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Ma F, Longo M, Meroni M, Bhattacharya D, Paolini E, Mughal S, Hussain S, Anand SK, Gupta N, Zhu Y, Navarro-Corcuera A, Li K, Prakash S, Cogliati B, Wang S, Huang X, Wang X, Yurdagul A, Rom O, Wang L, Fried SK, Dongiovanni P, Friedman SL, Cai B. EHBP1 suppresses liver fibrosis in metabolic dysfunction-associated steatohepatitis. Cell Metab 2025; 37:1152-1170.e7. [PMID: 40015280 PMCID: PMC12058419 DOI: 10.1016/j.cmet.2025.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/27/2024] [Accepted: 01/22/2025] [Indexed: 03/01/2025]
Abstract
Excess cholesterol accumulation contributes to fibrogenesis in metabolic dysfunction-associated steatohepatitis (MASH), but how hepatic cholesterol metabolism becomes dysregulated in MASH is not completely understood. We show that human fibrotic MASH livers have decreased EH-domain-binding protein 1 (EHBP1), a genome-wide association study (GWAS) locus associated with low-density lipoprotein (LDL) cholesterol, and that EHBP1 loss- and gain-of-function increase and decrease MASH fibrosis in mice, respectively. Mechanistic studies reveal that EHBP1 promotes sortilin-mediated PCSK9 secretion, leading to LDL receptor (LDLR) degradation, decreased LDL uptake, and reduced TAZ, a fibrogenic effector. At a cellular level, EHBP1 deficiency affects the intracellular localization of retromer, a protein complex required for sortilin stabilization. Our therapeutic approach to stabilizing retromer is effective in mitigating MASH fibrosis. Moreover, we show that the tumor necrosis factor alpha (TNF-α)/peroxisome proliferator-activated receptor alpha (PPARα) pathway suppresses EHBP1 in MASH. These data not only provide mechanistic insights into the role of EHBP1 in cholesterol metabolism and MASH fibrosis but also elucidate an interplay between inflammation and EHBP1-mediated cholesterol metabolism.
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Affiliation(s)
- Fanglin Ma
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Miriam Longo
- Medicine and Metabolic Diseases, Fondazione Ca' Granda IRCCS Ospedale Maggiore Policlinico, Milano 20122, Italy
| | - Marica Meroni
- Medicine and Metabolic Diseases, Fondazione Ca' Granda IRCCS Ospedale Maggiore Policlinico, Milano 20122, Italy
| | - Dipankar Bhattacharya
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Erika Paolini
- Medicine and Metabolic Diseases, Fondazione Ca' Granda IRCCS Ospedale Maggiore Policlinico, Milano 20122, Italy
| | - Shama Mughal
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Syed Hussain
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sumit Kumar Anand
- Department of Pathology and Translational Pathobiology, Louisiana State University Health Shreveport, Shreveport, LA 71103, USA
| | - Neha Gupta
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yiwei Zhu
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amaia Navarro-Corcuera
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kenneth Li
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Satya Prakash
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bruno Cogliati
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Shuang Wang
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Xin Huang
- Columbia Center for Human Development, Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Xiaobo Wang
- Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Arif Yurdagul
- Department of Molecular and Cellular Physiology, Louisiana State University Health Shreveport, Shreveport, LA 71103, USA
| | - Oren Rom
- Department of Pathology and Translational Pathobiology, Louisiana State University Health Shreveport, Shreveport, LA 71103, USA
| | - Liheng Wang
- Institute of Cardiovascular Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Susan K Fried
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Paola Dongiovanni
- Medicine and Metabolic Diseases, Fondazione Ca' Granda IRCCS Ospedale Maggiore Policlinico, Milano 20122, Italy
| | - Scott L Friedman
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bishuang Cai
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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Dron JS, Natarajan P, Peloso GM. The breadth and impact of the Global Lipids Genetics Consortium. Curr Opin Lipidol 2025; 36:61-70. [PMID: 39602359 PMCID: PMC11888832 DOI: 10.1097/mol.0000000000000966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
PURPOSE OF REVIEW This review highlights contributions of the Global Lipids Genetics Consortium (GLGC) in advancing the understanding of the genetic etiology of blood lipid traits, including total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, and non-HDL cholesterol. We emphasize the consortium's collaborative efforts, discoveries related to lipid and lipoprotein biology, methodological advancements, and utilization in areas extending beyond lipid research. RECENT FINDINGS The GLGC has identified over 923 genomic loci associated with lipid traits through genome-wide association studies (GWASs), involving more than 1.65 million individuals from globally diverse populations. Many loci have been functionally validated by individuals inside and outside the GLGC community. Recent GLGC studies show increased population diversity enhances variant discovery, fine-mapping of causal loci, and polygenic score prediction for blood lipid levels. Moreover, publicly available GWAS summary statistics have facilitated the exploration of lipid-related genetic influences on cardiovascular and noncardiovascular diseases, with implications for therapeutic development and drug repurposing. SUMMARY The GLGC has significantly advanced the understanding of the genetic basis of lipid levels and serves as the leading resource of GWAS summary statistics for these traits. Continued collaboration will be critical to further understand lipid and lipoprotein biology through large-scale genetic assessments in diverse populations.
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Affiliation(s)
- Jacqueline S. Dron
- Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge
| | - Pradeep Natarajan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge
- Cardiovascular Research Center, Massachusetts General Hospital
- Department of Medicine, Harvard Medical School
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
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Grasset L, Bis JC, Frenzel S, Kojis D, Simino J, Yaqub A, Beiser A, Berr C, Bressler J, Bülow R, DeCarli CS, Fohner AE, Harrington LB, Helmer C, Ikram MA, Lemaitre RN, Lopez OL, Longstreth WT, Neitzel J, Odden MC, Palta P, Schmidt CO, Talluri R, Vernooij MW, Völzke H, Voortman T, Whalen Q, Wittfeld K, Grabe HJ, Mosley TH, Psaty BM, Wolters FJ, Seshadri S, Dufouil C. Selected social and lifestyle correlates of brain health markers: the Cross-Cohort Collaboration Consortium. Alzheimers Dement 2025; 21:e70148. [PMID: 40207408 PMCID: PMC11982914 DOI: 10.1002/alz.70148] [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: 06/14/2024] [Revised: 01/20/2025] [Accepted: 03/06/2025] [Indexed: 04/11/2025]
Abstract
INTRODUCTION To investigate the associations of education level, marital status, and physical activity with dementia risk and brain MRI markers. METHODS Data from six community-based samples from the Cross-Cohort Collaboration Consortium were analyzed. Self-reported education level, marital status, and physical activity at age 60 to 75 years were harmonized. Subsamples of participants with brain MRI markers at time of exposure were selected. Associations with dementia risk and cross-sectional MRI markers were meta-analyzed. RESULTS Higher education level was associated with lower dementia risk (hazard ratio [HR] = 0.65, 95% confidence interval [CI] = 0.59; 0.72 vs low level) but not significantly with brain MRI markers. Compared with being unmarried, being married was only associated with higher total brain and hippocampal volumes. Being physically active was associated with lower dementia risk (HR = 0.73, 95% CI = 0.52; 1.04), as well as larger total brain volume and smaller white matter hyperintensity volume. DISCUSSION This study provides further evidence regarding the contribution of education level and physical activity to dementia resilience. HIGHLIGHTS Education level, marital status, and physical activity are thought to contribute to resilience against ADRD. We used random-effects meta-analysis to summarize results from six community-based samples from the CCC. In this cross-cohort meta-analysis, higher education level and being physically active were associated with lower risk of dementia. In cross-sectional analyses, being married was associated with larger TBV and HV, while being physically active was associated with larger TBV and lower WMHV.
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Jurado Vélez J, Anderson N, Datcher I, Foster C, Jackson P, Hidalgo B. Striving Towards Equity in Cardiovascular Genomics Research. Curr Atheroscler Rep 2025; 27:34. [PMID: 39964583 PMCID: PMC11836143 DOI: 10.1007/s11883-025-01277-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/29/2025] [Indexed: 02/21/2025]
Abstract
PURPOSE OF REVIEW Our review emphasizes recent advancements and persisting gaps in cardiovascular genomics, particularly highlighting how emerging studies involving underrepresented populations have uncovered new genetic variants associated with cardiovascular diseases. RECENT FINDINGS Initiatives like the H3Africa project, the Million Veterans Program, and the All of Us Research Program are working to address this gap by focusing on underrepresented groups. Additionally, emerging research is centering on the interplay between genetic factors and socio-environmental determinants of health, which disproportionately impact marginalized communities. As cardiovascular genomics research grows, increasing the inclusion of underrepresented populations is essential for gaining a more comprehensive understanding of genetic variability. This will lead to more accurate and clinically meaningful strategies for preventing and treating cardiovascular diseases across all ancestral backgrounds and diverse populations.
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Affiliation(s)
- Javier Jurado Vélez
- Marnix E Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nekayla Anderson
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ivree Datcher
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Christy Foster
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Pamela Jackson
- Department of Environmental Health Sciences, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Bertha Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA.
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Vogi V, Haschka D, Forer L, Schwendinger S, Petzer V, Coassin S, Tancevski I, Sonnweber T, Löffler-Ragg J, Puchhammer-Stöckl E, Graninger M, Wolf D, Kronenberg F, Zschocke J, Jukic E, Weiss G. Severe COVID-19 disease is associated with genetic factors affecting plasma ACE2 receptor and CRP concentrations. Sci Rep 2025; 15:4708. [PMID: 39922945 PMCID: PMC11807156 DOI: 10.1038/s41598-025-89306-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/04/2025] [Indexed: 02/10/2025] Open
Abstract
A hyperinflammatory state with highly elevated concentrations of inflammatory biomarkers such as C-reactive protein (CRP) is a characteristic feature of severe coronavirus disease 2019 (COVID-19). To examine a potential role of common genetic factors that may influence COVID-19 outcomes, we investigated whether individuals with a polygenic predisposition for a pro-inflammatory response (in the form of Polygenic Scores) are more likely to develop severe COVID-19. The innovative approach of polygenic scores to investigate genetic factors in COVID-19 severity should provide a comprehensive approach beyond single-gene studies. In our cohort of 156 patients of European ancestry, two overlapping Polygenic Scores (PGS) predicting a genetic predisposition to basal CRP concentrations were significantly different between non-severe and severe COVID-19 cases and were associated with less severe COVID-19 outcomes. Furthermore, specific single nucleotide polymorphisms (SNPs) that contribute to either of the two Polygenic Scores predicting basal CRP levels are associated with different traits that represent risk factors for COVID-19 disease initiation (ACE2 receptor, viral replication) and progression (CRP). We suggest that genetically determined enforced CRP formation may contribute to strengthening of innate immune responses and better initial pathogen control thereby reducing the risk of subsequent hyperinflammation and adverse course of COVID-19.
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Affiliation(s)
- Verena Vogi
- Institute of Human Genetics, Medical University Innsbruck, Innsbruck, 6020, Austria
| | - David Haschka
- Department of Internal Medicine II (Infectious Diseases, Immunology, Pneumology and Rheumatology), Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Lukas Forer
- Institute of Genetic Epidemiology, Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Simon Schwendinger
- Institute of Human Genetics, Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Verena Petzer
- Department of Internal Medicine V (Hematology and Internistic Oncology), Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Stefan Coassin
- Institute of Genetic Epidemiology, Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Ivan Tancevski
- Department of Internal Medicine II (Infectious Diseases, Immunology, Pneumology and Rheumatology), Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Thomas Sonnweber
- Department of Internal Medicine II (Infectious Diseases, Immunology, Pneumology and Rheumatology), Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Judith Löffler-Ragg
- Department of Internal Medicine II (Infectious Diseases, Immunology, Pneumology and Rheumatology), Medical University Innsbruck, Innsbruck, 6020, Austria
| | | | - Marianne Graninger
- Department of Virology, Medical University Vienna, Vienna, 1090, Austria
| | - Dominik Wolf
- Department of Internal Medicine V (Hematology and Internistic Oncology), Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Johannes Zschocke
- Institute of Human Genetics, Medical University Innsbruck, Innsbruck, 6020, Austria
| | - Emina Jukic
- Institute of Human Genetics, Medical University Innsbruck, Innsbruck, 6020, Austria.
| | - Günter Weiss
- Department of Internal Medicine II (Infectious Diseases, Immunology, Pneumology and Rheumatology), Medical University Innsbruck, Innsbruck, 6020, Austria.
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6
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Alemu R, Sharew NT, Arsano YY, Ahmed M, Tekola-Ayele F, Mersha TB, Amare AT. Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues. Hum Genomics 2025; 19:8. [PMID: 39891174 PMCID: PMC11786457 DOI: 10.1186/s40246-025-00718-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2024] [Accepted: 01/16/2025] [Indexed: 02/03/2025] Open
Abstract
Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.
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Affiliation(s)
- Robel Alemu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Anderson School of Management, University of California Los Angeles, Los Angeles, CA, USA.
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia.
| | - Nigussie T Sharew
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia
| | - Yodit Y Arsano
- Alpert Medical School, Lifespan Health Systems, Brown University, WarrenProvidence, Rhode Island, USA
| | - Muktar Ahmed
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Tesfaye B Mersha
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Azmeraw T Amare
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia.
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7
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Westerman KE, Kilpeläinen TO, Sevilla-Gonzalez M, Connelly MA, Wood AC, Tsai MY, Taylor KD, Rich SS, Rotter JI, Otvos JD, Bentley AR, Mora S, Aschard H, Rao DC, Gu C, Chasman DI, Manning AK, The CHARGE Gene-Lifestyle Interactions Working Group. Refinement of a Published Gene-Physical Activity Interaction Impacting HDL-Cholesterol: Role of Sex and Lipoprotein Subfractions. Genet Epidemiol 2025; 49:e22607. [PMID: 39764704 PMCID: PMC11934221 DOI: 10.1002/gepi.22607] [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: 04/27/2024] [Revised: 10/09/2024] [Accepted: 12/17/2024] [Indexed: 01/15/2025]
Abstract
Large-scale gene-environment interaction (GxE) discovery efforts often involve analytical compromises for the sake of data harmonization and statistical power. Refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C). We explored this GxE in the Women's Genome Health Study (WGHS; N = 23,294; the strongest cohort-specific signal in the original meta-analysis), the UK Biobank (UKB; N = 281,380), and the Multi-Ethnic Study of Atherosclerosis (MESA; N = 4587), using self-reported PA (MET-min/wk) and genotypes at rs295849 (nearest gene: LHX1). As originally reported, minor allele carriers of rs295849 in WGHS had a stronger positive association between PA and HDL-C (pint = 0.002). When testing available NMR metabolites to refine the HDL-C outcome, we found a stronger interaction effect on medium-sized HDL particle concentrations (M-HDL-P; pint = 1.0 × 10-4) than HDL-C. Meta-regression revealed a systematically larger interaction effect in cohorts from the original meta-analysis with a greater fraction of women (p = 0.018). In the UKB, GxE effects were stronger in women and using M-HDL-P as the outcome. In MESA, the primary interaction for HDL-C showed nominal significance (pint = 0.013), but without clear sex differences and with a greater magnitude for large HDL-P. Our work provides additional insights into a known gene-PA interaction while illustrating the importance of phenotype and model refinement toward understanding and replicating GxEs.
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Grants
- 75N92020D00005 NHLBI NIH HHS
- U54 HG003067 NHGRI NIH HHS
- UL1 TR001079 NCATS NIH HHS
- R01 HL043851 NHLBI NIH HHS
- N01HC95164 NHLBI NIH HHS
- P30 DK063491 NIDDK NIH HHS
- N01HC95165 NHLBI NIH HHS
- N01HC95159 NHLBI NIH HHS
- 75N92020D00007 NHLBI NIH HHS
- K24 HL136852 NHLBI NIH HHS
- UL1 TR000040 NCATS NIH HHS
- 75N92020D00002 NHLBI NIH HHS
- HHSN268201500003C NHLBI NIH HHS
- N01HC95160 NHLBI NIH HHS
- R01 HL160799 NHLBI NIH HHS
- R01 HL120393 NHLBI NIH HHS
- K01 DK133637 NIDDK NIH HHS
- N01HC95163 NHLBI NIH HHS
- 75N92020D00001 NHLBI NIH HHS
- N01HC95169 NHLBI NIH HHS
- N01HC95162 NHLBI NIH HHS
- 75N92020D00003 NHLBI NIH HHS
- R01 HL105756 NHLBI NIH HHS
- N01HC95168 NHLBI NIH HHS
- R01 HL118305 NHLBI NIH HHS
- UL1 TR001420 NCATS NIH HHS
- 75N92020D00004 NHLBI NIH HHS
- UM1 CA182913 NCI NIH HHS
- R01 HL156991 NHLBI NIH HHS
- N01HC95161 NHLBI NIH HHS
- R01 CA047988 NCI NIH HHS
- HHSN268201500003I NHLBI NIH HHS
- R01 HL080467 NHLBI NIH HHS
- N01HC95167 NHLBI NIH HHS
- R01 HL117626 NHLBI NIH HHS
- 75N92020D00006 NHLBI NIH HHS
- N01HC95166 NHLBI NIH HHS
- This investigation was supported by two grants from the U.S. National Heart, Lung, and Blood Institute (NHLBI), the National Institutes of Health, R01HL118305 and R01HL156991. K.E.W. was supported by K01DK133637. T.O.K. was supported by the Novo Nordisk Foundation (NNF18CC0034900, NNF21SA0072102). A.R.B. was supported by the Intramural Research Program of the National Human Genome Research Institute of the National Institutes of Health through the Center for Research on Genomics and Global Health (CRGGH). S.M. was supported by HL160799, HL117861, and K24HL136852. The WGHS is supported by the National Heart, Lung, and Blood Institute (HL043851 and HL080467) and the National Cancer Institute (CA047988 and UM1CA182913), with funding for genotyping provided by Amgen and funding for NMR assays by the American Heart Association.
