM, Rundus A, Sanz C, Sciaky L, Sommer V, Stewart FA, Tagg N, Tédonzong LR, van Schijndel J, Vendras E, Wessling EG, Willie J, Wittig RM, Yuh YG, Yurkiw K, Vigilant L, Piel A, Boesch C, Kühl HS, Dennis MY, Marques-Bonet T, Arandjelovic M, Andrés AM. Local genetic adaptation to habitat in wild chimpanzees. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.601734. [PMID: 39026872 PMCID: PMC11257515 DOI: 10.1101/2024.07.09.601734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
How populations adapt to their environment is a fundamental question in biology. Yet we know surprisingly little about this process, especially for endangered species such as non-human great apes. Chimpanzees, our closest living relatives, are particularly interesting because they inhabit diverse habitats, from rainforest to woodland-savannah. Whether genetic adaptation facilitates such habitat diversity remains unknown, despite having wide implications for evolutionary biology and conservation. Using 828 newly generated exomes from wild chimpanzees, we find evidence of fine-scale genetic adaptation to habitat. Notably, adaptation to malaria in forest chimpanzees is mediated by the same genes underlying adaptation to malaria in humans. This work demonstrates the power of non-invasive samples to reveal genetic adaptations in endangered populations and highlights the importance of adaptive genetic diversity for chimpanzees.
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Affiliation(s)
- Harrison J Ostridge
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Claudia Fontsere
- Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Esther Lizano
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Daniela C Soto
- University of California, Davis, Genome Center, MIND Institute, Department of Biochemistry & Molecular Medicine, One Shields Drive, Davis, CA, 95616, USA
| | - Joshua M Schmidt
- Flinders Health and Medical Research Institute (FHMRI), Department of Ophthalmology, Flinders University Sturt Rd, Bedford Park South Australia 5042 Australia
| | - Vrishti Saxena
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Marina Alvarez-Estape
- University of California, Davis, Genome Center, MIND Institute, Department of Biochemistry & Molecular Medicine, One Shields Drive, Davis, CA, 95616, USA
| | - Christopher D Barratt
- Naturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, the Netherlands
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Leipzig-Jena, Puschstrasse 4, 04103 Leipzig, Germany
| | - Paolo Gratton
- University of Rome "Tor Vergata" Department of Biology Via Cracovia, 1, Roma, Italia
| | - Gaëlle Bocksberger
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage, 60325 Frankfurt am Main, Germany
| | - Jack D Lester
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Paula Dieguez
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Leipzig-Jena, Puschstrasse 4, 04103 Leipzig, Germany
| | - Anthony Agbor
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Samuel Angedakin
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Alfred Kwabena Assumang
- Department of Wildlife and Range Management, Faculty of Renewable Natural Resources, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Emma Bailey
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Donatienne Barubiyo
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Mattia Bessone
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- University of Konstanz, Centre for the Advanced Study of Collective Behaviour, Universitätsstraße 10, 78464, Konstanz, Germany
| | - Gregory Brazzola
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Rebecca Chancellor
- West Chester University, Depts of Anthropology & Sociology and Psychology, West Chester, PA, 19382 USA
| | - Heather Cohen
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Leipzig-Jena, Puschstrasse 4, 04103 Leipzig, Germany
| | - Charlotte Coupland
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Emmanuel Danquah
- Department of Wildlife and Range Management, Faculty of Renewable Natural Resources, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Tobias Deschner
- Institute of Cognitive Science, University of Osnabrück, Artilleriestrasse 34, 49076 Osnabrück, Germany
| | - Laia Dotras
- Jane Goodall Institute Spain and Senegal, Dindefelo Biological Station, Dindefelo, Kedougou, Senegal
- Department of Social Psychology and Quantitative Psychology, Serra Hunter Programme, University of Barcelona, Barcelona, Spain
| | - Jef Dupain
- Antwerp Zoo Foundation, RZSA, Kon.Astridplein 26, 2018 Antwerp, Belgium
| | - Villard Ebot Egbe
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Anne-Céline Granjon
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Josephine Head
- The Biodiversity Consultancy, 3E Kings Parade, Cambridge, CB2 1SJ, UK
| | - Daniela Hedwig
- Elephant Listening Project, K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, 159 Sapsucker Woods Road, Ithaca, NY 14850, USA
| | - Veerle Hermans
- KMDA, Centre for Research and Conservation, Royal Zoological Society of Antwerp, Koningin Astridplein 20-26, B-2018 Antwerp, Belgium
| | - R Adriana Hernandez-Aguilar
- Jane Goodall Institute Spain and Senegal, Dindefelo Biological Station, Dindefelo, Kedougou, Senegal
- Department of Social Psychology and Quantitative Psychology, Serra Hunter Programme, University of Barcelona, Barcelona, Spain
| | - Kathryn J Jeffery
- School of Natural Sciences, University of Stirling, UK
- Agence National des Parcs Nationaux (ANPN) Batterie 4, BP20379, Libreville, Gabon
| | - Sorrel Jones
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Jessica Junker
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Leipzig-Jena, Puschstrasse 4, 04103 Leipzig, Germany
| | - Parag Kadam
- Greater Mahale Ecosystem Research and Conservation Project
| | - Michael Kaiser
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Ammie K Kalan
- Department of Anthropology, University of Victoria, 3800 Finnerty Rd, Victoria, BC V8P 5C2, Canada
| | - Mbangi Kambere
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Ivonne Kienast
- Department of Natural Resources and the Environment, Cornell University, Ithaca, NY 14850, USA
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
| | - Deo Kujirakwinja
- Wildlife Conservation Society (WCS), 2300 Southern Boulevard. Bronx, New York 10460, USA
| | - Kevin E Langergraber
- School of Human Evolution and Social Change, Institute of Human Origins, Arizona State University, 777 East University Drive, Tempe, AZ 85287 Arizona State University, PO Box 872402, Tempe, AZ 85287-2402 USA
- Institute of Human Origins, Arizona State University, 900 Cady Mall, Tempe, AZ 85287 Arizona State University, PO Box 872402, Tempe, AZ 85287-2402 USA
| | - Juan Lapuente
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | | | | | - Kevin C Lee
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
| | - Manuel Llana
- Jane Goodall Institute Spain and Senegal, Dindefelo Biological Station, Dindefelo, Kedougou, Senegal
| | - Giovanna Maretti
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Rumen Martín
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Amelia Meier
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
- Hawai'i Insititute of Marine Biology, University of Hawai'i at Manoa, 46-007 Lilipuna Place, Kaneohe, HI, 96744, USA
