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Hillary RF, Gadd DA, Kuncheva Z, Mangelis T, Lin T, Ferber K, McLaughlin H, Runz H, Marioni RE, Foley CN, Sun BB. Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank. Nat Commun 2024; 15:7346. [PMID: 39187491 PMCID: PMC11347662 DOI: 10.1038/s41467-024-51744-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 08/14/2024] [Indexed: 08/28/2024] Open
Abstract
Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.
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Affiliation(s)
- Robert F Hillary
- Optima Partners, Edinburgh, EH2 4HQ, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Danni A Gadd
- Optima Partners, Edinburgh, EH2 4HQ, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Zhana Kuncheva
- Optima Partners, Edinburgh, EH2 4HQ, UK
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
- Bayes Centre, The University of Edinburgh, Edinburgh, EH8 9BT, UK
| | - Tasos Mangelis
- Optima Partners, Edinburgh, EH2 4HQ, UK
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
- Bayes Centre, The University of Edinburgh, Edinburgh, EH8 9BT, UK
| | - Tinchi Lin
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Kyle Ferber
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Helen McLaughlin
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Riccardo E Marioni
- Optima Partners, Edinburgh, EH2 4HQ, UK.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
| | - Christopher N Foley
- Optima Partners, Edinburgh, EH2 4HQ, UK.
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
- Bayes Centre, The University of Edinburgh, Edinburgh, EH8 9BT, UK.
| | - Benjamin B Sun
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
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2
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Wang C, Wang T, Kiryluk K, Wei Y, Aschard H, Ionita-Laza I. Genome-wide discovery for biomarkers using quantile regression at biobank scale. Nat Commun 2024; 15:6460. [PMID: 39085219 PMCID: PMC11291931 DOI: 10.1038/s41467-024-50726-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: 07/06/2023] [Accepted: 07/18/2024] [Indexed: 08/02/2024] Open
Abstract
Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. Conventional GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. We draw attention here to an alternative, lesser known approach, namely quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest. Quantile regression can be applied efficiently at biobank scale, while having some unique advantages such as (1) identifying variants with heterogeneous effects across quantiles of the phenotype distribution; (2) accommodating a wide range of phenotype distributions including non-normal distributions, with invariance of results to trait transformations; and (3) providing more detailed information about genotype-phenotype associations even for those associations identified by conventional GWAS. We show in simulations that quantile regression is powerful across both homogeneous and various heterogeneous models. Applications to 39 quantitative traits in the UK Biobank demonstrate that quantile regression can be a helpful complement to linear regression in GWAS and can identify variants with larger effects on high-risk subgroups of individuals but with lower or no contribution overall.
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Affiliation(s)
- Chen Wang
- Department of Biostatistics, Columbia University, New York, NY, USA
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | | | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Ying Wei
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, NY, USA.
- Department of Statistics, Lund University, Lund, Sweden.