- R01 HL117861 NHLBI NIH HHS
- UL1 TR001881 NCATS NIH HHS
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Affiliation(s)
- Kenneth E. Westerman
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tuomas O. Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Magdalena Sevilla-Gonzalez
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alexis C. Wood
- USDA/ARS Children’s Nutrition Center, Baylor College of Medicine, Houston, TX, USA
| | - Michael Y. Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Kent D. Taylor
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - James D. Otvos
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Samia Mora
- Center for Lipid Metabolomics, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, USA
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université de Paris, Paris, FR
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - DC Rao
- Division of Biostatistics, Washington University, St. Louis, MO, USA
| | - Charles Gu
- Division of Biostatistics, Washington University, St. Louis, MO, USA
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alisa K. Manning
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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8
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Zhang DY, Levin MG, Duda JT, Landry LG, Witschey WR, Damrauer SM, Ritchie MD, Rader DJ. Protein-truncating variant in APOL3 increases chronic kidney disease risk in epistasis with APOL1 risk alleles. JCI Insight 2024; 9:e181238. [PMID: 39163132 PMCID: PMC11466179 DOI: 10.1172/jci.insight.181238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 08/13/2024] [Indexed: 08/22/2024] Open
Abstract
BACKGROUNDTwo coding alleles within the APOL1 gene, G1 and G2, found almost exclusively in individuals genetically similar to West African populations, contribute substantially to the pathogenesis of chronic kidney disease (CKD). The APOL gene cluster on chromosome 22 contains a total of 6 APOL genes that have arisen as a result of gene duplication.METHODSUsing a genome-first approach in the Penn Medicine BioBank, we identified 62 protein-altering variants in the 6 APOL genes with a minor allele frequency of >0.1% in a population of participants genetically similar to African reference populations and performed population-specific phenome-wide association studies.RESULTSWe identified rs1108978, a stop-gain variant in APOL3 (p.Q58*), to be significantly associated with increased CKD risk, even after conditioning on APOL1 G1/G2 carrier status. These findings were replicated in the Veterans Affairs Million Veteran Program and the All of Us Research Program. APOL3 p.Q58* was also significantly associated with a number of quantitative traits linked to CKD, including decreased kidney volume. This truncating variant contributed the most risk for CKD in patients monoallelic for APOL1 G1/G2, suggesting an epistatic interaction and a potential protective effect of wild-type APOL3 against APOL1-induced kidney disease.CONCLUSIONThis study demonstrates the utility of targeting population-specific variants in a genome-first approach, even in the context of well-studied gene-disease relationships.FUNDINGNational Heart, Lung, and Blood Institute (F30HL172382, R01HL169378, R01HL169458), Doris Duke Foundation (grant 2023-2024), National Institute of Biomedical Imaging and Bioengineering (P41EB029460), and National Center for Advancing Translational Sciences (UL1-TR-001878).
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Affiliation(s)
| | - Michael G. Levin
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA
| | - Jeffrey T. Duda
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Walter R. Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Scott M. Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA
- Department of Surgery, University of Pennsylvania, and
| | - Marylyn D. Ritchie
- Department of Genetics
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Ao L, van Heemst D, Luo J, Teder-Laving M, Mägi R, Frikke-Schmidt R, Willems van Dijk K, Noordam R. Large-scale genome-wide interaction analyses on multiple cardiometabolic risk factors to identify age-specific genetic risk factors. GeroScience 2024:10.1007/s11357-024-01348-0. [PMID: 39322921 DOI: 10.1007/s11357-024-01348-0] [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: 07/19/2024] [Accepted: 09/08/2024] [Indexed: 09/27/2024] Open
Abstract
The genetic landscape of cardiometabolic risk factors has been explored extensively. However, insight in the effects of genetic variation on these risk factors over the life course is sparse. Here, we performed genome-wide interaction studies (GWIS) on different cardiometabolic risk factors to identify age-specific genetic risks. This study included 270,276 unrelated European-ancestry participants from the UK Biobank (54.2% women, a median age of 58 [interquartile range (IQR): 50, 63] years). GWIS models with interaction terms between genetic variants and age were performed on apolipoprotein B (ApoB), low-density lipoprotein-cholesterol (LDL-C), log-transformed triglycerides (TG), body mass index (BMI) and systolic blood pressure (SBP). Replication was subsequently performed in the Copenhagen General Population Study (CGPS) and the Estonian Biobank (EstBB). Multiple lead variants were identified to have genome-wide significant interactions with age (Pinteraction < 1e - 08). In detail, rs429358 (tagging APOE4) was identified for ApoB (Pinteraction = 9.0e - 14) and TG (Pinteraction = 5.4e - 16). Three additional lead variants were identified for ApoB: rs11591147 (R46L in PCSK9, Pinteraction = 3.9e - 09), rs34601365 (near APOB, Pinteraction = 8.4e - 09) and rs17248720 (near LDLR, Pinteraction = 2.0e - 09). Effect sizes of the identified lead variants were generally closer to the null with increasing age. No variant-age interactions were identified for LDL-C, SBP and BMI. The significant interactions of rs429358 with age on ApoB and TG were replicated in both CGPS and EstBB. The majority of genetic effects on cardiometabolic risk factors remain relatively constant over age, with the noted exceptions of specific genetic effects on ApoB and TG.
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Affiliation(s)
- Linjun Ao
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden, the Netherlands
| | - Jiao Luo
- Department of Clinical Biochemistry, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Ruth Frikke-Schmidt
- Department of Clinical Biochemistry, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- The Copenhagen General Population Study, Herlev and Gentofte Hospital, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden, the Netherlands
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10
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Noordam R, Wang W, Nagarajan P, Wang H, Brown MR, Bentley AR, Hui Q, Kraja AT, Morrison JL, O'Connel JR, Lee S, Schwander K, Bartz TM, de las Fuentes L, Feitosa MF, Guo X, Hanfei X, Harris SE, Huang Z, Kals M, Lefevre C, Mangino M, Milaneschi Y, van der Most P, Pacheco NL, Palmer ND, Rao V, Rauramaa R, Sun Q, Tabara Y, Vojinovic D, Wang Y, Weiss S, Yang Q, Zhao W, Zhu W, Abu Yusuf Ansari M, Aschard H, Anugu P, Assimes TL, Attia J, Baker LD, Ballantyne C, Bazzano L, Boerwinkle E, Cade B, Chen HH, Chen W, Ida Chen YD, Chen Z, Cho K, De Anda-Duran I, Dimitrov L, Do A, Edwards T, Faquih T, Hingorani A, Fisher-Hoch SP, Gaziano JM, Gharib SA, Giri A, Ghanbari M, Grabe HJ, Graff M, Gu CC, He J, Heikkinen S, Hixson J, Ho YL, Hood MM, Houghton SC, Karvonen-Gutierrez CA, Kawaguchi T, Kilpeläinen TO, Komulainen P, Lin HJ, Linchangco GV, Luik AI, Ma J, Meigs JB, McCormick JB, Menni C, Nolte IM, Norris JM, Petty LE, Polikowsky HG, Raffield LM, Rich SS, Riha RL, Russ TC, Ruiz-Narvaez EA, Sitlani CM, Smith JA, Snieder H, Sofer T, Shen B, Tang J, Taylor KD, Teder-Laving M, Triatin R, et alNoordam R, Wang W, Nagarajan P, Wang H, Brown MR, Bentley AR, Hui Q, Kraja AT, Morrison JL, O'Connel JR, Lee S, Schwander K, Bartz TM, de las Fuentes L, Feitosa MF, Guo X, Hanfei X, Harris SE, Huang Z, Kals M, Lefevre C, Mangino M, Milaneschi Y, van der Most P, Pacheco NL, Palmer ND, Rao V, Rauramaa R, Sun Q, Tabara Y, Vojinovic D, Wang Y, Weiss S, Yang Q, Zhao W, Zhu W, Abu Yusuf Ansari M, Aschard H, Anugu P, Assimes TL, Attia J, Baker LD, Ballantyne C, Bazzano L, Boerwinkle E, Cade B, Chen HH, Chen W, Ida Chen YD, Chen Z, Cho K, De Anda-Duran I, Dimitrov L, Do A, Edwards T, Faquih T, Hingorani A, Fisher-Hoch SP, Gaziano JM, Gharib SA, Giri A, Ghanbari M, Grabe HJ, Graff M, Gu CC, He J, Heikkinen S, Hixson J, Ho YL, Hood MM, Houghton SC, Karvonen-Gutierrez CA, Kawaguchi T, Kilpeläinen TO, Komulainen P, Lin HJ, Linchangco GV, Luik AI, Ma J, Meigs JB, McCormick JB, Menni C, Nolte IM, Norris JM, Petty LE, Polikowsky HG, Raffield LM, Rich SS, Riha RL, Russ TC, Ruiz-Narvaez EA, Sitlani CM, Smith JA, Snieder H, Sofer T, Shen B, Tang J, Taylor KD, Teder-Laving M, Triatin R, Tsai MY, Völzke H, Westerman KE, Xia R, Yao J, Young KL, Zhang R, Zonderman AB, Zhu X, Below JE, Cox SR, Evans M, Fornage M, Fox ER, Franceschini N, Harlow SD, Holliday E, Ikram MA, Kelly T, Lakka TA, Lawlor DA, Li C, Liu CT, Mägi R, Manning AK, Matsuda F, Morrison AC, Nauck M, North KE, Penninx BW, Province MA, Psaty BM, Rotter JI, Spector TD, Wagenknecht LE, Willems van Dijk K, Study LC, Jaquish CE, Wilson PW, Peyser PA, Munroe PB, de Vries PS, Gauderman WJ, Sun YV, Chen H, Miller CL, Winkler TW, Rao DC, Redline S, van Heemst D. A Large-Scale Genome-Wide Gene-Sleep Interaction Study in 732,564 Participants Identifies Lipid Loci Explaining Sleep-Associated Lipid Disturbances. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.02.24312466. [PMID: 39281768 PMCID: PMC11398441 DOI: 10.1101/2024.09.02.24312466] [Show More Authors] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify novel genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. We collected data from 55 cohorts with a combined sample size of 732,564 participants (87% European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20% of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for the one-degree of freedom tests of interaction and two-degree of freedom joint tests of the main and interaction effect. In the cross-population meta-analyses, the one-degree of freedom variant-sleep interaction test identified 10 loci (P int <5.0e-9) not previously observed for lipids. Of interest, the ASPH locus (TG, LTST) is a target for aspartic and succinic acid metabolism previously shown to improve sleep and cardiovascular risk. The two-degree of freedom analyses identified an additional 7 loci that showed evidence for variant-sleep interaction (P joint <5.0e-9 in combination with P int <6.6e-6). Of these, the SLC8A1 locus (TG, STST) has been considered a potential treatment target for reduction of ischemic damage after acute myocardial infarction. Collectively, the 17 (9 with STST; 8 with LTST) loci identified in this large-scale initiative provides evidence into the biomolecular mechanisms underpinning sleep-duration-associated changes in lipid levels. The identified druggable targets may contribute to the development of novel therapies for dyslipidemia in people with sleep disturbances.
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11
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Leite JMRS, Pereira JL, Alves de Souza C, Pavan Soler JM, Mingroni-Netto RC, Fisberg RM, Rogero MM, Sarti FM. Novel loci linked to serum lipid traits are identified in a genome-wide association study of a highly admixed Brazilian population - the 2015 ISA Nutrition. Lipids Health Dis 2024; 23:229. [PMID: 39060932 PMCID: PMC11282745 DOI: 10.1186/s12944-024-02085-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 03/20/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND Cardiovascular diseases (CVDs) comprise major causes of death worldwide, leading to extensive burden on populations and societies. Alterations in normal lipid profiles, i.e., dyslipidemia, comprise important risk factors for CVDs. However, there is lack of comprehensive evidence on the genetic contribution to dyslipidemia in highly admixed populations. The identification of single nucleotide polymorphisms (SNPs) linked to blood lipid traits in the Brazilian population was based on genome-wide associations using data from the São Paulo Health Survey with Focus on Nutrition (ISA-Nutrition). METHODS A total of 667 unrelated individuals had genetic information on 330,656 SNPs available, and were genotyped with Axiom™ 2.0 Precision Medicine Research Array. Genetic associations were tested at the 10- 5 significance level for the following phenotypes: low-density lipoprotein cholesterol (LDL-c), very low-density lipoprotein cholesterol (VLDL-c), high-density lipoprotein cholesterol (HDL-c), HDL-c/LDL-c ratio, triglycerides (TGL), total cholesterol, and non-HDL-c. RESULTS There were 19 significantly different SNPs associated with lipid traits, the majority of which corresponding to intron variants, especially in the genes FAM81A, ZFHX3, PTPRD, and POMC. Three variants (rs1562012, rs16972039, and rs73401081) and two variants (rs8025871 and rs2161683) were associated with two and three phenotypes, respectively. Among the subtypes, non-HDL-c had the highest proportion of associated variants. CONCLUSIONS The results of the present genome-wide association study offer new insights into the genetic structure underlying lipid traits in underrepresented populations with high ancestry admixture. The associations were robust across multiple lipid phenotypes, and some of the phenotypes were associated with two or three variants. In addition, some variants were present in genes that encode ncRNAs, raising important questions regarding their role in lipid metabolism.
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Affiliation(s)
| | | | | | - Júlia M Pavan Soler
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | | | - Regina M Fisberg
- School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Marcelo M Rogero
- School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Flavia M Sarti
- School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, Brazil.
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12
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Zhu X, Yang Y, Lorincz-Comi N, Li G, Bentley AR, de Vries PS, Brown M, Morrison AC, Rotimi CN, Gauderman WJ, Rao DC, Aschard H. An approach to identify gene-environment interactions and reveal new biological insight in complex traits. Nat Commun 2024; 15:3385. [PMID: 38649715 PMCID: PMC11035594 DOI: 10.1038/s41467-024-47806-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Gen Li
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Michael Brown
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Dabeeru C Rao
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015, Paris, France
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
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13
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Jiang J, Wang D, Gao Y, Sun L, Li S, Hu X, Li Z, Zhang J, Ji F, Tian Y, Guan L, Li Z, He L, Wan C. Altered HCAR3 expression may underlying the blunted niacin responses of the psychiatric disorders and the risk of schizophrenia. Psychiatry Clin Neurosci 2024; 78:123-130. [PMID: 37984442 DOI: 10.1111/pcn.13617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 10/24/2023] [Accepted: 11/09/2023] [Indexed: 11/22/2023]
Abstract
AIM Blunted niacin response (BNR) was an endophenotype of schizophrenia, but the underlying mechanism remains unclarified. The objective of this study was to verify whether genes associated with BNR pathway constitute the genetic basis and the pathological mechanism of BNR phenotypic psychiatric patients. METHODS Two independent sample sets consisting of 971 subjects were enrolled in this study. A total of 62 variants were genotyped in the discovery set, then the related variants were verified in the verification set. The published PGC GWAS data were used to validate the associations between the variants and psychiatry disorders. RT-PCR analysis, eQTL data, and Dual-Luciferase Reporter experiment were used to investigate the potential molecular mechanisms of the variants underlying BNR. RESULTS The results showed that two SNPs, rs56959712 in HCAR2 and rs2454721 in HCAR3 were significantly associated with niacin response. The risk allele T of rs2454721 could affect the niacin responses of psychiatric patients through elevated HCAR3 gene expression. These two genes, especially HCAR3, were significantly associated with the risk of schizophrenia, as identified in this study and verified using the published GWAS data. CONCLUSION HCAR3 is a novel schizophrenia susceptibility gene which is significantly associated with blunted niacin response in schizophrenia. In-depth investigation of HCAR3 is of great significance for uncovering the pathogenesis and propose new therapeutic targets for psychiatric disorders, especially for the BNR subgroup patients.
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Affiliation(s)
- Jie Jiang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Dandan Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Gao
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Liya Sun
- Shanghai Mental Health Center, Shanghai Key Laboratory of Psychiatry Disorders, Shanghai Jiao Tong University, Shanghai, China
| | - Shuhui Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaowen Hu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Zhuyun Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Juan Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Feng Ji
- Institute of Mental Health, Jining Medical University, Jining, China
| | - Yusheng Tian
- National Clinical Research Center for Mental Disorders, Department of Psychiatry and Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Lili Guan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
| | - Zhiqiang Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
- The Affiliated Hospital of Qingdao University and the Biomedical Sciences Institute of Qingdao University, Qingdao Branch of SJTU Bio-X Institutes, Qingdao University, Qingdao, China
| | - Lin He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Chunling Wan
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Mental Health Center, Shanghai Key Laboratory of Psychiatry Disorders, Shanghai Jiao Tong University, Shanghai, China
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Li Y, Xiong Z, Jiang WL, Tian D, Zhou H, Hou Q, Xiao L, Zhang M, Huang L, Zhong L, Zhou L, Zeng GG. An innovative viewpoint on the existing and prospectiveness of SR-B1. Curr Probl Cardiol 2024; 49:102226. [PMID: 38040207 DOI: 10.1016/j.cpcardiol.2023.102226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 11/28/2023] [Indexed: 12/03/2023]
Abstract
Scavenger Receptor Class B Type 1 (SR-B1), a receptor protein expressed on the cell membrane, plays a crucial role in the metabolism and transport of cholesterol and other lipids, contributing significantly to the homeostasis of lipid levels within the body. Bibliometric analysis involves the application of mathematical and statistical methods to quantitatively analyze different types of documents. It involves the analysis of structural and temporal trends in scholarly articles, coupled with the identification of subject emphasis and variations. Through a bibliometric analysis, this study examines the historical background, current research trends, and future directions in the exploration of SR-B1. By offering insights into the research status and development of SR-B1, this paper aims to assist researchers in identifying novel pathways and areas of investigation in this field of study. Following the screening process, it can be concluded that research on SR-B1 has consistently remained a topic of significant interest over the past 17 years. Interestingly, SR-B1 has recently garnered attention in areas beyond its traditional research focus, including the field of cancer. The primary objective of this review is to provide a concise and accessible overview of the development process of SR-B1 that can help readers who are not well-versed in SR-B1 research quickly grasp its key aspects. Furthermore, this review aims to offer insights and suggestions to researchers regarding potential future research directions and areas of emphasis relating to SR-B1.