| | - David Morgan
- Lester E. Fisher Center for the Study and Conservation of Apes, Lincoln Park Zoo, 2001 North Clark Street, Chicago, Illinois 60614 USA
| | - Emily Neil
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Sonia Nicholl
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Stuart Nixon
- North of England Zoological Society, Chester Zoo, Upton by Chester, CH2 1LH, United Kingdom
| | | | - Christopher Orbell
- Panthera, 8 W 40TH ST, New York, NY 10018, USA
- School of Natural Sciences, University of Stirling, UK
| | - Lucy Jayne Ormsby
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Robinson Orume
- Korup Rainforest Conservation Society, c/o Korup National Park, P.O. Box 36 Mundemba, South West Region, Cameroon
| | - Liliana Pacheco
- Save the Dogs and Other Animals, DJ 223 Km 3, 905200 Cernavoda CT, Romania
| | - Jodie Preece
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | | | - Martha M Robbins
- Max Planck Institute for Evolutionary Anthropology, Department of Primate Behavior and Evolution, Deutscher Platz 6, 04103 Leipzig
| | - Aaron Rundus
- West Chester University, Depts of Anthropology & Sociology and Psychology, West Chester, PA, 19382 USA
| | - Crickette Sanz
- Washington University in Saint Louis, Department of Anthropology, One Brookings Drive, St. Louis, MO 63130, USA
- Congo Program, Wildlife Conservation Society, 151 Avenue Charles de Gaulle, Brazzaville, Republic of Congo
| | - Lilah Sciaky
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Volker Sommer
- University College London, Department of Anthropology, 14 Taviton Street, London WC1H 0BW, UK
| | - Fiona A Stewart
- University College London, Department of Anthropology, 14 Taviton Street, London WC1H 0BW, UK
- Department of Human Origins, Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Nikki Tagg
- KMDA, Centre for Research and Conservation, Royal Zoological Society of Antwerp, Koningin Astridplein 20-26, B-2018 Antwerp, Belgium
- Born Free Foundation, Floor 2 Frazer House, 14 Carfax, Horsham, RH12 1ER, UK
| | - Luc Roscelin Tédonzong
- KMDA, Centre for Research and Conservation, Royal Zoological Society of Antwerp, Koningin Astridplein 20-26, B-2018 Antwerp, Belgium
| | - Joost van Schijndel
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Elleni Vendras
- Frankfurt Zoological Society, Bernhard-Grzimek-Allee 1, 60316 Frankfurt, Germany
| | - Erin G Wessling
- Johann-Friedrich-Blumenbach Institute for Zoology and Anthropology, Georg-August-University Göttingen,Göttingen, Germany
- German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Jacob Willie
- KMDA, Centre for Research and Conservation, Royal Zoological Society of Antwerp, Koningin Astridplein 20-26, B-2018 Antwerp, Belgium
- Terrestrial Ecology Unit (TEREC), Department of Biology, Ghent University (UGent), K.L. Ledeganckstraat 35, 9000 Ghent, Belgium
| | - Roman M Wittig
- Ape Social Mind Lab, Institute for Cognitive Sciences Marc Jeannerod, CNRS UMR 5229 CNRS, 67 bd Pinel, 69675 Bron CEDEX, France
- Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques, BP 1301, Abidjan 01, CI
| | - Yisa Ginath Yuh
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Kyle Yurkiw
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Linda Vigilant
- Max Planck Institute for Evolutionary Anthropology (MPI EVAN), Deutscher Platz 6, 04103 Leipzig
| | - Alex Piel
- University College London, Department of Anthropology, 14 Taviton Street, London WC1H 0BW, UK
| | | | - Hjalmar S Kühl
- Senckenberg Museum for Natural History Görlitz, Senckenberg - Member of the Leibniz Association Am Museum 1, 02826 Görlitz, Germany
- International Institute Zittau, Technische Universität Dresden, Markt 23, 02763 Zittau, Germany
| | - Megan Y Dennis
- University of California, Davis, Genome Center, MIND Institute, Department of Biochemistry & Molecular Medicine, One Shields Drive, Davis, CA, 95616, USA
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain
- Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Mimi Arandjelovic
- Max Planck Institute for Evolutionary Anthropology, Department of Primate Behavior and Evolution, Deutscher Platz 6, 04103 Leipzig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103
| | - Aida M Andrés
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
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30
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Han QJ, Zhu YP, Sun J, Ding XY, Wang X, Zhang QZ. PTGES2 and RNASET2 identified as novel potential biomarkers and therapeutic targets for basal cell carcinoma: insights from proteome-wide mendelian randomization, colocalization, and MR-PheWAS analyses. Front Pharmacol 2024; 15:1418560. [PMID: 39035989 PMCID: PMC11257982 DOI: 10.3389/fphar.2024.1418560] [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: 04/16/2024] [Accepted: 06/12/2024] [Indexed: 07/23/2024] Open
Abstract
Introduction Basal cell carcinoma (BCC) is the most common skin cancer, lacking reliable biomarkers or therapeutic targets for effective treatment. Genome-wide association studies (GWAS) can aid in identifying drug targets, repurposing existing drugs, predicting clinical trial side effects, and reclassifying patients in clinical utility. Hence, the present study investigates the association between plasma proteins and skin cancer to identify effective biomarkers and therapeutic targets for BCC. Methods Proteome-wide mendelian randomization was performed using inverse-variance-weight and Wald Ratio methods, leveraging 1 Mb cis protein quantitative trait loci (cis-pQTLs) in the UK Biobank Pharma Proteomics Project (UKB-PPP) and the deCODE Health Study, to determine the causal relationship between plasma proteins and skin cancer and its subtypes in the FinnGen R10 study and the SAIGE database of Lee lab. Significant association with skin cancer and its subtypes was defined as a false discovery rate (FDR) < 0.05. pQTL to GWAS colocalization analysis was executed using a Bayesian model to evaluate five exclusive hypotheses. Strong colocalization evidence was defined as a posterior probability for shared causal variants (PP.H4) of ≥0.85. Mendelian randomization-Phenome-wide association studies (MR-PheWAS) were used to evaluate potential biomarkers and therapeutic targets for skin cancer and its subtypes within a phenome-wide human disease category. Results PTGES2, RNASET2, SF3B4, STX8, ENO2, and HS3ST3B1 (besides RNASET2, five other plasma proteins were previously unknown in expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL)) were significantly associated with BCC after FDR correction in the UKB-PPP and deCODE studies. Reverse MR showed no association between BCC and these proteins. PTGES2 and RNASET2 exhibited strong evidence of colocalization with BCC based on a posterior probability PP.H4 >0.92. Furthermore, MR-PheWAS analysis showed that BCC was the most significant phenotype associated with PTGES2 and RNASET2 among 2,408 phenotypes in the FinnGen R10 study. Therefore, PTGES2 and RNASET2 are highlighted as effective biomarkers and therapeutic targets for BCC within the phenome-wide human disease category. Conclusion The study identifies PTGES2 and RNASET2 plasma proteins as novel, reliable biomarkers and therapeutic targets for BCC, suggesting more effective clinical application strategies for patients.
