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Agrawal S, Luan J, Cummings BB, Weiss EJ, Wareham NJ, Khera AV. Relationship of Fat Mass Ratio, a Biomarker for Lipodystrophy, With Cardiometabolic Traits. Diabetes 2024; 73:1099-1111. [PMID: 38345889 DOI: 10.2337/db23-0575] [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: 07/23/2023] [Accepted: 02/06/2024] [Indexed: 06/22/2024]
Abstract
Familial partial lipodystrophy (FPLD) is a heterogenous group of syndromes associated with a high prevalence of cardiometabolic diseases. Prior work has proposed DEXA-derived fat mass ratio (FMR), defined as trunk fat percentage divided by leg fat percentage, as a biomarker of FPLD, but this metric has not previously been characterized in large cohort studies. We set out to 1) understand the cardiometabolic burden of individuals with high FMR in up to 40,796 participants in the UK Biobank and 9,408 participants in the Fenland study, 2) characterize the common variant genetic underpinnings of FMR, and 3) build and test a polygenic predictor for FMR. Participants with high FMR were at higher risk for type 2 diabetes (odds ratio [OR] 2.30, P = 3.5 × 10-41) and metabolic dysfunction-associated liver disease or steatohepatitis (OR 2.55, P = 4.9 × 10-7) in UK Biobank and had higher fasting insulin (difference 19.8 pmol/L, P = 5.7 × 10-36) and fasting triglycerides (difference 36.1 mg/dL, P = 2.5 × 10-28) in the Fenland study. Across FMR and its component traits, 61 conditionally independent variant-trait pairs were discovered, including 13 newly identified pairs. A polygenic score for FMR was associated with an increased risk of cardiometabolic diseases. This work establishes the cardiometabolic significance of high FMR, a biomarker for FPLD, in two large cohort studies and may prove useful in increasing diagnosis rates of patients with metabolically unhealthy fat distribution to enable treatment or a preventive therapy. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Saaket Agrawal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, U.K
| | | | | | - Nick J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, U.K
| | - Amit V Khera
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston, MA
- Verve Therapeutics, Boston, MA
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2
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Liu A, Genovese G, Zhao Y, Pirinen M, Zekavat SM, Kentistou KA, Yang Z, Yu K, Vlasschaert C, Liu X, Brown DW, Hudjashov G, Gorman BR, Dennis J, Zhou W, Momozawa Y, Pyarajan S, Tuzov V, Pajuste FD, Aavikko M, Sipilä TP, Ghazal A, Huang WY, Freedman ND, Song L, Gardner EJ, Sankaran VG, Palotie A, Ollila HM, Tukiainen T, Chanock SJ, Mägi R, Natarajan P, Daly MJ, Bick A, McCarroll SA, Terao C, Loh PR, Ganna A, Perry JRB, Machiela MJ. Genetic drivers and cellular selection of female mosaic X chromosome loss. Nature 2024:10.1038/s41586-024-07533-7. [PMID: 38867047 DOI: 10.1038/s41586-024-07533-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/07/2024] [Indexed: 06/14/2024]
Abstract
Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.
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Affiliation(s)
- Aoxing Liu
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Giulio Genovese
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Seyedeh M Zekavat
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | | | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Derek W Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Bryan R Gorman
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Booz Allen Hamilton, McLean, VA, USA
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Weiyin Zhou
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saiju Pyarajan
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Valdislav Tuzov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Mervi Aavikko
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Timo P Sipilä
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Awaisa Ghazal
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pradeep Natarajan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexander Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
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Nauffal V, Klarqvist MDR, Hill MC, Pace DF, Di Achille P, Choi SH, Rämö JT, Pirruccello JP, Singh P, Kany S, Hou C, Ng K, Philippakis AA, Batra P, Lubitz SA, Ellinor PT. Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping. Nat Med 2024; 30:1749-1760. [PMID: 38806679 DOI: 10.1038/s41591-024-03010-w] [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: 09/05/2023] [Accepted: 04/22/2024] [Indexed: 05/30/2024]
Abstract
Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77-6.19; P = 1.78 × 10-4). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.
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Affiliation(s)
- Victor Nauffal
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Matthew C Hill
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Danielle F Pace
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joel T Rämö
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - James P Pirruccello
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA
| | - Pulkit Singh
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shinwan Kany
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany
| | - Cody Hou
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Anthony A Philippakis
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven A Lubitz
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
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4
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Rahman MS, Harrison E, Biggs H, Seikus C, Elliott P, Breen G, Kingston N, Bradley JR, Hill SM, Tom BDM, Chinnery PF. Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants. Nat Med 2024; 30:1739-1748. [PMID: 38745010 DOI: 10.1038/s41591-024-02960-5] [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: 11/21/2023] [Accepted: 03/28/2024] [Indexed: 05/16/2024]
Abstract
A leading explanation for translational failure in neurodegenerative disease is that new drugs are evaluated late in the disease course when clinical features have become irreversible. Here, to address this gap, we cognitively profiled 21,051 people aged 17-85 years as part of the Genes and Cognition cohort within the National Institute for Health and Care Research BioResource across England. We describe the cohort, present cognitive trajectories and show the potential utility. Surprisingly, when studied at scale, the APOE genotype had negligible impact on cognitive performance. Different cognitive domains had distinct genetic architectures, with one indicating brain region-specific activation of microglia and another with glycogen metabolism. Thus, the molecular and cellular mechanisms underpinning cognition are distinct from dementia risk loci, presenting different targets to slow down age-related cognitive decline. Participants can now be recalled stratified by genotype and cognitive phenotype for natural history and interventional studies of neurodegenerative and other disorders.
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Affiliation(s)
- Md Shafiqur Rahman
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Emma Harrison
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Heather Biggs
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Chloe Seikus
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research BioResource, Cambridge, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health Research Biomedical Research Centre for Mental Health, South London and Maudsley Hospital, London, UK
| | - Nathalie Kingston
- National Institute for Health and Care Research BioResource, Cambridge, UK
- Dept of Haematology, Cambridge University, Cambridge, UK
| | - John R Bradley
- National Institute for Health and Care Research BioResource, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Steven M Hill
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
| | - Brian D M Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Patrick F Chinnery
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- National Institute for Health and Care Research BioResource, Cambridge, UK.
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK.
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5
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Guo Y, Liu Q, Zheng Z, Qing M, Yao T, Wang B, Zhou M, Wang D, Ke Q, Ma J, Shan Z, Chen W. Genetic association of inflammatory marker GlycA with lung function and respiratory diseases. Nat Commun 2024; 15:3751. [PMID: 38704398 PMCID: PMC11069551 DOI: 10.1038/s41467-024-47845-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: 05/03/2023] [Accepted: 04/12/2024] [Indexed: 05/06/2024] Open
Abstract
Association of circulating glycoprotein acetyls (GlycA), a systemic inflammation biomarker, with lung function and respiratory diseases remain to be investigated. We examined the genetic correlation, shared genetics, and potential causality of GlycA (N = 115,078) with lung function and respiratory diseases (N = 497,000). GlycA showed significant genetic correlation with FEV1 (rg = -0.14), FVC (rg = -0.18), asthma (rg = 0.21) and COPD (rg = 0.31). We consistently identified ten shared loci (including chr3p21.31 and chr8p23.1) at both SNP and gene level revealing potential shared biological mechanisms involving ubiquitination, immune response, Wnt/β-catenin signaling, cell growth and differentiation in tissues or cells including blood, epithelium, fibroblast, fetal thymus, and fetal intestine. Genetically elevated GlycA was significantly correlated with lung function and asthma susceptibility (354.13 ml decrement of FEV1, 442.28 ml decrement of FVC, and 144% increased risk of asthma per SD increment of GlycA) from MR analyses. Our findings provide insights into biological mechanisms of GlycA in relating to lung function, asthma, and COPD.
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Affiliation(s)
- Yanjun Guo
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
- Department of Epidemiology, Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA.
| | - Quanhong Liu
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Zhilin Zheng
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Mengxia Qing
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Tianci Yao
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bin Wang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Min Zhou
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Dongming Wang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Qinmei Ke
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jixuan Ma
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Zhilei Shan
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weihong Chen
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
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6
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Jiang J. MPH: fast REML for large-scale genome partitioning of quantitative genetic variation. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae298. [PMID: 38688661 DOI: 10.1093/bioinformatics/btae298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024]
Abstract
MOTIVATION Genome partitioning of quantitative genetic variation is useful for dissecting the genetic architecture of complex traits. However, existing methods, such as Haseman-Elston regression and linkage disequilibrium score regression, often face limitations when handling extensive farm animal datasets, as demonstrated in this study. RESULTS To overcome this challenge, we present MPH, a novel software tool designed for efficient genome partitioning analyses using restricted maximum likelihood. The computational efficiency of MPH primarily stems from two key factors: the utilization of stochastic trace estimators and the comprehensive implementation of parallel computation. Evaluations with simulated and real datasets demonstrate that MPH achieves comparable accuracy and significantly enhances convergence, speed, and memory efficiency compared to widely used tools like GCTA and LDAK. These advancements facilitate large-scale, comprehensive analyses of complex genetic architectures in farm animals. AVAILABILITY AND IMPLEMENTATION The MPH software is available at https://jiang18.github.io/mph/.
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Affiliation(s)
- Jicai Jiang
- Department of Animal Science, North Carolina State University, Raleigh, NC 27695, United States
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7
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Mitteroecker P, Merola GP. The cliff edge model of the evolution of schizophrenia: Mathematical, epidemiological, and genetic evidence. Neurosci Biobehav Rev 2024; 160:105636. [PMID: 38522813 DOI: 10.1016/j.neubiorev.2024.105636] [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: 12/19/2023] [Revised: 02/27/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
How has schizophrenia, a condition that significantly reduces an individual's evolutionary fitness, remained common across generations and cultures? Numerous theories about the evolution of schizophrenia have been proposed, most of which are not consistent with modern epidemiological and genetic evidence. Here, we briefly review this evidence and explore the cliff edge model of schizophrenia. It suggests that schizophrenia is the extreme manifestation of a polygenic trait or a combination of traits that, within a normal range of variation, confer cognitive, linguistic, and/or social advantages. Only beyond a certain threshold, these traits precipitate the onset of schizophrenia and reduce fitness. We provide the first mathematical model of this qualitative concept and show that it requires only very weak positive selection of the underlying trait(s) to explain today's schizophrenia prevalence. This prediction, along with expectations about the effect size of schizophrenia risk alleles, are surprisingly well matched by empirical evidence. The cliff edge model predicts a dynamic change of selection of risk alleles, which explains the contradictory findings of evolutionary genetic studies.
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Affiliation(s)
- Philipp Mitteroecker
- Unit for Theoretical Biology, Department of Evolutionary Biology, University of Vienna, Djerassiplatz 1, Vienna, Austria; Konrad Lorenz Institute for Evolution and Cognition Research, Martinstrasse 12, Klosterneuburg, Vienna, Austria.
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8
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Durvasula A, Price AL. Distinct explanations underlie gene-environment interactions in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.22.23295969. [PMID: 37790574 PMCID: PMC10543037 DOI: 10.1101/2023.09.22.23295969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation r g < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N = 325 K ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r g significantly < 1 (FDR<5%) (average r g = 0.95 ); for example, white blood cell count had r g = 0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.
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Affiliation(s)
- Arun Durvasula
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Genetics, Harvard Medical School, Cambridge, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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9
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Kanchibhotla SC, Mather KA, Armstrong NJ, Ciobanu LG, Baune BT, Catts VS, Schofield PR, Trollor JN, Ames D, Sachdev PS, Thalamuthu A. Heritability of Gene Expression Measured from Peripheral Blood in Older Adults. Genes (Basel) 2024; 15:495. [PMID: 38674429 PMCID: PMC11049887 DOI: 10.3390/genes15040495] [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: 03/13/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
The contributions of genetic variation and the environment to gene expression may change across the lifespan. However, few studies have investigated the heritability of blood gene expression in older adults. The current study therefore aimed to investigate this question in a community sample of older adults. A total of 246 adults (71 MZ and 52 DZ twins, 69.91% females; mean age-75.79 ± 5.44) were studied. Peripheral blood gene expression was assessed using Illumina microarrays. A heritability analysis was performed using structural equation modelling. There were 5269 probes (19.9%) from 4603 unique genes (23.9%) (total 26,537 probes from 19,256 genes) that were significantly heritable (mean h2 = 0.40). A pathway analysis of the top 10% of significant genes showed enrichment for the immune response and ageing-associated genes. In a comparison with two other gene expression twin heritability studies using adults from across the lifespan, there were 38 out of 9479 overlapping genes that were significantly heritable. In conclusion, our study found ~24% of the available genes for analysis were heritable in older adults, with only a small number common across studies that used samples from across adulthood, indicating the importance of examining gene expression in older age groups.