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Affiliation(s)
- Yonggui Li
- The Second Affiliated Hospital, Department of Digestive Internal Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Zhijie Xiong
- The Second Affiliated Hospital, Department of Digestive Internal Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Wan-Li Jiang
- Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, Hunan International Scientific and Technological Cooperation Base of Arteriosclerotic Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China; Departments of Clinical Medicine, Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
| | - Dandan Tian
- School of Nursing, Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
| | - Haiyou Zhou
- The Second Affiliated Hospital, Department of Digestive Internal Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, China; Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, Hunan International Scientific and Technological Cooperation Base of Arteriosclerotic Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China; 2020 Grade Excellent Doctor Class of Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
| | - Qin Hou
- Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
| | - Liang Xiao
- The Second Affiliated Hospital, Department of Digestive Internal Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, China; Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, Hunan International Scientific and Technological Cooperation Base of Arteriosclerotic Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China; 2020 Grade Excellent Doctor Class of Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
| | - Mengjie Zhang
- Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, Hunan International Scientific and Technological Cooperation Base of Arteriosclerotic Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China; Departments of Clinical Medicine, Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
| | - Liubin Huang
- Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, Hunan International Scientific and Technological Cooperation Base of Arteriosclerotic Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China; Departments of Clinical Medicine, Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
| | - Lianping Zhong
- The Second Affiliated Hospital, Department of Digestive Internal Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, China; Department of Gastroenterology, the Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Li Zhou
- Department of Pathology, Chongqing Public Health Medical Center, Southwest University Public Health Hospital, Chongqing, China
| | - Guang-Gui Zeng
- The Second Affiliated Hospital, Department of Digestive Internal Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, China; Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, Hunan International Scientific and Technological Cooperation Base of Arteriosclerotic Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China; 2020 Grade Excellent Doctor Class of Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China.
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15
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Westerman KE, Kilpeläinen TO, Sevilla-Gonzalez M, Connelly MA, Wood AC, Tsai MY, Taylor KD, Rich SS, Rotter JI, Otvos JD, Bentley AR, Mora S, Aschard H, Rao DC, Gu C, Chasman DI, Manning AK. Refinement of a published gene-physical activity interaction impacting HDL-cholesterol: role of sex and lipoprotein subfractions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.23.24301689. [PMID: 38313294 PMCID: PMC10836120 DOI: 10.1101/2024.01.23.24301689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Large-scale gene-environment interaction (GxE) discovery efforts often involve compromises in the definition of outcomes and choice of covariates for the sake of data harmonization and statistical power. Consequently, refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C). This GxE was originally identified by Kilpeläinen et al., with the strongest cohort-specific signal coming from the Women's Genome Health Study (WGHS). We thus explored this GxE further in the WGHS (N = 23,294), with follow-up in the UK Biobank (UKB; N = 281,380), and the Multi-Ethnic Study of Atherosclerosis (MESA; N = 4,587). Self-reported PA (MET-hrs/wk), genotypes at rs295849 (nearest gene: LHX1), and NMR metabolomics data were available in all three cohorts. As originally reported, minor allele carriers of rs295849 in WGHS had a stronger positive association between PA and HDL-C (pint = 0.002). When testing a range of NMR metabolites (primarily lipoprotein and lipid subfractions) to refine the HDL-C outcome, we found a stronger interaction effect on medium-sized HDL particle concentrations (M-HDL-P; pint = 1.0×10-4) than HDL-C. Meta-regression revealed a systematically larger interaction effect in cohorts from the original meta-analysis with a greater fraction of women (p = 0.018). In the UKB, GxE effects were stronger both in women and using M-HDL-P as the outcome. In MESA, the primary interaction for HDL-C showed nominal significance (pint = 0.013), but without clear differences by sex and with a greater magnitude using large, rather than medium, HDL-P as an outcome. Towards reconciling these observations, further exploration leveraging NMR platform-specific HDL subfraction diameter annotations revealed modest agreement across all cohorts in the interaction affecting medium-to-large particles. Taken together, our work provides additional insights into a specific known gene-PA interaction while illustrating the importance of phenotype and model refinement towards understanding and replicating GxEs.
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Affiliation(s)
- Kenneth E. Westerman
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tuomas O. Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Magdalena Sevilla-Gonzalez
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alexis C. Wood
- USDA/ARS Children’s Nutrition Center, Baylor College of Medicine, Houston, TX, USA
| | - Michael Y. Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Kent D. Taylor
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - James D. Otvos
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Samia Mora
- Center for Lipid Metabolomics, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, USA
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université de Paris, Paris, FR
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - DC Rao
- Division of Biostatistics, Washington University, St. Louis, MO, USA
| | - Charles Gu
- Division of Biostatistics, Washington University, St. Louis, MO, USA
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alisa K. Manning
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Wang J, Wang YC, Zhang P, Wang X, Zong RR, Jiang J, Zhang Y, Qian YW, Li QJ, Wang ZL. Retinal thickness and fundus blood flow density changes in chest pain subjects with dyslipidemia. Int J Ophthalmol 2023; 16:1860-1866. [PMID: 38028521 PMCID: PMC10626367 DOI: 10.18240/ijo.2023.11.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 08/24/2023] [Indexed: 12/01/2023] Open
Abstract
AIM To assess the retinal thickness and fundus blood flow density changes in chest pain patients with dyslipidemia using optical coherence tomography angiography (OCTA). METHODS All subjects with chest pain as the main symptom accepted a comprehensive ophthalmological examination. According to the serum lipid levels, the participants were divided into the control group and the dyslipidemia group. The retina thickness and fundus blood flow density were determined using OCTA. RESULTS The study enrolled 87 left eyes from 87 adults with dyslipidemia and 87 left eyes from age- and sex-matched participants without dyslipidemia. The retina of dyslipidemia subjects was significantly thinner than that of the controls in the inferior (P=0.004 and P=0.014, respectively) and temporal (P=0.015 and P=0.019, respectively) regions, both inner and outer layers. In terms of blood flow density in the macula or optic disk, there was a decreasing trend in the dyslipidemia group compared with the control group, especially in the inferior and temporal regions. CONCLUSION Dyslipidemia may contribute to the decrease in retinal thickness and fundus blood flow density. Further validation of the association between abnormal lipid metabolism and fundus microcirculation alterations needs to be carried out in chest pain patients.
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Affiliation(s)
- Jin Wang
- Department of Cardiology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200010, China
| | - Yu-Cen Wang
- Department of Ophthalmology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Pei Zhang
- Department of Ophthalmology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Ophthalmology, Shanghai Gonghui Hospital, Shanghai 200040, China
| | - Xin Wang
- Department of Ophthalmology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Rong-Rong Zong
- Fujian Provincial Key Laboratory of Ophthalmology and Visual Science; Fujian Engineering and Research Center of Eye Regenerative Medicine; Eye Institute of Xiamen University; Xiamen University School of Medicine, Xiamen 361005, Fujian Province, China
| | - Jing Jiang
- Department of Ophthalmology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yu Zhang
- Department of Ophthalmology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yi-Wen Qian
- Department of Ophthalmology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Qing-Jian Li
- Department of Ophthalmology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Zhi-Liang Wang
- Department of Ophthalmology, Huashan Hospital, Fudan University, Shanghai 200040, China
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Zhu X, Yang Y, Lorincz-Comi N, Li G, Bentley A, de Vries PS, Brown M, Morrison AC, Rotimi C, James Gauderman W, Rao DC, Aschard H. A new Approach to Identify Gene-Environment Interactions and Reveal New Biological Insight in Complex traits. RESEARCH SQUARE 2023:rs.3.rs-3338723. [PMID: 37886448 PMCID: PMC10602131 DOI: 10.21203/rs.3.rs-3338723/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the CHARGE Gene-Lifestyle Interactions Working Group has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identified and confirmed 5 loci (6 independent signals) interacting with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrated that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated contribution ranges from 1.76% to 14.05% of SNP heritability of serum lipids in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Gen Li
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Amy Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Michael Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - W. James Gauderman
- Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - DC Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
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18
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Doumatey AP, Bentley AR, Akinyemi R, Olanrewaju TO, Adeyemo A, Rotimi C. Genes, environment, and African ancestry in cardiometabolic disorders. Trends Endocrinol Metab 2023; 34:601-621. [PMID: 37598069 PMCID: PMC10548552 DOI: 10.1016/j.tem.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/21/2023]
Abstract
The past two decades have been characterized by a substantial global increase in cardiometabolic diseases, but the prevalence and incidence of these diseases and related traits differ across populations. African ancestry populations are among the most affected yet least included in research. Populations of African descent manifest significant genetic and environmental diversity and this under-representation is a missed opportunity for discovery and could exacerbate existing health disparities and curtail equitable implementation of precision medicine. Here, we discuss cardiometabolic diseases and traits in the context of African descent populations, including both genetic and environmental contributors and emphasizing novel discoveries. We also review new initiatives to include more individuals of African descent in genomics to address current gaps in the field.
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Affiliation(s)
- Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Rufus Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training and Centre for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria; Department of Neurology, University College Hospital, Ibadan, Nigeria
| | - Timothy O Olanrewaju
- Division of Nephrology, Department of Medicine, University of Ilorin & University of Ilorin Teaching Hospital, Ilorin, Nigeria
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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Li C, Pan Y, Zhang R, Huang Z, Li D, Han Y, Larkin C, Rao V, Sun X, Kelly TN. Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment. Circ Res 2023; 132:1628-1647. [PMID: 37289909 PMCID: PMC10328558 DOI: 10.1161/circresaha.123.321999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.
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Affiliation(s)
- Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Davey Li
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Yunan Han
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Claire Larkin
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Varun Rao
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
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Wuni R, Ventura EF, Curi-Quinto K, Murray C, Nunes R, Lovegrove JA, Penny M, Favara M, Sanchez A, Vimaleswaran KS. Interactions between genetic and lifestyle factors on cardiometabolic disease-related outcomes in Latin American and Caribbean populations: A systematic review. Front Nutr 2023; 10:1067033. [PMID: 36776603 PMCID: PMC9909204 DOI: 10.3389/fnut.2023.1067033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The prevalence of cardiometabolic diseases has increased in Latin American and the Caribbean populations (LACP). To identify gene-lifestyle interactions that modify the risk of cardiometabolic diseases in LACP, a systematic search using 11 search engines was conducted up to May 2022. Methods Eligible studies were observational and interventional studies in either English, Spanish, or Portuguese. A total of 26,171 publications were screened for title and abstract; of these, 101 potential studies were evaluated for eligibility, and 74 articles were included in this study following full-text screening and risk of bias assessment. The Appraisal tool for Cross-Sectional Studies (AXIS) and the Risk Of Bias In Non-Randomized Studies-of Interventions (ROBINS-I) assessment tool were used to assess the methodological quality and risk of bias of the included studies. Results We identified 122 significant interactions between genetic and lifestyle factors on cardiometabolic traits and the vast majority of studies come from Brazil (29), Mexico (15) and Costa Rica (12) with FTO, APOE, and TCF7L2 being the most studied genes. The results of the gene-lifestyle interactions suggest effects which are population-, gender-, and ethnic-specific. Most of the gene-lifestyle interactions were conducted once, necessitating replication to reinforce these results. Discussion The findings of this review indicate that 27 out of 33 LACP have not conducted gene-lifestyle interaction studies and only five studies have been undertaken in low-socioeconomic settings. Most of the studies were cross-sectional, indicating a need for longitudinal/prospective studies. Future gene-lifestyle interaction studies will need to replicate primary research of already studied genetic variants to enable comparison, and to explore the interactions between genetic and other lifestyle factors such as those conditioned by socioeconomic factors and the built environment. The protocol has been registered on PROSPERO, number CRD42022308488. Systematic review registration https://clinicaltrials.gov, identifier CRD420223 08488.
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Affiliation(s)
- Ramatu Wuni
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
| | - Eduard F. Ventura
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
| | | | - Claudia Murray
- Department of Real Estate and Planning, University of Reading, Reading, United Kingdom
| | - Richard Nunes
- Department of Real Estate and Planning, University of Reading, Reading, United Kingdom
| | - Julie A. Lovegrove
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
| | - Mary Penny
- Instituto de Investigación Nutricional, Lima, Peru
| | - Marta Favara
- Oxford Department of International Development, University of Oxford, Oxford, United Kingdom
| | - Alan Sanchez
- Grupo de Análisis para el Desarrollo (GRADE), Lima, Peru
| | - Karani Santhanakrishnan Vimaleswaran
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
- Institute for Food, Nutrition and Health (IFNH), University of Reading, Reading, United Kingdom
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21
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Quinlan RA, Clark JI. Insights into the biochemical and biophysical mechanisms mediating the longevity of the transparent optics of the eye lens. J Biol Chem 2022; 298:102537. [PMID: 36174677 PMCID: PMC9638808 DOI: 10.1016/j.jbc.2022.102537] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 11/18/2022] Open
Abstract
In the human eye, a transparent cornea and lens combine to form the "refracton" to focus images on the retina. This requires the refracton to have a high refractive index "n," mediated largely by extracellular collagen fibrils in the corneal stroma and the highly concentrated crystallin proteins in the cytoplasm of the lens fiber cells. Transparency is a result of short-range order in the spatial arrangement of corneal collagen fibrils and lens crystallins, generated in part by post-translational modifications (PTMs). However, while corneal collagen is remodeled continuously and replaced, lens crystallins are very long-lived and are not replaced and so accumulate PTMs over a lifetime. Eventually, a tipping point is reached when protein aggregation results in increased light scatter, inevitably leading to the iconic protein condensation-based disease, age-related cataract (ARC). Cataracts account for 50% of vision impairment worldwide, affecting far more people than other well-known protein aggregation-based diseases. However, because accumulation of crystallin PTMs begins before birth and long before ARC presents, we postulate that the lens protein PTMs contribute to a "cataractogenic load" that not only increases with age but also has protective effects on optical function by stabilizing lens crystallins until a tipping point is reached. In this review, we highlight decades of experimental findings that support the potential for PTMs to be protective during normal development. We hypothesize that ARC is preventable by protecting the biochemical and biophysical properties of lens proteins needed to maintain transparency, refraction, and optical function.
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Affiliation(s)
- Roy A Quinlan
- Department of Biosciences, Durham University, South Road Science Site, Durham, United Kingdom; Department of Biological Structure, University of Washington, Seattle, Washington, USA.
| | - John I Clark
- Department of Biological Structure, University of Washington, Seattle, Washington, USA.