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3
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Lin WY. Detecting gene-environment interactions from multiple continuous traits. Bioinformatics 2024; 40:btae419. [PMID: 38917408 PMCID: PMC11254352 DOI: 10.1093/bioinformatics/btae419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 06/17/2024] [Accepted: 06/24/2024] [Indexed: 06/27/2024] Open
Abstract
MOTIVATION Genetic variants present differential effects on humans according to various environmental exposures, the so-called "gene-environment interactions" (GxE). Many diseases can be diagnosed with multiple traits, such as obesity, diabetes, and dyslipidemia. I developed a multivariate scale test (MST) for detecting the GxE of a disease with several continuous traits. Given a significant MST result, I continued to search for which trait and which E enriched the GxE signals. Simulation studies were performed to compare MST with the univariate scale test (UST). RESULTS MST can gain more power than UST because of (1) integrating more traits with GxE information and (2) the less harsh penalty on multiple testing. However, if only few traits account for GxE, MST may lose power due to aggregating non-informative traits into the test statistic. As an example, MST was applied to a discovery set of 93 708 Taiwan Biobank (TWB) individuals and a replication set of 25 200 TWB individuals. From among 2 570 487 SNPs with minor allele frequencies ≥5%, MST identified 18 independent variance quantitative trait loci (P < 2.4E-9 in the discovery cohort and P < 2.8E-5 in the replication cohort) and 41 GxE signals (P < .00027) based on eight trait domains (including 29 traits). AVAILABILITY AND IMPLEMENTATION https://github.com/WanYuLin/Multivariate-scale-test-MST.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei 100, Taiwan
- Master of Public Health Degree Program, College of Public Health, National Taiwan University, Taipei 100, Taiwan
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4
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Zhukovsky P, Tio ES, Coughlan G, Bennett DA, Wang Y, Hohman TJ, Pizzagalli DA, Mulsant BH, Voineskos AN, Felsky D. Genetic influences on brain and cognitive health and their interactions with cardiovascular conditions and depression. Nat Commun 2024; 15:5207. [PMID: 38890310 PMCID: PMC11189393 DOI: 10.1038/s41467-024-49430-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/04/2024] [Indexed: 06/20/2024] Open
Abstract
Approximately 40% of dementia cases could be prevented or delayed by modifiable risk factors related to lifestyle and environment. These risk factors, such as depression and vascular disease, do not affect all individuals in the same way, likely due to inter-individual differences in genetics. However, the precise nature of how genetic risk profiles interact with modifiable risk factors to affect brain health is poorly understood. Here we combine multiple data resources, including genotyping and postmortem gene expression, to map the genetic landscape of brain structure and identify 367 loci associated with cortical thickness and 13 loci associated with white matter hyperintensities (P < 5×10-8), with several loci also showing a significant association with cognitive function. We show that among 220 unique genetic loci associated with cortical thickness in our genome-wide association studies (GWAS), 95 also showed evidence of interaction with depression or cardiovascular conditions. Polygenic risk scores based on our GWAS of inferior frontal thickness also interacted with hypertension in predicting executive function in the Canadian Longitudinal Study on Aging. These findings advance our understanding of the genetic underpinning of brain structure and show that genetic risk for brain and cognitive health is in part moderated by treatable mid-life factors.
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Grants
- P30 AG072975 NIA NIH HHS
- U01 AG046152 NIA NIH HHS
- U01 AG061356 NIA NIH HHS
- R01 AG017917 NIA NIH HHS
- P30 AG010161 NIA NIH HHS
- R01 AG059716 NIA NIH HHS
- Wellcome Trust
- R01 AG015819 NIA NIH HHS
- Gouvernement du Canada | Instituts de Recherche en Santé du Canada | CIHR Skin Research Training Centre (Skin Research Training Centre)
- D.F. is supported by the generous contributions from the Michael and Sonja Koerner Foundation and the Krembil Family Foundation. D.F. is also supported in part by the Centre for Addiction and Mental Health (CAMH) Discovery Fund and CIHR.
- PZ was funded by the Canadian Institute of Health Research Postdoctoral Fellowship.
- Over the past 3 years, D.A.P has received consulting fees from Albright Stonebridge Group, Boehringer Ingelheim, Compass Pathways, Engrail Therapeutics, Neumora Therapeutics (formerly BlackThorn Therapeutics), Neurocrine Biosciences, Neuroscience Software, Otsuka, Sunovion, and Takeda; he has received honoraria from the Psychonomic Society and American Psychological Association (for editorial work) and from Alkermes; he has received research funding from the Brain and Behavior Research Foundation, the Dana Foundation, Millennium Pharmaceuticals, Wellcome Leap MCPsych, and NIMH; he has received stock options from Compass Pathways, Engrail Therapeutics, Neumora Therapeutics, and Neuroscience Software. No funding from these entities was used to support the current work, and all views expressed are solely those of the authors.
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- A.N.V. currently receives funding from CIHR, the NIH, the National Sciences and Engineering Research Council (NSERC), the CAMH Foundation, and the University of Toronto. E.S.T. was funded by the Ontario Graduate Scholarship.