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Affiliation(s)
- Sri C. Kanchibhotla
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - Karen A. Mather
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
| | - Nicola J. Armstrong
- Department of Mathematics and Statistics, Curtin University, Perth, WA 6845, Australia
| | - Liliana G. Ciobanu
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Bernhard T. Baune
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5005, Australia
- Department of Psychiatry, University of Münster, 48149 Münster, Germany
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3052, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Vibeke S. Catts
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Julian N. Trollor
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Department of Developmental Disability Neuropsychiatry, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - David Ames
- Academic Unit for Psychiatry of Old Age, University of Melbourne, St George’s Hospital, Kew, Melbourne, VIC 3010, Australia
- National Ageing Research Institute, Parkville, VIC 3052, Australia
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Sydney, NSW 2031, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
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10
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Wang H, Pang J, Zhou Y, Qi Q, Tang Y, Gul S, Sheng M, Dan J, Tang W. Identification of potential drug targets for allergic diseases from a genetic perspective: A mendelian randomization study. Clin Transl Allergy 2024; 14:e12350. [PMID: 38573314 PMCID: PMC10994001 DOI: 10.1002/clt2.12350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/26/2024] [Accepted: 03/16/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Allergic diseases typically refer to a heterogeneous group of conditions primarily caused by the activation of mast cells or eosinophils, including atopic dermatitis (AD), allergic rhinitis (AR), and asthma. Asthma, AR, and AD collectively affect approximately one-fifth of the global population, imposing a significant economic burden on society. Despite the availability of drugs to treat allergic diseases, they have been shown to be insufficient in controlling relapses and halting disease progression. Therefore, new drug targets are needed to prevent the onset of allergic diseases. METHOD We employed a Mendelian randomization approach to identify potential drug targets for the treatment of allergic diseases. Leveraging 1798 genetic instruments for 1537 plasma proteins from the latest reported Genome-Wide Association Studies (GWAS), we analyzed the GWAS summary statistics of Ferreira MA et al. (nCase = 180,129, nControl = 180,709) using the Mendelian randomization method. Furthermore, we validated our findings in the GWAS data from the FinnGen and UK Biobank cohorts. Subsequently, we conducted sensitivity tests through reverse causal analysis, Bayesian colocalization analysis, and phenotype scanning. Additionally, we performed protein-protein interaction analysis to determine the interaction between causal proteins. Finally, based on the potential protein targets, we conducted molecular docking to identify potential drugs for the treatment of allergic diseases. RESULTS At Bonferroni significance (p < 3.25 × 10-5), the Mendelian randomization analysis revealed 11 significantly associated protein-allergic disease pairs. Among these, the increased levels of TNFAIP3, ERBB3, TLR1, and IL1RL2 proteins were associated with a reduced risk of allergic diseases, with corresponding odds ratios of 0.82 (0.76-0.88), 0.74 (0.66-0.82), 0.49 (0.45-0.55), and 0.81 (0.75-0.87), respectively. Conversely, increased levels of IL6R, IL1R1, ITPKA, IL1RL1, KYNU, LAYN, and LRP11 proteins were linked to an elevated risk of allergic diseases, with corresponding odds ratios of 1.04 (1.03-1.05), 1.25 (1.18-1.34), 1.48 (1.25-1.75), 1.14 (1.11-1.18), 1.09 (1.05-1.12), 1.96 (1.56-2.47), and 1.05 (1.03-1.07), respectively. Bayesian colocalization analysis suggested that LAYN (coloc.abf-PPH4 = 0.819) and TNFAIP3 (coloc.abf-PPH4 = 0.930) share the same variant associated with allergic diseases. CONCLUSIONS Our study demonstrates a causal association between the expression levels of TNFAIP3 and LAYN and the risk of allergic diseases, suggesting them as potential drug targets for these conditions, warranting further clinical investigation.
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Affiliation(s)
- Hui Wang
- Laboratory of Molecular Genetics of Aging & TumorMedicine SchoolKunming University of Science and TechnologyKunmingYunnanChina
| | - Jianyu Pang
- Laboratory of Molecular Genetics of Aging & TumorMedicine SchoolKunming University of Science and TechnologyKunmingYunnanChina
| | - Yuguan Zhou
- Laboratory of Molecular Genetics of Aging & TumorMedicine SchoolKunming University of Science and TechnologyKunmingYunnanChina
| | - Qi Qi
- Laboratory of Molecular Genetics of Aging & TumorMedicine SchoolKunming University of Science and TechnologyKunmingYunnanChina
| | - Yuheng Tang
- Laboratory of Molecular Genetics of Aging & TumorMedicine SchoolKunming University of Science and TechnologyKunmingYunnanChina
| | - Samina Gul
- Laboratory of Molecular Genetics of Aging & TumorMedicine SchoolKunming University of Science and TechnologyKunmingYunnanChina
| | - Miaomiao Sheng
- Laboratory of Molecular Genetics of Aging & TumorMedicine SchoolKunming University of Science and TechnologyKunmingYunnanChina
| | - Juhua Dan
- Laboratory of Molecular Genetics of Aging & TumorMedicine SchoolKunming University of Science and TechnologyKunmingYunnanChina
| | - Wenru Tang
- Laboratory of Molecular Genetics of Aging & TumorMedicine SchoolKunming University of Science and TechnologyKunmingYunnanChina
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11
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Dong Z, Zhao H, DeWan AT. A mediation analysis framework based on variance component to remove genetic confounding effect. J Hum Genet 2024:10.1038/s10038-024-01232-x. [PMID: 38528049 DOI: 10.1038/s10038-024-01232-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/27/2024]
Abstract
Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.
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Affiliation(s)
- Zihan Dong
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
| | - Andrew T DeWan
- Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, New Haven, CT, USA.
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA.
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12
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Peyrot WJ, Panagiotaropoulou G, Olde Loohuis LM, Adams MJ, Awasthi S, Ge T, McIntosh AM, Mitchell BL, Mullins N, O'Connell KS, Penninx BWJH, Posthuma D, Ripke S, Ruderfer DM, Uffelmann E, Vilhjalmsson BJ, Zhu Z, Smoller JW, Price AL. Distinguishing different psychiatric disorders using DDx-PRS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.02.24302228. [PMID: 38352307 PMCID: PMC10862992 DOI: 10.1101/2024.02.02.24302228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.
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13
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Zhang MJ, Durvasula A, Chiang C, Koch EM, Strober BJ, Shi H, Barton AR, Kim SS, Weissbrod O, Loh PR, Gazal S, Sunyaev S, Price AL. Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection. RESEARCH SQUARE 2023:rs.3.rs-3707248. [PMID: 38168385 PMCID: PMC10760228 DOI: 10.21203/rs.3.rs-3707248/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.
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Affiliation(s)
- Martin Jinye Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Arun Durvasula
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Colby Chiang
- Department of Pediatrics, Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA
| | - Evan M. Koch
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Benjamin J. Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alison R. Barton
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Samuel S. Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California
| | - Shamil Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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14
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Matkarimov BT, Saparbaev MK. Chargaff's second parity rule lies at the origin of additive genetic interactions in quantitative traits to make omnigenic selection possible. PeerJ 2023; 11:e16671. [PMID: 38107580 PMCID: PMC10725672 DOI: 10.7717/peerj.16671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/22/2023] [Indexed: 12/19/2023] Open
Abstract
Background Francis Crick's central dogma provides a residue-by-residue mechanistic explanation of the flow of genetic information in living systems. However, this principle may not be sufficient for explaining how random mutations cause continuous variation of quantitative highly polygenic complex traits. Chargaff's second parity rule (CSPR), also referred to as intrastrand DNA symmetry, defined as near-exact equalities G ≈ C and A ≈ T within a single DNA strand, is a statistical property of cellular genomes. The phenomenon of intrastrand DNA symmetry was discovered more than 50 years ago; at present, it remains unclear what its biological role is, what the mechanisms are that force cellular genomes to comply strictly with CSPR, and why genomes of certain noncellular organisms have broken intrastrand DNA symmetry. The present work is aimed at studying a possible link between intrastrand DNA symmetry and the origin of genetic interactions in quantitative traits. Methods Computational analysis of single-nucleotide polymorphisms in human and mouse populations and of nucleotide composition biases at different codon positions in bacterial and human proteomes. Results The analysis of mutation spectra inferred from single-nucleotide polymorphisms observed in murine and human populations revealed near-exact equalities of numbers of reverse complementary mutations, indicating that random genetic variations obey CSPR. Furthermore, nucleotide compositions of coding sequences proved to be statistically interwoven via CSPR because pyrimidine bias at the 3rd codon position compensates purine bias at the 1st and 2nd positions. Conclusions According to Fisher's infinitesimal model, we propose that accumulation of reverse complementary mutations results in a continuous phenotypic variation due to small additive effects of statistically interwoven genetic variations. Therefore, additive genetic interactions can be inferred as a statistical entanglement of nucleotide compositions of separate genetic loci. CSPR challenges the neutral theory of molecular evolution-because all random mutations participate in variation of a trait-and provides an alternative solution to Haldane's dilemma by making a gene function diffuse. We propose that CSPR is symmetry of Fisher's infinitesimal model and that genetic information can be transferred in an implicit contactless manner.
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Affiliation(s)
- Bakhyt T. Matkarimov
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
- L.N.Gumilev Eurasian National University, Astana, Kazakhstan
| | - Murat K. Saparbaev
- Groupe «Mechanisms of DNA Repair and Carcinogenesis», CNRS UMR9019, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- Al-Farabi Kazakh National University, Almaty, Kazakhstan
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15
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Borbye-Lorenzen N, Zhu Z, Agerbo E, Albiñana C, Benros ME, Bian B, Børglum AD, Bulik CM, Debost JCPG, Grove J, Hougaard DM, McRae AF, Mors O, Mortensen PB, Musliner KL, Nordentoft M, Petersen LV, Privé F, Sidorenko J, Skogstrand K, Werge T, Wray NR, Vilhjálmsson BJ, McGrath JJ. The correlates of neonatal complement component 3 and 4 protein concentrations with a focus on psychiatric and autoimmune disorders. CELL GENOMICS 2023; 3:100457. [PMID: 38116117 PMCID: PMC10726496 DOI: 10.1016/j.xgen.2023.100457] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 09/03/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023]
Abstract
Complement components have been linked to schizophrenia and autoimmune disorders. We examined the association between neonatal circulating C3 and C4 protein concentrations in 68,768 neonates and the risk of six mental disorders. We completed genome-wide association studies (GWASs) for C3 and C4 and applied the summary statistics in Mendelian randomization and phenome-wide association studies related to mental and autoimmune disorders. The GWASs for C3 and C4 protein concentrations identified 15 and 36 independent loci, respectively. We found no associations between neonatal C3 and C4 concentrations and mental disorders in the total sample (both sexes combined); however, post-hoc analyses found that a higher C3 concentration was associated with a reduced risk of schizophrenia in females. Mendelian randomization based on C4 summary statistics found an altered risk of five types of autoimmune disorders. Our study adds to our understanding of the associations between C3 and C4 concentrations and subsequent mental and autoimmune disorders.
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Affiliation(s)
- Nis Borbye-Lorenzen
- Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Zhihong Zhu
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Esben Agerbo
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Center for Integrated Register-based Research, Aarhus University, CIRRAU, 8210 Aarhus V, Denmark
| | - Clara Albiñana
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
| | - Michael E. Benros
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen University Hospital, Hellerup, Denmark
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Beilei Bian
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Anders D. Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Department of Biomedicine and the iSEQ Center, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, CGPM, Aarhus, Denmark
| | - Cynthia M. Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jean-Christophe Philippe Goldtsche Debost
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- Department of Psychosis, Aarhus University Hospital Skejby, Aarhus Nord, Denmark
| | - Jakob Grove
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Center for Genomics and Personalized Medicine, CGPM, Aarhus, Denmark
- Department of Biomedicine (Human Genetics), Aarhus University, Aarhus, Denmark
- Bioinformatics Research Center, Aarhus University, 8000 Aarhus C, Denmark
| | - David M. Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Department for Congenital Disorders, Statens Serum Institut, 2300 Copenhagen S, Denmark
| | - Allan F. McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Psychosis Research Unit, Aarhus University Hospital – Psychiatry, Aarhus, Denmark
| | - Preben Bo Mortensen
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Center for Integrated Register-based Research, Aarhus University, CIRRAU, 8210 Aarhus V, Denmark
| | - Katherine L. Musliner
- Department of Affective Disorders, Aarhus University and Aarhus University Hospital –Psychiatry, Aarhus, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Liselotte V. Petersen
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Florian Privé
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Kristin Skogstrand
- Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark
- Department of Clinical Medicine, Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, University of Copenhagen, 2200 Copenhagen N, Denmark
- Lundbeck Center for Geogenetics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
- Big Data Institute, University of Oxford, Oxford OX3 7LF, UK
| | - Bjarni J. Vilhjálmsson
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
- Bioinformatics Research Center, Aarhus University, 8000 Aarhus C, Denmark
| | - John J. McGrath
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, QLD 4076, Australia
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16
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Jabalameli M, Lin JR, Zhang Q, Wang Z, Mitra J, Nguyen N, Gao T, Khusidman M, Atzmon G, Milman S, Vijg J, Barzilai N, Zhang ZD. Polygenic prediction of human longevity on the supposition of pervasive pleiotropy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.10.23299795. [PMID: 38168353 PMCID: PMC10760260 DOI: 10.1101/2023.12.10.23299795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The highly polygenic nature of human longevity renders cross-trait pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between the aging-related traits (ARTs), we sought to model the additive variance in lifespan as a function of cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores (iLGSs) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of i L G S , we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with i L G S highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.
