1
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Motsinger-Reif AA, Reif DM, Akhtari FS, House JS, Campbell CR, Messier KP, Fargo DC, Bowen TA, Nadadur SS, Schmitt CP, Pettibone KG, Balshaw DM, Lawler CP, Newton SA, Collman GW, Miller AK, Merrick BA, Cui Y, Anchang B, Harmon QE, McAllister KA, Woychik R. Gene-environment interactions within a precision environmental health framework. CELL GENOMICS 2024:100591. [PMID: 38925123 DOI: 10.1016/j.xgen.2024.100591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/26/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024]
Abstract
Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.
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Affiliation(s)
- Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - C Ryan Campbell
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kyle P Messier
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA; Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David C Fargo
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Tiffany A Bowen
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Srikanth S Nadadur
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Charles P Schmitt
- Office of the Scientific Director, Office of Data Science, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kristianna G Pettibone
- Program Analysis Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David M Balshaw
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Cindy P Lawler
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Shelia A Newton
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Gwen W Collman
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Aubrey K Miller
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - B Alex Merrick
- Mechanistic Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Benedict Anchang
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Quaker E Harmon
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kimberly A McAllister
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Rick Woychik
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
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2
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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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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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4
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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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5
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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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6
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Jiang Z, Sullivan PF, Li T, Zhao B, Wang X, Luo T, Huang S, Guan PY, Chen J, Yang Y, Stein JL, Li Y, Liu D, Sun L, Zhu H. The pivotal role of the X-chromosome in the genetic architecture of the human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.30.23294848. [PMID: 37693466 PMCID: PMC10491353 DOI: 10.1101/2023.08.30.23294848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Genes on the X-chromosome are extensively expressed in the human brain. However, little is known for the X-chromosome's impact on the brain anatomy, microstructure, and functional network. We examined 1,045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X-chromosome interactions, while proposing an atlas outlining dosage compensation (DC) for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we discovered unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X-chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.
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7
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Littleton SH, Trang KB, Volpe CM, Cook K, DeBruyne N, Maguire JA, Weidekamp MA, Hodge KM, Boehm K, Lu S, Chesi A, Bradfield JP, Pippin JA, Anderson SA, Wells AD, Pahl MC, Grant SFA. Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3' UTR of FAIM2. CELL GENOMICS 2024; 4:100556. [PMID: 38697123 PMCID: PMC11099382 DOI: 10.1016/j.xgen.2024.100556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 03/21/2024] [Accepted: 04/08/2024] [Indexed: 05/04/2024]
Abstract
The ch12q13 locus is among the most significant childhood obesity loci identified in genome-wide association studies. This locus resides in a non-coding region within FAIM2; thus, the underlying causal variant(s) presumably influence disease susceptibility via cis-regulation. We implicated rs7132908 as a putative causal variant by leveraging our in-house 3D genomic data and public domain datasets. Using a luciferase reporter assay, we observed allele-specific cis-regulatory activity of the immediate region harboring rs7132908. We generated isogenic human embryonic stem cell lines homozygous for either rs7132908 allele to assess changes in gene expression and chromatin accessibility throughout a differentiation to hypothalamic neurons, a key cell type known to regulate feeding behavior. The rs7132908 obesity risk allele influenced expression of FAIM2 and other genes and decreased the proportion of neurons produced by differentiation. We have functionally validated rs7132908 as a causal obesity variant that temporally regulates nearby effector genes and influences neurodevelopment and survival.