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Affiliation(s)
- Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Earvin S Tio
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Gillian Coughlan
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02129, USA
| | - David A Bennett
- Department of Neurological Sciences, RUSH Medical College, Chicago, IL, 60612, USA
| | - Yanling Wang
- Department of Neurological Sciences, RUSH Medical College, Chicago, IL, 60612, USA
| | - Timothy J Hohman
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, 02478, USA
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada.
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada.
| | - Daniel Felsky
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5T 1R8, Canada.
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Rotman Research Institute, Baycrest Hospital, Toronto, ON, M6A 2E1, Canada.
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5
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Asiimwe IG, Walker L, Sofat R, Jorgensen AL, Pirmohamed M. Genetic Determinants of Thiazide-Induced Hyperuricemia, Hyperglycemia, and Urinary Electrolyte Disturbances - A Genome-Wide Evaluation of the UK Biobank. Clin Pharmacol Ther 2024; 115:1408-1417. [PMID: 38425181 DOI: 10.1002/cpt.3229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Thiazide diuretics, widely used in hypertension, cause a variety of adverse reactions, including hyperglycemia, hyperuricemia, and electrolyte abnormalities. In this study, we aimed to identify genetic variants that interact with thiazide-use to increase the risk of these adverse reactions. Using UK Biobank data, we first performed genomewide variance quantitative trait locus (vQTL) analysis of ~ 6.2 million SNPs on 95,493 unrelated hypertensive White British participants (24,313 on self-reported bendroflumethiazide treatment at recruitment) for 2 blood (glucose and urate) and 2 urine (potassium and sodium) biomarkers. Second, we conducted direct gene-environment interaction (GEI) tests on the significant (P < 2.5 × 10-9) vQTLs, included a second UK Biobank cohort comprising 13,647 unrelated hypertensive White British participants (3,478 on thiazides other than bendroflumethiazide) and set significance at P = 0.05 divided by the number of vQTL SNPs tested for GEIs. The vQTL analysis identified eight statistically significant SNPs for blood glucose (5 SNPs) and serum urate (3 SNPs), with none being identified for the urinary biomarkers. Two of the SNPs (1 glucose SNP: CDKAL1 intron rs35612982, GEI P = 6.24 × 10-3; and 1 serum urate SNP: SLC2A9 intron rs938564, GEI P = 4.51 × 10-4) demonstrated significant GEI effects in the first, but not the second, cohort. Both genes are biologically plausible candidates, with the SLC2A9-mediated interaction having been previously reported. In conclusion, we used a two-stage approach to detect two biologically plausible genetic loci that can interact with thiazides to increase the risk of thiazide-associated biochemical abnormalities. Understanding how environmental exposures (including medications such as thiazides) and genetics interact, is an important step toward precision medicine and improved patient outcomes.
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Affiliation(s)
- Innocent G Asiimwe
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Lauren Walker
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Reecha Sofat
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Andrea L Jorgensen
- Department of Health Data Science, Institute of Population Health Sciences, University of Liverpool, Liverpool, UK
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
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6
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Boye C, Nirmalan S, Ranjbaran A, Luca F. Genotype × environment interactions in gene regulation and complex traits. Nat Genet 2024; 56:1057-1068. [PMID: 38858456 DOI: 10.1038/s41588-024-01776-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 04/25/2024] [Indexed: 06/12/2024]
Abstract
Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.
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Affiliation(s)
- Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Shreya Nirmalan
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Ali Ranjbaran
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US.
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, US.
- Department of Biology, University of Rome "Tor Vergata", Rome, Italy.