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Affiliation(s)
- M.Reza Jabalameli
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Jhih-Rong Lin
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Quanwei Zhang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Zhen Wang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Joydeep Mitra
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Tina Gao
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Mark Khusidman
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Gil Atzmon
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Sofiya Milman
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Nir Barzilai
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
| | - Zhengdong D. Zhang
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
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17
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Privé F, Albiñana C, Arbel J, Pasaniuc B, Vilhjálmsson BJ. Inferring disease architecture and predictive ability with LDpred2-auto. Am J Hum Genet 2023; 110:2042-2055. [PMID: 37944514 PMCID: PMC10716363 DOI: 10.1016/j.ajhg.2023.10.010] [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: 06/14/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.
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Affiliation(s)
- Florian Privé
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
| | - Clara Albiñana
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Julyan Arbel
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bjarni J Vilhjálmsson
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark; Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
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18
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Zhang MJ, Durvasula A, Chiang C, Koch EM, Strober BJ, Shi H, Barton AR, Kim SS, Weissbrod O, Loh PR, Gazal S, Sunyaev S, Price AL. Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.04.23299391. [PMID: 38106023 PMCID: PMC10723494 DOI: 10.1101/2023.12.04.23299391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.
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Affiliation(s)
- Martin Jinye Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Arun Durvasula
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Colby Chiang
- Department of Pediatrics, Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA
| | - Evan M Koch
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Benjamin J Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alison R Barton
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Samuel S Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California
| | - Shamil Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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19
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Li H, Mazumder R, Lin X. Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix. Nat Commun 2023; 14:7954. [PMID: 38040712 PMCID: PMC10692177 DOI: 10.1038/s41467-023-43565-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 11/14/2023] [Indexed: 12/03/2023] Open
Abstract
Existing SNP-heritability estimators that leverage summary statistics from genome-wide association studies (GWAS) are much less efficient (i.e., have larger standard errors) than the restricted maximum likelihood (REML) estimators which require access to individual-level data. We introduce a new method for local heritability estimation-Heritability Estimation with high Efficiency using LD and association Summary Statistics (HEELS)-that significantly improves the statistical efficiency of summary-statistics-based heritability estimator and attains comparable statistical efficiency as REML (with a relative statistical efficiency >92%). Moreover, we propose representing the empirical LD matrix as the sum of a low-rank matrix and a banded matrix. We show that this way of modeling the LD can not only reduce the storage and memory cost, but also improve the computational efficiency of heritability estimation. We demonstrate the statistical efficiency of HEELS and the advantages of our proposed LD approximation strategies both in simulations and through empirical analyses of the UK Biobank data.
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Affiliation(s)
- Hui Li
- Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, MA, USA
| | - Rahul Mazumder
- Massachusetts Institute of Technology, Operations Research and Statistics group, Cambridge, MA, USA
| | - Xihong Lin
- Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, MA, USA.
- Harvard University, Department of Statistics, Cambridge, MA, USA.
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20
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Guo Y, Chung W, Shan Z, Zhu Z, Costenbader KH, Liang L. Genome-Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases. J Am Heart Assoc 2023; 12:e030211. [PMID: 37947095 PMCID: PMC10727280 DOI: 10.1161/jaha.123.030211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 10/16/2023] [Indexed: 11/12/2023]
Abstract
Background Patients with rheumatoid arthritis (RA) have a 2- to 10-fold increased risk of cardiovascular disease (CVD), but the biological mechanisms and existence of causality underlying such associations remain to be investigated. We aimed to investigate the genetic associations and underlying mechanisms between RA and CVD by leveraging large-scale genomic data and genetic cross-trait analytic approaches. Methods and Results Within UK Biobank data, we examined the genetic correlation, shared genetics, and potential causality between RA (Ncases=6754, Ncontrols=452 384) and cardiovascular diseases (CVD, Ncases=44 238, Ncontrols=414 900) using linkage disequilibrium score regression, cross-trait meta-analysis, and Mendelian randomization. We observed significant genetic correlations of RA with myocardial infarction (rg:0.40 [95% CI, 0.24-0.56), angina (rg:0.42 [95% CI, 0.28-0.56]), coronary heart diseases (rg:0.41 [95% CI, 0.27-0.55]), and CVD (rg:0.43 [95% CI, 0.29-0.57]) after correcting for multiple testing (P<0.05/5). When stratified by frequent use of analgesics, we found increased genetic correlation between RA and CVD among participants without aspirin usage (rg:0.54 [95% CI, 0.30-0.78] for angina; Pvalue=6.69×10-6) and among participants with paracetamol usage (rg:0.75 [95% CI, 0.20-1.29] for myocardial infarction; Pvalue=8.90×10-3), whereas others remained similar. Cross-trait meta-analysis identified 9 independent shared loci between RA and CVD, including PTPN22 at chr1p13.2, BCL2L11 at chr2q13, and CCR3 at chr3p21.31 (Psingle trait<1×10-3 and Pmeta<5×10-8), highlighting potential shared pathogenesis including accelerating atherosclerosis, upregulating oxidative stress, and vascular permeability. Finally, Mendelian randomization estimates showed limited evidence of causality between RA and CVD. Conclusions Our results supported shared genetic pathogenesis rather than causality in explaining the observed association between RA and CVD. The identified shared genetic factors provided insights into potential novel therapeutic target for RA-CVD comorbidities.
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Affiliation(s)
- Yanjun Guo
- Department of Occupational and Environmental Health, School of Public HealthTongji Medical College, Huazhong University of Science and TechnologyWuhanChina
- Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
- Division of Preventive MedicineBrigham and Women’s HospitalBostonMA
- Harvard Medical SchoolBostonMA
| | - Wonil Chung
- Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
- Department of Statistics and Actuarial ScienceSoongsil UniversitySeoulKorea
| | - Zhilei Shan
- Department of NutritionHarvard T.H. Chan School of Public HealthBostonMA
| | - Zhaozhong Zhu
- Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
| | - Karen H. Costenbader
- Division of Preventive MedicineBrigham and Women’s HospitalBostonMA
- Division of Rheumatology, Inflammation and Immunity, Department of MedicineBrigham and Women’s HospitalBostonMA
| | - Liming Liang
- Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMAUSA
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMA
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21
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Ratajczak F, Joblin M, Hildebrandt M, Ringsquandl M, Falter-Braun P, Heinig M. Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases. Nat Commun 2023; 14:7206. [PMID: 37938585 PMCID: PMC10632370 DOI: 10.1038/s41467-023-42975-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 10/27/2023] [Indexed: 11/09/2023] Open
Abstract
Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.
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Affiliation(s)
- Florin Ratajczak
- Institute of Network Biology (INET), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Munich, Neuherberg, Germany
| | | | | | | | - Pascal Falter-Braun
- Institute of Network Biology (INET), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Munich, Neuherberg, Germany.
- Microbe-Host Interactions, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
| | - Matthias Heinig
- Institute of Computational Biology (ICB), Helmholtz Munich, Neuherberg, Germany.
- Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- German Centre for Cardiovascular Research (DZHK), Munich Heart Association, Partner Site Munich, Berlin, Germany.
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22
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Chan TF, Rui X, Conti DV, Fornage M, Graff M, Haessler J, Haiman C, Highland HM, Jung SY, Kenny EE, Kooperberg C, Le Marchand L, North KE, Tao R, Wojcik G, Gignoux CR, Chiang CWK, Mancuso N. Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics. Am J Hum Genet 2023; 110:1853-1862. [PMID: 37875120 PMCID: PMC10645552 DOI: 10.1016/j.ajhg.2023.09.012] [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: 04/18/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 10/26/2023] Open
Abstract
The heritability explained by local ancestry markers in an admixed population (hγ2) provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of hγ2 can be susceptible to biases due to population structure in ancestral populations. Here, we present heritability estimation from admixture mapping summary statistics (HAMSTA), an approach that uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA hγ2 estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ∼5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe hˆγ2 in the 20 phenotypes range from 0.0025 to 0.033 (mean hˆγ2 = 0.012 ± 9.2 × 10-4), which translates to hˆ2 ranging from 0.062 to 0.85 (mean hˆ2 = 0.30 ± 0.023). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 ± 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.
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Affiliation(s)
- Tsz Fung Chan
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinyue Rui
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christopher Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Su Yon Jung
- Translational Sciences Section, School of Nursing, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Genevieve Wojcik
- Department of Epidemiology, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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23
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Patel AP, Fahed AC. Pragmatic Approach to Applying Polygenic Risk Scores to Diverse Populations. Curr Protoc 2023; 3:e911. [PMID: 37921506 DOI: 10.1002/cpz1.911] [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] [Indexed: 11/04/2023]
Abstract
Polygenic risk scores (PRS) estimate genetic susceptibility of an individual to disease and have the potential of providing utility in multiple clinical contexts. However, their performance, computation, and reporting in diverse populations remain challenging. Here, we present a pragmatic approach to optimize a PRS for a population of interest that leverages publicly available data and methods and consists of seven steps that are easily implemented without the requirement of expertise in complex genetics: step 1, selecting source genome-wide association studies (GWAS) and imputation; step 2, selecting methods to compute polygenic score; step 3, adjusting scores using principal components of genetic ancestry; step 4, selecting the best performing score; step 5, defining percentiles of a population distribution; step 6, validating performance of the optimized polygenic score; and step 7, implementing the optimized polygenic score in clinical practice. © 2023 Wiley Periodicals LLC.
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Affiliation(s)
- Aniruddh P Patel
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Akl C Fahed
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
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Cho Y, Lin K, Lee SH, Yu C, Valle DS, Avery D, Lv J, Jung K, Li L, Smith GD, China Kadoorie Biobank Collaborative Group, Sun D, Chen Z, Millwood IY, Hemani G, Walters RG. Genetic influences on alcohol flushing in East Asian populations. BMC Genomics 2023; 24:638. [PMID: 37875790 PMCID: PMC10594868 DOI: 10.1186/s12864-023-09721-7] [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: 05/26/2023] [Accepted: 10/06/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Although it is known that variation in the aldehyde dehydrogenase 2 (ALDH2) gene family influences the East Asian alcohol flushing response, knowledge about other genetic variants that affect flushing symptoms is limited. METHODS We performed a genome-wide association study meta-analysis and heritability analysis of alcohol flushing in 15,105 males of East Asian ancestry (Koreans and Chinese) to identify genetic associations with alcohol flushing. We also evaluated whether self-reported flushing can be used as an instrumental variable for alcohol intake. RESULTS We identified variants in the region of ALDH2 strongly associated with alcohol flushing, replicating previous studies conducted in East Asian populations. Additionally, we identified variants in the alcohol dehydrogenase 1B (ADH1B) gene region associated with alcohol flushing. Several novel variants were identified after adjustment for the lead variants (ALDH2-rs671 and ADH1B-rs1229984), which need to be confirmed in larger studies. The estimated SNP-heritability on the liability scale was 13% (S.E. = 4%) for flushing, but the heritability estimate decreased to 6% (S.E. = 4%) when the effects of the lead variants were controlled for. Genetic instrumentation of higher alcohol intake using these variants recapitulated known associations of alcohol intake with hypertension. Using self-reported alcohol flushing as an instrument gave a similar association pattern of higher alcohol intake and cardiovascular disease-related traits (e.g. stroke). CONCLUSION This study confirms that ALDH2-rs671 and ADH1B-rs1229984 are associated with alcohol flushing in East Asian populations. Our findings also suggest that self-reported alcohol flushing can be used as an instrumental variable in future studies of alcohol consumption.