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Affiliation(s)
- Qiu-Ju Han
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Yi-Pan Zhu
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Jing Sun
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Xin-Yu Ding
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Xiuyu Wang
- Department of Neurosurgery, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Qiang-Zhe Zhang
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
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Gilliland T, Dron JS, Selvaraj MS, Trinder M, Paruchuri K, Urbut SM, Haidermota S, Bernardo R, Uddin MM, Honigberg MC, Peloso GM, Natarajan P. Genetic Architecture and Clinical Outcomes of Combined Lipid Disturbances. Circ Res 2024; 135:265-276. [PMID: 38828614 PMCID: PMC11223949 DOI: 10.1161/circresaha.123.323973] [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: 11/10/2023] [Accepted: 05/20/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUND Dyslipoproteinemia often involves simultaneous derangements of multiple lipid traits. We aimed to evaluate the phenotypic and genetic characteristics of combined lipid disturbances in a general population-based cohort. METHODS Among UK Biobank participants without prevalent coronary artery disease, we used blood lipid and apolipoprotein B concentrations to ascribe individuals into 1 of 6 reproducible and mutually exclusive dyslipoproteinemia subtypes. Incident coronary artery disease risk was estimated for each subtype using Cox proportional hazards models. Phenome-wide analyses and genome-wide association studies were performed for each subtype, followed by in silico causal gene prioritization and heritability analyses. Additionally, the prevalence of disruptive variants in causal genes for Mendelian lipid disorders was assessed using whole-exome sequence data. RESULTS Among 450 636 UK Biobank participants: 63 (0.01%) had chylomicronemia; 40 005 (8.9%) had hypercholesterolemia; 94 785 (21.0%) had combined hyperlipidemia; 13 998 (3.1%) had remnant hypercholesterolemia; 110 389 (24.5%) had hypertriglyceridemia; and 49 (0.01%) had mixed hypertriglyceridemia and hypercholesterolemia. Over a median (interquartile range) follow-up of 11.1 (10.4-11.8) years, incident coronary artery disease risk varied across subtypes, with combined hyperlipidemia exhibiting the largest hazard (hazard ratio, 1.92 [95% CI, 1.84-2.01]; P=2×10-16), even when accounting for non-HDL-C (hazard ratio, 1.45 [95% CI, 1.30-1.60]; P=2.6×10-12). Genome-wide association studies revealed 250 loci significantly associated with dyslipoproteinemia subtypes, of which 72 (28.8%) were not detected in prior single lipid trait genome-wide association studies. Mendelian lipid variant carriers were rare (2.0%) among individuals with dyslipoproteinemia, but polygenic heritability was high, ranging from 23% for remnant hypercholesterolemia to 54% for combined hyperlipidemia. CONCLUSIONS Simultaneous assessment of multiple lipid derangements revealed nuanced differences in coronary artery disease risk and genetic architectures across dyslipoproteinemia subtypes. These findings highlight the importance of looking beyond single lipid traits to better understand combined lipid and lipoprotein phenotypes and implications for disease risk.
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Affiliation(s)
- Thomas Gilliland
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Jacqueline S. Dron
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Margaret Sunitha Selvaraj
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Mark Trinder
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC
| | - Kaavya Paruchuri
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Sarah M. Urbut
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Sara Haidermota
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Rachel Bernardo
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Md Mesbah Uddin
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Michael C. Honigberg
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
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Kumagai T, Iwata A, Furuya H, Kato K, Okabe A, Toda Y, Kanai M, Fujimura L, Sakamoto A, Kageyama T, Tanaka S, Suto A, Hatano M, Kaneda A, Nakajima H. A distal enhancer of GATA3 regulates Th2 differentiation and allergic inflammation. Proc Natl Acad Sci U S A 2024; 121:e2320727121. [PMID: 38923989 PMCID: PMC11228505 DOI: 10.1073/pnas.2320727121] [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: 11/24/2023] [Accepted: 05/15/2024] [Indexed: 06/28/2024] Open
Abstract
Asthma is a widespread airway disorder where GATA3-dependent Type-2 helper T (Th2) cells and group 2 innate lymphoid cells (ILC2s) play vital roles. Asthma-associated single nucleotide polymorphisms (SNPs) are enriched in a region located 926-970 kb downstream from GATA3 in the 10p14 (hG900). However, it is unknown how hG900 affects the pathogenesis of allergic airway inflammation. To investigate the roles of the asthma-associated GATA3 enhancer region in experimental allergic airway inflammation, we first examined the correlation between GATA3 expression and the activation of the hG900 region was analyzed by flow cytometry and ChIP-qPCR. We found that The activation of enhancers in the hG900 region was strongly correlated to the levels of GATA3 in human peripheral T cell subsets. We next generated mice lacking the mG900 region (mG900KO mice) were generated by the CRISPR-Cas9 system, and the development and function of helper T cells and ILCs in mG900KO mice were analyzed in steady-state conditions and allergic airway inflammation induced by papain or house dust mite (HDM). The deletion of the mG900 did not affect the development of lymphocytes in steady-state conditions or allergic airway inflammation induced by papain. However, mG900KO mice exhibited reduced allergic inflammation and Th2 differentiation in the HDM-induced allergic airway inflammation. The analysis of the chromatin conformation around Gata3 by circular chromosome conformation capture coupled to high-throughput sequencing (4C-seq) revealed that the mG900 region interacted with the transcription start site of Gata3 with an influencing chromatin conformation in Th2 cells. These findings indicate that the mG900 region plays a pivotal role in Th2 differentiation and thus enhances allergic airway inflammation.