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Affiliation(s)
- Sheridan H Littleton
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Khanh B Trang
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Christina M Volpe
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kieona Cook
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Nicole DeBruyne
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jean Ann Maguire
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Mary Ann Weidekamp
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kenyaita M Hodge
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Keith Boehm
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Sumei Lu
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jonathan P Bradfield
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Quantinuum Research LLC, San Diego, CA 92101, USA
| | - James A Pippin
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Stewart A Anderson
- Department of Child and Adolescent Psychiatry, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew C Pahl
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
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8
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Kemper KE, Sidorenko J, Wang H, Hayes BJ, Wray NR, Yengo L, Keller MC, Goddard M, Visscher PM. Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank. Nat Commun 2024; 15:3776. [PMID: 38710707 PMCID: PMC11074304 DOI: 10.1038/s41467-024-47802-7] [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: 05/11/2023] [Accepted: 04/10/2024] [Indexed: 05/08/2024] Open
Abstract
The causes of temporal fluctuations in adult traits are poorly understood. Here, we investigate the genetic determinants of within-person trait variability of 8 repeatedly measured anthropometric traits in 50,117 individuals from the UK Biobank. We found that within-person (non-directional) variability had a SNP-based heritability of 2-5% for height, sitting height, body mass index (BMI) and weight (P ≤ 2.4 × 10-3). We also analysed longitudinal trait change and show a loss of both average height and weight beyond about 70 years of age. A variant tracking the Alzheimer's risk APOE- E 4 allele (rs429358) was significantly associated with weight loss ( β = -0.047 kg per yr, s.e. 0.007, P = 2.2 × 10-11), and using 2-sample Mendelian Randomisation we detected a relationship consistent with causality between decreased lumbar spine bone mineral density and height loss (bxy = 0.011, s.e. 0.003, P = 3.5 × 10-4). Finally, population-level variance quantitative trait loci (vQTL) were consistent with within-person variability for several traits, indicating an overlap between trait variability assessed at the population or individual level. Our findings help elucidate the genetic influence on trait-change within an individual and highlight disease risks associated with these changes.
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Affiliation(s)
- Kathryn E Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
| | - Julia Sidorenko
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Huanwei Wang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
| | - Michael Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, Australia
- Biosciences Research Division, Agriculture Victoria, Bundoora, VIC, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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9
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Bass AJ, Bian S, Wingo AP, Wingo TS, Cutler DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. Genome Med 2024; 16:62. [PMID: 38664839 PMCID: PMC11044415 DOI: 10.1186/s13073-024-01329-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).
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Affiliation(s)
- Andrew J Bass
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - Aliza P Wingo
- Department of Psychiatry, Emory University, Atlanta, GA, 30322, USA
| | - Thomas S Wingo
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
- Department of Neurology, Emory University, Atlanta, GA, 30322, USA
| | - David J Cutler
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
| | - Michael P Epstein
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
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10
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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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11
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Willems YE, Raffington L, Ligthart L, Pool R, Hottenga JJ, Finkenauer C, Bartels M. No gene by stressful life events interaction on individual differences in adults' self-control. Front Psychiatry 2024; 15:1388264. [PMID: 38693999 PMCID: PMC11061522 DOI: 10.3389/fpsyt.2024.1388264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/03/2024] [Indexed: 05/03/2024] Open
Abstract
Background Difficulty with self-control, or the ability to alter impulses and behavior in a goal-directed way, predicts interpersonal conflict, lower socioeconomic attainments, and more adverse health outcomes. Etiological understanding, and intervention for low self-control is, therefore, a public health goal. A prominent developmental theory proposes that individuals with high genetic propensity for low self-control that are also exposed to stressful environments may be most at-risk of low levels of self-control. Here we examine if polygenic measures associated with behaviors marked by low self-control interact with stressful life events in predicting self-control. Methods Leveraging molecular data from a large population-based Dutch sample (N = 7,090, Mage = 41.2) to test for effects of genetics (i.e., polygenic scores for ADHD and aggression), stressful life events (e.g., traffic accident, violent assault, financial problems), and a gene-by-stress interaction on self-control (measured with the ASEBA Self-Control Scale). Results Both genetics (β =.03 -.04, p <.001) and stressful life events (β = .11 -.14, p <.001) were associated with individual differences in self-control. We find no evidence of a gene-by-stressful life events interaction on individual differences in adults' self-control. Conclusion Our findings are consistent with the notion that genetic influences and stressful life events exert largely independent effects on adult self-control. However, the small effect sizes of polygenic scores increases the likelihood of null results. Genetically-informed longitudinal research in large samples can further inform the etiology of individual differences in self-control from early childhood into later adulthood and its downstream implications for public health.