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Affiliation(s)
- Yoonsu Cho
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Su-Hyun Lee
- Department of Epidemiology and Health Promotion, Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Dan Schmidt Valle
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Keumji Jung
- Department of Epidemiology and Health Promotion, Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, UK
| | | | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- MRC Population Health Research Unit, University of Oxford, Oxford, UK.
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, UK.
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- MRC Population Health Research Unit, University of Oxford, Oxford, UK.
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Urbut SM, Koyama S, Hornsby W, Bhukar R, Kheterpal S, Truong B, Selvaraj MS, Neale B, O’Donnell CJ, Peloso GM, Natarajan P. Bayesian multivariate genetic analysis improves translational insights. iScience 2023; 26:107854. [PMID: 37766997 PMCID: PMC10520309 DOI: 10.1016/j.isci.2023.107854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/15/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.
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Affiliation(s)
- Sarah M. Urbut
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Whitney Hornsby
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Rohan Bhukar
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Sumeet Kheterpal
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Buu Truong
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Margaret S. Selvaraj
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin Neale
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
- Analytic Translational and Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Christopher J. O’Donnell
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
- VA Boston Department of Veterans Affairs, Boston, MA 02130, USA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02218, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
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26
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Lee SM, Shivakumar M, Xiao B, Jung SH, Nam Y, Yun JS, Choe EK, Jung YM, Oh S, Park JS, Jun JK, Kim D. Genome-wide polygenic risk scores for hypertensive disease during pregnancy can also predict the risk for long-term cardiovascular disease. Am J Obstet Gynecol 2023; 229:298.e1-298.e19. [PMID: 36933686 PMCID: PMC10504416 DOI: 10.1016/j.ajog.2023.03.013] [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: 10/31/2022] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND Hypertensive disorders during pregnancy are associated with the risk of long-term cardiovascular disease after pregnancy, but it has not yet been determined whether genetic predisposition for hypertensive disorders during pregnancy can predict the risk for long-term cardiovascular disease. OBJECTIVE This study aimed to evaluate the risk for long-term atherosclerotic cardiovascular disease according to polygenic risk scores for hypertensive disorders during pregnancy. STUDY DESIGN Among UK Biobank participants, we included European-descent women (n=164,575) with at least 1 live birth. Participants were divided according to genetic risk categorized by polygenic risk scores for hypertensive disorders during pregnancy (low risk, score ≤25th percentile; medium risk, score 25th∼75th percentile; high risk, score >75th percentile), and were evaluated for incident atherosclerotic cardiovascular disease, defined as the new occurrence of one of the following: coronary artery disease, myocardial infarction, ischemic stroke, or peripheral artery disease. RESULTS Among the study population, 2427 (1.5%) had a history of hypertensive disorders during pregnancy, and 8942 (5.6%) developed incident atherosclerotic cardiovascular disease after enrollment. Women with high genetic risk for hypertensive disorders during pregnancy had a higher prevalence of hypertension at enrollment. After enrollment, women with high genetic risk for hypertensive disorders during pregnancy had an increased risk for incident atherosclerotic cardiovascular disease, including coronary artery disease, myocardial infarction, and peripheral artery disease, compared with those with low genetic risk, even after adjustment for history of hypertensive disorders during pregnancy. CONCLUSION High genetic risk for hypertensive disorders during pregnancy was associated with increased risk for atherosclerotic cardiovascular disease. This study provides evidence on the informative value of polygenic risk scores for hypertensive disorders during pregnancy in prediction of long-term cardiovascular outcomes later in life.
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Affiliation(s)
- Seung Mi Lee
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Brenda Xiao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jae-Seung Yun
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Department of Internal Medicine, Catholic University of Korea School of Medicine, Seoul, Korea
| | - Eun Kyung Choe
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Young Mi Jung
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Sohee Oh
- Department of Biostatistics, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Joong Shin Park
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Jong Kwan Jun
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
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27
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Sweeny AR, Lemon H, Ibrahim A, Watt KA, Wilson K, Childs DZ, Nussey DH, Free A, McNally L. A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance. mSystems 2023; 8:e0004023. [PMID: 37489890 PMCID: PMC10469806 DOI: 10.1128/msystems.00040-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/08/2023] [Indexed: 07/26/2023] Open
Abstract
Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.
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Affiliation(s)
- Amy R. Sweeny
- Institute of Ecology & Evolution, University of Edinburgh, Edinburgh, United Kingdom
- School of Biosciences, University of Sheffield, Sheffield, United Kingdom
| | - Hannah Lemon
- Institute of Ecology & Evolution, University of Edinburgh, Edinburgh, United Kingdom
| | - Anan Ibrahim
- Biochemistry and Biotechnology, Institute of Quantitative Biology, University of Edinburgh, Edinburgh, United Kingdom
| | - Kathryn A. Watt
- Institute of Ecology & Evolution, University of Edinburgh, Edinburgh, United Kingdom
| | - Kenneth Wilson
- Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom
| | - Dylan Z. Childs
- School of Biosciences, University of Sheffield, Sheffield, United Kingdom
| | - Daniel H. Nussey
- Institute of Ecology & Evolution, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew Free
- Biochemistry and Biotechnology, Institute of Quantitative Biology, University of Edinburgh, Edinburgh, United Kingdom
| | - Luke McNally
- Institute of Ecology & Evolution, University of Edinburgh, Edinburgh, United Kingdom
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28
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Wang X, Khurshid S, Choi SH, Friedman S, Weng LC, Reeder C, Pirruccello JP, Singh P, Lau ES, Venn R, Diamant N, Di Achille P, Philippakis A, Anderson CD, Ho JE, Ellinor PT, Batra P, Lubitz SA. Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:340-349. [PMID: 37278238 PMCID: PMC10524395 DOI: 10.1161/circgen.122.003808] [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: 04/30/2022] [Accepted: 04/11/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates. METHODS We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model. RESULTS In the ECG-AI GWAS, we identified 3 signals (P<5×10-8) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A. We also identified 2 novel loci near the genes VGLL2 and EXT1. In contrast, the clinical variable model prediction GWAS indicated a different genetic profile. In genetic correlation analysis, the prediction from the ECG-AI model was estimated to have a higher correlation with AF than that from the clinical variable model. CONCLUSIONS Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.
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Affiliation(s)
- Xin Wang
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Shaan Khurshid
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Samuel Friedman
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Lu-Chen Weng
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | | | - James P. Pirruccello
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Pulkit Singh
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Emily S. Lau
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Rachael Venn
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Nate Diamant
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Paolo Di Achille
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Anthony Philippakis
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
- Eric & Wendy Schmidt Ctr, The Broad Institute of MIT & Harvard, Cambridge
| | - Christopher D. Anderson
- Dept of Neurology, Brigham and Women’s Hospital
- Ctr for Genomic Medicine, Massachusetts General Hospital, Boston
- Henry & Allison McCance Ctr for Brain Health, Massachusetts General Hospital, Boston
| | - Jennifer E. Ho
- CardioVascular Institute & Division of Cardiology, Dept of Medicine, Beth Israel Deaconess Medical Ctr, Boston, MA
| | - Patrick T. Ellinor
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Ctr for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
| | - Puneet Batra
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Steven A. Lubitz
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Ctr for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
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29
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Gómez-Vecino A, Corchado-Cobos R, Blanco-Gómez A, García-Sancha N, Castillo-Lluva S, Martín-García A, Mendiburu-Eliçabe M, Prieto C, Ruiz-Pinto S, Pita G, Velasco-Ruiz A, Patino-Alonso C, Galindo-Villardón P, Vera-Pedrosa ML, Jalife J, Mao JH, Macías de Plasencia G, Castellanos-Martín A, Sáez-Freire MDM, Fraile-Martín S, Rodrigues-Teixeira T, García-Macías C, Galvis-Jiménez JM, García-Sánchez A, Isidoro-García M, Fuentes M, García-Cenador MB, García-Criado FJ, García-Hernández JL, Hernández-García MÁ, Cruz-Hernández JJ, Rodríguez-Sánchez CA, García-Sancho AM, Pérez-López E, Pérez-Martínez A, Gutiérrez-Larraya F, Cartón AJ, García-Sáenz JÁ, Patiño-García A, Martín M, Alonso-Gordoa T, Vulsteke C, Croes L, Hatse S, Van Brussel T, Lambrechts D, Wildiers H, Chang H, Holgado-Madruga M, González-Neira A, Sánchez PL, Pérez Losada J. Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk. Cells 2023; 12:1956. [PMID: 37566035 PMCID: PMC10417374 DOI: 10.3390/cells12151956] [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: 04/17/2023] [Revised: 07/14/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.
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Affiliation(s)
- Aurora Gómez-Vecino
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Roberto Corchado-Cobos
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Adrián Blanco-Gómez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Natalia García-Sancha
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Sonia Castillo-Lluva
- Departamento de Bioquímica y Biología Molecular, Facultad de Ciencias Químicas, Universidad Complutense, 28040 Madrid, Spain;
- Instituto de Investigaciones Sanitarias San Carlos (IdISSC), 24040 Madrid, Spain
| | - Ana Martín-García
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca (CIBER.CV), 37007 Salamanca, Spain
| | - Marina Mendiburu-Eliçabe
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Carlos Prieto
- Servicio de Bioinformática, Nucleus, Universidad de Salamanca, 37007 Salamanca, Spain;
| | - Sara Ruiz-Pinto
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; (S.R.-P.); (G.P.); (A.V.-R.)
| | - Guillermo Pita
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; (S.R.-P.); (G.P.); (A.V.-R.)
| | - Alejandro Velasco-Ruiz
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; (S.R.-P.); (G.P.); (A.V.-R.)
| | - Carmen Patino-Alonso
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Estadística, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Purificación Galindo-Villardón
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Estadística, Universidad de Salamanca, 37007 Salamanca, Spain
- Escuela Superior Politécnica del Litoral, ESPOL, Centro de Estudios e Investigaciones Estadísticas, Campus Gustavo Galindo, Km. 30.5 Via Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador
| | | | - José Jalife
- Centro Nacional de Investigaciones Cardiovasculares (CNIC) Carlos III, 28029 Madrid, Spain; (M.L.V.-P.); (J.J.)
| | - Jian-Hua Mao
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 92720, USA
| | - Guillermo Macías de Plasencia
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca (CIBER.CV), 37007 Salamanca, Spain
| | - Andrés Castellanos-Martín
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - María del Mar Sáez-Freire
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Susana Fraile-Martín
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, 37007 Salamanca, Spain; (S.F.-M.); (T.R.-T.); (C.G.-M.)
| | - Telmo Rodrigues-Teixeira
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, 37007 Salamanca, Spain; (S.F.-M.); (T.R.-T.); (C.G.-M.)
| | - Carmen García-Macías
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, 37007 Salamanca, Spain; (S.F.-M.); (T.R.-T.); (C.G.-M.)
| | - Julie Milena Galvis-Jiménez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Instituto Nacional de Cancerología de Colombia, Bogotá 111511-110411001, Colombia
| | - Asunción García-Sánchez
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Bioquímica Clínica, Hospital Universitario de Salamanca, 37007 Salamanca, Spain
| | - María Isidoro-García
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Bioquímica Clínica, Hospital Universitario de Salamanca, 37007 Salamanca, Spain
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Manuel Fuentes
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
- Unidad de Proteómica y Servicio General de Citometría de Flujo, Nucleus, Universidad de Salamanca, 37007 Salamanca, Spain
| | - María Begoña García-Cenador
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Cirugía, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Francisco Javier García-Criado
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Cirugía, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Juan Luis García-Hernández
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | | | - Juan Jesús Cruz-Hernández
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
- Servicio de Oncología, Hospital Universitario de Salamanca, 37007 Salamanca, Spain
| | - César Augusto Rodríguez-Sánchez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
- Servicio de Oncología, Hospital Universitario de Salamanca, 37007 Salamanca, Spain
| | - Alejandro Martín García-Sancho
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Hematología, Hospital Universitario de Salamanca, CIBERONC, 37007 Salamanca, Spain;
| | - Estefanía Pérez-López
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Hematología, Hospital Universitario de Salamanca, CIBERONC, 37007 Salamanca, Spain;
| | - Antonio Pérez-Martínez
- Department of Paediatric Hemato-Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain;
| | - Federico Gutiérrez-Larraya
- Department of Paediatric Cardiology, Hospital Universitario La Paz, 28046 Madrid, Spain; (F.G.-L.); (A.J.C.)
| | - Antonio J. Cartón
- Department of Paediatric Cardiology, Hospital Universitario La Paz, 28046 Madrid, Spain; (F.G.-L.); (A.J.C.)
| | - José Ángel García-Sáenz
- Medical Oncology Service, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Hospital Clínico San Carlos, 28040 Madrid, Spain;
| | - Ana Patiño-García
- Department of Pediatrics, Solid Tumor Program, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, IdisNA, 31008 Pamplona, Spain;
| | - Miguel Martín
- Department of Medicine, Gregorio Marañón Health Research Institute (IISGM), Centro de Investigación Biomédica en Red Oncológica (CIBERONC), Universidad Complutense, 28007 Madrid, Spain;
| | - Teresa Alonso-Gordoa
- Department of Medical Oncology, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain;
| | - Christof Vulsteke
- Department of Molecular Imaging, Pathology, Radiotherapy and Oncology (MIPRO), Center for Oncological Research (CORE), Antwerp University, 2610 Antwerp, Belgium; (C.V.); (L.C.)