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Affiliation(s)
- Takashi Kumagai
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Arifumi Iwata
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Hiroki Furuya
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Kodai Kato
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Atsushi Okabe
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Health and Disease Omics Center, Chiba University, Chiba260-8670, Japan
| | - Yosuke Toda
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Mizuki Kanai
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Lisa Fujimura
- Biomedical Research Center, Chiba University, Chiba260-8670, Japan
| | - Akemi Sakamoto
- Biomedical Research Center, Chiba University, Chiba260-8670, Japan
- Department of Biomedical Science, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Takahiro Kageyama
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Shigeru Tanaka
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Akira Suto
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Masahiko Hatano
- Biomedical Research Center, Chiba University, Chiba260-8670, Japan
- Department of Biomedical Science, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
| | - Atsushi Kaneda
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Health and Disease Omics Center, Chiba University, Chiba260-8670, Japan
| | - Hiroshi Nakajima
- Department of Allergy and Clinical Immunology, Graduate School of Medicine, Chiba University, Chiba260-8670, Japan
- Chiba University Synergy Institute for Futuristic Mucosal Vaccine Research and Development, Chiba260-8670, Japan
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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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Yaacov O, Mathiyalagan P, Berk-Rauch HE, Ganesh SK, Zhu L, Hoffmann TJ, Iribarren C, Risch N, Lee D, Chakravarti A. Identification of the Molecular Components of Enhancer-Mediated Gene Expression Variation in Multiple Tissues Regulating Blood Pressure. Hypertension 2024; 81:1500-1510. [PMID: 38747164 PMCID: PMC11168860 DOI: 10.1161/hypertensionaha.123.22538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/24/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND Inter-individual variation in blood pressure (BP) arises in part from sequence variants within enhancers modulating the expression of causal genes. We propose that these genes, active in tissues relevant to BP physiology, can be identified from tissue-level epigenomic data and genotypes of BP-phenotyped individuals. METHODS We used chromatin accessibility data from the heart, adrenal, kidney, and artery to identify cis-regulatory elements (CREs) in these tissues and estimate the impact of common human single-nucleotide variants within these CREs on gene expression, using machine learning methods. To identify causal genes, we performed a gene-wise association test. We conducted analyses in 2 separate large-scale cohorts: 77 822 individuals from the Genetic Epidemiology Research on Adult Health and Aging and 315 270 individuals from the UK Biobank. RESULTS We identified 309, 259, 331, and 367 genes (false discovery rate <0.05) for diastolic BP and 191, 184, 204, and 204 genes for systolic BP in the artery, kidney, heart, and adrenal, respectively, in Genetic Epidemiology Research on Adult Health and Aging; 50% to 70% of these genes were replicated in the UK Biobank, significantly higher than the 12% to 15% expected by chance (P<0.0001). These results enabled tissue expression prediction of these 988 to 2875 putative BP genes in individuals of both cohorts to construct an expression polygenic score. This score explained ≈27% of the reported single-nucleotide variant heritability, substantially higher than expected from prior studies. CONCLUSIONS Our work demonstrates the power of tissue-restricted comprehensive CRE analysis, followed by CRE-based expression prediction, for understanding BP regulation in relevant tissues and provides dual-modality supporting evidence, CRE and expression, for the causality genes.
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Affiliation(s)
- Or Yaacov
- Center for Human Genetics and Genomics, NYU Grossman School of Medicine, New York, NY, USA
| | - Prabhu Mathiyalagan
- Center for Human Genetics and Genomics, NYU Grossman School of Medicine, New York, NY, USA
- Benthos Prime Central, Houston, TX, USA
| | - Hanna E. Berk-Rauch
- Center for Human Genetics and Genomics, NYU Grossman School of Medicine, New York, NY, USA
| | - Santhi K. Ganesh
- Department of Internal Medicine & Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Luke Zhu
- Center for Human Genetics and Genomics, NYU Grossman School of Medicine, New York, NY, USA
| | - Thomas J. Hoffmann
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Carlos Iribarren
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
| | - Neil Risch
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
| | - Dongwon Lee
- Department of Pediatrics, Division of Nephrology, Boston Children’s Hospital, Boston & Harvard Medical School, Boston, MA, USA
| | - Aravinda Chakravarti
- Center for Human Genetics and Genomics, NYU Grossman School of Medicine, New York, NY, USA
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Mielnicka M, Tabaro F, Sureka R, Acurzio B, Paoletti R, Scavizzi F, Raspa M, Crevenna AH, Lapouge K, Remans K, Boulard M. Trim66's paternal deficiency causes intrauterine overgrowth. Life Sci Alliance 2024; 7:e202302512. [PMID: 38719749 PMCID: PMC11077763 DOI: 10.26508/lsa.202302512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/12/2024] Open
Abstract
The tripartite motif-containing protein 66 (TRIM66, also known as TIF1-delta) is a PHD-Bromo-containing protein primarily expressed in post-meiotic male germ cells known as spermatids. Biophysical assays showed that the TRIM66 PHD-Bromodomain binds to H3 N-terminus only when lysine 4 is unmethylated. We addressed TRIM66's role in reproduction by loss-of-function genetics in the mouse. Males homozygous for Trim66-null mutations produced functional spermatozoa. Round spermatids lacking TRIM66 up-regulated a network of genes involved in histone acetylation and H3K4 methylation. Profiling of H3K4me3 patterns in the sperm produced by the Trim66-null mutant showed minor alterations below statistical significance. Unexpectedly, Trim66-null males, but not females, sired pups overweight at birth, hence revealing that Trim66 mutations cause a paternal effect phenotype.