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Affiliation(s)
- Yayouk Eva Willems
- Max Planck Institute for Human Development, Max Planck Research Group Biosocial – Biology, Social Disparities, and Development, Berlin, Germany
| | - Laurel Raffington
- Max Planck Institute for Human Development, Max Planck Research Group Biosocial – Biology, Social Disparities, and Development, Berlin, Germany
| | - Lannie Ligthart
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Rene Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Catrin Finkenauer
- Department of Interdisciplinary Social Science, Universiteit Utrecht, Utrecht, Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centres, Amsterdam, Netherlands
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12
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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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13
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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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14
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Reay WR, Clarke E, Eslick S, Riveros C, Holliday EG, McEvoy MA, Peel R, Hancock S, Scott RJ, Attia JR, Collins CE, Cairns MJ. Using Genetics to Inform Interventions Related to Sodium and Potassium in Hypertension. Circulation 2024; 149:1019-1032. [PMID: 38131187 PMCID: PMC10962430 DOI: 10.1161/circulationaha.123.065394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Hypertension is a key risk factor for major adverse cardiovascular events but remains difficult to treat in many individuals. Dietary interventions are an effective approach to lower blood pressure (BP) but are not equally effective across all individuals. BP is heritable, and genetics may be a useful tool to overcome treatment response heterogeneity. We investigated whether the genetics of BP could be used to identify individuals with hypertension who may receive a particular benefit from lowering sodium intake and boosting potassium levels. METHODS In this observational genetic study, we leveraged cross-sectional data from up to 296 475 genotyped individuals drawn from the UK Biobank cohort for whom BP and urinary electrolytes (sodium and potassium), biomarkers of sodium and potassium intake, were measured. Biologically directed genetic scores for BP were constructed specifically among pathways related to sodium and potassium biology (pharmagenic enrichment scores), as well as unannotated genome-wide scores (conventional polygenic scores). We then tested whether there was a gene-by-environment interaction between urinary electrolytes and these genetic scores on BP. RESULTS Genetic risk and urinary electrolytes both independently correlated with BP. However, urinary sodium was associated with a larger BP increase among individuals with higher genetic risk in sodium- and potassium-related pathways than in those with comparatively lower genetic risk. For example, each SD in urinary sodium was associated with a 1.47-mm Hg increase in systolic BP for those in the top 10% of the distribution of genetic risk in sodium and potassium transport pathways versus a 0.97-mm Hg systolic BP increase in the lowest 10% (P=1.95×10-3). This interaction with urinary sodium remained when considering estimated glomerular filtration rate and indexing sodium to urinary creatinine. There was no strong evidence of an interaction between urinary sodium and a standard genome-wide polygenic score of BP. CONCLUSIONS The data suggest that genetic risk in sodium and potassium pathways could be used in a precision medicine model to direct interventions more specifically in the management of hypertension. Intervention studies are warranted.
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Affiliation(s)
- William R. Reay
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
| | - Erin Clarke
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Shaun Eslick
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
| | - Carlos Riveros
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Elizabeth G. Holliday
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Mark A. McEvoy
- Rural Health School, La Trobe University, Bendigo, Victoria, Australia (M.A.M.)