- Department of Oncology, Integrated Cancer Center in Ghent, AZ Maria Middelares, 9000 Ghent, Belgium
| | - Lieselot Croes
- Department of Molecular Imaging, Pathology, Radiotherapy and Oncology (MIPRO), Center for Oncological Research (CORE), Antwerp University, 2610 Antwerp, Belgium; (C.V.); (L.C.)
- Department of Oncology, Integrated Cancer Center in Ghent, AZ Maria Middelares, 9000 Ghent, Belgium
| | - Sigrid Hatse
- Laboratory of Experimental Oncology (LEO), Department of Oncology, Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium;
| | - Thomas Van Brussel
- VIB Center for Cancer Biology, VIB, 3000 Leuven, Belgium; (T.V.B.); (D.L.)
- Laboratory of Translational Genetics, Department of Human Genetics, Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium
| | - Diether Lambrechts
- VIB Center for Cancer Biology, VIB, 3000 Leuven, Belgium; (T.V.B.); (D.L.)
- Laboratory of Translational Genetics, Department of Human Genetics, Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium
| | - Hans Wildiers
- Department of General Medical Oncology and Multidisciplinary Breast Unit, Leuven Cancer Institute, and Laboratory of Experimental Oncology (LEO), Department of Oncology, Leuven Cancer Institute and University Hospital Leuven, Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium;
| | - Hang Chang
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 92720, USA
| | - Marina Holgado-Madruga
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Fisiología y Farmacología, Universidad de Salamanca, 37007 Salamanca, Spain
- Instituto de Neurociencias de Castilla y León (INCyL), 37007 Salamanca, Spain
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; (S.R.-P.); (G.P.); (A.V.-R.)
| | - Pedro L. Sánchez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca (CIBER.CV), 37007 Salamanca, Spain
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Jesús Pérez Losada
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
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De Lillo A, Wendt FR, Pathak GA, Polimanti R. Characterizing the polygenic architecture of complex traits in populations of East Asian and European descent. Hum Genomics 2023; 17:67. [PMID: 37475089 PMCID: PMC10360343 DOI: 10.1186/s40246-023-00514-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 07/14/2023] [Indexed: 07/22/2023] Open
Abstract
To investigate the polygenicity of complex traits in populations of East Asian (EAS) and European (EUR) descents, we leveraged genome-wide data from Biobank Japan, UK Biobank, and FinnGen cohorts. Specifically, we analyzed up to 215 outcomes related to 18 health domains, assessing their polygenic architecture via descriptive statistics, such as the proportion of susceptibility SNPs per trait (πc). While we did not observe EAS-EUR differences in the overall distribution of polygenicity parameters across the phenotypes investigated, there were ancestry-specific patterns in the polygenicity differences between health domains. In EAS, pairwise comparisons across health domains showed enrichment for πc differences related to hematological and metabolic traits (hematological fold-enrichment = 4.45, p = 2.15 × 10-7; metabolic fold-enrichment = 4.05, p = 4.01 × 10-6). For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains (EAS-hematological median πc = 0.15%, EAS-metabolic median πc = 0.18%) with the strongest πc difference with respect to respiratory traits (EAS-respiratory median πc = 0.50%; hematological-p = 2.26 × 10-3; metabolic-p = 3.48 × 10-3). In EUR, pairwise comparisons showed multiple πc differences related to the endocrine category (fold-enrichment = 5.83, p = 4.76 × 10-6), where these traits showed a low proportion of susceptibility SNPs (EUR-endocrine median πc = 0.01%) with the strongest difference with respect to psychiatric phenotypes (EUR-psychiatric median πc = 0.50%; p = 1.19 × 10-4). Simulating sample sizes of 1,000,000 and 5,000,000 individuals, we also showed that ancestry-specific polygenicity patterns translate into differences across health domains in the genetic variance explained by susceptibility SNPs projected to be genome-wide significant (e.g., EAS hematological-neoplasm p = 2.18 × 10-4; EUR endocrine-gastrointestinal p = 6.80 × 10-4). These findings highlight that traits related to the same health domains may present ancestry-specific variability in their polygenicity.
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Affiliation(s)
- Antonella De Lillo
- Department of Psychiatry, Yale University School of Medicine, 60 Temple, Suite 7A, New Haven, CT, 06510, USA
- Department of Biology, University of Rome "Tor Vergata", Rome, Italy
| | - Frank R Wendt
- Department of Psychiatry, Yale University School of Medicine, 60 Temple, Suite 7A, New Haven, CT, 06510, USA
- Department of Anthropology, University of Toronto, Mississauga, ON, Canada
- Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, 60 Temple, Suite 7A, New Haven, CT, 06510, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, 60 Temple, Suite 7A, New Haven, CT, 06510, USA.
- VA CT Healthcare Center, West Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
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Li B, Wang Y, Wang Z, Li X, Kay S, Chupp GL, Zhao H, Gomez JL. Shared genetic architecture of blood eosinophil counts and asthma in UK Biobank. ERJ Open Res 2023; 9:00291-2023. [PMID: 37650091 PMCID: PMC10463033 DOI: 10.1183/23120541.00291-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/07/2023] [Indexed: 09/01/2023] Open
Abstract
Rationale Asthma is a complex, heterogeneous disease strongly associated with type 2 inflammation, and blood eosinophil counts guide therapeutic interventions in moderate and severe asthma. Eosinophils are leukocytes involved in type 2 immune responses. Despite these critical associations between asthma and blood eosinophil counts, the shared genetic architecture of these two traits remains unknown. The objective of the present study was to characterise the genetic architecture of blood eosinophil counts and asthma in the UK Biobank. Methods We performed genome-wide association studies (GWAS) of doctor-diagnosed asthma, blood eosinophil, neutrophil, lymphocyte and monocyte counts in the UK Biobank. Genetic correlation analysis was performed on GWAS results and validated in the Trans-National Asthma Genetic Consortium (TAGC) study of asthma. Results GWAS of doctor-diagnosed asthma and blood eosinophil counts in the UK Biobank identified 585 and 3429 significant variants, respectively. STAT6, a transcription factor involved in interleukin-4 signalling, was a key shared pathway between asthma and blood eosinophil counts. Genetic correlation analysis demonstrated a positive correlation between doctor-diagnosed asthma and blood eosinophil counts (r=0.38±0.10, correlation±se; p=4.7×10-11). As a validation of this association, we found a similar correlation between TAGC and blood eosinophil counts in the UK Biobank (0.37±0.08, correlation±se; p=1.2×10-6). Conclusions These findings define the shared genetic architecture between blood eosinophil counts and asthma risk in subjects of European ancestry and point to a genetic link to the STAT6 signalling pathway in these two traits.
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Affiliation(s)
- Boyang Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- These authors contributed equally to this work
| | - Yuxuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- These authors contributed equally to this work
| | - Zixiao Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Xinyue Li
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Shannon Kay
- Pulmonary, Critical Care and Sleep Medicine Section, Yale University, New Haven, CT, USA
- Center for Precision Pulmonary Medicine (P2MED), Yale University, New Haven, CT, USA
| | - Geoffrey L. Chupp
- Pulmonary, Critical Care and Sleep Medicine Section, Yale University, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- These authors share senior authorship
| | - Jose L. Gomez
- Pulmonary, Critical Care and Sleep Medicine Section, Yale University, New Haven, CT, USA
- Center for Precision Pulmonary Medicine (P2MED), Yale University, New Haven, CT, USA
- These authors share senior authorship
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Sato G, Shirai Y, Namba S, Edahiro R, Sonehara K, Hata T, Uemura M, Matsuda K, Doki Y, Eguchi H, Okada Y. Pan-cancer and cross-population genome-wide association studies dissect shared genetic backgrounds underlying carcinogenesis. Nat Commun 2023; 14:3671. [PMID: 37340002 DOI: 10.1038/s41467-023-39136-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/31/2023] [Indexed: 06/22/2023] Open
Abstract
Integrating genomic data of multiple cancers allows de novo cancer grouping and elucidating the shared genetic basis across cancers. Here, we conduct the pan-cancer and cross-population genome-wide association study (GWAS) meta-analysis and replication studies on 13 cancers including 250,015 East Asians (Biobank Japan) and 377,441 Europeans (UK Biobank). We identify ten cancer risk variants including five pleiotropic associations (e.g., rs2076295 at DSP on 6p24 associated with lung cancer and rs2525548 at TRIM4 on 7q22 nominally associated with six cancers). Quantifying shared heritability among the cancers detects positive genetic correlations between breast and prostate cancer across populations. Common genetic components increase the statistical power, and the large-scale meta-analysis of 277,896 breast/prostate cancer cases and 901,858 controls identifies 91 newly genome-wide significant loci. Enrichment analysis of pathways and cell types reveals shared genetic backgrounds across said cancers. Focusing on genetically correlated cancers can contribute to enhancing our insights into carcinogenesis.
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Affiliation(s)
- Go Sato
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Hata
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Mamoru Uemura
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Yuichiro Doki
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Hidetoshi Eguchi
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
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33
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De Lillo A, Wendt FR, Pathak GA, Polimanti R. Characterizing the polygenic architecture of complex traits in populations of East Asian and European descent. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.25.23290542. [PMID: 37398225 PMCID: PMC10312887 DOI: 10.1101/2023.05.25.23290542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
To investigate the polygenicity of complex traits in populations of East Asian (EAS) and European (EUR) descents, we leveraged genome-wide data from Biobank Japan, UK Biobank, and FinnGen cohorts. Specifically, we analyzed up to 215 outcomes related to 18 health domains, assessing their polygenic architecture via descriptive statistics, such as the proportion of susceptibility SNPs per trait (π c ). While we did not observe EAS-EUR differences in the overall distribution of polygenicity parameters across the phenotypes investigated, there were ancestry-specific patterns in the polygenicity differences between health domains. In EAS, pairwise comparisons across health domains showed enrichment for π c differences related to hematological and metabolic traits (hematological fold-enrichment=4.45, p=2.15×10 -7 ; metabolic fold-enrichment=4.05, p=4.01×10 -6 ). For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains (EAS-hematological median π c =0.15%, EAS-metabolic median π c =0.18%) with the strongest π c difference with respect to respiratory traits (EAS-respiratory median π c =0.50%; Hematological-p=2.26×10 -3 ; Metabolic-p=3.48×10 -3 ). In EUR, pairwise comparisons showed multiple π c differences related to the endocrine category (fold-enrichment=5.83, p=4.76×10 -6 ), where these traits showed a low proportion of susceptibility SNPs (EUR-endocrine median π c =0.01%) with the strongest difference with respect to psychiatric phenotypes (EUR-psychiatric median π c =0.50%; p=1.19×10 -4 ). Simulating sample sizes of 1,000,000 and 5,000,000 individuals, we also showed that ancestry-specific polygenicity patterns translate into differences across health domains in the genetic variance explained by susceptibility SNPs projected to be genome-wide significant (e.g., EAS hematological-neoplasm p=2.18×10 -4 ; EUR endocrine-gastrointestinal p=6.80×10 -4 ). These findings highlight that traits related to the same health domains may present ancestry-specific variability in their polygenicity.