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Affiliation(s)
- Monika Mielnicka
- https://ror.org/01yr73893 Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, Italy
| | - Francesco Tabaro
- https://ror.org/01yr73893 Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, Italy
| | - Rahul Sureka
- https://ror.org/01yr73893 Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, Italy
| | - Basilia Acurzio
- https://ror.org/01yr73893 Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, Italy
| | | | - Ferdinando Scavizzi
- National Research Council (IBBC), CNR-Campus International Development (EMMA-INFRAFRONTIER-IMPC), Monterotondo, Italy
| | - Marcello Raspa
- National Research Council (IBBC), CNR-Campus International Development (EMMA-INFRAFRONTIER-IMPC), Monterotondo, Italy
| | - Alvaro H Crevenna
- https://ror.org/01yr73893 Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, Italy
| | - Karine Lapouge
- https://ror.org/01yr73893 European Molecular Biology Laboratory, Protein Expression and Purification Core Facility, Heidelberg, Germany
| | - Kim Remans
- https://ror.org/01yr73893 European Molecular Biology Laboratory, Protein Expression and Purification Core Facility, Heidelberg, Germany
| | - Matthieu Boulard
- https://ror.org/01yr73893 Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, Italy
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36
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Im C, Neupane A, Baedke JL, Lenny B, Delaney A, Dixon SB, Chow EJ, Mostoufi-Moab S, Yang T, Richard MA, Gramatges MM, Lupo PJ, Sharafeldin N, Bhatia S, Armstrong GT, Hudson MM, Ness KK, Robison LL, Yasui Y, Wilson CL, Sapkota Y. Trans-Ancestral Genetic Risk Factors for Treatment-Related Type 2 Diabetes Mellitus in Survivors of Childhood Cancer. J Clin Oncol 2024; 42:2306-2316. [PMID: 38652878 PMCID: PMC11209771 DOI: 10.1200/jco.23.02281] [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: 10/19/2023] [Revised: 02/01/2024] [Accepted: 02/28/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE Type 2 diabetes mellitus (T2D) is a prevalent long-term complication of treatment in survivors of childhood cancer, with marked racial/ethnic differences in burden. In this study, we investigated trans-ancestral genetic risks for treatment-related T2D. PATIENTS AND METHODS Leveraging whole-genome sequencing data from the St Jude Lifetime Cohort (N = 3,676, 304 clinically ascertained cases), we conducted ancestry-specific genome-wide association studies among survivors of African and European genetic ancestry (AFR and EUR, respectively) followed by trans-ancestry meta-analysis. Trans-/within-ancestry replication including data from the Childhood Cancer Survivor Study (N = 5,965) was required for prioritization. Three external general population T2D polygenic risk scores (PRSs) were assessed, including multiancestry PRSs. Treatment risk effect modification was evaluated for prioritized loci. RESULTS Four novel T2D risk loci showing trans-/within-ancestry replication evidence were identified, with three loci achieving genome-wide significance (P < 5 × 10-8). Among these, common variants at 5p15.2 (LINC02112), 2p25.3 (MYT1L), and 19p12 (ZNF492) showed evidence of modifying alkylating agent-related T2D risk in both ancestral groups, but showed disproportionately greater risk in AFR survivors (AFR odds ratios [ORs], 3.95-17.81; EUR ORs, 2.37-3.32). In survivor-specific RNA-sequencing data (N = 207), the 19p12 locus variant was associated with greater ZNF492 expression dysregulation after exposures to alkylators. Elevated T2D risks across ancestry groups were only observed with increasing values for multiancestry T2D PRSs and were especially increased among survivors treated with alkylators (top v bottom quintiles: ORAFR, 20.18; P = .023; OREUR, 13.44; P = 1.3 × 10-9). CONCLUSION Our findings suggest therapy-related genetic risks contribute to the increased T2D burden among non-Hispanic Black childhood cancer survivors. Additional study of how therapy-related genetic susceptibility contributes to this disparity is needed.
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Affiliation(s)
- Cindy Im
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Achal Neupane
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Jessica L. Baedke
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Brian Lenny
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Angela Delaney
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Division of Endocrinology, Department of Pediatric Medicine, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Stephanie B. Dixon
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Eric J. Chow
- Public Health Sciences and Clinical Research Divisions, Fred Hutchinson Research Center, Seattle, WA, 98109, USA
| | - Sogol Mostoufi-Moab
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19146, USA
| | - Tianzhong Yang
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Melissa A. Richard
- Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - M. Monica Gramatges
- Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Philip J. Lupo
- Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Noha Sharafeldin
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, 35223, USA
| | - Smita Bhatia
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, 35223, USA
| | - Gregory T. Armstrong
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Melissa M. Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Kirsten K. Ness
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Leslie L. Robison
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Yutaka Yasui
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Carmen L. Wilson
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Yadav Sapkota
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
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Benstock SE, Weaver K, Hettema JM, Verhulst B. Using Alternative Definitions of Controls to Increase Statistical Power in GWAS. Behav Genet 2024; 54:353-366. [PMID: 38869698 DOI: 10.1007/s10519-024-10187-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: 01/12/2024] [Accepted: 05/29/2024] [Indexed: 06/14/2024]
Abstract
Genome-wide association studies (GWAS) are often underpowered due to small effect sizes of common single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the greatest statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.
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Affiliation(s)
- Sarah E Benstock
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA
| | - Katherine Weaver
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA
| | - John M Hettema
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA
| | - Brad Verhulst
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, College Station, TX, USA.
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38
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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; 56:1527-1536. [PMID: 38872030 DOI: 10.1038/s41588-024-01793-9] [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: 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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Yang Y, Dong L, Li Y, Huang Y, Zeng X. Summary data-based Mendelian randomization and single-cell RNA sequencing analyses identify immune associations with low-level LGALS9 in sepsis. J Cell Mol Med 2024; 28:e18559. [PMID: 39044269 PMCID: PMC11265992 DOI: 10.1111/jcmm.18559] [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: 05/02/2024] [Revised: 06/15/2024] [Accepted: 07/08/2024] [Indexed: 07/25/2024] Open
Abstract
Sepsis is one of the major challenges in intensive care units, characterized by the complexity of the host immune status. To gain a deeper understanding of the pathogenesis of sepsis, it is crucial to study the phenotypic changes in immune cells and their underlying molecular mechanisms. We conducted Summary data-based Mendelian randomization analysis by integrating genome-wide association studies data for sepsis with expression quantitative trait locus data, revealing a significant decrease in the expression levels of 17 biomarkers in sepsis patients. Furthermore, based on single-cell RNA sequencing data, we elucidated potential molecular mechanisms at single-cell resolution and identified that LGALS9 inhibition in sepsis patients leads to the activation and differentiation of monocyte and T-cell subtypes. These findings are expected to assist researchers in gaining a more in-depth understanding of the immune dysregulation in sepsis.