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Wang Z, Emmerich A, Pillon NJ, Moore T, Hemerich D, Cornelis MC, Mazzaferro E, Broos S, Ahluwalia TS, Bartz TM, Bentley AR, Bielak LF, Chong M, Chu AY, Berry D, Dorajoo R, Dueker ND, Kasbohm E, Feenstra B, Feitosa MF, Gieger C, Graff M, Hall LM, Haller T, Hartwig FP, Hillis DA, Huikari V, Heard-Costa N, Holzapfel C, Jackson AU, Johansson Å, Jørgensen AM, Kaakinen MA, Karlsson R, Kerr KF, Kim B, Koolhaas CM, Kutalik Z, Lagou V, Lind PA, Lorentzon M, Lyytikäinen LP, Mangino M, Metzendorf C, Monroe KR, Pacolet A, Pérusse L, Pool R, Richmond RC, Rivera NV, Robiou-du-Pont S, Schraut KE, Schulz CA, Stringham HM, Tanaka T, Teumer A, Turman C, van der Most PJ, Vanmunster M, van Rooij FJA, van Vliet-Ostaptchouk JV, Zhang X, Zhao JH, Zhao W, Balkhiyarova Z, Balslev-Harder MN, Baumeister SE, Beilby J, Blangero J, Boomsma DI, Brage S, Braund PS, Brody JA, Bruinenberg M, Ekelund U, Liu CT, Cole JW, Collins FS, Cupples LA, Esko T, Enroth S, Faul JD, Fernandez-Rhodes L, Fohner AE, Franco OH, Galesloot TE, Gordon SD, Grarup N, Hartman CA, Heiss G, Hui J, Illig T, Jago R, James A, Joshi PK, Jung T, Kähönen M, Kilpeläinen TO, Koh WP, Kolcic I, et alWang Z, Emmerich A, Pillon NJ, Moore T, Hemerich D, Cornelis MC, Mazzaferro E, Broos S, Ahluwalia TS, Bartz TM, Bentley AR, Bielak LF, Chong M, Chu AY, Berry D, Dorajoo R, Dueker ND, Kasbohm E, Feenstra B, Feitosa MF, Gieger C, Graff M, Hall LM, Haller T, Hartwig FP, Hillis DA, Huikari V, Heard-Costa N, Holzapfel C, Jackson AU, Johansson Å, Jørgensen AM, Kaakinen MA, Karlsson R, Kerr KF, Kim B, Koolhaas CM, Kutalik Z, Lagou V, Lind PA, Lorentzon M, Lyytikäinen LP, Mangino M, Metzendorf C, Monroe KR, Pacolet A, Pérusse L, Pool R, Richmond RC, Rivera NV, Robiou-du-Pont S, Schraut KE, Schulz CA, Stringham HM, Tanaka T, Teumer A, Turman C, van der Most PJ, Vanmunster M, van Rooij FJA, van Vliet-Ostaptchouk JV, Zhang X, Zhao JH, Zhao W, Balkhiyarova Z, Balslev-Harder MN, Baumeister SE, Beilby J, Blangero J, Boomsma DI, Brage S, Braund PS, Brody JA, Bruinenberg M, Ekelund U, Liu CT, Cole JW, Collins FS, Cupples LA, Esko T, Enroth S, Faul JD, Fernandez-Rhodes L, Fohner AE, Franco OH, Galesloot TE, Gordon SD, Grarup N, Hartman CA, Heiss G, Hui J, Illig T, Jago R, James A, Joshi PK, Jung T, Kähönen M, Kilpeläinen TO, Koh WP, Kolcic I, Kraft PP, Kuusisto J, Launer LJ, Li A, Linneberg A, Luan J, Vidal PM, Medland SE, Milaneschi Y, Moscati A, Musk B, Nelson CP, Nolte IM, Pedersen NL, Peters A, Peyser PA, Power C, Raitakari OT, Reedik M, Reiner AP, Ridker PM, Rudan I, Ryan K, Sarzynski MA, Scott LJ, Scott RA, Sidney S, Siggeirsdottir K, Smith AV, Smith JA, Sonestedt E, Strøm M, Tai ES, Teo KK, Thorand B, Tönjes A, Tremblay A, Uitterlinden AG, Vangipurapu J, van Schoor N, Völker U, Willemsen G, Williams K, Wong Q, Xu H, Young KL, Yuan JM, Zillikens MC, Zonderman AB, Ameur A, Bandinelli S, Bis JC, Boehnke M, Bouchard C, Chasman DI, Smith GD, de Geus EJC, Deldicque L, Dörr M, Evans MK, Ferrucci L, Fornage M, Fox C, Garland T, Gudnason V, Gyllensten U, Hansen T, Hayward C, Horta BL, Hyppönen E, Jarvelin MR, Johnson WC, Kardia SLR, Kiemeney LA, Laakso M, Langenberg C, Lehtimäki T, Marchand LL, Magnusson PKE, Martin NG, Melbye M, Metspalu A, Meyre D, North KE, Ohlsson C, Oldehinkel AJ, Orho-Melander M, Pare G, Park T, Pedersen O, Penninx BWJH, Pers TH, Polasek O, Prokopenko I, Rotimi CN, Samani NJ, Sim X, Snieder H, Sørensen TIA, Spector TD, Timpson NJ, van Dam RM, van der Velde N, van Duijn CM, Vollenweider P, Völzke H, Voortman T, Waeber G, Wareham NJ, Weir DR, Wichmann HE, Wilson JF, Hevener AL, Krook A, Zierath JR, Thomis MAI, Loos RJF, Hoed MD. Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention. Nat Genet 2022; 54:1332-1344. [PMID: 36071172 PMCID: PMC9470530 DOI: 10.1038/s41588-022-01165-1] [Show More Authors] [Citation(s) in RCA: 111] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 07/18/2022] [Indexed: 02/02/2023]
Abstract
Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.
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Affiliation(s)
- Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Andrew Emmerich
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Nicolas J Pillon
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Tim Moore
- Division of Cardiology, Department of Medicine, University of California, Los Angeles, CA, USA
| | - Daiane Hemerich
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marilyn C Cornelis
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Eugenia Mazzaferro
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Siacia Broos
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Exercise Physiology Research Group, KU Leuven, Leuven, Belgium
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Physical Activity, Sports & Health Research Group, KU Leuven, Leuven, Belgium
| | - Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Mike Chong
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Audrey Y Chu
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- GlaxoSmithKline, Cambridge, MA, USA
| | - Diane Berry
- Division of Population, Policy and Practice, Great Ormond Street Hospital Institute for Child Health, University College London, London, UK
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Nicole D Dueker
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
- Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Elisa Kasbohm
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- Institute of Mathematics and Computer Science, University of Greifswald, Greifswald, Germany
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Leanne M Hall
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Toomas Haller
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fernando P Hartwig
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
- MRC Integrative Epidemiology Unit, NIHR Bristol Biomedical Research Center, University of Bristol, Bristol, UK
| | - David A Hillis
- Genetics, Genomics, and Bioinformatics Graduate Program, University of California, Riverside, CA, USA
| | - Ville Huikari
- Institute of Health Sciences, University of Oulu, Oulu, Finland
| | - Nancy Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Christina Holzapfel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
- Institute for Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Anja Moltke Jørgensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marika A Kaakinen
- Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kathleen F Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Boram Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Chantal M Koolhaas
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Zoltan Kutalik
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | | | - Penelope A Lind
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Biomedical Science, Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Mattias Lorentzon
- Geriatric Medicine, Institute of Medicine, University of Gothenburg and Sahlgrenska University Hospital Mölndal, Gothenburg, Sweden
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Christoph Metzendorf
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Kristine R Monroe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alexander Pacolet
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Exercise Physiology Research Group, KU Leuven, Leuven, Belgium
| | - Louis Pérusse
- Department of Kinesiology, Université Laval, Quebec, Quebec, Canada
- Centre Nutrition Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Quebec, Canada
| | - Rene Pool
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit and Avon Longitudinal Study of Parents and Children, University of Bristol Medical School, Population Health Sciences and Avon Longitudinal Study of Parents and Children, University of Bristol, Bristol, UK
| | - Natalia V Rivera
- Respiratory Division, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Rheumatology Division, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Center of Molecular Medicine (CMM), Karolinska Institutet, Stockholm, Sweden
| | - Sebastien Robiou-du-Pont
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Katharina E Schraut
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Christina-Alexandra Schulz
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, Germany
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Mathias Vanmunster
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Exercise Physiology Research Group, KU Leuven, Leuven, Belgium
| | - Frank J A van Rooij
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jana V van Vliet-Ostaptchouk
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Xiaoshuai Zhang
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- School of Public Health, Department of Biostatistics, Shandong University, Jinan, China
| | - Jing-Hua Zhao
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Zhanna Balkhiyarova
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Clinical and Experimental Medicine, University of Surrey, Guilford, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guilford, UK
| | - Marie N Balslev-Harder
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian E Baumeister
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- University of Münster, Münster, Germany
| | - John Beilby
- Diagnostic Genomics, PathWest Laboratory Medicine WA, Perth, Western Australia, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Peter S Braund
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | | | - Ulf Ekelund
- Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - John W Cole
- Vascular Neurology, Department of Neurology, University of Maryland School of Medicine and the Baltimore VAMC, Baltimore, MD, USA
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, NIH, Bethesda, MD, USA
| | - L Adrienne Cupples
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stefan Enroth
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Lindsay Fernandez-Rhodes
- Department of Biobehavioral Health, College of Health and Human Development, Pennsylvania State University, University Park, PA, USA
| | - Alison E Fohner
- Department of Epidemiology, Institute of Public Health Genetics, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Oscar H Franco
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Tessel E Galesloot
- Radboud Institute for Health Sciences, Department for Health Evidence, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Scott D Gordon
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Catharina A Hartman
- Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jennie Hui
- Diagnostic Genomics, PathWest Laboratory Medicine WA, Perth, Western Australia, Australia
- School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
- Busselton Population Medical Research Institute, Busselton, Western Australia, Australia
| | - Thomas Illig
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Russell Jago
- Centre for Exercise Nutrition & Health Sciences, School for Policy Studies, University of Bristol, Bristol, UK
| | - Alan James
- Department of Pulmonary Physiology and Sleep Medicine, Sir Charles Gairdner Hospital, Western Australia, Perth, Australia
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Humanity Inc, Boston, MA, USA
| | - Taeyeong Jung
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Mika Kähönen
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Woon-Puay Koh
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore, Singapore
| | - Ivana Kolcic
- Department of Public Health, University of Split School of Medicine, Split, Croatia
| | - Peter P Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institutes of Health, Baltimore, MD, USA
| | - Aihua Li
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Pedro Marques Vidal
- Division of Internal Medicine, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sarah E Medland
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Psychology and Faculty of Medicine, University of Queensland, St Lucia, Queensland, Australia
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bill Musk
- Busselton Population Medical Research Institute, Busselton, Western Australia, Australia
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Christine Power
- Division of Population, Policy and Practice, Great Ormond Street Hospital Institute for Child Health, University College London, London, UK
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Mägi Reedik
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Alex P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Kathy Ryan
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Stephen Sidney
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | | | - Albert V Smith
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Icelandic Heart Association, Kópavogur, Iceland
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Emily Sonestedt
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Marin Strøm
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- Faculty of Health Sciences, University of the Faroe Islands, Tórshavn, Faroe Islands
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Koon K Teo
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Angelo Tremblay
- Department of Kinesiology, Université Laval, Quebec, Quebec, Canada
- Centre Nutrition Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Quebec, Canada
| | - Andre G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jagadish Vangipurapu
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Natasja van Schoor
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, the Netherlands
| | - Uwe Völker
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Kayleen Williams
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin L Young
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jian Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - M Carola Zillikens
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Science, National Instiute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Adam Ameur
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, NIHR Bristol Biomedical Research Center, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, NIHR Bristol Biomedical Research Center, University of Bristol, Bristol, UK
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Louise Deldicque
- Faculty of Movement and Rehabilitation Sciences, Institute of Neuroscience, UC Louvain, Louvain-la-Neuve, Belgium
| | - Marcus Dörr
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Michele K Evans
- Laboratory of Epidemiology and Population Science, National Instiute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Caroline Fox
- Genetics and Pharmacogenomics (GpGx), Merck Research Labs, Boston, MA, USA
| | - Theodore Garland
- Department of Evolution, Ecology, and Organismal Biology, University of California, Riverside, Riverside, CA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kópavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Ulf Gyllensten
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Bernardo L Horta
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Elina Hyppönen
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Population, Policy and Practice, Great Ormond Street Hospital Institute for Child Health, University College London, London, UK
| | - Marjo-Riitta Jarvelin
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics and HPA-MRC Center, School of Public Health, Imperial College London, London, UK
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lambertus A Kiemeney
- Radboud Institute for Health Sciences, Department for Health Evidence, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Nicholas G Martin
- Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Mads Melbye
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- K.G.Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Center for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - David Meyre
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Albertine J Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Guillaume Pare
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Tune H Pers
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ozren Polasek
- University of Split School of Medicine, Split, Croatia
| | - Inga Prokopenko
- Department of Clinical and Experimental Medicine, University of Surrey, Guilford, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guilford, UK
- UMR 8199 - EGID, Institut Pasteur de Lille, CNRS, University of Lille, Lille, France
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol Medical School, University of Bristol, Bristol, UK
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | - Nathalie van der Velde
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
- Section of Geriatrics, Department of Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Aging and Later Life, Amsterdam, the Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Peter Vollenweider
- Division of Internal Medicine, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Gérard Waeber
- Division of Internal Medicine, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Heinz-Erich Wichmann
- Institute of Epidemiology, Helmholtz Zentrum München -Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Andrea L Hevener
- Division of Endocrinology, Department of Medicine, University of California, Los Angeles, CA, USA
| | - Anna Krook
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Juleen R Zierath
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Martine A I Thomis
- Faculty of Movement and Rehabilitation Sciences, Department of Movement Sciences - Physical Activity, Sports & Health Research Group, KU Leuven, Leuven, Belgium
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marcel den Hoed
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden.
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Identification of an Epigenetic Signature for Coronary Heart Disease in Postmenopausal Women’s PBMC DNA. Mediators Inflamm 2022; 2022:2185198. [PMID: 36032780 PMCID: PMC9417773 DOI: 10.1155/2022/2185198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/25/2022] [Accepted: 08/02/2022] [Indexed: 11/24/2022] Open
Abstract
Menopause is accompanied with an increased risk of cardiovascular disease. DNA methylation may have a significant impact on postmenopausal women's development of coronary heart disease. DNA methylation alterations in peripheral blood mononuclear cells (PBMCs) from women with coronary heart disease and healthy controls were detected using the Illumina Infinium MethylationEPIC BeadChip platform in this work. We employed Sangerbox technology and the GO and KEGG databases to further study the pathogenesis of coronary heart disease in postmenopausal women. After that, we used functional epigenetic module analysis and Cytoscape to remove the hub genes from the protein–protein interaction networks. Five genes (FOXA2, PTRD, CREB1, CTNAP2, and FBN2) were the hub genes. Lipid accumulation, endothelial cell failure, inflammatory responses, monocyte recruitment and aggregation, and other critical biological processes were all influenced by these genes. Finally, we employed methylation-specific PCR to demonstrate that FOXA2 was methylated at a high level in postmenopausal women with coronary heart disease. To better understand coronary heart disease in postmenopausal women's molecular mechanisms, our study examine the major factors contributing to the state of DNA methylation modification, which will help discover novel diagnostic tools and treatment options.
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24
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Winkler TW, Rasheed H, Teumer A, Gorski M, Rowan BX, Stanzick KJ, Thomas LF, Tin A, Hoppmann A, Chu AY, Tayo B, Thio CHL, Cusi D, Chai JF, Sieber KB, Horn K, Li M, Scholz M, Cocca M, Wuttke M, van der Most PJ, Yang Q, Ghasemi S, Nutile T, Li Y, Pontali G, Günther F, Dehghan A, Correa A, Parsa A, Feresin A, de Vries APJ, Zonderman AB, Smith AV, Oldehinkel AJ, De Grandi A, Rosenkranz AR, Franke A, Teren A, Metspalu A, Hicks AA, Morris AP, Tönjes A, Morgan A, Podgornaia AI, Peters A, Körner A, Mahajan A, Campbell A, Freedman BI, Spedicati B, Ponte B, Schöttker B, Brumpton B, Banas B, Krämer BK, Jung B, Åsvold BO, Smith BH, Ning B, Penninx BWJH, Vanderwerff BR, Psaty BM, Kammerer CM, Langefeld CD, Hayward C, Spracklen CN, Robinson-Cohen C, Hartman CA, Lindgren CM, Wang C, Sabanayagam C, Heng CK, Lanzani C, Khor CC, Cheng CY, Fuchsberger C, Gieger C, Shaffer CM, Schulz CA, Willer CJ, Chasman DI, Gudbjartsson DF, Ruggiero D, Toniolo D, Czamara D, Porteous DJ, Waterworth DM, Mascalzoni D, Mook-Kanamori DO, Reilly DF, Daw EW, Hofer E, Boerwinkle E, Salvi E, Bottinger EP, Tai ES, Catamo E, Rizzi F, Guo F, et alWinkler TW, Rasheed H, Teumer A, Gorski M, Rowan BX, Stanzick KJ, Thomas LF, Tin A, Hoppmann A, Chu AY, Tayo B, Thio CHL, Cusi D, Chai JF, Sieber KB, Horn K, Li M, Scholz M, Cocca M, Wuttke M, van der Most PJ, Yang Q, Ghasemi S, Nutile T, Li Y, Pontali G, Günther F, Dehghan A, Correa A, Parsa A, Feresin A, de Vries APJ, Zonderman AB, Smith AV, Oldehinkel AJ, De Grandi A, Rosenkranz AR, Franke A, Teren A, Metspalu A, Hicks AA, Morris AP, Tönjes A, Morgan A, Podgornaia AI, Peters A, Körner A, Mahajan A, Campbell A, Freedman BI, Spedicati B, Ponte B, Schöttker B, Brumpton B, Banas B, Krämer BK, Jung B, Åsvold BO, Smith BH, Ning B, Penninx BWJH, Vanderwerff BR, Psaty BM, Kammerer CM, Langefeld CD, Hayward C, Spracklen CN, Robinson-Cohen C, Hartman CA, Lindgren CM, Wang C, Sabanayagam C, Heng CK, Lanzani C, Khor CC, Cheng CY, Fuchsberger C, Gieger C, Shaffer CM, Schulz CA, Willer CJ, Chasman DI, Gudbjartsson DF, Ruggiero D, Toniolo D, Czamara D, Porteous DJ, Waterworth DM, Mascalzoni D, Mook-Kanamori DO, Reilly DF, Daw EW, Hofer E, Boerwinkle E, Salvi E, Bottinger EP, Tai ES, Catamo E, Rizzi F, Guo F, Rivadeneira F, Guilianini F, Sveinbjornsson G, Ehret G, Waeber G, Biino G, Girotto G, Pistis G, Nadkarni GN, Delgado GE, Montgomery GW, Snieder H, Campbell H, White HD, Gao H, Stringham HM, Schmidt H, Li H, Brenner H, Holm H, Kirsten H, Kramer H, Rudan I, Nolte IM, Tzoulaki I, Olafsson I, Martins J, Cook JP, Wilson JF, Halbritter J, Felix JF, Divers J, Kooner JS, Lee JJM, O'Connell J, Rotter JI, Liu J, Xu J, Thiery J, Ärnlöv J, Kuusisto J, Jakobsdottir J, Tremblay J, Chambers JC, Whitfield JB, Gaziano JM, Marten J, Coresh J, Jonas JB, Mychaleckyj JC, Christensen K, Eckardt KU, Mohlke KL, Endlich K, Dittrich K, Ryan KA, Rice KM, Taylor KD, Ho K, Nikus K, Matsuda K, Strauch K, Miliku K, Hveem K, Lind L, Wallentin L, Yerges-Armstrong LM, Raffield LM, Phillips LS, Launer LJ, Lyytikäinen LP, Lange LA, Citterio L, Klaric L, Ikram MA, Ising M, Kleber ME, Francescatto M, Concas MP, Ciullo M, Piratsu M, Orho-Melander M, Laakso M, Loeffler M, Perola M, de Borst MH, Gögele M, Bianca ML, Lukas MA, Feitosa MF, Biggs ML, Wojczynski MK, Kavousi M, Kanai M, Akiyama M, Yasuda M, Nauck M, Waldenberger M, Chee ML, Chee ML, Boehnke M, Preuss MH, Stumvoll M, Province MA, Evans MK, O'Donoghue ML, Kubo M, Kähönen M, Kastarinen M, Nalls MA, Kuokkanen M, Ghanbari M, Bochud M, Josyula NS, Martin NG, Tan NYQ, Palmer ND, Pirastu N, Schupf N, Verweij N, Hutri-Kähönen N, Mononen N, Bansal N, Devuyst O, Melander O, Raitakari OT, Polasek O, Manunta P, Gasparini P, Mishra PP, Sulem P, Magnusson PKE, Elliott P, Ridker PM, Hamet P, Svensson PO, Joshi PK, Kovacs P, Pramstaller PP, Rossing P, Vollenweider P, van der Harst P, Dorajoo R, Sim RZH, Burkhardt R, Tao R, Noordam R, Mägi R, Schmidt R, de Mutsert R, Rueedi R, van Dam RM, Carroll RJ, Gansevoort RT, Loos RJF, Felicita SC, Sedaghat S, Padmanabhan S, Freitag-Wolf S, Pendergrass SA, Graham SE, Gordon SD, Hwang SJ, Kerr SM, Vaccargiu S, Patil SB, Hallan S, Bakker SJL, Lim SC, Lucae S, Vogelezang S, Bergmann S, Corre T, Ahluwalia TS, Lehtimäki T, Boutin TS, Meitinger T, Wong TY, Bergler T, Rabelink TJ, Esko T, Haller T, Thorsteinsdottir U, Völker U, Foo VHX, Salomaa V, Vitart V, Giedraitis V, Gudnason V, Jaddoe VWV, Huang W, Zhang W, Wei WB, Kiess W, März W, Koenig W, Lieb W, Gao X, Sim X, Wang YX, Friedlander Y, Tham YC, Kamatani Y, Okada Y, Milaneschi Y, Yu Z, Stark KJ, Stefansson K, Böger CA, Hung AM, Kronenberg F, Köttgen A, Pattaro C, Heid IM. Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals. Commun Biol 2022; 5:580. [PMID: 35697829 PMCID: PMC9192715 DOI: 10.1038/s42003-022-03448-z] [Show More Authors] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 05/04/2022] [Indexed: 01/14/2023] Open
Abstract
Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.