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34
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Ortiz-Fernández L, Carmona EG, Kerick M, Lyons P, Carmona FD, López Mejías R, Khor CC, Grayson PC, Tombetti E, Jiang L, Direskeneli H, Saruhan-Direskeneli G, Callejas-Rubio JL, Vaglio A, Salvarani C, Hernández-Rodríguez J, Cid MC, Morgan AW, Merkel PA, Burgner D, Smith KG, Gonzalez-Gay MA, Sawalha AH, Martin J, Marquez A. Identification of new risk loci shared across systemic vasculitides points towards potential target genes for drug repurposing. Ann Rheum Dis 2023; 82:837-847. [PMID: 36797040 PMCID: PMC10314028 DOI: 10.1136/ard-2022-223697] [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: 11/30/2022] [Accepted: 02/04/2023] [Indexed: 02/18/2023]
Abstract
OBJECTIVES The number of susceptibility loci currently associated with vasculitis is lower than in other immune-mediated diseases due in part to small cohort sizes, a consequence of the low prevalence of vasculitides. This study aimed to identify new genetic risk loci for the main systemic vasculitides through a comprehensive analysis of their genetic overlap. METHODS Genome-wide data from 8467 patients with any of the main forms of vasculitis and 29 795 healthy controls were meta-analysed using ASSET. Pleiotropic variants were functionally annotated and linked to their target genes. Prioritised genes were queried in DrugBank to identify potentially repositionable drugs for the treatment of vasculitis. RESULTS Sixteen variants were independently associated with two or more vasculitides, 15 of them representing new shared risk loci. Two of these pleiotropic signals, located close to CTLA4 and CPLX1, emerged as novel genetic risk loci in vasculitis. Most of these polymorphisms appeared to affect vasculitis by regulating gene expression. In this regard, for some of these common signals, potential causal genes were prioritised based on functional annotation, including CTLA4, RNF145, IL12B, IL5, IRF1, IFNGR1, PTK2B, TRIM35, EGR2 and ETS2, each of which has key roles in inflammation. In addition, drug repositioning analysis showed that several drugs, including abatacept and ustekinumab, could be potentially repurposed in the management of the analysed vasculitides. CONCLUSIONS We identified new shared risk loci with functional impact in vasculitis and pinpointed potential causal genes, some of which could represent promising targets for the treatment of vasculitis.
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Affiliation(s)
| | - Elio G Carmona
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
- Unidad de Enfermedades Autoinmunes Sistémicas, Hospital Clínico San Cecilio, Instituto de Investigación Biosanitaria de Granada ibs.GRANADA, Granada, Spain
| | - Martin Kerick
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
| | - Paul Lyons
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Francisco David Carmona
- Departamento de Genética e Instituto de Biotecnología, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Raquel López Mejías
- Research Group on Genetic Epidemiology and Atherosclerosis in Systemic Diseases and in Metabolic Bone Diseases of the Musculoskeletal System, IDIVAL, Santander, Spain
| | - Chiea Chuen Khor
- Duke-NUS Medical School, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Peter C Grayson
- Systemic Autoimmunity Branch, NIAMS, National Institutes of Health, Bethesda, Maryland, USA
| | - Enrico Tombetti
- Division of Immunology, Transplantation and Infectious Diseases, San Raffaele Hospital, Milan, Italy
- Department of Biomedical and Clinical Sciences L. Sacco, Milan University, Milan, Italy
| | - Lindi Jiang
- Department of Rheumatology, Zhongshan Hospital, Fudan University, Shanghai, China
- Evidence-Based Medicine Center, Fudan University, Shanghai, China
| | - Haner Direskeneli
- Department of Internal Medicine, Division of Rheumatology, Marmara University, Faculty of Medicine, Istanbul, Turkey
| | | | - José-Luis Callejas-Rubio
- Systemic Autoimmune Diseases Unit, San Cecilio University Hospital, Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Augusto Vaglio
- Department of Biomedical Experimental and Clinical Sciences "Mario Serio", University of Florence, Florence, Italy
- Nephrology and Dialysis Unit, Meyer Children's Hospital, Florence, Italy
| | - Carlo Salvarani
- Rheumatology Unit, Azienda USL-IRCCS di Reggio Emilia and Azienda Ospedaliero - Universitaria di Modena, Università di Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Jose Hernández-Rodríguez
- Department of Autoimmune Diseases, Hospital Clinic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Maria Cinta Cid
- Department of Autoimmune Diseases, Hospital Clinic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Ann W Morgan
- School of Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds Medtech and In vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Peter A Merkel
- Division of Rheumatology, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Division of Epidemiology, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David Burgner
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Department of General Medicine, Royal Children's Hospital, Parkville, Victoria, Australia
- Department of Paediatrics, Monash University, Clayton, Victoria, Australia
| | - Kenneth Gc Smith
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Miguel Angel Gonzalez-Gay
- Research Group on Genetic Epidemiology and Atherosclerosis in Systemic Diseases and in Metabolic Bone Diseases of the Musculoskeletal System, IDIVAL, University of Cantabria, Santander, Spain
| | - Amr H Sawalha
- Division of Rheumatology, Department of Pediatrics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Lupus Center of Excellence, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Javier Martin
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
| | - Ana Marquez
- Institute of Parasitology and Biomedicine "López- Neyra", CSIC, Granada, Spain
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35
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Zhang BC, Biddanda A, Gunnarsson ÁF, Cooper F, Palamara PF. Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits. Nat Genet 2023; 55:768-776. [PMID: 37127670 PMCID: PMC10181934 DOI: 10.1038/s41588-023-01379-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 03/22/2023] [Indexed: 05/03/2023]
Abstract
Genome-wide genealogies compactly represent the evolutionary history of a set of genomes and inferring them from genetic data has the potential to facilitate a wide range of analyses. We introduce a method, ARG-Needle, for accurately inferring biobank-scale genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies to perform association and other complex trait analyses. We use these methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and test for association across seven complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 134, frequency range 0.0007-0.1%) than genotype imputation using ~65,000 sequenced haplotypes (N = 64). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants enriched (4.8×) for loss-of-function variation. These results demonstrate that inferred genome-wide genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.
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Affiliation(s)
- Brian C Zhang
- Department of Statistics, University of Oxford, Oxford, UK
| | - Arjun Biddanda
- Department of Statistics, University of Oxford, Oxford, UK
| | - Árni Freyr Gunnarsson
- Department of Statistics, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Fergus Cooper
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Pier Francesco Palamara
- Department of Statistics, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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36
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Nauffal V, Di Achille P, Klarqvist MDR, Cunningham JW, Hill MC, Pirruccello JP, Weng LC, Morrill VN, Choi SH, Khurshid S, Friedman SF, Nekoui M, Roselli C, Ng K, Philippakis AA, Batra P, Ellinor PT, Lubitz SA. Genetics of myocardial interstitial fibrosis in the human heart and association with disease. Nat Genet 2023; 55:777-786. [PMID: 37081215 PMCID: PMC11107861 DOI: 10.1038/s41588-023-01371-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/13/2023] [Indexed: 04/22/2023]
Abstract
Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor β1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.
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Affiliation(s)
- Victor Nauffal
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jonathan W Cunningham
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew C Hill
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - James P Pirruccello
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Lu-Chen Weng
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Valerie N Morrill
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel F Friedman
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mahan Nekoui
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Carolina Roselli
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Anthony A Philippakis
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
| | - Steven A Lubitz
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
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37
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Chan TF, Rui X, Conti DV, Fornage M, Graff M, Haessler J, Haiman C, Highland HM, Jung SY, Kenny E, Kooperberg C, Marchland LL, North KE, Tao R, Wojcik G, Gignoux CR, Chiang CWK, Mancuso N. Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.10.536252. [PMID: 37131817 PMCID: PMC10153181 DOI: 10.1101/2023.04.10.536252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The heritability explained by local ancestry markers in an admixed population h γ 2 provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of h γ 2 can be susceptible to biases due to population structure in ancestral populations. Here, we present a novel approach, Heritability estimation from Admixture Mapping Summary STAtistics (HAMSTA), which uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA h γ 2 estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ~5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe h ˆ γ 2 in the 20 phenotypes range from 0.0025 to 0.033 (mean h ˆ γ 2 = 0.012 + / - 9.2 × 10 - 4 ), which translates to h ˆ 2 ranging from 0.062 to 0.85 (mean h ˆ 2 = 0.30 + / - 0.023 ). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 +/- 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.
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Affiliation(s)
- Tsz Fung Chan
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Xinyue Rui
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christopher Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Su Yon Jung
- Translational Sciences Section, School of Nursing, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Eimear Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Loic Le Marchland
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Genevieve Wojcik
- Department of Epidemiology, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
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38
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Hou K, Ding Y, Xu Z, Wu Y, Bhattacharya A, Mester R, Belbin GM, Buyske S, Conti DV, Darst BF, Fornage M, Gignoux C, Guo X, Haiman C, Kenny EE, Kim M, Kooperberg C, Lange L, Manichaikul A, North KE, Peters U, Rasmussen-Torvik LJ, Rich SS, Rotter JI, Wheeler HE, Wojcik GL, Zhou Y, Sankararaman S, Pasaniuc B. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat Genet 2023; 55:549-558. [PMID: 36941441 PMCID: PMC11120833 DOI: 10.1038/s41588-023-01338-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023]
Abstract
Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93-0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
| | - Yi Ding
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Ziqi Xu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Yue Wu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Rachel Mester
- Graduate Program in Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Gillian M Belbin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - David V Conti
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Burcu F Darst
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Christopher Haiman
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michelle Kim
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Leslie Lange
- Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Kari E North
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ulrike Peters
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Heather E Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, USA
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Ying Zhou
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Sriram Sankararaman
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
- Department of Computer Science, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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39
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Jung HU, Kim DJ, Baek EJ, Chung JY, Ha TW, Kim HK, Kang JO, Lim JE, Oh B. Gene-environment interaction explains a part of missing heritability in human body mass index. Commun Biol 2023; 6:324. [PMID: 36966243 PMCID: PMC10039928 DOI: 10.1038/s42003-023-04679-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 03/07/2023] [Indexed: 03/27/2023] Open
Abstract
Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships-software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.
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Affiliation(s)
- Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Dong Jun Kim
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Eun Ju Baek
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Tae Woong Ha
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Han-Kyul Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea.
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea.
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea.
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40
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Li H, Mazumder R, Lin X. Accurate and Efficient Estimation of Local Heritability using Summary Statistics and LD Matrix. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.08.527759. [PMID: 36798290 PMCID: PMC9934676 DOI: 10.1101/2023.02.08.527759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Existing SNP-heritability estimation methods that leverage GWAS summary statistics produce estimators that are less efficient than the restricted maximum likelihood (REML) estimator using individual-level data under linear mixed models (LMMs). Increasing the precision of a heritability estimator is particularly important for regional analyses, as local genetic variances tend to be small. We introduce a new estimator for local heritability, "HEELS", which attains comparable statistical efficiency as REML (\emph{i.e.} relative efficiency greater than 92%) but only requires summary-level statistics -- Z-scores from the marginal association tests plus the empirical LD matrix. HEELS significantly improves the statistical efficiency of the existing summary-statistics-based heritability estimators-- for instance, HEELS produces heritability estimates that are more than 3-fold and 7-times less variable than GRE and LDSC, respectively. Moreover, we introduce a unified framework to evaluate and compare the performance of different LD approximation strategies. We propose representing the empirical LD as the sum of a low-rank matrix and a banded matrix. This approximation not only reduces the storage and memory cost of using the LD matrix, but also improves the computational efficiency of the HEELS estimation. We demonstrate the statistical efficiency of HEELS and the advantages of our proposed LD approximation strategies both in simulations and through empirical analyses of the UK Biobank data.
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41
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Ghouse J, Tragante V, Ahlberg G, Rand SA, Jespersen JB, Leinøe EB, Vissing CR, Trudsø L, Jonsdottir I, Banasik K, Brunak S, Ostrowski SR, Pedersen OB, Sørensen E, Erikstrup C, Bruun MT, Nielsen KR, Køber L, Christensen AH, Iversen K, Jones D, Knowlton KU, Nadauld L, Halldorsson GH, Ferkingstad E, Olafsson I, Gretarsdottir S, Onundarson PT, Sulem P, Thorsteinsdottir U, Thorgeirsson G, Gudbjartsson DF, Stefansson K, Holm H, Olesen MS, Bundgaard H. Genome-wide meta-analysis identifies 93 risk loci and enables risk prediction equivalent to monogenic forms of venous thromboembolism. Nat Genet 2023; 55:399-409. [PMID: 36658437 DOI: 10.1038/s41588-022-01286-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/13/2022] [Indexed: 01/21/2023]
Abstract
We report a genome-wide association study of venous thromboembolism (VTE) incorporating 81,190 cases and 1,419,671 controls sampled from six cohorts. We identify 93 risk loci, of which 62 are previously unreported. Many of the identified risk loci are at genes encoding proteins with functions converging on the coagulation cascade or platelet function. A VTE polygenic risk score (PRS) enabled effective identification of both high- and low-risk individuals. Individuals within the top 0.1% of PRS distribution had a VTE risk similar to homozygous or compound heterozygous carriers of the variants G20210A (c.*97 G > A) in F2 and p.R534Q in F5. We also document that F2 and F5 mutation carriers in the bottom 10% of the PRS distribution had a risk similar to that of the general population. We further show that PRS improved individual risk prediction beyond that of genetic and clinical risk factors. We investigated the extent to which venous and arterial thrombosis share clinical risk factors using Mendelian randomization, finding that some risk factors for arterial thrombosis were directionally concordant with VTE risk (for example, body mass index and smoking) whereas others were discordant (for example, systolic blood pressure and triglyceride levels).