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Affiliation(s)
- Yongsan Yang
- Intensive Care Unit and West China Biomedical Big Data CenterWest China Hospital, Sichuan UniversityChengduChina
- Med‐X Center for InformaticsSichuan UniversityChengduChina
| | - Lei Dong
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of SciencesBeijingChina
| | - Yanguo Li
- Institute of Drug Discovery Technology, Ningbo UniversityNingboChina
| | - Ye Huang
- Department of Emergency MedicineXiyuan Hospital of China Academy of Chinese Medical SciencesBeijingChina
| | - Xiaoxi Zeng
- Med‐X Center for InformaticsSichuan UniversityChengduChina
- West China Biomedical Big Data CenterWest China Hospital, Sichuan UniversityChengduChina
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40
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Schmidt AF, Finan C, Chopade S, Ellmerich S, Rossor MN, Hingorani AD, Pepys M. Genetic evidence for serum amyloid P component as a drug target in neurodegenerative disorders. Open Biol 2024; 14:230419. [PMID: 39013416 PMCID: PMC11251762 DOI: 10.1098/rsob.230419] [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: 11/14/2023] [Accepted: 05/23/2024] [Indexed: 07/18/2024] Open
Abstract
The mechanisms responsible for neuronal death causing cognitive loss in Alzheimer's disease (AD) and many other dementias are not known. Serum amyloid P component (SAP) is a constitutive plasma protein, which is cytotoxic for cerebral neurones and also promotes formation and persistence of cerebral Aβ amyloid and neurofibrillary tangles. Circulating SAP, which is produced exclusively by the liver, is normally almost completely excluded from the brain. Conditions increasing brain exposure to SAP increase dementia risk, consistent with a causative role in neurodegeneration. Furthermore, neocortex content of SAP is strongly and independently associated with dementia at death. Here, seeking genomic evidence for a causal link of SAP with neurodegeneration, we meta-analysed three genome-wide association studies of 44 288 participants, then conducted cis-Mendelian randomization assessment of associations with neurodegenerative diseases. Higher genetically instrumented plasma SAP concentrations were associated with AD (odds ratio 1.07, 95% confidence interval (CI) 1.02; 1.11, p = 1.8 × 10-3), Lewy body dementia (odds ratio 1.37, 95%CI 1.19; 1.59, p = 1.5 × 10-5) and plasma tau concentration (0.06 log2(ng l-1) 95%CI 0.03; 0.08, p = 4.55 × 10-6). These genetic findings are consistent with neuropathogenicity of SAP. Depletion of SAP from the blood and the brain, by the safe, well tolerated, experimental drug miridesap may thus be neuroprotective.
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Affiliation(s)
- A. Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, 69-75 Chenies Mews, London WC1E 6HX, UK
- UCL British Heart Foundation Research Accelerator, 69-75 Chenies Mews, London WC1E 6HX, UK
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam UMC, locatie AMC Postbus 22660, 1100 DD Amsterdam, Zuidoost, The Netherlands
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, 69-75 Chenies Mews, London WC1E 6HX, UK
- UCL British Heart Foundation Research Accelerator, 69-75 Chenies Mews, London WC1E 6HX, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Sandesh Chopade
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, 69-75 Chenies Mews, London WC1E 6HX, UK
- UCL British Heart Foundation Research Accelerator, 69-75 Chenies Mews, London WC1E 6HX, UK
| | - Stephan Ellmerich
- Wolfson Drug Discovery Unit, Division of Medicine, University College London, Royal Free Campus, Rowland Hill Street, London NW3 2PF, UK
| | - Martin N. Rossor
- UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, Queen Square, London WC1N 3BG, UK
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, 69-75 Chenies Mews, London WC1E 6HX, UK
- UCL British Heart Foundation Research Accelerator, 69-75 Chenies Mews, London WC1E 6HX, UK
| | - Mark B. Pepys
- Wolfson Drug Discovery Unit, Division of Medicine, University College London, Royal Free Campus, Rowland Hill Street, London NW3 2PF, UK
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Liu YS, Lin YC, Lin MC, Wu CC, Wang TN. Association of blood lipid profiles and asthma: A bidirectional two-sample Mendelian randomization study. Ann Hum Genet 2024; 88:307-319. [PMID: 38305494 DOI: 10.1111/ahg.12545] [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: 06/09/2023] [Revised: 11/22/2023] [Accepted: 12/01/2023] [Indexed: 02/03/2024]
Abstract
BACKGROUND Observational studies and meta-analyses have indicated associations between blood lipid profiles and asthma. However, the causal association is unknown. Therefore, this study investigated the causal relationship between blood lipid profiles and asthma using bidirectional Mendelian randomization analysis. METHODS AND MATERIALS Our analyses were performed using individual data from the Taiwan Biobank and summary statistics from the Asian Genetic Epidemiology Network (AGEN). The causal estimates between all genetic variants, exposures of interest and asthma were calculated using an inverse-variance weighted method based on Taiwan Biobank data from 24,853 participants (mean age, 48.8 years; 49.8% women). Sensitivity analyses, including the weighted median, MR Egger regression, MR-PRESSO, mode-based estimate, contamination mixture methods, and leave-one-out analysis, were applied to validate the results and detect pleiotropy. RESULTS In the inverse-variance weighted (IVW) analyses, we found evidence of a significant causal effect of an increased level of low-density lipoprotein cholesterol on asthma risk (βIVW = 1.338, p = 0.001). A genetically decreased level of high-density lipoprotein cholesterol was also associated with asthma risk (βIVW = -0.338, p = 0.01). We also found that an increased level of total cholesterol was associated with an increased risk of asthma (βIVW = 1.343, p = 0.001). Several sensitivity analyses generated consistent findings. We did not find evidence to support the causality between asthma and blood lipid profiles in either direction. CONCLUSION Our results supported the causal relationship between higher levels of LDL cholesterol and total cholesterol and lower levels of HDL cholesterol with an increased risk of asthma.
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Affiliation(s)
- Yi-Shian Liu
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yu-Chun Lin
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Meng-Chih Lin
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Chang Gung Memorial Hospital-Kaohsiung Medical Center, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chao-Chien Wu
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Chang Gung Memorial Hospital-Kaohsiung Medical Center, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Tsu-Nai Wang
- Department of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung, Taiwan
- Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
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Wen J, Tian YE, Skampardoni I, Yang Z, Cui Y, Anagnostakis F, Mamourian E, Zhao B, Toga AW, Zalesky A, Davatzikos C. The genetic architecture of biological age in nine human organ systems. NATURE AGING 2024:10.1038/s43587-024-00662-8. [PMID: 38942983 DOI: 10.1038/s43587-024-00662-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 05/30/2024] [Indexed: 06/30/2024]
Abstract
Investigating the genetic underpinnings of human aging is essential for unraveling the etiology of and developing actionable therapies for chronic diseases. Here, we characterize the genetic architecture of the biological age gap (BAG; the difference between machine learning-predicted age and chronological age) across nine human organ systems in 377,028 participants of European ancestry from the UK Biobank. The BAGs were computed using cross-validated support vector machines, incorporating imaging, physical traits and physiological measures. We identify 393 genomic loci-BAG pairs (P < 5 × 10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary and renal systems. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system (organ specificity) while exerting pleiotropic links with other organ systems (interorgan cross-talk). We find that genetic correlation between the nine BAGs mirrors their phenotypic correlation. Further, a multiorgan causal network established from two-sample Mendelian randomization and latent causal variance models revealed potential causality between chronic diseases (for example, Alzheimer's disease and diabetes), modifiable lifestyle factors (for example, sleep duration and body weight) and multiple BAGs. Our results illustrate the potential for improving human organ health via a multiorgan network, including lifestyle interventions and drug repurposing strategies.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), University of Southern California, Los Angeles, CA, USA.