| | - Roseanne Peel
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Stephen Hancock
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Rodney J. Scott
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Cancer Detection and Therapy Research Program (R.J.S.), New Lambton, NSW, Australia
| | - John R. Attia
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Clare E. Collins
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Murray J. Cairns
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
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15
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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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16
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Wang X, Zhang Z, Ding Y, Chen T, Mucci L, Albanes D, Landi MT, Caporaso NE, Lam S, Tardon A, Chen C, Bojesen SE, Johansson M, Risch A, Bickeböller H, Wichmann HE, Rennert G, Arnold S, Brennan P, McKay JD, Field JK, Shete SS, Le Marchand L, Liu G, Andrew AS, Kiemeney LA, Zienolddiny-Narui S, Behndig A, Johansson M, Cox A, Lazarus P, Schabath MB, Aldrich MC, Hung RJ, Amos CI, Lin X, Christiani DC. Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification. Genome Med 2024; 16:22. [PMID: 38317189 PMCID: PMC10840262 DOI: 10.1186/s13073-024-01298-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/26/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored. METHODS Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold. RESULTS Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12-3.50, P-value = 4.13 × 10-15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99-2.49, P-value = 5.70 × 10-46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72-0.74). CONCLUSIONS Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.
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Affiliation(s)
- Xinan Wang
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, 667 Huntington Ave, Boston, MA, 02115, USA
| | - Ziwei Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, USA
| | - Tony Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Lorelei Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Demetrios Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Neil E Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen Lam
- Department of Medicine, British Columbia Cancer Agency, University of British Columbia, Vancouver, Canada
| | - Adonina Tardon
- Faculty of Medicine, University of Oviedo and CIBERESP, Oviedo, Spain
| | - Chu Chen
- Department of Epidemiology, University of Washington School of Public Health, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Stig E Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Angela Risch
- Department of Biosciences and Medical Biology, Allergy-Cancer-BioNano Research Centre, University of Salzburg, and Cancer Cluster Salzburg, Salzburg, Austria
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg August University Göttingen, Göttingen, Germany
| | - H-Erich Wichmann
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians University, Munich, Germany
| | - Gadi Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Carmel, Haifa, Israel
| | - Susanne Arnold
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA
| | - Paul Brennan
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - James D McKay
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - John K Field
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Sanjay S Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Geoffrey Liu
- Princess Margaret Cancer Centre, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Angeline S Andrew
- Department of Epidemiology, Department of Community and Family Medicine, Dartmouth Geisel School of Medicine, Hanover, NH, USA
| | - Lambertus A Kiemeney
- Department for Health Evidence, Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Annelie Behndig
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | | | - Angie Cox
- Department of Oncology and Metabolism, The Medical School, University of Sheffield, Sheffield, UK
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, WA, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Melinda C Aldrich
- Department of Medicine, Department of Biomedical Informatics and Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, 667 Huntington Ave, Boston, MA, 02115, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
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17
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Mersha TB. From Mendel to multi-omics: shifting paradigms. Eur J Hum Genet 2024; 32:139-142. [PMID: 37468578 PMCID: PMC10853174 DOI: 10.1038/s41431-023-01420-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 05/24/2023] [Accepted: 06/22/2023] [Indexed: 07/21/2023] Open
Affiliation(s)
- Tesfaye B Mersha
- Cincinnati Children's Hospital Medical Center, Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
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18
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Kar A, Alvarez M, Garske KM, Huang H, Lee SHT, Deal M, Das SS, Koka A, Jamal Z, Mohlke KL, Laakso M, Heinonen S, Pietiläinen KH, Pajukanta P. Age-dependent genes in adipose stem and precursor cells affect regulation of fat cell differentiation and link aging to obesity via cellular and genetic interactions. Genome Med 2024; 16:19. [PMID: 38297378 PMCID: PMC10829214 DOI: 10.1186/s13073-024-01291-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/19/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner. METHODS We integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI). RESULTS We found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level. CONCLUSIONS Overall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.
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Affiliation(s)
- Asha Kar
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Huiling Huang
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA
| | - Seung Hyuk T Lee
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Milena Deal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Sankha Subhra Das
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Amogha Koka
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Zoeb Jamal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Sini Heinonen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HealthyWeightHub, Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA.
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA.
- Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, USA.