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Affiliation(s)
- Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
| | - Humaira Rasheed
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
| | - Mathias Gorski
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Bryce X Rowan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Kira J Stanzick
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- BioCore-Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway
| | - Adrienne Tin
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Anselm Hoppmann
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | | | - Bamidele Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
| | - Chris H L Thio
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Daniele Cusi
- Institute of Biomedical Technologies, National Research Council of Italy, Milan, Italy
- Bio4Dreams-Business Nursery for Life Sciences, Milan, Italy
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Karsten B Sieber
- Target Sciences-Genetics, GlaxoSmithKline, Collegeville, PA, USA
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Man Li
- Division of Nephrology and Hypertension, Department of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Massimiliano Cocca
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
- Renal Division, Department of Medicine IV, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Sahar Ghasemi
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Teresa Nutile
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
| | - Yong Li
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Giulia Pontali
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
- University of Trento, Department of Cellular, Computational and Integrative Biology-CIBIO, Trento, Italy
| | - Felix Günther
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
- Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich, Munich, Germany
| | - Abbas Dehghan
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Dementia Research Institute, Imperial College London, London, UK
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Afshin Parsa
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Agnese Feresin
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Aiko P J de Vries
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Baltimore, MD, USA
| | - Albert V Smith
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
| | - Albertine J Oldehinkel
- Interdisciplinary Center of Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Alessandro De Grandi
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Alexander R Rosenkranz
- Department of Internal Medicine, Division of Nephrology, Medical University Graz, Graz, Austria
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Andrej Teren
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andrew A Hicks
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Andrew P Morris
- Department of Health Data Science, University of Liverpool, Liverpool, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Anke Tönjes
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Anna Morgan
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | | | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Chair of Epidemiology, IBE, Faculty of Medicine, Ludwig-Maximilians-Universität München, München, Germany
| | - Antje Körner
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Center for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Archie Campbell
- Center for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Barry I Freedman
- Section on Nephrology, Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Beatrice Spedicati
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Belen Ponte
- Service de Néphrologie et Hypertension, Medicine Department, Geneva University Hospitals, Geneva, Switzerland
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Ben Brumpton
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, 7030, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, 7600, Norway
| | - Bernhard Banas
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Bernhard K Krämer
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology, Pneumology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Bettina Jung
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany
| | - Bjørn Olav Åsvold
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Boting Ning
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, VU University Medical Centre, Amsterdam, The Netherlands
| | - Brett R Vanderwerff
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Candace M Kammerer
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Cassianne Robinson-Cohen
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Medical Center, Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease and Integrated Program for Acute Kidney Injury Research, and Vanderbilt Precision Nephrology Program Nashville, Nashville, TN, USA
| | - Catharina A Hartman
- Interdisciplinary Center of Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Cecilia M Lindgren
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Chaolong Wang
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, Singapore
| | - Chiara Lanzani
- Nephrology and Dialysis Unit, Genomics of Renal Diseases and Hypertension Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Christian Fuchsberger
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian M Shaffer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Iceland School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | | | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - David J Porteous
- Center for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Center for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | | | - Deborah Mascalzoni
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
- Centre for Research Ethics & Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Dennis O Mook-Kanamori
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - E Warwick Daw
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, TX, USA
| | - Erika Salvi
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Erwin P Bottinger
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Hasso Plattner Institute and University of Potsdam, Potsdam, Germany
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke - NUS Medical School, Singapore, Singapore
| | - Eulalia Catamo
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Federica Rizzi
- Bio4Dreams-Business Nursery for Life Sciences, Milan, Italy
- ePhood Scientific Unit, ePhood SRL, Milano, Italy
| | - Feng Guo
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Franco Guilianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Georg Ehret
- Cardiology, Geneva University Hospitals, Geneva, Switzerland
| | - Gerard Waeber
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ginevra Biino
- Institute of Molecular Genetics "Luigi Luca Cavalli-Sforza", National Research Council of Italy, Pavia, Italy
| | - Giorgia Girotto
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Giorgio Pistis
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Harvey D White
- Green Lane Cardiovascular Service, Auckland City Hospital and University of Auckland, Auckland, New Zealand
| | - He Gao
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Helena Schmidt
- Research Unit Genetic Epidemiology, Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Hengtong Li
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Hilma Holm
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Holgen Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Holly Kramer
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
- Division of Nephrology and Hypertension, Loyola University Chicago, Chicago, IL, USA
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ioanna Tzoulaki
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Dementia Research Institute, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Jade Martins
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - James F Wilson
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Jan Halbritter
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Janine F Felix
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jasmin Divers
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jeannette Jen-Mai Lee
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | | | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institutefor Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jie Xu
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
| | - Johan Ärnlöv
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- School of Health and Social Studies, Dalarna University, Stockholm, Sweden
| | - Johanna Kuusisto
- University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Johanna Jakobsdottir
- Icelandic Heart Association, Kopavogur, Iceland
- The Center of Public Health Sciences, University of Iceland, Reykjavík, Iceland
| | - Johanne Tremblay
- Montreal University Hospital Research Center, CHUM, Montreal, QC, Canada
- CRCHUM, Montreal, QC, Canada
| | - John C Chambers
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - John B Whitfield
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - John M Gaziano
- Department of Internal Medicine, Harvard Medical School, Boston, MA, USA
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jost B Jonas
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany
- Instituteof Molecular and Clinical Ophthalmology, Basel, Switzerland
- Privatpraxis Prof Jonas und Dr Panda-Jonas, Heidelberg, Germany
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Charlottesville, VA, USA
| | - Kaare Christensen
- Danish Aging Research Center, University of Southern Denmark, Odense C, Denmark
| | - Kai-Uwe Eckardt
- Intensive Care Medicine, Charité, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Karlhans Endlich
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany
| | - Katalin Dittrich
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Center for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Kathleen A Ryan
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institutefor Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kevin Ho
- Geisinger Research, Biomedical and Translational Informatics Institute, Rockville, MD, USA
- Department of Nephrology, Geisinger, Danville, PA, USA
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland
- Department of Cardiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, Ludwig-Maximilians-Universität München, München, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Kozeta Miliku
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lars Lind
- Cardiovascular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Lars Wallentin
- Cardiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Lawrence S Phillips
- Atlanta VA Health Care System, Decatur, GA, USA
- Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Bethesda, MD, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado Denver-Anschutz Medical Campus, Aurora, CO, USA
| | - Lorena Citterio
- Nephrology and Dialysis Unit, Genomics of Renal Diseases and Hypertension Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Lucija Klaric
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marcus Ising
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | | | - Maria Pina Concas
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Marina Ciullo
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Mario Piratsu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, Cagliari, Italy
| | | | - Markku Laakso
- University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Markus Perola
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Martin H de Borst
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Martin Gögele
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Martina La Bianca
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Mary Ann Lukas
- Target Sciences-Genetics, GlaxoSmithKline, Albuquerque, NM, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Mary L Biggs
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Mary K Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Masahiro Kanai
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masayuki Yasuda
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Miao-Ling Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Stumvoll
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Baltimore, MD, USA
| | - Michelle L O'Donoghue
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
- TIMI Study Group, Boston, MA, USA
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences (IMS), Yokohama (Kanagawa), Japan
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
| | - Mikko Kuokkanen
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- The Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Murielle Bochud
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, 1010, Lausanne, Switzerland
| | - Navya Shilpa Josyula
- Department of Population Health Sciences, Geisinger Health, 100 N. Academy Ave., Danville, PA, USA
| | | | - Nicholas Y Q Tan
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | | | - Nicola Pirastu
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Nicole Schupf
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY, USA
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Nina Hutri-Kähönen
- Tampere Centre for Skills Training and Simulation, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, WA, USA
- Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Olivier Devuyst
- Institute of Physiology, University of Zurich, Zurich, Switzerland
| | - Olle Melander
- Department of Clincial Sciences in Malmö, Lund University, Malmö, Sweden
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Center of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
- Algebra University College, Ilica 242, Zagreb, Croatia
| | - Paolo Manunta
- Nephrology and Dialysis Unit, Genomics of Renal Diseases and Hypertension Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Gasparini
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Paul Elliott
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Dementia Research Institute, Imperial College London, London, UK
- Imperial College NIHR Biomedical Research Center, Imperial College London, London, UK
- Health Data Research UK-London, London, UK
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Pavel Hamet
- Montreal University Hospital Research Center, CHUM, Montreal, QC, Canada
- Medpharmgene, Montreal, QC, Canada
| | - Per O Svensson
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
- Department of Cardiology, Södersjukhuset, Stockholm, Sweden
| | - Peter K Joshi
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Peter P Pramstaller
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Peter Rossing
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Ralene Z H Sim
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Ralph Burkhardt
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ron T Gansevoort
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Department of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Sanaz Sedaghat
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Sandra Freitag-Wolf
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Sarah A Pendergrass
- Geisinger Research, Biomedical and Translational Informatics Institute, Danville, PA, USA
| | - Sarah E Graham
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Shih-Jen Hwang
- NHLBI's Framingham Heart Study, Framingham, MA, USA
- The Center for Population Studies, NHLBI, Framingham, MA, USA
| | - Shona M Kerr
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Simona Vaccargiu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, Cagliari, Italy
| | - Snehal B Patil
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Stein Hallan
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Nephrology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Stephan J L Bakker
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Su-Chi Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Diabetes Center, Khoo Teck Puat Hospital, Singapore, Singapore
| | | | - Suzanne Vogelezang
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tanguy Corre
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, 1010, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- The Bioinformatics Center, Department of Biology, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Thibaud S Boutin
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Thomas Meitinger
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Tobias Bergler
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Ton J Rabelink
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory of Experimental Vascular Research, Leiden University Medical Center, Leiden, The Netherlands
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Toomas Haller
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Unnur Thorsteinsdottir
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Valencia Hui Xian Foo
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Vilmantas Giedraitis
- Molecular Geriatrics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Vilmundur Gudnason
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Vincent W V Jaddoe
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center, Shanghai, China
- Shanghai Industrial Technology Institute, Shanghai, China
| | - Weihua Zhang
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UK
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wieland Kiess
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Center for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Wolfgang Koenig
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank Popgen, Kiel University, Kiel, Germany
| | - Xin Gao
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yechiel Friedlander
- School of Public Health and Community Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences (IMS), Osaka, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuri Milaneschi
- Department of Psychiatry, VU University Medical Centre, Amsterdam, The Netherlands
| | - Zhi Yu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
| | - Klaus J Stark
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Kari Stefansson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Carsten A Böger
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany
| | - Adriana M Hung
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Medical Center, Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease and Integrated Program for Acute Kidney Injury Research, and Vanderbilt Precision Nephrology Program Nashville, Nashville, TN, USA
| | - Florian Kronenberg
- Department of Genetics and Pharmacology, Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
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Yang X, Li Q, Liu D, Han M, Qie R, Huang S, Zhang Y, Wu X, Zhao Y, Feng Y, Wu Y, Li Y, Hu D, Sun L. Joint effect of physical activity and blood lipid levels on all-cause and cardiovascular disease mortality: The Rural Chinese Cohort Study. Nutr Metab Cardiovasc Dis 2022; 32:1445-1453. [PMID: 35459605 DOI: 10.1016/j.numecd.2022.03.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND AND AIMS We aimed to evaluate the joint effect of physical activity (PA) and blood lipid levels on all-cause and cardiovascular disease (CVD) mortality. METHODS AND RESULTS We analyzed 17,236 participants from the Rural Chinese Cohort Study. Cox's proportional-hazards regression models were used to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) between the joint effect of PA and blood lipid levels and risk of all-cause and CVD mortality. Restricted cubic splines were used to estimate the dose-response relationship of PA with risk of all-cause and CVD mortality. During a median follow-up of 6.01 years there were 1106 deaths (484 from CVD) among participants. For all-cause mortality, compared with the group with dyslipidemia and extremely light PA (ELPA), the HRs with dyslipidemia and light PA (LPA), moderate PA (MPA), and heavy PA (HPA) were 0.56 (95% CI 0.45-0.70), 0.59 (0.46-0.75), and 0.59 (0.45-0.78), respectively, while the HRs of groups with normal lipid levels and ELPA, LPA, MPA, and HPA were 0.88 (0.72-1.04), 0.59 (0.48-0.73), 0.53 (0.41-0.67), and 0.38 (0.29-0.50), respectively. We observed similar effects on CVD mortality. Restricted cubic splines showed a curvilinear relationship between PA and risk of all-cause and CVD mortality with normal lipid levels and with dyslipidemia. CONCLUSION Higher PA reduces the risk of all-cause and CVD mortality. Higher levels of PA are needed in the population.
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Affiliation(s)
- Xingjin Yang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Quanman Li
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Dechen Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China; Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Minghui Han
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Ranran Qie
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Shengbing Huang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yanyan Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Xiaoyan Wu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Yang Zhao
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yifei Feng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yuying Wu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Yang Li
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
| | - Liang Sun
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
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26
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Khankari NK, Keaton JM, Walker VM, Lee KM, Shuey MM, Clarke SL, Heberer KR, Miller DR, Reaven PD, Lynch JA, Vujkovic M, Edwards TL. Using Mendelian randomisation to identify opportunities for type 2 diabetes prevention by repurposing medications used for lipid management. EBioMedicine 2022; 80:104038. [PMID: 35500537 PMCID: PMC9062817 DOI: 10.1016/j.ebiom.2022.104038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/14/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Maintaining a healthy lifestyle to reduce type 2 diabetes (T2D) risk is challenging and additional strategies for T2D prevention are needed. We evaluated several lipid control medications as potential therapeutic options for T2D prevention using tissue-specific predicted gene expression summary statistics in a two-sample Mendelian randomisation (MR) design. METHODS Large-scale European genome-wide summary statistics for lipids and T2D were leveraged in our multi-stage analysis to estimate changes in either lipid levels or T2D risk driven by tissue-specific predicted gene expression. We incorporated tissue-specific predicted gene expression summary statistics to proxy therapeutic effects of three lipid control medications [i.e., statins, icosapent ethyl (IPE), and proprotein convertase subtilisin/kexin type-9 inhibitors (PCSK-9i)] on T2D susceptibility using two-sample Mendelian randomisation (MR). FINDINGS IPE, as proxied via increased FADS1 expression, was predicted to lower triglycerides and was associated with a 53% reduced risk of T2D. Statins and PCSK-9i, as proxied by reduced HMGCR and PCSK9 expression, respectively, were predicted to lower LDL-C levels but were not associated with T2D susceptibility. INTERPRETATION Triglyceride lowering via IPE may reduce the risk of developing T2D in populations of European ancestry. However, experimental validation using animal models is needed to substantiate our results and to motivate randomized control trials (RCTs) for IPE as putative treatment for T2D prevention. FUNDING Only summary statistics were used in this analysis. Funding information is detailed under Acknowledgments.