| | - Ye Ella Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600283. [PMID: 38979289 PMCID: PMC11230257 DOI: 10.1101/2024.06.25.600283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). Results The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. Conclusions VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. Availability VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Arul S. Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Currently at: Illumina, Foster City, California 94404, USA
| | - Steven E. Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
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Magalhães Borges V, Horimoto ARVR, Wijsman EM, Kimura L, Nunes K, Nato AQ, Mingroni-Netto RC. Genomic Exploration of Essential Hypertension in African-Brazilian Quilombo Populations: A Comprehensive Approach with Pedigree Analysis and Family-Based Association Studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.26.24309531. [PMID: 38978678 PMCID: PMC11230341 DOI: 10.1101/2024.06.26.24309531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Essential Hypertension (EH) is a major global health concern, causing about 9.4 million deaths annually. Its prevalence varies across different regions, affecting 17% of the population in the Americas, 19.2% in the Western Pacific, 23.2% in Europe, 25.1% in Southeast Asia, 26.3% in the Eastern Mediterranean, and 27.2% in Africa. EH is a multifactorial disease influenced by both genetic and environmental factors. While genetic factors contribute 30-60% to blood pressure variation, the genetic complexity of EH remains largely unexplained due to limited knowledge of candidate genes and population-specific differences. Various methods, including candidate gene studies, genome-wide linkage analysis (GWLA), and genome-wide association studies (GWAS), have been employed to identify genetic factors, yet much of the heritability of EH is still unknown. This study aimed to investigate the genetic basis of EH by mapping regions of interest (ROIs) and identifying candidate genes and variants influencing EH in African-derived individuals from partially isolated populations of quilombo remnants in Vale do Ribeira, São Paulo, Brazil. Samples from 431 individuals (167 affected, 261 unaffected, 3 with unknown phenotype) from eight quilombo remnant populations were genotyped using a 650k SNP array. The global ancestry proportions were estimated at 47% African, 36% European, and 16% Native American. Genealogical information from 673 individuals was used to construct six pedigrees comprising 1104 individuals. The mapping strategy consisted of a multi-level computational approach. We constructed pedigrees based on interviews and kinship coefficient, pruned the dataset to obtain three non-overlapping markers subpanels, phased the haplotype and performed local ancestry to account for admixture. We performed GWLA and dense linkage analyses using markers subpanels and performed fine-mapping using family-based association studies (FBAS) based on population and pedigree imputed data, investigating EH-related genes and variants. The linkage analysis identified 22 ROIs with LOD scores 1.45-3.03, containing markers co-segregating with the phenotype. These ROIs encompassed 2363 genes. Fine-mapping identified 60 EH-related candidate genes and 118 suggestive or significant variants (FBAS). Among these, 14 genes, including PHGDH, S100A10, MFN2, and RYR2, were highlighted with strong evidence of association with hypertension. These genes, harboring 29 SNPs, were implicated in regulating blood pressure, sodium and potassium levels, and the aldosterone pathway. This study revealed, through a complementary approach - combining admixture-adjusted genome-wide linkage analysis based on Markov chain Monte Carlo (MCMC) methods, association studies on imputed data, and in silico investigations - genetic regions, variants and candidate genes that shed light on the genetic basis of essential hypertension, with significant potential to explain the genetic etiology in quilombo remnant populations.
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Affiliation(s)
- Vinícius Magalhães Borges
- Centro de Estudos sobre o Genoma Humano e Células Tronco, Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo 05508-090, Brazil
- Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV 25755, USA
| | - Andrea R V R Horimoto
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, 98105 USA
| | - Ellen Marie Wijsman
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, 98105 USA
| | - Lilian Kimura
- Centro de Estudos sobre o Genoma Humano e Células Tronco, Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo 05508-090, Brazil
| | - Kelly Nunes
- Centro de Estudos sobre o Genoma Humano e Células Tronco, Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo 05508-090, Brazil
| | - Alejandro Q Nato
- Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV 25755, USA
| | - Regina Célia Mingroni-Netto
- Centro de Estudos sobre o Genoma Humano e Células Tronco, Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo 05508-090, Brazil
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Mustelin T, Andrade F. Autoimmunity: the neoantigen hypothesis. Front Immunol 2024; 15:1432985. [PMID: 38994353 PMCID: PMC11236689 DOI: 10.3389/fimmu.2024.1432985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 06/17/2024] [Indexed: 07/13/2024] Open
Affiliation(s)
- Tomas Mustelin
- Division of Rheumatology, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Felipe Andrade
- Division of Rheumatology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Pakha DN, Yudhani RD, Irham LM. Investigation of missense mutation-related type 1 diabetes mellitus through integrating genomic databases and bioinformatic approach. Genomics Inform 2024; 22:8. [PMID: 38926794 PMCID: PMC11201337 DOI: 10.1186/s44342-024-00005-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/03/2024] [Indexed: 06/28/2024] Open
Abstract
Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.
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Affiliation(s)
- Dyonisa Nasirochmi Pakha
- Department of Pharmacology, Faculty of Medicine, Universitas Sebelas Maret, Surakarta, 57126, Indonesia
| | - Ratih Dewi Yudhani
- Department of Pharmacology, Faculty of Medicine, Universitas Sebelas Maret, Surakarta, 57126, Indonesia.
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Newsham I, Sendera M, Jammula SG, Samarajiwa SA. Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns. Biol Methods Protoc 2024; 9:bpae028. [PMID: 38903861 PMCID: PMC11186673 DOI: 10.1093/biomethods/bpae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/30/2024] [Accepted: 04/29/2024] [Indexed: 06/22/2024] Open
Abstract
Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.