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19
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Miao J, Wu Y, Lu Q. Statistical methods for gene-environment interaction analysis. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL STATISTICS 2024; 16:e1635. [PMID: 38699459 PMCID: PMC11064894 DOI: 10.1002/wics.1635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/12/2023] [Indexed: 05/05/2024]
Abstract
Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene-environment interactions (G × E). In this review, we present state-of-the-art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single-variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.
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Affiliation(s)
- Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Yixuan Wu
- University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, Wisconsin, USA
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20
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Wang KW, Yuan YX, Zhu B, Zhang Y, Wei YF, Meng FS, Zhang S, Wang JX, Zhou JY. X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study. Front Aging Neurosci 2023; 15:1277731. [PMID: 38035272 PMCID: PMC10682795 DOI: 10.3389/fnagi.2023.1277731] [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: 08/15/2023] [Accepted: 10/20/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a complex neurodegenerative disease with high heritability. Compared to autosomes, a higher proportion of disorder-associated genes on X chromosome are expressed in the brain. However, only a few studies focused on the identification of the susceptibility loci for AD on X chromosome. Methods Using the data from the Alzheimer's Disease Neuroimaging Initiative Study, we conducted an X chromosome-wide association study between 16 AD quantitative biomarkers and 19,692 single nucleotide polymorphisms (SNPs) based on both the cross-sectional and longitudinal studies. Results We identified 15 SNPs statistically significantly associated with different quantitative biomarkers of the AD. For the cross-sectional study, six SNPs (rs5927116, rs4596772, rs5929538, rs2213488, rs5920524, and rs5945306) are located in or near to six genes DMD, TBX22, LOC101928437, TENM1, SPANXN1, and ZFP92, which have been reported to be associated with schizophrenia or neuropsychiatric diseases in literature. For the longitudinal study, four SNPs (rs4829868, rs5931111, rs6540385, and rs763320) are included in or near to two genes RAC1P4 and AFF2, which have been demonstrated to be associated with brain development or intellectual disability in literature, while the functional annotations of other five novel SNPs (rs12157031, rs428303, rs5953487, rs10284107, and rs5955016) have not been found. Discussion 15 SNPs were found statistically significantly associated with the quantitative biomarkers of the AD. Follow-up study in molecular genetics is needed to verify whether they are indeed related to AD. The findings in this article expand our understanding of the role of the X chromosome in exploring disease susceptibility, introduce new insights into the molecular genetics behind the AD, and may provide a mechanistic clue to further AD-related studies.
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Affiliation(s)
- Kai-Wen Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yu-Xin Yuan
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Bin Zhu
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi-Fang Wei
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Fan-Shuo Meng
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Shun Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jing-Xuan Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ji-Yuan Zhou
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
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21
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Wang X, Hivert V, Groot S, Wang Y, Yengo L, McGrath JJ, Kemper KE, Visscher PM, Wray NR, Revez JA. Cross-ancestry analyses identify new genetic loci associated with 25-hydroxyvitamin D. PLoS Genet 2023; 19:e1011033. [PMID: 37963177 PMCID: PMC10684098 DOI: 10.1371/journal.pgen.1011033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 11/28/2023] [Accepted: 10/24/2023] [Indexed: 11/16/2023] Open
Abstract
Vitamin D status-a complex trait influenced by environmental and genetic factors-is tightly associated with skin colour and ancestry. Yet very few studies have investigated the genetic underpinnings of vitamin D levels across diverse ancestries, and the ones that have, relied on small sample sizes, resulting in inconclusive results. Here, we conduct genome-wide association studies (GWAS) of 25 hydroxyvitamin D (25OHD)-the main circulating form of vitamin D-in 442,435 individuals from four broad genetically-determined ancestry groups represented in the UK Biobank: European (N = 421,867), South Asian (N = 9,983), African (N = 8,306) and East Asian (N = 2,279). We identify a new genetic determinant of 25OHD (rs146759773) in individuals of African ancestry, which was not detected in previous analysis of much larger European cohorts due to low minor allele frequency. We show genome-wide significant evidence of dominance effects in 25OHD that protect against vitamin D deficiency. Given that key events in the synthesis of 25OHD occur in the skin and are affected by pigmentation levels, we conduct GWAS of 25OHD stratified by skin colour and identify new associations. Lastly, we test the interaction between skin colour and variants associated with variance in 25OHD levels and identify two loci (rs10832254 and rs1352846) whose association with 25OHD differs in individuals of distinct complexions. Collectively, our results provide new insights into the complex relationship between 25OHD and skin colour and highlight the importance of diversity in genomic studies. Despite the much larger rates of vitamin D deficiency that we and others report for ancestry groups with dark skin (e.g., South Asian), our study highlights the importance of considering ancestral background and/or skin colour when assessing the implications of low vitamin D.