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7
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Herrera-Luis E, Benke K, Volk H, Ladd-Acosta C, Wojcik GL. Gene-environment interactions in human health. Nat Rev Genet 2024:10.1038/s41576-024-00731-z. [PMID: 38806721 DOI: 10.1038/s41576-024-00731-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2024] [Indexed: 05/30/2024]
Abstract
Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kelly Benke
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Heather Volk
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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8
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Ghouse J, Sveinbjörnsson G, Vujkovic M, Seidelin AS, Gellert-Kristensen H, Ahlberg G, Tragante V, Rand SA, Brancale J, Vilarinho S, Lundegaard PR, Sørensen E, Erikstrup C, Bruun MT, Jensen BA, Brunak S, Banasik K, Ullum H, Verweij N, Lotta L, Baras A, Mirshahi T, Carey DJ, Kaplan DE, Lynch J, Morgan T, Schwantes-An TH, Dochtermann DR, Pyarajan S, Tsao PS, Laisk T, Mägi R, Kozlitina J, Tybjærg-Hansen A, Jones D, Knowlton KU, Nadauld L, Ferkingstad E, Björnsson ES, Ulfarsson MO, Sturluson Á, Sulem P, Pedersen OB, Ostrowski SR, Gudbjartsson DF, Stefansson K, Olesen MS, Chang KM, Holm H, Bundgaard H, Stender S. Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis. Nat Genet 2024; 56:827-837. [PMID: 38632349 PMCID: PMC11096111 DOI: 10.1038/s41588-024-01720-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.
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Affiliation(s)
- Jonas Ghouse
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | | | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne-Sofie Seidelin
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Helene Gellert-Kristensen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Gustav Ahlberg
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Søren A Rand
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joseph Brancale
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Silvia Vilarinho
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Pia Rengtved Lundegaard
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | | | - Søren Brunak
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
| | | | - Niek Verweij
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Luca Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Tooraj Mirshahi
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David E Kaplan
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Julie Lynch
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Timothy Morgan
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medicine, University of California, Irvine, CA, USA
| | - Tae-Hwi Schwantes-An
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Daniel R Dochtermann
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Julia Kozlitina
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - David Jones
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - Lincoln Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
- Stanford University, School of Medicine, Stanford, CA, USA
| | | | - Einar S Björnsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Magnus O Ulfarsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | | | | | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Morten Salling Olesen
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hilma Holm
- deCODE Genetics/Amgen, Reykjavik, Iceland
| | - Henning Bundgaard
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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9
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Durvasula A, Price AL. Distinct explanations underlie gene-environment interactions in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.22.23295969. [PMID: 37790574 PMCID: PMC10543037 DOI: 10.1101/2023.09.22.23295969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation r g < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N = 325 K ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r g significantly < 1 (FDR<5%) (average r g = 0.95 ); for example, white blood cell count had r g = 0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.
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Affiliation(s)
- Arun Durvasula
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Genetics, Harvard Medical School, Cambridge, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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10
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Xiang R, Liu Y, Ben-Eghan C, Ritchie S, Lambert SA, Xu Y, Takeuchi F, Inouye M. Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305830. [PMID: 38699308 PMCID: PMC11065006 DOI: 10.1101/2024.04.15.24305830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N~408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N~40,466), where genetically less variable individuals (low vPGS) had increased conventional PGS accuracy (by ~19%) than genetically more variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.
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Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC, 3086, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, VIC, 3010, Australia
| | - Yang Liu
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Chief Ben-Eghan
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Scott Ritchie
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Samuel A. Lambert
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Fumihiko Takeuchi
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
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11
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Westerman KE, Sofer T. Many roads to a gene-environment interaction. Am J Hum Genet 2024; 111:626-635. [PMID: 38579668 PMCID: PMC11023920 DOI: 10.1016/j.ajhg.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 04/07/2024] Open
Abstract
Despite the importance of gene-environment interactions (GxEs) in improving and operationalizing genetic discovery, interpretation of any GxEs that are discovered can be surprisingly difficult. There are many potential biological and statistical explanations for a statistically significant finding and, likewise, it is not always clear what can be claimed based on a null result. A better understanding of the possible underlying mechanisms leading to a detected GxE can help investigators decide which are and which are not relevant to their hypothesis. Here, we provide a detailed explanation of five "phenomena," or data-generating mechanisms, that can lead to nonzero interaction estimates, as well as a discussion of specific instances in which they might be relevant. We hope that, given this framework, investigators can design more targeted experiments and provide cleaner interpretations of the associated results.