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Affiliation(s)
- Nikhil K Khankari
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, 2525 West End Ave, Suite 700, Nashville, TN 37203, USA.
| | - Jacob M Keaton
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Venexia M Walker
- Medical Research Council, Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kyung Min Lee
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Megan M Shuey
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, 2525 West End Ave, Suite 700, Nashville, TN 37203, USA
| | - Shoa L Clarke
- Departments of Medicine and Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Kent R Heberer
- VA Palo Alto Health Care System, Palo Alto, CA, USA; Departments of Medicine and Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
| | - Donald R Miller
- Center for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, MA, USA; Center for Population Health, Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA
| | - Peter D Reaven
- Phoenix VA Health Care Center, Phoenix, AZ, USA; College of Medicine, University of Arizona, Phoenix, AZ, USA
| | - Julie A Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; College of Nursing and Health Sciences, University of Massachusetts, Lowell, MA, USA
| | - Marijana Vujkovic
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Todd L Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, 2525 West End Ave, Suite 700, Nashville, TN 37203, USA; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Nashville VA Medical Center, Nashville, TN, USA.
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27
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Huang RDL, Nguyen XMT, Peloso GM, Trinder M, Posner DC, Aragam KG, Ho YL, Lynch JA, Damrauer SM, Chang KM, Tsao PS, Natarajan P, Assimes T, Gaziano JM, Djousse L, Cho K, Wilson PWF, Huffman JE, O’Donnell CJ, on behalf of the Veterans Affairs’ Million Veteran Program. Genome-wide and phenome-wide analysis of ideal cardiovascular health in the VA Million Veteran Program. PLoS One 2022; 17:e0267900. [PMID: 35613103 PMCID: PMC9132265 DOI: 10.1371/journal.pone.0267900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 04/18/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Genetic studies may help identify causal pathways; therefore, we sought to identify genetic determinants of ideal CVH and their association with CVD outcomes in the multi-population Veteran Administration Million Veteran Program. METHODS An ideal health score (IHS) was calculated from 3 clinical factors (blood pressure, total cholesterol, and blood glucose levels) and 3 behavioral factors (smoking status, physical activity, and BMI), ascertained at baseline. Multi-population genome-wide association study (GWAS) was performed on IHS and binary ideal health using linear and logistic regression, respectively. Using the genome-wide significant SNPs from the IHS GWAS, we created a weighted IHS polygenic risk score (PRSIHS) which was used (i) to conduct a phenome-wide association study (PheWAS) of associations between PRSIHS and ICD-9 phenotypes and (ii) to further test for associations with mortality and selected CVD outcomes using logistic and Cox regression and, as an instrumental variable, in Mendelian Randomization. RESULTS The discovery and replication cohorts consisted of 142,404 (119,129 European American (EUR); 16,495 African American (AFR)), and 45,766 (37,646 EUR; 5,366 AFR) participants, respectively. The mean age was 65.8 years (SD = 11.2) and 92.7% were male. Overall, 4.2% exhibited ideal CVH based on the clinical and behavioral factors. In the multi-population meta-analysis, variants at 17 loci were associated with IHS and each had known GWAS associations with multiple components of the IHS. PheWAS analysis in 456,026 participants showed that increased PRSIHS was associated with a lower odds ratio for many CVD outcomes and risk factors. Both IHS and PRSIHS measures of ideal CVH were associated with significantly less CVD outcomes and CVD mortality. CONCLUSION A set of high interest genetic variants contribute to the presence of ideal CVH in a multi-ethnic cohort of US Veterans. Genetically influenced ideal CVH is associated with lower odds of CVD outcomes and mortality.
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Affiliation(s)
- Rose D. L. Huang
- Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Xuan-Mai T. Nguyen
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America
| | - Gina M. Peloso
- Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Mark Trinder
- Centre for Heart Lung Innovation, University of British Columbia, Vancouver, Canada
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Daniel C. Posner
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Krishna G. Aragam
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yuk-Lam Ho
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Julie A. Lynch
- VA Informatics & Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah, United States of America
- College of Nursing & Health Sciences, University of Massachusetts Boston, Boston, Massachusetts, United States of America
| | - Scott M. Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States of America
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Kyong-Mi Chang
- Corporal Michael Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States of America
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Philip S. Tsao
- VA Palo Alto Health Care System, Palo Alto, California, United States of America
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Themistocles Assimes
- VA Palo Alto Health Care System, Palo Alto, California, United States of America
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - J. Michael Gaziano
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Luc Djousse
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Kelly Cho
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Peter W. F. Wilson
- Atlanta VA Medical Center, Decatur, Georgia, United States of America
- Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Jennifer E. Huffman
- Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Christopher J. O’Donnell
- Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Heart & Vascular Center, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
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SARZYNSKI MARKA, RICE TREVAK, DESPRÉS JEANPIERRE, PÉRUSSE LOUIS, TREMBLAY ANGELO, STANFORTH PHILIPR, TCHERNOF ANDRÉ, BARBER JACOBL, FALCIANI FRANCESCO, CLISH CLARY, ROBBINS JEREMYM, GHOSH SUJOY, GERSZTEN ROBERTE, LEON ARTHURS, SKINNER JAMESS, RAO DC, BOUCHARD CLAUDE. The HERITAGE Family Study: A Review of the Effects of Exercise Training on Cardiometabolic Health, with Insights into Molecular Transducers. Med Sci Sports Exerc 2022; 54:S1-S43. [PMID: 35611651 PMCID: PMC9012529 DOI: 10.1249/mss.0000000000002859] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The aim of the HERITAGE Family Study was to investigate individual differences in response to a standardized endurance exercise program, the role of familial aggregation, and the genetics of response levels of cardiorespiratory fitness and cardiovascular disease and diabetes risk factors. Here we summarize the findings and their potential implications for cardiometabolic health and cardiorespiratory fitness. It begins with overviews of background and planning, recruitment, testing and exercise program protocol, quality control measures, and other relevant organizational issues. A summary of findings is then provided on cardiorespiratory fitness, exercise hemodynamics, insulin and glucose metabolism, lipid and lipoprotein profiles, adiposity and abdominal visceral fat, blood levels of steroids and other hormones, markers of oxidative stress, skeletal muscle morphology and metabolic indicators, and resting metabolic rate. These summaries document the extent of the individual differences in response to a standardized and fully monitored endurance exercise program and document the importance of familial aggregation and heritability level for exercise response traits. Findings from genomic markers, muscle gene expression studies, and proteomic and metabolomics explorations are reviewed, along with lessons learned from a bioinformatics-driven analysis pipeline. The new opportunities being pursued in integrative -omics and physiology have extended considerably the expected life of HERITAGE and are being discussed in relation to the original conceptual model of the study.
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Affiliation(s)
- MARK A. SARZYNSKI
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC
| | - TREVA K. RICE
- Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO
| | - JEAN-PIERRE DESPRÉS
- Department of Kinesiology, Faculty of Medicine, Laval University, Quebec, QC, CANADA
- Quebec Heart and Lung Institute Research Center, Laval University, Québec, QC, CANADA
| | - LOUIS PÉRUSSE
- Department of Kinesiology, Faculty of Medicine, Laval University, Quebec, QC, CANADA
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec, QC, CANADA
| | - ANGELO TREMBLAY
- Department of Kinesiology, Faculty of Medicine, Laval University, Quebec, QC, CANADA
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec, QC, CANADA
| | - PHILIP R. STANFORTH
- Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX
| | - ANDRÉ TCHERNOF
- Quebec Heart and Lung Institute Research Center, Laval University, Québec, QC, CANADA
- School of Nutrition, Laval University, Quebec, QC, CANADA
| | - JACOB L. BARBER
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC
| | - FRANCESCO FALCIANI
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UNITED KINGDOM
| | - CLARY CLISH
- Metabolomics Platform, Broad Institute and Harvard Medical School, Boston, MA
| | - JEREMY M. ROBBINS
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, MA
| | - SUJOY GHOSH
- Cardiovascular and Metabolic Disorders Program and Centre for Computational Biology, Duke-National University of Singapore Medical School, SINGAPORE
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA
| | - ROBERT E. GERSZTEN
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, MA
| | - ARTHUR S. LEON
- School of Kinesiology, University of Minnesota, Minneapolis, MN
| | | | - D. C. RAO
- Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO
| | - CLAUDE BOUCHARD
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA
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29
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Yeung MW, Wang S, van de Vegte YJ, Borisov O, van Setten J, Snieder H, Verweij N, Said MA, van der Harst P. Twenty-Five Novel Loci for Carotid Intima-Media Thickness: A Genome-Wide Association Study in >45 000 Individuals and Meta-Analysis of >100 000 Individuals. Arterioscler Thromb Vasc Biol 2022; 42:484-501. [PMID: 34852643 PMCID: PMC8939707 DOI: 10.1161/atvbaha.121.317007] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 11/22/2021] [Indexed: 01/06/2023]
Abstract
OBJECTIVE Carotid artery intima-media thickness (cIMT) is a widely accepted marker of subclinical atherosclerosis. Twenty susceptibility loci for cIMT were previously identified and the identification of additional susceptibility loci furthers our knowledge on the genetic architecture underlying atherosclerosis. APPROACH AND RESULTS We performed 3 genome-wide association studies in 45 185 participants from the UK Biobank study who underwent cIMT measurements and had data on minimum, mean, and maximum thickness. We replicated 15 known loci and identified 20 novel loci associated with cIMT at P<5×10-8. Seven novel loci (ZNF385D, ADAMTS9, EDNRA, HAND2, MYOCD, ITCH/EDEM2/MMP24, and MRTFA) were identified in all 3 phenotypes. An additional new locus (LOXL1) was identified in the meta-analysis of the 3 phenotypes. Sex interaction analysis revealed sex differences in 7 loci including a novel locus (SYNE3) in males. Meta-analysis of UK Biobank data with a previous meta-analysis led to identification of three novel loci (APOB, FIP1L1, and LOXL4). Transcriptome-wide association analyses implicated additional genes ARHGAP42, NDRG4, and KANK2. Gene set analysis showed an enrichment in extracellular organization and the PDGF (platelet-derived growth factor) signaling pathway. We found positive genetic correlations of cIMT with coronary artery disease rg=0.21 (P=1.4×10-7), peripheral artery disease rg=0.45 (P=5.3×10-5), and systolic blood pressure rg=0.30 (P=4.0×10-18). A negative genetic correlation between average of maximum cIMT and high-density lipoprotein was found rg=-0.12 (P=7.0×10-4). CONCLUSIONS Genome-wide association meta-analyses in >100 000 individuals identified 25 novel loci associated with cIMT providing insights into genes and tissue-specific regulatory mechanisms of proatherosclerotic processes. We found evidence for shared biological mechanisms with cardiovascular diseases.
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Affiliation(s)
- Ming Wai Yeung
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Siqi Wang
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
- Department of Epidemiology (S.W., H.S.), University of Groningen, University Medical Center Groningen, the Netherlands
- Division of Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, the Netherlands (M.W.Y., J.v.S., P.v.d.H.)
| | - Yordi J. van de Vegte
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Oleg Borisov
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Germany (O.B.)
| | - Jessica van Setten
- Department of Epidemiology (S.W., H.S.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology (S.W., H.S.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Niek Verweij
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - M. Abdullah Said
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
| | - Pim van der Harst
- Department of Cardiology (M.W.Y., S.W., Y.J.v.d.V., N.V., M.A.S., P.v.d.H.), University of Groningen, University Medical Center Groningen, the Netherlands
- Division of Heart & Lungs, Department of Cardiology, University Medical Center Utrecht, University of Utrecht, the Netherlands (M.W.Y., J.v.S., P.v.d.H.)
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30
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Lin H, Xuan L, Xiang J, Hou Y, Dai H, Wang T, Zhao Z, Wang S, Lu J, Xu Y, Chen Y, Wang W, Ning G, Bi Y, Li M, Xu M. Changes in adiposity modulate the APOA5 genetic effect on blood lipids: A longitudinal cohort study. Atherosclerosis 2022; 350:1-8. [DOI: 10.1016/j.atherosclerosis.2022.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/27/2022] [Accepted: 03/30/2022] [Indexed: 11/30/2022]
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31
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Cao X, Wang X, Zhang S, Sha Q. Gene-based association tests using GWAS summary statistics and incorporating eQTL. Sci Rep 2022; 12:3553. [PMID: 35241742 PMCID: PMC8894384 DOI: 10.1038/s41598-022-07465-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 02/11/2022] [Indexed: 01/29/2023] Open
Abstract
Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Xuexia Wang
- Department of Mathematics, University of North Texas, Denton, TX, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA.
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32
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San-Cristobal R, de Toro-Martín J, Vohl MC. Appraisal of Gene-Environment Interactions in GWAS for Evidence-Based Precision Nutrition Implementation. Curr Nutr Rep 2022; 11:563-573. [PMID: 35948824 PMCID: PMC9750926 DOI: 10.1007/s13668-022-00430-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2022] [Indexed: 01/31/2023]
Abstract
PURPOSE OF REVIEW This review aims to analyse the currently reported gene-environment (G × E) interactions in genome-wide association studies (GWAS), involving environmental factors such as lifestyle and dietary habits related to metabolic syndrome phenotypes. For this purpose, the present manuscript reviews the available GWAS registered on the GWAS Catalog reporting the interaction between environmental factors and metabolic syndrome traits. RECENT FINDINGS Advances in omics-related analytical and computational approaches in recent years have led to a better understanding of the biological processes underlying these G × E interactions. A total of 42 GWAS were analysed, reporting over 300 loci interacting with environmental factors. Alcohol consumption, sleep time, smoking habit and physical activity were the most studied environmental factors with significant G × E interactions. The implementation of more comprehensive GWAS will provide a better understanding of the metabolic processes that determine individual responses to environmental exposures and their association with the development of chronic diseases such as obesity and the metabolic syndrome. This will facilitate the development of precision approaches for better prevention, management and treatment of these diseases.
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Affiliation(s)
- Rodrigo San-Cristobal
- grid.23856.3a0000 0004 1936 8390Centre Nutrition, Santé Et Société (NUTRISS), Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Québec, QC Canada ,grid.23856.3a0000 0004 1936 8390School of Nutrition, Université Laval, Quebec, QC G1V 0A6 Canada
| | - Juan de Toro-Martín
- grid.23856.3a0000 0004 1936 8390Centre Nutrition, Santé Et Société (NUTRISS), Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Québec, QC Canada ,grid.23856.3a0000 0004 1936 8390School of Nutrition, Université Laval, Quebec, QC G1V 0A6 Canada
| | - Marie-Claude Vohl
- grid.23856.3a0000 0004 1936 8390Centre Nutrition, Santé Et Société (NUTRISS), Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Québec, QC Canada ,grid.23856.3a0000 0004 1936 8390School of Nutrition, Université Laval, Quebec, QC G1V 0A6 Canada
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33
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Kernel-based gene-environment interaction tests for rare variants with multiple quantitative phenotypes. PLoS One 2022; 17:e0275929. [PMID: 36223383 PMCID: PMC9555665 DOI: 10.1371/journal.pone.0275929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 09/26/2022] [Indexed: 11/19/2022] Open
Abstract
Previous studies have suggested that gene-environment interactions (GEIs) between a common variant and an environmental factor can influence multiple correlated phenotypes simultaneously, that is, GEI pleiotropy, and that analyzing multiple phenotypes jointly is more powerful than analyzing phenotypes separately by using single-phenotype GEI tests. Methods to test the GEI for rare variants with multiple phenotypes are, however, lacking. In our work, we model the correlation among the GEI effects of a variant on multiple quantitative phenotypes through four kernels and propose four multiphenotype GEI tests for rare variants, which are a test with a homogeneous kernel (Hom-GEI), a test with a heterogeneous kernel (Het-GEI), a test with a projection phenotype kernel (PPK-GEI) and a test with a linear phenotype kernel (LPK-GEI). Through numerical simulations, we show that correlation among phenotypes can enhance the statistical power except for LPK-GEI, which simply combines statistics from single-phenotype GEI tests and ignores the phenotypic correlations. Among almost all considered scenarios, Het-GEI and PPK-GEI are more powerful than Hom-GEI and LPK-GEI. We apply Het-GEI and PPK-GEI in the genome-wide GEI analysis of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the UK Biobank. We analyze 18,101 genes and find that LEUTX is associated with SBP and DBP (p = 2.20×10-6) through its interaction with hemoglobin. The single-phenotype GEI test and our multiphenotype GEI tests Het-GEI and PPK-GEI are also used to evaluate the gene-hemoglobin interactions for 22 genes that were previously reported to be associated with SBP or DBP in a meta-analysis of genetic main effects. MYO1C shows nominal significance (p < 0.05) by the Het-GEI test. NOS3 shows nominal significance in DBP and MYO1C in both SBP and DBP by the single-phenotype GEI test.