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Affiliation(s)
- Izzy Newsham
- MRC Cancer Unit, University of Cambridge, Cambridge, CB2 0XZ, United Kingdom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, United Kingdom
| | - Marcin Sendera
- MRC Cancer Unit, University of Cambridge, Cambridge, CB2 0XZ, United Kingdom
- Jagiellonian University, Faculty of Mathematics and Computer Science, 30-348 Kraków, Poland
| | - Sri Ganesh Jammula
- CRUK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, United Kingdom
- MedGenome labs, Bengaluru, 560099, India
| | - Shamith A Samarajiwa
- MRC Cancer Unit, University of Cambridge, Cambridge, CB2 0XZ, United Kingdom
- Imperial College London, Hammersmith Campus, London, W12 0NN, United Kingdom
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Zou Y, Carbonetto P, Xie D, Wang G, Stephens M. Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.14.536893. [PMID: 37425935 PMCID: PMC10327118 DOI: 10.1101/2023.04.14.536893] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
We introduce mvSuSiE, a multi-trait fine-mapping method for identifying putative causal variants from genetic association data (individual-level or summary data). mvSuSiE learns patterns of shared genetic effects from data, and exploits these patterns to improve power to identify causal SNPs. Comparisons on simulated data show that mvSuSiE is competitive in speed, power and precision with existing multi-trait methods, and uniformly improves on single-trait fine-mapping (SuSiE) in each trait separately. We applied mvSuSiE to jointly fine-map 16 blood cell traits using data from the UK Biobank. By jointly analyzing the traits and modeling heterogeneous effect sharing patterns, we discovered a much larger number of causal SNPs (>3,000) compared with single-trait fine-mapping, and with narrower credible sets. mvSuSiE also more comprehensively characterized the ways in which the genetic variants affect one or more blood cell traits; 68% of causal SNPs showed significant effects in more than one blood cell type.
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Affiliation(s)
- Yuxin Zou
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
| | - Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Dongyue Xie
- Department of Statistics, University of Chicago, Chicago, IL, USA
| | - Gao Wang
- Gertrude. H. Sergievsky Center, Department of Neurology, Columbia University, New York, NY, USA
| | - Matthew Stephens
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
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Santiago-Lamelas L, Castro-Santos P, Carracedo Á, Olloquequi J, Díaz-Peña R. Unveiling the Significance of HLA and KIR Diversity in Underrepresented Populations. Biomedicines 2024; 12:1333. [PMID: 38927540 PMCID: PMC11202227 DOI: 10.3390/biomedicines12061333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Human leukocyte antigen (HLA) molecules and their relationships with natural killer (NK) cells, specifically through their interaction with killer-cell immunoglobulin-like receptors (KIRs), exhibit robust associations with the outcomes of diverse diseases. Moreover, genetic variations in HLA and KIR immune system genes offer limitless depths of complexity. In recent years, a surge of high-powered genome-wide association studies (GWASs) utilizing single nucleotide polymorphism (SNP) arrays has occurred, significantly advancing our understanding of disease pathogenesis. Additionally, advances in HLA reference panels have enabled higher resolution and more reliable imputation, allowing for finer-grained evaluation of the association between sequence variations and disease risk. However, it is essential to note that the majority of these GWASs have focused primarily on populations of Caucasian and Asian origins, neglecting underrepresented populations in Latin America and Africa. This omission not only leads to disparities in health care access but also restricts our knowledge of novel genetic variants involved in disease pathogenesis within these overlooked populations. Since the KIR and HLA haplotypes prevalent in each population are clearly modelled by the specific environment, the aim of this review is to encourage studies investigating HLA/KIR involvement in infection and autoimmune diseases, reproduction, and transplantation in underrepresented populations.
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Affiliation(s)
- Lucía Santiago-Lamelas
- Fundación Pública Galega de Medicina Xenómica, SERGAS, Grupo de Medicina Xenomica-USC, Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain; (L.S.-L.); (P.C.-S.); (Á.C.)
| | - Patricia Castro-Santos
- Fundación Pública Galega de Medicina Xenómica, SERGAS, Grupo de Medicina Xenomica-USC, Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain; (L.S.-L.); (P.C.-S.); (Á.C.)
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca 3460000, Chile
| | - Ángel Carracedo
- Fundación Pública Galega de Medicina Xenómica, SERGAS, Grupo de Medicina Xenomica-USC, Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain; (L.S.-L.); (P.C.-S.); (Á.C.)
- Grupo de Medicina Xenómica, CIMUS, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Jordi Olloquequi
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca 3460000, Chile
- Departament de Bioquímica i Fisiologia, Facultat de Farmàcia i Ciències de l’Alimentació, Universitat de Barcelona, 08028 Barcelona, Spain
| | - Roberto Díaz-Peña
- Fundación Pública Galega de Medicina Xenómica, SERGAS, Grupo de Medicina Xenomica-USC, Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain; (L.S.-L.); (P.C.-S.); (Á.C.)
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca 3460000, Chile
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Ralli S, Vira T, Robles-Espinoza CD, Adams DJ, Brooks-Wilson AR. Variant ranking pipeline for complex familial disorders. Sci Rep 2024; 14:13599. [PMID: 38866901 PMCID: PMC11169219 DOI: 10.1038/s41598-024-64169-3] [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: 08/03/2023] [Accepted: 06/05/2024] [Indexed: 06/14/2024] Open
Abstract
Identifying genetic susceptibility factors for complex disorders remains a challenging task. To analyze collections of small and large pedigrees where genetic heterogeneity is likely, but biological commonalities are plausible, we have developed a weights-based pipeline to prioritize variants and genes. The Weights-based vAriant Ranking in Pedigrees (WARP) pipeline prioritizes variants using 5 weights: disease incidence rate, number of cases in a family, genome fraction shared amongst cases in a family, allele frequency and variant deleteriousness. Weights, except for the population allele frequency weight, are normalized between 0 and 1. Weights are combined multiplicatively to produce family-specific-variant weights that are then averaged across all families in which the variant is observed to generate a multifamily weight. Sorting multifamily weights in descending order creates a ranked list of variants and genes for further investigation. WARP was validated using familial melanoma sequence data from the European Genome-phenome Archive. The pipeline identified variation in known germline melanoma genes POT1, MITF and BAP1 in 4 out of 13 families (31%). Analysis of the other 9 families identified several interesting genes, some of which might have a role in melanoma. WARP provides an approach to identify disease predisposing genes in studies with small and large pedigrees.
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Affiliation(s)
- Sneha Ralli
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Tariq Vira
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | | | - David J Adams
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK
| | - Angela R Brooks-Wilson
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, V5Z 1L3, Canada.
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.
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