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Affiliation(s)
- Kenneth E Westerman
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA; Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Tamar Sofer
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
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12
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Zhang X, Bell JT. Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison. G3 (BETHESDA, MD.) 2024; 14:jkae022. [PMID: 38289865 PMCID: PMC10989912 DOI: 10.1093/g3journal/jkae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/01/2024]
Abstract
Genetically associated phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in genetically associated phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. Overall, with a minor allele frequency (MAF) of less than 0.2, the squared residual value linear model (SVLM) and the deviation regression model (DRM) are optimal when the data follow normal and non-normal distributions, respectively. In addition, the Brown-Forsythe (BF) test is one of the optimal methods when the MAF is 0.2 or larger, irrespective of phenotype distribution. Additionally, a larger sample size and more balanced sample distribution in different exposure categories increase the power of BF, SVLM, and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the phenotype distribution, allele frequency, sample size, and the type of exposure in the interaction model underlying the vQTL.
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Affiliation(s)
- Xiaopu Zhang
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK
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13
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Lin WY. Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes. Front Genet 2024; 15:1357238. [PMID: 38516378 PMCID: PMC10956579 DOI: 10.3389/fgene.2024.1357238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 02/27/2024] [Indexed: 03/23/2024] Open
Abstract
Introduction: After the era of genome-wide association studies (GWAS), thousands of genetic variants have been identified to exhibit main effects on human phenotypes. The next critical issue would be to explore the interplay between genes, the so-called "gene-gene interactions" (GxG) or epistasis. An exhaustive search for all single-nucleotide polymorphism (SNP) pairs is not recommended because this will induce a harsh penalty of multiple testing. Limiting the search of epistasis on SNPs reported by previous GWAS may miss essential interactions between SNPs without significant marginal effects. Moreover, most methods are computationally intensive and can be challenging to implement genome-wide. Methods: I here searched for GxG through variance quantitative trait loci (vQTLs) of 29 continuous Taiwan Biobank (TWB) phenotypes. A discovery cohort of 86,536 and a replication cohort of 25,460 TWB individuals were analyzed, respectively. Results: A total of 18 nearly independent vQTLs with linkage disequilibrium measure r 2 < 0.01 were identified and replicated from nine phenotypes. 15 significant GxG were found with p-values <1.1E-5 (in the discovery cohort) and false discovery rates <2% (in the replication cohort). Among these 15 GxG, 11 were detected for blood traits including red blood cells, hemoglobin, and hematocrit; 2 for total bilirubin; 1 for fasting glucose; and 1 for total cholesterol (TCHO). All GxG were observed for gene pairs on the same chromosome, except for the APOA5 (chromosome 11)-TOMM40 (chromosome 19) interaction for TCHO. Discussion: This study provided a computationally feasible way to search for GxG genome-wide and applied this approach to 29 phenotypes.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan
- Master of Public Health Degree Program, College of Public Health, National Taiwan University, Taipei, Taiwan
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14
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Li X, Lv X, Li H, Zhang G, Long Y, Li K, Fan Y, Jin D, Zhou F, Liu H. Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer. Adv Biol (Weinh) 2023; 7:e2300189. [PMID: 37423953 DOI: 10.1002/adbi.202300189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/17/2023] [Indexed: 07/11/2023]
Abstract
This work hypothesizes that some genes undergo radically changed transcription regulations (TRs) in breast cancer (BC), but don't show differential expressions for unknown reasons. The TR of a gene is quantitatively formulated by a regression model between the expression of this gene and multiple transcription factors (TFs). The difference between the predicted and real expression levels of a gene in a query sample is defined as the mqTrans value of this gene, which quantitatively reflects its regulatory changes. This work systematically screens the undifferentially expressed genes with differentially expressed mqTrans values in 1036 samples across five datasets and three ethnic groups. This study calls the 25 genes satisfying the above hypothesis in at least four datasets as dark biomarkers, and the strong dark biomarker gene CXXC5 (CXXC Finger Protein 5) is even supported by all the five independent BC datasets. Although CXXC5 does not show differential expressions in BC, its transcription regulations show quantitative associations with BCs in diversified cohorts. The overlapping long noncoding RNAs (lncRNAs) may have contributed their transcripts to the expression miscalculations of dark biomarkers. The mqTrans analysis serves as a complementary view of the transcriptome-based detections of biomarkers that are ignored by many existing studies.