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Affiliation(s)
- Jonas Ghouse
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | | | - Gustav Ahlberg
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren A Rand
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jakob B Jespersen
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Eva Birgitte Leinøe
- Department of Hematology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Linea Trudsø
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ingileif Jonsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Iceland Department of Immunology, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Karina Banasik
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Næstved Hospital, Næstved, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Kaspar Rene Nielsen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Lars Køber
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Alex H Christensen
- Department of Cardiology, Copenhagen University Hospital, Herlev-Gentofte Hospital, Herlev, Denmark
| | - Kasper Iversen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology, Copenhagen University Hospital, Herlev-Gentofte Hospital, Herlev, Denmark
| | - David Jones
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - Lincoln Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
- Stanford University, School of Medicine, Stanford, CA, USA
| | | | | | | | | | - Pall T Onundarson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Laboratory Hematology, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Gudmundur Thorgeirsson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Department of Medicine, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Morten Salling Olesen
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henning Bundgaard
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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42
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Aung N, Lopes LR, van Duijvenboden S, Harper AR, Goel A, Grace C, Ho CY, Weintraub WS, Kramer CM, Neubauer S, Watkins HC, Petersen SE, Munroe PB. Genome-Wide Analysis of Left Ventricular Maximum Wall Thickness in the UK Biobank Cohort Reveals a Shared Genetic Background With Hypertrophic Cardiomyopathy. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e003716. [PMID: 36598836 PMCID: PMC9946169 DOI: 10.1161/circgen.122.003716] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 10/13/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND Left ventricular maximum wall thickness (LVMWT) is an important biomarker of left ventricular hypertrophy and provides diagnostic and prognostic information in hypertrophic cardiomyopathy (HCM). Limited information is available on the genetic determinants of LVMWT. METHODS We performed a genome-wide association study of LVMWT measured from the cardiovascular magnetic resonance examinations of 42 176 European individuals. We evaluated the genetic relationship between LVMWT and HCM by performing pairwise analysis using the data from the Hypertrophic Cardiomyopathy Registry in which the controls were randomly selected from UK Biobank individuals not included in the cardiovascular magnetic resonance sub-study. RESULTS Twenty-one genetic loci were discovered at P<5×10-8. Several novel candidate genes were identified including PROX1, PXN, and PTK2, with known functional roles in myocardial growth and sarcomere organization. The LVMWT genetic risk score is predictive of HCM in the Hypertrophic Cardiomyopathy Registry (odds ratio per SD: 1.18 [95% CI, 1.13-1.23]) with pairwise analyses demonstrating a moderate genetic correlation (rg=0.53) and substantial loci overlap (19/21). CONCLUSIONS Our findings provide novel insights into the genetic underpinning of LVMWT and highlight its shared genetic background with HCM, supporting future endeavours to elucidate the genetic etiology of HCM.
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Affiliation(s)
- Nay Aung
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (N.A., S.v.D., S.E.P., P.B.M.)
- National Institute for Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London (N.A., S.v.D., S.E.P., P.B.M.)
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield (N.A., L.R.L., S.E.P.)
| | - Luis R Lopes
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield (N.A., L.R.L., S.E.P.)
- Centre for Heart Muscle Disease, Institute of Cardiovascular Science, University College London (L.R.L.)
| | - Stefan van Duijvenboden
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (N.A., S.v.D., S.E.P., P.B.M.)
- National Institute for Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London (N.A., S.v.D., S.E.P., P.B.M.)
| | - Andrew R Harper
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine (A.R.H., A.G., C.G., S.N., H.C.W.)
- Wellcome Centre for Human Genetics, University of Oxford, United Kingdom (A.R.H., A.G., C.G., H.C.W.)
| | - Anuj Goel
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine (A.R.H., A.G., C.G., S.N., H.C.W.)
- Wellcome Centre for Human Genetics, University of Oxford, United Kingdom (A.R.H., A.G., C.G., H.C.W.)
| | - Christopher Grace
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine (A.R.H., A.G., C.G., S.N., H.C.W.)
- Wellcome Centre for Human Genetics, University of Oxford, United Kingdom (A.R.H., A.G., C.G., H.C.W.)
| | - Carolyn Y Ho
- Cardiovascular Division, Department of Medicine and Department of Radiology, Brigham and Women's Hospital, Boston, MA (C.Y.H.)
| | | | - Christopher M Kramer
- Cardiovascular Division, University of Virginia Health System, Charlottesville (C.M.K.)
| | - Stefan Neubauer
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine (A.R.H., A.G., C.G., S.N., H.C.W.)
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, United Kingdom (S.N., H.C.W.)
| | - Hugh C Watkins
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine (A.R.H., A.G., C.G., S.N., H.C.W.)
- Wellcome Centre for Human Genetics, University of Oxford, United Kingdom (A.R.H., A.G., C.G., H.C.W.)
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, United Kingdom (S.N., H.C.W.)
| | - Steffen E Petersen
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (N.A., S.v.D., S.E.P., P.B.M.)
- National Institute for Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London (N.A., S.v.D., S.E.P., P.B.M.)
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield (N.A., L.R.L., S.E.P.)
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (N.A., S.v.D., S.E.P., P.B.M.)
- National Institute for Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London (N.A., S.v.D., S.E.P., P.B.M.)
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Cunningham JW, Di Achille P, Morrill VN, Weng LC, Hoan Choi S, Khurshid S, Nauffal V, Pirruccello JP, Solomon SD, Batra P, Ho JE, Philippakis AA, Ellinor PT, Lubitz SA. Machine Learning to Understand Genetic and Clinical Factors Associated With the Pulse Waveform Dicrotic Notch. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e003676. [PMID: 36580284 PMCID: PMC9975074 DOI: 10.1161/circgen.121.003676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 09/30/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Absence of a dicrotic notch on finger photoplethysmography is an easily ascertainable and inexpensive trait that has been associated with age and prevalent cardiovascular disease. However, the trait exists along a continuum, and little is known about its genetic underpinnings or prognostic value for incident cardiovascular disease. METHODS In 169 787 participants in the UK Biobank, we identified absent dicrotic notch on photoplethysmography and created a novel continuous trait reflecting notch smoothness using machine learning. Next, we determined the heritability, genetic basis, polygenic risk, and clinical relations for the binary absent notch trait and the newly derived continuous notch smoothness trait. RESULTS Heritability of the continuous notch smoothness trait was 7.5%, compared with 5.6% for the binary absent notch trait. A genome-wide association study of notch smoothness identified 15 significant loci, implicating genes including NT5C2 (P=1.2×10-26), IGFBP3 (P=4.8×10-18), and PHACTR1 (P=1.4×10-13), compared with 6 loci for the binary absent notch trait. Notch smoothness stratified risk of incident myocardial infarction or coronary artery disease, stroke, heart failure, and aortic stenosis. A polygenic risk score for notch smoothness was associated with incident cardiovascular disease and all-cause death in UK Biobank participants without available photoplethysmography data. CONCLUSIONS We found that a machine learning derived continuous trait reflecting dicrotic notch smoothness on photoplethysmography was heritable and associated with genes involved in vascular stiffness. Greater notch smoothness was associated with greater risk of incident cardiovascular disease. Raw digital phenotyping may identify individuals at risk for disease via specific genetic pathways.
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Affiliation(s)
- Jonathan W. Cunningham
- Cardiovascular Division, Brigham & Women’s Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Paolo Di Achille
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Valerie N. Morrill
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Lu-Chen Weng
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Cardiovascular Research Center, Massachusetts General Hospital
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital
| | - Victor Nauffal
- Cardiovascular Division, Brigham & Women’s Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - James P Pirruccello
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital
| | | | - Puneet Batra
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Jennifer E. Ho
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | | | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Cardiovascular Research Center, Massachusetts General Hospital
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Cardiovascular Research Center, Massachusetts General Hospital
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital
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44
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Weiner DJ, Nadig A, Jagadeesh KA, Dey KK, Neale BM, Robinson EB, Karczewski KJ, O'Connor LJ. Polygenic architecture of rare coding variation across 394,783 exomes. Nature 2023; 614:492-499. [PMID: 36755099 PMCID: PMC10614218 DOI: 10.1038/s41586-022-05684-z] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/22/2022] [Indexed: 02/10/2023]
Abstract
Both common and rare genetic variants influence complex traits and common diseases. Genome-wide association studies have identified thousands of common-variant associations, and more recently, large-scale exome sequencing studies have identified rare-variant associations in hundreds of genes1-3. However, rare-variant genetic architecture is not well characterized, and the relationship between common-variant and rare-variant architecture is unclear4. Here we quantify the heritability explained by the gene-wise burden of rare coding variants across 22 common traits and diseases in 394,783 UK Biobank exomes5. Rare coding variants (allele frequency < 1 × 10-3) explain 1.3% (s.e. = 0.03%) of phenotypic variance on average-much less than common variants-and most burden heritability is explained by ultrarare loss-of-function variants (allele frequency < 1 × 10-5). Common and rare variants implicate the same cell types, with similar enrichments, and they have pleiotropic effects on the same pairs of traits, with similar genetic correlations. They partially colocalize at individual genes and loci, but not to the same extent: burden heritability is strongly concentrated in significant genes, while common-variant heritability is more polygenic, and burden heritability is also more strongly concentrated in constrained genes. Finally, we find that burden heritability for schizophrenia and bipolar disorder6,7 is approximately 2%. Our results indicate that rare coding variants will implicate a tractable number of large-effect genes, that common and rare associations are mechanistically convergent, and that rare coding variants will contribute only modestly to missing heritability and population risk stratification.
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Affiliation(s)
- Daniel J Weiner
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Ajay Nadig
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Karthik A Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elise B Robinson
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luke J O'Connor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Liu A, Genovese G, Zhao Y, Pirinen M, Zekavat MM, Kentistou K, Yang Z, Yu K, Vlasschaert C, Liu X, Brown DW, Hudjashov G, Gorman B, Dennis J, Zhou W, Momozawa Y, Pyarajan S, Tuzov V, Pajuste FD, Aavikko M, Sipilä TP, Ghazal A, Huang WY, Freedman N, Song L, Gardner EJ, Sankaran VG, Palotie A, Ollila HM, Tukiainen T, Chanock SJ, Mägi R, Natarajan P, Daly MJ, Bick A, McCarroll SA, Terao C, Loh PR, Ganna A, Perry JRB, Machiela MJ. Population analyses of mosaic X chromosome loss identify genetic drivers and widespread signatures of cellular selection. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.28.23285140. [PMID: 36778285 PMCID: PMC9915812 DOI: 10.1101/2023.01.28.23285140] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Mosaic loss of the X chromosome (mLOX) is the most commonly occurring clonal somatic alteration detected in the leukocytes of women, yet little is known about its genetic determinants or phenotypic consequences. To address this, we estimated mLOX in >900,000 women across eight biobanks, identifying 10% of women with detectable X loss in approximately 2% of their leukocytes. Out of 1,253 diseases examined, women with mLOX had an elevated risk of myeloid and lymphoid leukemias and pneumonia. Genetic analyses identified 49 common variants influencing mLOX, implicating genes with established roles in chromosomal missegregation, cancer predisposition, and autoimmune diseases. Complementary exome-sequence analyses identified rare missense variants in FBXO10 which confer a two-fold increased risk of mLOX. A small fraction of these associations were shared with mosaic Y chromosome loss in men, suggesting different biological processes drive the formation and clonal expansion of sex chromosome missegregation events. Allelic shift analyses identified alleles on the X chromosome which are preferentially retained, demonstrating that variation at many loci across the X chromosome is under cellular selection. A novel polygenic score including 44 independent X chromosome allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Collectively our results support a model where germline variants predispose women to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of subsequent clonal expansion.