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34
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Venkataraman GR, DeBoever C, Tanigawa Y, Aguirre M, Ioannidis AG, Mostafavi H, Spencer CCA, Poterba T, Bustamante CD, Daly MJ, Pirinen M, Rivas MA. Bayesian model comparison for rare-variant association studies. Am J Hum Genet 2021; 108:2354-2367. [PMID: 34822764 PMCID: PMC8715195 DOI: 10.1016/j.ajhg.2021.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 11/02/2021] [Indexed: 12/12/2022] Open
Abstract
Whole-genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery not addressed by the traditional one variant, one phenotype association study. Here, we introduce a Bayesian model comparison approach called MRP (multiple rare variants and phenotypes) for rare-variant association studies that considers correlation, scale, and direction of genetic effects across a group of genetic variants, phenotypes, and studies, requiring only summary statistic data. We apply our method to exome sequencing data (n = 184,698) across 2,019 traits from the UK Biobank, aggregating signals in genes. MRP demonstrates an ability to recover signals such as associations between PCSK9 and LDL cholesterol levels. We additionally find MRP effective in conducting meta-analyses in exome data. Non-biomarker findings include associations between MC1R and red hair color and skin color, IL17RA and monocyte count, and IQGAP2 and mean platelet volume. Finally, we apply MRP in a multi-phenotype setting; after clustering the 35 biomarker phenotypes based on genetic correlation estimates, we find that joint analysis of these phenotypes results in substantial power gains for gene-trait associations, such as in TNFRSF13B in one of the clusters containing diabetes- and lipid-related traits. Overall, we show that the MRP model comparison approach improves upon useful features from widely used meta-analysis approaches for rare-variant association analyses and prioritizes protective modifiers of disease risk.
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Affiliation(s)
| | - Christopher DeBoever
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Matthew Aguirre
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | | | | | | | - Timothy Poterba
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Carlos D Bustamante
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Mark J Daly
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00014, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki 00014, Finland.
| | - Manuel A Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
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Bentley AR, Chen G, Doumatey AP, Shriner D, Meeks KAC, Gouveia MH, Ekoru K, Zhou J, Adeyemo A, Rotimi CN. GWAS in Africans identifies novel lipids loci and demonstrates heterogenous association within Africa. Hum Mol Genet 2021; 30:2205-2214. [PMID: 34196372 PMCID: PMC8561421 DOI: 10.1093/hmg/ddab174] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/22/2021] [Accepted: 06/23/2021] [Indexed: 01/11/2023] Open
Abstract
Serum lipids are biomarkers of cardiometabolic disease risk, and understanding genomic factors contributing to their distribution is of interest. Studies of lipids in Africans are rare, though it is expected that such studies could identify novel loci. We conducted a GWAS of 4317 Africans enrolled from Nigeria, Ghana and Kenya. We evaluated linear mixed models of high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), total cholesterol (CHOL), triglycerides (TG) and TG/HDLC. Replication was attempted in 9542 African Americans (AA). In our main analysis, we identified 28 novel associations in Africans. Of the 18 of these that could be tested in AA, three associations replicated (GPNMB-TG, ENPP1-TG and SMARCA4-LDLC). Five additional novel loci were discovered upon meta-analysis with AA (rs138282551-TG, PGBD5-HDLC, CD80-TG/HDLC, SLC44A1-CHOL and TLL2-CHOL). Analyses considering only those with predominantly West African ancestry (Nigeria, Ghana and AA) yielded new insights: ORC5-LDLC and chr20:60973327-CHOL. Among our novel findings are some loci with known connections to lipids pathways. For instance, rs147706369 (TLL2) alters a regulatory motif for sterol regulatory element-binding proteins, a family of transcription factors that control the expression of a range of enzymes involved in cholesterol, fatty acid and TG synthesis, and rs115749422 (SMARCA4), an independent association near the known LDLR locus that is rare or absent in populations without African ancestry. These findings demonstrate the utility of conducting genomic analyses in Africans for discovering novel loci and provide some preliminary evidence for caution against treating 'African ancestry' as a monolithic category.
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Affiliation(s)
- Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Karlijn A C Meeks
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Mateus H Gouveia
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Kenneth Ekoru
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
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Hartiala JA, Hilser JR, Biswas S, Lusis AJ, Allayee H. Gene-Environment Interactions for Cardiovascular Disease. Curr Atheroscler Rep 2021; 23:75. [PMID: 34648097 PMCID: PMC8903169 DOI: 10.1007/s11883-021-00974-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/12/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE OF REVIEW We provide an overview of recent findings with respect to gene-environment (GxE) interactions for cardiovascular disease (CVD) risk and discuss future opportunities for advancing the field. RECENT FINDINGS Over the last several years, GxE interactions for CVD have mostly been identified for smoking and coronary artery disease (CAD) or related risk factors. By comparison, there is more limited evidence for GxE interactions between CVD outcomes and other exposures, such as physical activity, air pollution, diet, and sex. The establishment of large consortia and population-based cohorts, in combination with new computational tools and mouse genetics platforms, can potentially overcome some of the limitations that have hindered human GxE interaction studies and reveal additional association signals for CVD-related traits. The identification of novel GxE interactions is likely to provide a better understanding of the pathogenesis and genetic liability of CVD, with significant implications for healthy lifestyles and therapeutic strategies.
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Affiliation(s)
- Jaana A Hartiala
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2250 Alcazar Street, CSC202, Los Angeles, CA, 90033, USA
| | - James R Hilser
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2250 Alcazar Street, CSC202, Los Angeles, CA, 90033, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Subarna Biswas
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2250 Alcazar Street, CSC202, Los Angeles, CA, 90033, USA
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Aldons J Lusis
- Department of Medicine, David Geffen School of Medicine of UCLA, Los Angeles, CA, 90095, USA
- Department of Microbiology, David Geffen School of Medicine of UCLA, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA, 90095, USA
| | - Hooman Allayee
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2250 Alcazar Street, CSC202, Los Angeles, CA, 90033, USA.
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
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37
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A genetic sum score of effect alleles associated with serum lipid concentrations interacts with educational attainment. Sci Rep 2021; 11:16541. [PMID: 34400708 PMCID: PMC8368036 DOI: 10.1038/s41598-021-95970-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) levels are influenced by both genes and the environment. The aim was to investigate whether education and income as indicators of socioeconomic position (SEP) interact with lipid-increasing genetic effect allele scores (GES) in a population-based cohort. Using baseline data of 4516 study participants, age- and sex-adjusted linear regression models were fitted to investigate associations between GES and lipids stratified by SEP as well as including GES×SEP interaction terms. In the highest education group compared to the lowest stronger effects per GES standard deviation were observed for HDL-C (2.96 mg/dl [95%-CI: 2.19, 3.83] vs. 2.45 mg/dl [95%-CI: 1.12, 3.72]), LDL-C (6.57 mg/dl [95%-CI: 4.73, 8.37] vs. 2.66 mg/dl [95%-CI: −0.50, 5.76]) and TC (8.06 mg/dl [95%-CI: 6.14, 9.98] vs. 4.37 mg/dl [95%-CI: 0.94, 7.80]). Using the highest education group as reference, interaction terms showed indication of GES by low education interaction for LDL-C (ßGES×Education: −3.87; 95%-CI: −7.47, −0.32), which was slightly attenuated after controlling for GESLDL-C×Diabetes interaction (ßGES×Education: −3.42; 95%-CI: −6.98, 0.18). The present study showed stronger genetic effects on LDL-C in higher SEP groups and gave indication for a GESLDL-C×Education interaction, demonstrating the relevance of SEP for the expression of genetic health risks.
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Abstract
PURPOSE OF REVIEW Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. RECENT FINDINGS More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.
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Affiliation(s)
- Germán D. Carrasquilla
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Mærsk Building, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Malene Revsbech Christiansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Mærsk Building, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Tuomas O. Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Mærsk Building, Blegdamsvej 3B, 2200 Copenhagen, Denmark
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Bos MM, de Vries L, Rensen PC, Willems van Dijk K, Blauw GJ, van Heemst D, Noordam R. Apolipoprotein E genotype, lifestyle and coronary artery disease: Gene-environment interaction analyses in the UK Biobank population. Atherosclerosis 2021; 328:33-37. [PMID: 34082327 DOI: 10.1016/j.atherosclerosis.2021.05.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 05/13/2021] [Accepted: 05/20/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND AIMS The APOE ε4 genotype has a higher risk for developing coronary artery disease (CAD), but there is preliminary evidence that antioxidative lifestyle factors interact with APOE genotype on CAD risk. Here, we assessed the effect modification of physical activity, oily fish and polyunsaturated fatty acid (PUFA) intake with APOE genotype on risk of incident CAD. METHODS The present study comprised 345,659 white European participants from UK Biobank (mean age: 56.5 years, 45.7% men) without a history of CAD. Information regarding physical activity, oily fish intake and PUFA intake was collected through questionnaires, and information on incident CAD through linkage with hospital admission records. Analyses were performed using Cox proportional hazard models adjusted for age and sex. RESULTS Higher physical activity level and oily fish intake were both associated with a lower incidence of CAD. However, these associations were similar across the different APOE genotypes (p-values for interaction > 0.05). Most notable, higher PUFA intake was associated with a lower CAD risk in APOE ε4 genotype carriers (hazard ratio: 0.76, 95% confidence interval: 0.63-0.92), and not in APOE ε3/ε3 genotype carriers (0.90; 0.79, 1.02), but without statistical evidence for effect modification (p-valueinteraction = 0.137). CONCLUSIONS While higher physical activity and high fish and PUFA intake were associated with a lower risk of incident CAD, no evidence for interaction of these lifestyle factors with APOE genotype was observed in UK Biobank participants. Interventions intended to reduce cardiovascular risk might therefore be similarly effective across the APOE genotype carriers.
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Affiliation(s)
- Maxime M Bos
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Lina de Vries
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick Cn Rensen
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands; Einthoven Laboratory of Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Ko Willems van Dijk
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands; Einthoven Laboratory of Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Gerard Jan Blauw
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands.
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Thio CHL, van Zon SKR, van der Most PJ, Snieder H, Bültmann U, Gansevoort RT. Associations of Genetic Factors, Educational Attainment, and Their Interaction With Kidney Function Outcomes. Am J Epidemiol 2021; 190:864-874. [PMID: 33089864 PMCID: PMC8096480 DOI: 10.1093/aje/kwaa237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 10/02/2020] [Accepted: 10/19/2020] [Indexed: 11/16/2022] Open
Abstract
Both genetic predisposition and low educational attainment (EA) are associated with higher risk of chronic kidney disease. We examined the interaction of EA and genetic risk in kidney function outcomes. We included 3,597 participants from the Prevention of Renal and Vascular End-Stage Disease Cohort Study, a longitudinal study in a community-based sample from Groningen, the Netherlands (median follow-up, 11 years; 1997–2012). Kidney function was approximated by obtaining estimated glomerular filtration rate (eGFR) from serum creatinine and cystatin C. Individual longitudinal linear eGFR trajectories were derived from linear mixed models. Genotype data on 63 single-nucleotide polymorphisms, with known associations with eGFR, were used to calculate an allele-weighted genetic score (WGS). EA was categorized into high, medium, and low. In ordinary least squares analysis, higher WGS and lower EA showed additive effects on reduced baseline eGFR; the interaction term was nonsignificant. In analysis of eGFR decline, the significant interaction term suggested amplification of genetic risk by low EA. Adjustment for known renal risk factors did not affect our results. This study presents the first evidence of gene-environment interaction between EA and a WGS for eGFR decline and provides population-level insights into the mechanisms underlying socioeconomic disparities in chronic kidney disease.
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Affiliation(s)
- Chris H L Thio
- Correspondence to Dr. Chris H. L. Thio, Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology (HPC FA40), University Medical Center Groningen, University of Groningen Hanzeplein 1, PO Box 30.001, 9700RB Groningen, the Netherlands (e-mail: )
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Xu H, Schwander K, Brown MR, Wang W, Waken RJ, Boerwinkle E, Cupples LA, de las Fuentes L, van Heemst D, Osazuwa-Peters O, de Vries PS, van Dijk KW, Sung YJ, Zhang X, Morrison AC, Rao DC, Noordam R, Liu CT. Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions. Eur J Hum Genet 2021; 29:839-850. [PMID: 33500576 PMCID: PMC8110957 DOI: 10.1038/s41431-021-00808-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 11/30/2020] [Accepted: 01/05/2021] [Indexed: 01/29/2023] Open
Abstract
Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naïve, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered due to lower sample size, and the Naïve Approach, which utilized all available data and ignored the missingness, was slightly inflated. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach, a straightforward and non-inflated approach, to handle heterogeneity among cohorts in the LRS based genome-wide interaction meta-analyses.
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Affiliation(s)
- Hanfei Xu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
| | - Karen Schwander
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas School of Public health, Houston, TX, USA
| | - Wenyi Wang
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - R J Waken
- Field and Environmental Data Science, Benson Hill Inc, St. Louis, MO, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas School of Public health, Houston, TX, USA
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- NHLBI and Boston University Framingham Heart Study, Framingham, MA, USA
| | - Lisa de las Fuentes
- Department of Medicine, Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
| | - Diana van Heemst
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas School of Public health, Houston, TX, USA
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Xiaoyu Zhang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas School of Public health, Houston, TX, USA
| | - D C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
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Drivas TG, Lucas A, Zhang X, Ritchie MD. Mendelian pathway analysis of laboratory traits reveals distinct roles for ciliary subcompartments in common disease pathogenesis. Am J Hum Genet 2021; 108:482-501. [PMID: 33636100 PMCID: PMC8008498 DOI: 10.1016/j.ajhg.2021.02.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/05/2021] [Indexed: 12/17/2022] Open
Abstract
Rare monogenic disorders of the primary cilium, termed ciliopathies, are characterized by extreme presentations of otherwise common diseases, such as diabetes, hepatic fibrosis, and kidney failure. However, despite a recent revolution in our understanding of the cilium's role in rare disease pathogenesis, the organelle's contribution to common disease remains largely unknown. Hypothesizing that common genetic variants within Mendelian ciliopathy genes might contribute to common complex diseases pathogenesis, we performed association studies of 16,874 common genetic variants across 122 ciliary genes with 12 quantitative laboratory traits characteristic of ciliopathy syndromes in 452,593 individuals in the UK Biobank. We incorporated tissue-specific gene expression analysis, expression quantitative trait loci, and Mendelian disease phenotype information into our analysis and replicated our findings in meta-analysis. 101 statistically significant associations were identified across 42 of the 122 examined ciliary genes (including eight novel replicating associations). These ciliary genes were widely expressed in tissues relevant to the phenotypes being studied, and eQTL analysis revealed strong evidence for correlation between ciliary gene expression levels and laboratory traits. Perhaps most interestingly, our analysis identified different ciliary subcompartments as being specifically associated with distinct sets of phenotypes. Taken together, our data demonstrate the utility of a Mendelian pathway-based approach to genomic association studies, challenge the widely held belief that the cilium is an organelle important mainly in development and in rare syndromic disease pathogenesis, and provide a framework for the continued integration of common and rare disease genetics to provide insight into the pathophysiology of human diseases of immense public health burden.
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Affiliation(s)
- Theodore George Drivas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
| | - Anastasia Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA
| | - Xinyuan Zhang
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA
| | - Marylyn DeRiggi Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA.
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Lipid Traffic Analysis reveals the impact of high paternal carbohydrate intake on offsprings' lipid metabolism. Commun Biol 2021; 4:163. [PMID: 33547386 PMCID: PMC7864968 DOI: 10.1038/s42003-021-01686-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/08/2021] [Indexed: 12/12/2022] Open
Abstract
In this paper we present an investigation of parental-diet-driven metabolic programming in offspring using a novel computational network analysis tool. The impact of high paternal carbohydrate intake on offsprings’ phospholipid and triglyceride metabolism in F1 and F2 generations is described. Detailed lipid profiles were acquired from F1 neonate (3 weeks), F1 adult (16 weeks) and F2 neonate offspring in serum, liver, brain, heart and abdominal adipose tissues by MS and NMR. Using a purpose-built computational tool for analysing both phospholipid and fat metabolism as a network, we characterised the number, type and abundance of lipid variables in and between tissues (Lipid Traffic Analysis), finding a variety of reprogrammings associated with paternal diet. These results are important because they describe the long-term metabolic result of dietary intake by fathers. This analytical approach is important because it offers unparalleled insight into possible mechanisms for alterations in lipid metabolism throughout organisms. Furse et al. use a purpose-built computational tool called Lipid Traffic Analysis to determine the spatial distribution of lipids throughout an organism. They use it to show that high paternal carbohydrate intake influences lipid metabolism in offspring two generations hence.
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Majumdar A, Burch KS, Haldar T, Sankararaman S, Pasaniuc B, Gauderman WJ, Witte JS. A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics 2021; 36:5640-5648. [PMID: 33453114 DOI: 10.1093/bioinformatics/btaa1083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 12/09/2020] [Accepted: 12/17/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes. RESULTS Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18%-43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two loci with an overall GxE effect on the vector of lipids, one of which was missed by the competing approaches. AVAILABILITY We provide an R package MPGE implementing the proposed approach which is available from CRAN: https://cran.r-project.org/web/packages/MPGE/index.html.
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Affiliation(s)
- Arunabha Majumdar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - W James Gauderman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
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Williams PT. Quantile-Dependent Expressivity and Gene-Lifestyle Interactions Involving High-Density Lipoprotein Cholesterol. Lifestyle Genom 2020; 14:1-19. [PMID: 33296900 DOI: 10.1159/000511421] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 09/04/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The phenotypic expression of a high-density lipoprotein (HDL) genetic risk score has been shown to depend upon whether the phenotype (HDL-cholesterol) is high or low relative to its distribution in the population (quantile-dependent expressivity). This may be due to the effects of genetic mutations on HDL-metabolism being concentration dependent. METHOD The purpose of this article is to assess whether some previously reported HDL gene-lifestyle interactions could potentially be attributable to quantile-dependent expressivity. SUMMARY Seventy-three published examples of HDL gene-lifestyle interactions were interpreted from the perspective of quantile-dependent expressivity. These included interactive effects of diet, alcohol, physical activity, adiposity, and smoking with genetic variants associated with the ABCA1, ADH3, ANGPTL4, APOA1, APOA4, APOA5, APOC3, APOE, CETP, CLASP1, CYP7A1, GALNT2, LDLR, LHX1, LIPC, LIPG, LPL, MVK-MMAB, PLTP, PON1, PPARα, SIRT1, SNTA1,and UCP1genes. The selected examples showed larger genetic effect sizes for lifestyle conditions associated with higher vis-à-vis lower average HDL-cholesterol concentrations. This suggests these reported interactions could be the result of selecting subjects for conditions that differentiate high from low HDL-cholesterol (e.g., lean vs. overweight, active vs. sedentary, high-fat vs. high-carbohydrate diets, alcohol drinkers vs. abstainers, nonsmokers vs. smokers) producing larger versus smaller genetic effect sizes. Key Message: Quantile-dependent expressivity provides a potential explanation for some reported gene-lifestyle interactions for HDL-cholesterol. Although overall genetic heritability appears to be quantile specific, this may vary by genetic variant and environmental exposure.