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Affiliation(s)
- Xue Li
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Xiaoying Lv
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Haijun Li
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Gongyou Zhang
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Yaohang Long
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Kewei Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Yusi Fan
- College of Software, Jilin University, Changchun, 130012, China
| | - Dawei Jin
- Research Institute of Guizhou Huada Life Big Data, Guiyang, Guizhou, 550025, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
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15
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Snaebjarnarson AS, Helgadottir A, Arnadottir GA, Ivarsdottir EV, Thorleifsson G, Ferkingstad E, Einarsson G, Sveinbjornsson G, Thorgeirsson TE, Ulfarsson MO, Halldorsson BV, Olafsson I, Erikstrup C, Pedersen OB, Nyegaard M, Bruun MT, Ullum H, Brunak S, Iversen KK, Christensen AH, Olesen MS, Ghouse J, Banasik K, Knowlton KU, Arnar DO, Thorgeirsson G, Nadauld L, Ostrowski SR, Bundgaard H, Holm H, Sulem P, Stefansson K, Gudbjartsson DF. Complex effects of sequence variants on lipid levels and coronary artery disease. Cell 2023; 186:4085-4099.e15. [PMID: 37714134 DOI: 10.1016/j.cell.2023.08.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 05/06/2023] [Accepted: 08/10/2023] [Indexed: 09/17/2023]
Abstract
Many sequence variants have additive effects on blood lipid levels and, through that, on the risk of coronary artery disease (CAD). We show that variants also have non-additive effects and interact to affect lipid levels as well as affecting variance and correlations. Variance and correlation effects are often signatures of epistasis or gene-environmental interactions. These complex effects can translate into CAD risk. For example, Trp154Ter in FUT2 protects against CAD among subjects with the A1 blood group, whereas it associates with greater risk of CAD in others. His48Arg in ADH1B interacts with alcohol consumption to affect lipid levels and CAD. The effect of variants in TM6SF2 on blood lipids is greatest among those who never eat oily fish but absent from those who often do. This work demonstrates that variants that affect variance of quantitative traits can allow for the discovery of epistasis and interactions of variants with the environment.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Magnus O Ulfarsson
- deCODE genetics/Amgen, Inc., Reykjavik 102, Iceland; Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik 102, Iceland
| | | | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali - National University Hospital of Iceland, Hringbraut, Reykjavik 101, Iceland
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus 8200, Denmark; Department of Clinical Medicine, Health, Aarhus University, Aarhus 8200, Denmark
| | - Ole B Pedersen
- Department of Clinical Immunology, Zealand University Hospital, Køge 4600, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen 1165, Denmark
| | - Mette Nyegaard
- Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Aalborg 9220, Denmark
| | - Mie T Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense 5000, Denmark
| | - Henrik Ullum
- Statens Serum Institut, Copenhagen 2300, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Kasper Karmark Iversen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen 1165, Denmark; Department of Emergency Medicine, Copenhagen University Hospital Herlev and Gentofte, Herlev 2900, Denmark; Department of Cardiology, Copenhagen University Hospital, Herlev-Gentofte Hospital, Herlev 2900, Denmark
| | - Alex Hoerby Christensen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen 1165, Denmark; Department of Cardiology, Copenhagen University Hospital, Herlev-Gentofte Hospital, Herlev 2900, Denmark
| | - Morten S Olesen
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark; Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen 1165, Denmark
| | - Jonas Ghouse
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark; Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen 1165, Denmark
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT 84143, USA
| | - David O Arnar
- deCODE genetics/Amgen, Inc., Reykjavik 102, Iceland; Faculty of Medicine, University of Iceland, Vatnsmyrarvegur, Reykjavik 101, Iceland; Division of Cardiology, Department of Internal Medicine, Landspitali - National University Hospital of Iceland, Hringbraut, Reykjavik 101, Iceland
| | - Gudmundur Thorgeirsson
- deCODE genetics/Amgen, Inc., Reykjavik 102, Iceland; Faculty of Medicine, University of Iceland, Vatnsmyrarvegur, Reykjavik 101, Iceland; Division of Cardiology, Department of Internal Medicine, Landspitali - National University Hospital of Iceland, Hringbraut, Reykjavik 101, Iceland