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Affiliation(s)
- Aoxing Liu
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- These authors contributed equally: Aoxing Liu, Giulio Genovese, Yajie Zhao
| | - Giulio Genovese
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- These authors contributed equally: Aoxing Liu, Giulio Genovese, Yajie Zhao
| | - Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- These authors contributed equally: Aoxing Liu, Giulio Genovese, Yajie Zhao
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Publich Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Maryam M Zekavat
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Katherine Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | | | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Derek W Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Bryan Gorman
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Booz Allen Hamilton, McLean, VA, USA
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Weiyin Zhou
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saiju Pyarajan
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Vlad Tuzov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mervi Aavikko
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Timo P Sipilä
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Awaisa Ghazal
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Neal Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center of Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Center of Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alexander Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- These authors jointly supervised this work: Po-Ru Loh, Andrea Ganna, John R.B. Perry, Mitchell J. Machiela
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- These authors jointly supervised this work: Po-Ru Loh, Andrea Ganna, John R.B. Perry, Mitchell J. Machiela
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- These authors jointly supervised this work: Po-Ru Loh, Andrea Ganna, John R.B. Perry, Mitchell J. Machiela
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- These authors jointly supervised this work: Po-Ru Loh, Andrea Ganna, John R.B. Perry, Mitchell J. Machiela
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46
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Devi A, Jain K. Polygenic adaptation dynamics in large, finite populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525607. [PMID: 36747829 PMCID: PMC9901025 DOI: 10.1101/2023.01.25.525607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Although many phenotypic traits are determined by a large number of genetic variants, how a polygenic trait adapts in response to a change in the environment is not completely understood. In the framework of diffusion theory, we study the steady state and the adaptation dynamics of a large but finite population evolving under stabilizing selection and symmetric mutations when selection and mutation are moderately large. We find that in the stationary state, the allele frequency distribution at a locus is unimodal if its effect size is below a threshold effect and bimodal otherwise; these results are the stochastic analog of the deterministic ones where the stable allele frequency becomes bistable when the effect size exceeds a threshold. It is known that following a sudden shift in the phenotypic optimum, in an infinitely large population, selective sweeps at a large-effect locus are prevented and adaptation proceeds exclusively via subtle changes in the allele frequency; in contrast, we find that the chance of sweep is substantially enhanced in large, finite populations and the allele frequency at a large-effect locus can reach a high frequency at short times even for small shifts in the phenotypic optimum.
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Affiliation(s)
- Archana Devi
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287, USA
| | - Kavita Jain
- Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
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47
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Fritzsche MC, Akyüz K, Cano Abadía M, McLennan S, Marttinen P, Mayrhofer MT, Buyx AM. Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges. Front Genet 2023; 14:1098439. [PMID: 36816027 PMCID: PMC9933509 DOI: 10.3389/fgene.2023.1098439] [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: 11/14/2022] [Accepted: 01/04/2023] [Indexed: 01/27/2023] Open
Abstract
Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.
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Affiliation(s)
- Marie-Christine Fritzsche
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany,Department of Science, Technology and Society (STS), School of Social Sciences and Technology, Technical University of Munich, Munich, Germany,*Correspondence: Marie-Christine Fritzsche,
| | - Kaya Akyüz
- Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria,Department of Science and Technology Studies, University of Vienna, Vienna, Austria
| | - Mónica Cano Abadía
- Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Stuart McLennan
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany,Department of Science, Technology and Society (STS), School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
| | - Pekka Marttinen
- Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Michaela Th. Mayrhofer
- Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Alena M. Buyx
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany,Department of Science, Technology and Society (STS), School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
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48
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Gómez-Vecino A, Corchado-Cobos R, Blanco-Gómez A, García-Sancha N, Castillo-Lluva S, Martín-García A, Mendiburu-Eliçabe M, Prieto C, Ruiz-Pinto S, Pita G, Velasco-Ruiz A, Patino-Alonso C, Galindo-Villardón P, Vera-Pedrosa ML, Jalife J, Mao JH, de Plasencia GM, Castellanos-Martín A, Freire MDMS, Fraile-Martín S, Rodrigues-Teixeira T, García-Macías C, Galvis-Jiménez JM, García-Sánchez A, Isidoro-García M, Fuentes M, García-Cenador MB, García-Criado FJ, García JL, Hernández-García MÁ, Hernández JJC, Rodríguez-Sánchez CA, Martín-Ruiz A, Pérez-López E, Pérez-Martínez A, Gutiérrez-Larraya F, Cartón AJ, García-Sáenz JÁ, Patiño-García A, Martín M, Gordoa TA, Vulsteke C, Croes L, Hatse S, Brussel TV, Lambrechts D, Wildiers H, Hang C, Holgado-Madruga M, González-Neira A, Sánchez PL, Losada JP. Intermediate molecular phenotypes to identify genetic markers of anthracycline-induced cardiotoxicity risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522844. [PMID: 36712139 PMCID: PMC9881971 DOI: 10.1101/2023.01.05.522844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex disease whose polygenic component is mainly unidentified. We propose that levels of intermediate molecular phenotypes in the myocardium associated with histopathological damage could explain CDA susceptibility; so that variants of genes encoding these intermediate molecular phenotypes could identify patients susceptible to this complication. A genetically heterogeneous cohort of mice generated by backcrossing (N = 165) was treated with doxorubicin and docetaxel. Cardiac histopathological damage was measured by fibrosis and cardiomyocyte size by an Ariol slide scanner. We determine intramyocardial levels of intermediate molecular phenotypes of CDA associated with histopathological damage and quantitative trait loci (ipQTLs) linked to them. These ipQTLs seem to contribute to the missing heritability of CDA because they improve the heritability explained by QTL directly linked to CDA (cda-QTLs) through genetic models. Genes encoding these molecular subphenotypes were evaluated as genetic markers of CDA in three cancer patient cohorts (N = 517) whose cardiac damage was quantified by echocardiography or Cardiac Magnetic Resonance. Many SNPs associated with CDA were found using genetic models. LASSO multivariate regression identified two risk score models, one for pediatric cancer patients and the other for women with breast cancer. Molecular intermediate phenotypes associated with heart damage can identify genetic markers of CDA risk, thereby allowing a more personalized patient management. A similar strategy could be applied to identify genetic markers of other complex trait diseases.
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Affiliation(s)
- Aurora Gómez-Vecino
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Roberto Corchado-Cobos
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Adrián Blanco-Gómez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Natalia García-Sancha
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Sonia Castillo-Lluva
- Departamento de Bioquímica y Biología Molecular, Facultad de Ciencias Químicas, Universidad Complutense, Madrid, 28040, Spain
- Instituto de Investigaciones Sanitarias San Carlos (IdISSC), Madrid, Spain
| | - Ana Martín-García
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca, and CIBER.CV, Salamanca, 37007, Spain
| | - Marina Mendiburu-Eliçabe
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Carlos Prieto
- Servicio de Bioinformática, Nucleus, Universidad de Salamanca, Salamanca, 37007, Spain
| | - Sara Ruiz-Pinto
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Spain
| | - Guillermo Pita
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Spain
| | - Alejandro Velasco-Ruiz
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Spain
| | - Carmen Patino-Alonso
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Estadística, Universidad de Salamanca, Salamanca, 37007, Spain; and Centro de Investigación Institucional (CII). Universidad Bernardo O’Higgins, 1497. Santiago, Chile
| | - Purificación Galindo-Villardón
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Estadística, Universidad de Salamanca, Salamanca, 37007, Spain; and Centro de Investigación Institucional (CII). Universidad Bernardo O’Higgins, 1497. Santiago, Chile
| | | | - José Jalife
- Centro Nacional de Investigaciones Cardiovasculares (CNIC) Carlos III, Madrid, 28029, Spain
| | - Jian-Hua Mao
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Guillermo Macías de Plasencia
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca, and CIBER.CV, Salamanca, 37007, Spain
| | - Andrés Castellanos-Martín
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - María del Mar Sáez Freire
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Susana Fraile-Martín
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, Salamanca, 37007, Spain
| | - Telmo Rodrigues-Teixeira
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, Salamanca, 37007, Spain
| | - Carmen García-Macías
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, Salamanca, 37007, Spain
| | - Julie Milena Galvis-Jiménez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Instituto Nacional de Cancerología de Colombia, Bogotá D. C., Colombia
| | - Asunción García-Sánchez
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Bioquímica Clínica, Hospital Universitario de Salamanca, Salamanca, 37007, Spain
| | - María Isidoro-García
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Bioquímica Clínica, Hospital Universitario de Salamanca, Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
| | - Manuel Fuentes
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
- Unidad de Proteómica y Servicio General de Citometría de Flujo, Nucleus, Universidad de Salamanca, 37007, Spain
| | - María Begoña García-Cenador
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Cirugía, Universidad de Salamanca. Salamanca, 37007, Spain
| | - Francisco Javier García-Criado
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Cirugía, Universidad de Salamanca. Salamanca, 37007, Spain
| | - Juan Luis García
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | | | - Juan Jesús Cruz Hernández
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
- Servicio de Oncología, Hospital Universitario de Salamanca, Salamanca, 37007, Spain
| | - César Augusto Rodríguez-Sánchez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
- Servicio de Oncología, Hospital Universitario de Salamanca, Salamanca, 37007, Spain
| | - Alejandro Martín-Ruiz
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Hematología, Hospital Universitario de Salamanca, CIBERONC, Salamanca, 37007, Spain
| | - Estefanía Pérez-López
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Hematología, Hospital Universitario de Salamanca, CIBERONC, Salamanca, 37007, Spain
| | - Antonio Pérez-Martínez
- Department of Paediatric Hemato-Oncology, Hospital Universitario La Paz, Madrid, 28046, Spain
| | | | - Antonio J. Cartón
- Department of Paediatric Hemato-Oncology, Hospital Universitario La Paz, Madrid, 28046, Spain
| | - José Ángel García-Sáenz
- Medical Oncology Service, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Hospital Clínico San Carlos, Madrid, 28040, Spain
| | - Ana Patiño-García
- Department of Pediatrics, University Clinic of Navarra, Solid Tumor Program, CIMA, Universidad de Navarra, IdisNA, Pamplona, 31008, Spain
| | - Miguel Martín
- Gregorio Marañón Health Research Institute (IISGM), CIBERONC, Department of Medicine, Universidad Complutense, Madrid, 28007, Spain
| | - Teresa Alonso Gordoa
- Department of Medical Oncology, Hospital Universitario Ramón y Cajal, Madrid, 28034, Spain
| | - Christof Vulsteke
- Department of Molecular Imaging, Pathology, Radiotherapy and Oncology (MIPRO), Center for Oncological Research (CORE), Antwerp University, Antwerp, Belgium
- Department of Oncology, Integrated Cancer Center in Ghent, AZ Maria Middelares, Ghent, Belgium
| | - Lieselot Croes
- Department of Molecular Imaging, Pathology, Radiotherapy and Oncology (MIPRO), Center for Oncological Research (CORE), Antwerp University, Antwerp, Belgium
- Department of Oncology, Integrated Cancer Center in Ghent, AZ Maria Middelares, Ghent, Belgium
| | - Sigrid Hatse
- Laboratory of Experimental Oncology (LEO), Department of Oncology, KU Leuven, and Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium
| | - Thomas Van Brussel
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Diether Lambrechts
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Hans Wildiers
- Department of General Medical Oncology and Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven Cancer Institute, and Laboratory of Experimental Oncology (LEO), Department of Oncology, KU Leuven, Leuven, Belgium
| | - Chang Hang
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Marina Holgado-Madruga
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Fisiología y Farmacología, Universidad de Salamanca, 37007, Salamanca. Spain
- Instituto de Neurociencias de Castilla y León (INCyL), Salamanca, 37007, Spain
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Spain
| | - Pedro L Sánchez
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca, and CIBER.CV, Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
| | - Jesús Pérez Losada
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
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49
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Song J, Zou Y, Wu Y, Miao J, Yu Z, Fletcher JM, Lu Q. Decomposing heritability and genetic covariance by direct and indirect effect paths. PLoS Genet 2023; 19:e1010620. [PMID: 36689559 PMCID: PMC9894552 DOI: 10.1371/journal.pgen.1010620] [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: 09/16/2022] [Revised: 02/02/2023] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.
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Affiliation(s)
- Jie Song
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Yiqing Zou
- Department of Statistics, Stanford University, Stanford, CA, United States of America
| | - Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
| | - Ze Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
| | - Jason M. Fletcher
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Sociology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Qiongshi Lu
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- * E-mail:
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50
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Kanai M, Elzur R, Zhou W, Daly MJ, Finucane HK. Meta-analysis fine-mapping is often miscalibrated at single-variant resolution. CELL GENOMICS 2022; 2:100210. [PMID: 36643910 PMCID: PMC9839193 DOI: 10.1016/j.xgen.2022.100210] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7×; Fisher's exact p = 7.3 × 10-4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.
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Affiliation(s)
- Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Corresponding author
| | - Roy Elzur
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Mark J. Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Hilary K. Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Corresponding author
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