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Affiliation(s)
- Paul T Williams
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, USA,
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Zhang Q, Cai Z, Lhomme M, Sahana G, Lesnik P, Guerin M, Fredholm M, Karlskov-Mortensen P. Inclusion of endophenotypes in a standard GWAS facilitate a detailed mechanistic understanding of genetic elements that control blood lipid levels. Sci Rep 2020; 10:18434. [PMID: 33116219 PMCID: PMC7595098 DOI: 10.1038/s41598-020-75612-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 10/15/2020] [Indexed: 12/13/2022] Open
Abstract
Dyslipidemia is the primary cause of cardiovascular disease, which is a serious human health problem in large parts of the world. Therefore, it is important to understand the genetic and molecular mechanisms that regulate blood levels of cholesterol and other lipids. Discovery of genetic elements in the regulatory machinery is often based on genome wide associations studies (GWAS) focused on end-point phenotypes such as total cholesterol level or a disease diagnosis. In the present study, we add endophenotypes, such as serum levels of intermediate metabolites in the cholesterol synthesis pathways, to a GWAS analysis and use the pig as an animal model. We do this to increase statistical power and to facilitate biological interpretation of results. Although the study population was limited to ~ 300 individuals, we identify two genome-wide significant associations and ten suggestive associations. Furthermore, we identify 28 tentative associations to loci previously associated with blood lipids or dyslipidemia associated diseases. The associations with endophenotypes may inspire future studies that can dissect the biological mechanisms underlying these previously identified associations and add a new level of understanding to previously identified associations.
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Affiliation(s)
- Qianqian Zhang
- Bioinformatics Research Centre (BiRC), Aarhus University, C.F.Møllers Allé 8, 8000, Aarhus C, Denmark
| | - Zexi Cai
- Center for Quantitativ Genetics and Genomics, Aarhus University, Blichers Allé 20, 8830, Tjele, Danmark
| | - Marie Lhomme
- ICANalytics, Institute of Cardiometabolism and Nutrition (ICAN), 47-83 boulevard de l'hôpital, 75013, Paris, France
| | - Goutam Sahana
- Center for Quantitativ Genetics and Genomics, Aarhus University, Blichers Allé 20, 8830, Tjele, Danmark
| | - Philippe Lesnik
- Unité de Recherche sur les maladies cardiovasculaires, le métabolisme et la nutrition, INSERM UMR_S 1166, ICAN Institute of Cardiometabolism & Nutrition, Faculté de Médecine Sorbonne Université, Sorbonne Université, 4ème étage, Bureau 421,91, boulevard de l'Hôpital, 75634, Paris Cedex 13, France
| | - Maryse Guerin
- Unité de Recherche sur les maladies cardiovasculaires, le métabolisme et la nutrition, INSERM UMR_S 1166, ICAN Institute of Cardiometabolism & Nutrition, Faculté de Médecine Sorbonne Université, Sorbonne Université, 4ème étage, Bureau 421,91, boulevard de l'Hôpital, 75634, Paris Cedex 13, France
| | - Merete Fredholm
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Gronnegaardsvej 3, 1870, Frederikgsberg C, Denmark
| | - Peter Karlskov-Mortensen
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Gronnegaardsvej 3, 1870, Frederikgsberg C, Denmark.
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Osazuwa-Peters OL, Waken RJ, Schwander KL, Sung YJ, de Vries PS, Hartz SM, Chasman DI, Morrison AC, Bierut LJ, Xiong C, de las Fuentes L, Rao DC. Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures. Genet Epidemiol 2020; 44:629-641. [PMID: 32227373 PMCID: PMC7717887 DOI: 10.1002/gepi.22292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/30/2019] [Accepted: 03/06/2020] [Indexed: 12/27/2022]
Abstract
Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10-8 ) and 11 suggestive (p < 1 × 10-6 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.
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Affiliation(s)
| | - R J Waken
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Karen L Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Sarah M Hartz
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Daniel I Chasman
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Lisa de las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
- Cardiovascular Division, Department of Medicine, Washington University, St. Louis, Missouri
| | - D C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
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Głuszek S, Ciesla E, Głuszek-Osuch M, Kozieł D, Kiebzak W, Wypchło Ł, Suliga E. Anthropometric indices and cut-off points in the diagnosis of metabolic disorders. PLoS One 2020; 15:e0235121. [PMID: 32569336 PMCID: PMC7307766 DOI: 10.1371/journal.pone.0235121] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 06/09/2020] [Indexed: 01/23/2023] Open
Abstract
OBJECTIVE Identifying metabolic disorders at the earliest phase of their development allows for an early intervention and the prevention of serious consequences of diseases. However, it is difficult to determine which of the anthropometric indices of obesity is the best tool for diagnosing metabolic disorders. The aims of this study were to evaluate the usefulness of selected anthropometric indices and to determine optimal cut-off points for the identification of single metabolic disorders that are components of metabolic syndrome (MetS). DESIGN Cross-sectional study. PARTICIPANTS We analyzed the data of 12,328 participants aged 55.7±5.4 years. All participants were of European descent. PRIMARY OUTCOME MEASURE Four MetS components were included: high glucose concentration, high blood triglyceride concentration, low high-density lipoprotein cholesterol concentration, and elevated blood pressure. The following obesity indices were considered: waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), body fat percentage (%BF), Clínica Universidad de Navarra-body adiposity estimator (CUN-BAE), body roundness index (BRI), and a body shape index (ABSI). RESULTS The following indices had the highest discriminatory power for the identification of at least one MetS component: CUN-BAE, BMI, and WC in men (AUC = 0.734, 0.728, and 0.728, respectively) and WHtR, CUN-BAE, and WC in women (AUC = 0.715, 0.714, and 0.712, respectively) (p<0.001 for all). The other indices were similarly useful, except for the ABSI. CONCLUSIONS For the BMI, the optimal cut-off point for the identification of metabolic abnormalities was 27.2 kg/m2 for both sexes. For the WC, the optimal cut-off point was of 94 cm for men and 87 cm for women. Prospective studies are needed to identify those indices in which changes in value predict the occurrence of metabolic disorders best.
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Affiliation(s)
- Stanisław Głuszek
- Institute of Medical Sciences, Medical College, Jan Kochanowski University, Kielce, Poland
| | - Elzbieta Ciesla
- Institute of Health Sciences, Medical College, Jan Kochanowski University, Kielce, Poland
| | - Martyna Głuszek-Osuch
- Institute of Health Sciences, Medical College, Jan Kochanowski University, Kielce, Poland
| | - Dorota Kozieł
- Institute of Health Sciences, Medical College, Jan Kochanowski University, Kielce, Poland
| | - Wojciech Kiebzak
- Institute of Health Sciences, Medical College, Jan Kochanowski University, Kielce, Poland
| | - Łukasz Wypchło
- Institute of Medical Sciences, Medical College, Jan Kochanowski University, Kielce, Poland
| | - Edyta Suliga
- Institute of Health Sciences, Medical College, Jan Kochanowski University, Kielce, Poland
- * E-mail:
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Wang Z, Chen H, Bartz TM, Bielak LF, Chasman DI, Feitosa MF, Franceschini N, Guo X, Lim E, Noordam R, Richard MA, Wang H, Cade B, Cupples LA, de Vries PS, Giulanini F, Lee J, Lemaitre RN, Martin LW, Reiner AP, Rich SS, Schreiner PJ, Sidney S, Sitlani CM, Smith JA, Willems van Dijk K, Yao J, Zhao W, Fornage M, Kardia SLR, Kooperberg C, Liu CT, Mook-Kanamori DO, Province MA, Psaty BM, Redline S, Ridker PM, Rotter JI, Boerwinkle E, Morrison AC. Role of Rare and Low-Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2020; 13:e002772. [PMID: 32510982 DOI: 10.1161/circgen.119.002772] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Alcohol intake influences plasma lipid levels, and such effects may be moderated by genetic variants. We aimed to characterize the role of aggregated rare and low-frequency protein-coding variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels. METHODS In the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, fasting plasma triglycerides and high- and low-density lipoprotein cholesterol were measured in 34 153 individuals with European ancestry from 5 discovery studies and 32 277 individuals from 6 replication studies. Rare and low-frequency functional protein-coding variants (minor allele frequency, ≤5%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction test and a joint test of genetic main and gene-environment interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least 2 drinks per week, were considered. RESULTS We discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci (PCSK9, LPA, LPL, LIPG, ANGPTL4, APOB, APOC3, and CD300LG) remained significant after conditioning on the common index single-nucleotide polymorphism identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on triglycerides in a novel locus was significantly discovered (P=6.65×10-6 for the interaction test) and replicated at nominal significance level (P=0.013) in SMC5. CONCLUSIONS In conclusion, this study applied new gene-based statistical approaches and suggested that rare and low-frequency genetic variants interacted with alcohol consumption on lipid levels.
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Affiliation(s)
- Zhe Wang
- Department of Epidemiology, Human Genetics Center, Human Genetics and Environmental Sciences, School of Public Health (Z.W., H.C., P.S.d.V., M.F., E.B., A.C.M.), University of Texas Health Science Center at Houston, Houston, TX
| | - Han Chen
- Department of Epidemiology, Human Genetics Center, Human Genetics and Environmental Sciences, School of Public Health (Z.W., H.C., P.S.d.V., M.F., E.B., A.C.M.), University of Texas Health Science Center at Houston, Houston, TX
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Biostatistics and Medicine (T.M.B.), University of Washington, Seattle, WA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health (L.F.B., J.A.S., W.Z., S.L.R.K.), University of Michigan, Ann Arbor, MI
| | - Daniel I Chasman
- Division of Preventive Medicine (D.I.C., F.G.), Brigham and Women's Hospital, Boston, MA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO (M.F.F., M.A.P.)
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill (N.F.)
| | - Xiuqing Guo
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (X.G., J.Y., J.I.R.)
| | - Elise Lim
- Biostatistics Department, Boston University School of Public Health, MA (E.L., L.A.C., C.-T.L.)
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine (R.N.), Leiden University Medical Center, the Netherlands
| | - Melissa A Richard
- Brown Foundation Institute of Molecular Medicine (M.A.R., M.F.), University of Texas Health Science Center at Houston, Houston, TX
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine (H.W., B.C., J.L., S.R., P.M.R.), Brigham and Women's Hospital, Boston, MA.,Harvard Medical School, Boston, MA (D.I.C., H.W., B.C., J.L., S.R., P.M.R.)
| | - Brian Cade
- Division of Sleep and Circadian Disorders, Department of Medicine (H.W., B.C., J.L., S.R., P.M.R.), Brigham and Women's Hospital, Boston, MA.,Harvard Medical School, Boston, MA (D.I.C., H.W., B.C., J.L., S.R., P.M.R.)
| | - L Adrienne Cupples
- Biostatistics Department, Boston University School of Public Health, MA (E.L., L.A.C., C.-T.L.).,NHLBI Framingham Heart Study, MA (L.A.C.)
| | - Paul S de Vries
- Department of Epidemiology, Human Genetics Center, Human Genetics and Environmental Sciences, School of Public Health (Z.W., H.C., P.S.d.V., M.F., E.B., A.C.M.), University of Texas Health Science Center at Houston, Houston, TX
| | - Franco Giulanini
- Division of Preventive Medicine (D.I.C., F.G.), Brigham and Women's Hospital, Boston, MA
| | - Jiwon Lee
- Division of Sleep and Circadian Disorders, Department of Medicine (H.W., B.C., J.L., S.R., P.M.R.), Brigham and Women's Hospital, Boston, MA.,Harvard Medical School, Boston, MA (D.I.C., H.W., B.C., J.L., S.R., P.M.R.)
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine (R.N.L., C.M.S.), University of Washington, Seattle, WA
| | - Lisa W Martin
- George Washington University School of Medicine and Health Sciences, DC (L.W.M.)
| | - Alex P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (A.P.R., C.K.)
| | - Stephen S Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville (S.S.R.)
| | - Pamela J Schreiner
- Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (P.J.S.)
| | - Stephen Sidney
- Division of Research, Kaiser Permanente Northern California, Oakland (S.S.)
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Department of Medicine (R.N.L., C.M.S.), University of Washington, Seattle, WA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health (L.F.B., J.A.S., W.Z., S.L.R.K.), University of Michigan, Ann Arbor, MI.,Institute for Social Research, Survey Research Center (J.A.S.), University of Michigan, Ann Arbor, MI
| | - Ko Willems van Dijk
- Department of Human Genetics (K.W.v.D.), Leiden University Medical Center, the Netherlands.,Division of Endocrinology, Department of Internal Medicine (K.W.v.D.), Leiden University Medical Center, the Netherlands
| | - Jie Yao
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (X.G., J.Y., J.I.R.)
| | - Wei Zhao
- Department of Epidemiology, School of Public Health (L.F.B., J.A.S., W.Z., S.L.R.K.), University of Michigan, Ann Arbor, MI
| | - Myriam Fornage
- Department of Epidemiology, Human Genetics Center, Human Genetics and Environmental Sciences, School of Public Health (Z.W., H.C., P.S.d.V., M.F., E.B., A.C.M.), University of Texas Health Science Center at Houston, Houston, TX.,Brown Foundation Institute of Molecular Medicine (M.A.R., M.F.), University of Texas Health Science Center at Houston, Houston, TX
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health (L.F.B., J.A.S., W.Z., S.L.R.K.), University of Michigan, Ann Arbor, MI
| | - Charles Kooperberg
- Department of Epidemiology, Human Genetics Center, Human Genetics and Environmental Sciences, School of Public Health (Z.W., H.C., P.S.d.V., M.F., E.B., A.C.M.), University of Texas Health Science Center at Houston, Houston, TX
| | - Ching-Ti Liu
- Department of Epidemiology, Human Genetics Center, Human Genetics and Environmental Sciences, School of Public Health (Z.W., H.C., P.S.d.V., M.F., E.B., A.C.M.), University of Texas Health Science Center at Houston, Houston, TX
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology (D.O.M.-K.), Leiden University Medical Center, the Netherlands.,Department of Public Health and Primary Care (D.O.M.-K.), Leiden University Medical Center, the Netherlands
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO (M.F.F., M.A.P.)
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services (B.M.P.), University of Washington, Seattle, WA.,Kaiser Permanente Washington Health Research Institute, Seattle, WA (B.M.P.)
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine (H.W., B.C., J.L., S.R., P.M.R.), Brigham and Women's Hospital, Boston, MA.,Harvard Medical School, Boston, MA (D.I.C., H.W., B.C., J.L., S.R., P.M.R.)
| | - Paul M Ridker
- Division of Sleep and Circadian Disorders, Department of Medicine (H.W., B.C., J.L., S.R., P.M.R.), Brigham and Women's Hospital, Boston, MA.,Harvard Medical School, Boston, MA (D.I.C., H.W., B.C., J.L., S.R., P.M.R.)
| | - Jerome I Rotter
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (X.G., J.Y., J.I.R.)
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics Center, Human Genetics and Environmental Sciences, School of Public Health (Z.W., H.C., P.S.d.V., M.F., E.B., A.C.M.), University of Texas Health Science Center at Houston, Houston, TX.,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX (E.B.)
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics Center, Human Genetics and Environmental Sciences, School of Public Health (Z.W., H.C., P.S.d.V., M.F., E.B., A.C.M.), University of Texas Health Science Center at Houston, Houston, TX
| | -
- Department of Epidemiology, Human Genetics Center, Human Genetics and Environmental Sciences, School of Public Health (Z.W., H.C., P.S.d.V., M.F., E.B., A.C.M.), University of Texas Health Science Center at Houston, Houston, TX
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50
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McQuillan MA, Zhang C, Tishkoff SA, Platt A. The importance of including ethnically diverse populations in studies of quantitative trait evolution. Curr Opin Genet Dev 2020; 62:30-35. [PMID: 32604012 PMCID: PMC7942184 DOI: 10.1016/j.gde.2020.05.037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 05/26/2020] [Accepted: 05/28/2020] [Indexed: 10/24/2022]
Abstract
For many traits, human variation is less a matter of categorical differences than quantitative variation, such as height, where individuals fall along a continuum from short to tall. Most recent studies utilize large population-based samples with whole-genome sequences to study the evolution of these traits and have made significant progress implementing a broad spectrum of techniques. However, relatively few studies of quantitative trait evolution include ethnically diverse populations, which often harbor the highest levels of genetic and phenotypic diversity. Thus, our ability to draw inferences about quantitative trait adaptation has been limited. Here, we review recent studies examining human quantitative trait adaptation, and argue that including ethnically diverse populations, particularly from Africa, will be especially informative for our understanding of how humans adapt to the world around them.
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Affiliation(s)
- Michael A McQuillan
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chao Zhang
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah A Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Alexander Platt
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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