| | - Lincoln Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT 84790, USA
| | - Sisse Rye Ostrowski
- Department of Clinical Medicine, University of Copenhagen, Copenhagen 1165, Denmark; Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen 2100, Denmark
| | - Henning Bundgaard
- Department of Clinical Medicine, University of Copenhagen, Copenhagen 1165, Denmark; Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik 102, Iceland
| | | | - Kari Stefansson
- deCODE genetics/Amgen, Inc., Reykjavik 102, Iceland; Faculty of Medicine, University of Iceland, Vatnsmyrarvegur, Reykjavik 101, Iceland.
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Inc., Reykjavik 102, Iceland; School of Engineering and Natural Sciences, University of Iceland, Reykjavik 102, Iceland.
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16
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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17
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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: 1.0] [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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Wang C, Wang T, Wei Y, Aschard H, Ionita-Laza I. Quantile Regression for biomarkers in the UK Biobank. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543699. [PMID: 37333162 PMCID: PMC10274625 DOI: 10.1101/2023.06.05.543699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. An alternative and easy to apply approach is quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest by modeling conditional quantiles within a regression framework. Quantile regression can be applied efficiently at biobank scale using standard statistical packages in much the same way as linear regression, while having some unique advantages such as identifying variants with heterogeneous effects across different quantiles, including non-additive effects and variants involved in gene-environment interactions; accommodating a wide range of phenotype distributions with invariance to trait transformation; and overall providing more detailed information about the underlying genotype-phenotype associations. Here, we demonstrate the value of quantile regression in the context of GWAS by applying it to 39 quantitative traits in the UK Biobank (n > 300 , 000 individuals). Across these 39 traits we identify 7,297 significant loci, including 259 loci only detected by quantile regression. We show that quantile regression can help uncover replicable but unmodelled gene-environment interactions, and can provide additional key insights into poorly understood genotype-phenotype correlations for clinically relevant biomarkers at minimal additional cost.
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Affiliation(s)
- Chen Wang
- Department of Biostatistics, Columbia University, New York, USA
| | - Tianying Wang
- Center for Statistical Science & Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Ying Wei
- Department of Biostatistics, Columbia University, New York, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, France
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19
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Bartell E, Lin K, Tsuo K, Gan W, Vedantam S, Cole JB, Baronas JM, Yengo L, Marouli E, Amariuta T, Chen Z, Li L, Renthal NE, Jacobsen CM, Salem RM, Walters RG, Hirschhorn JN. Genetics of skeletal proportions in two different populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.22.541772. [PMID: 37292977 PMCID: PMC10245876 DOI: 10.1101/2023.05.22.541772] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Human height can be divided into sitting height and leg length, reflecting growth of different parts of the skeleton whose relative proportions are captured by the ratio of sitting to total height (as sitting height ratio, SHR). Height is a highly heritable trait, and its genetic basis has been well-studied. However, the genetic determinants of skeletal proportion are much less well-characterized. Expanding substantially on past work, we performed a genome-wide association study (GWAS) of SHR in ∼450,000 individuals with European ancestry and ∼100,000 individuals with East Asian ancestry from the UK and China Kadoorie Biobanks. We identified 565 loci independently associated with SHR, including all genomic regions implicated in prior GWAS in these ancestries. While SHR loci largely overlap height-associated loci (P < 0.001), the fine-mapped SHR signals were often distinct from height. We additionally used fine-mapped signals to identify 36 credible sets with heterogeneous effects across ancestries. Lastly, we used SHR, sitting height, and leg length to identify genetic variation acting on specific body regions rather than on overall human height.
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20
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Costanzo MC, von Grotthuss M, Massung J, Jang D, Caulkins L, Koesterer R, Gilbert C, Welch RP, Kudtarkar P, Hoang Q, Boughton AP, Singh P, Sun Y, Duby M, Moriondo A, Nguyen T, Smadbeck P, Alexander BR, Brandes M, Carmichael M, Dornbos P, Green T, Huellas-Bruskiewicz KC, Ji Y, Kluge A, McMahon AC, Mercader JM, Ruebenacker O, Sengupta S, Spalding D, Taliun D, Smith P, Thomas MK, Akolkar B, Brosnan MJ, Cherkas A, Chu AY, Fauman EB, Fox CS, Kamphaus TN, Miller MR, Nguyen L, Parsa A, Reilly DF, Ruetten H, Wholley D, Zaghloul NA, Abecasis GR, Altshuler D, Keane TM, McCarthy MI, Gaulton KJ, Florez JC, Boehnke M, Burtt NP, Flannick J. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits. Cell Metab 2023; 35:695-710.e6. [PMID: 36963395 PMCID: PMC10231654 DOI: 10.1016/j.cmet.2023.03.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 10/23/2022] [Accepted: 02/28/2023] [Indexed: 03/26/2023]
Abstract
Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.
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Affiliation(s)
- Maria C Costanzo
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Marcin von Grotthuss
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Jeffrey Massung
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Dongkeun Jang
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Lizz Caulkins
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Ryan Koesterer
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Clint Gilbert
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Ryan P Welch
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Quy Hoang
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Andrew P Boughton
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Preeti Singh
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Ying Sun
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Marc Duby
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Annie Moriondo
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Trang Nguyen
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Patrick Smadbeck
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Benjamin R Alexander
- Simulation and Modeling Sciences, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - MacKenzie Brandes
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Mary Carmichael
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Peter Dornbos
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Todd Green
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Kenneth C Huellas-Bruskiewicz
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Yue Ji
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Alexandria Kluge
- Genomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Aoife C McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Josep M Mercader
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Oliver Ruebenacker
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Sebanti Sengupta
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Dylan Spalding
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Daniel Taliun
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Philip Smith
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Melissa K Thomas
- Tailored Therapeutics-Diabetes, Eli Lilly and Company, Lilly Corporate Center DC 0545, Indianapolis, IN 46285, USA
| | - Beena Akolkar
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - M Julia Brosnan
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Andriy Cherkas
- Team Early Projects Type 1 Diabetes, Therapeutic Area Diabetes and Cardiovascular Medicine, Research & Development, Sanofi, Industriepark Höchst-H831, Frankfurt am Main 65926, Germany
| | - Audrey Y Chu
- Merck Research Laboratories, Boston, MA 02115, USA
| | - Eric B Fauman
- Integrative Biology, Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | | | | | - Melissa R Miller
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Lynette Nguyen
- Foundation for the National Institutes of Health, North Bethesda, MD 20852, USA
| | - Afshin Parsa
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | | | - Hartmut Ruetten
- CardioMetabolism & Respiratory Medicine, Boehringer Ingelheim International GmbH, 55216 Ingelheim/Rhein, Germany
| | - David Wholley
- Foundation for the National Institutes of Health, North Bethesda, MD 20852, USA
| | - Norann A Zaghloul
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Gonçalo R Abecasis
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA
| | - David Altshuler
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Thomas M Keane
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 9DU, UK; Oxford Centre for Diabetes Endocrinology & Metabolism, University of Oxford, Oxford OX3 7BN, UK
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Michael Boehnke
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Noël P Burtt
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA.
| | - Jason Flannick
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
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