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Affiliation(s)
- Xiaotong Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Valentin Hivert
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Shiane Groot
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - John J. McGrath
- National Centre for Register-Based Research, Aarhus University, Aarhus V, Denmark
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Peter M. Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Joana A. Revez
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
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22
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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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23
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Bass AJ, Bian S, Wingo AP, Wingo TS, Cutler DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557155. [PMID: 37745553 PMCID: PMC10515795 DOI: 10.1101/2023.09.11.557155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.
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Affiliation(s)
- Andrew J. Bass
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Aliza P. Wingo
- Department of Psychiatry, Emory University, Atlanta, GA 30322, USA
| | - Thomas S. Wingo
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
| | - David J. Cutler
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
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24
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Lea AJ, Clark AG, Dahl AW, Devinsky O, Garcia AR, Golden CD, Kamau J, Kraft TS, Lim YAL, Martins DJ, Mogoi D, Pajukanta P, Perry GH, Pontzer H, Trumble BC, Urlacher SS, Venkataraman VV, Wallace IJ, Gurven M, Lieberman DE, Ayroles JF. Applying an evolutionary mismatch framework to understand disease susceptibility. PLoS Biol 2023; 21:e3002311. [PMID: 37695771 PMCID: PMC10513379 DOI: 10.1371/journal.pbio.3002311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 09/21/2023] [Indexed: 09/13/2023] Open
Abstract
Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.
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Affiliation(s)
- Amanda J. Lea
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Andrew G. Clark
- Department of Molecular Biology & Genetics, Cornell University, Ithaca, New York, United States of America
| | - Andrew W. Dahl
- Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Orrin Devinsky
- Department of Neurology, NYU Langone Comprehensive Epilepsy Center, NYU Grossman School of Medicine, New York, New York, United States of America
| | - Angela R. Garcia
- Department of Anthropology, Stanford University, Stanford, California, United States of America
| | - Christopher D. Golden
- Department of Nutrition, Harvard T H Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Joseph Kamau
- One Health Centre, Institute of Primate Research, Karen, Nairobi, Kenya
| | - Thomas S. Kraft
- Department of Anthropology, University of Utah, Salt Lake City, Utah, United States of America
| | - Yvonne A. L. Lim
- Department of Parasitology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Dino J. Martins
- Turkana Basin Institute, Stony Brook University, Stony Brook, New York, United States of America
| | - Donald Mogoi
- Department of Medical Services and Public Health, Ministry of Health Laikipia County, Nanyuki, Kenya
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, United States of America
| | - George H. Perry
- Departments of Anthropology and Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Herman Pontzer
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, United States of America
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
| | - Benjamin C. Trumble
- School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, United States of America
| | - Samuel S. Urlacher
- Department of Anthropology, Baylor University, Waco, Texas, United States of America
| | - Vivek V. Venkataraman
- Department of Anthropology and Archaeology, University of Calgary, Calgary, Alberta, Canada
| | - Ian J. Wallace
- Department of Anthropology, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Michael Gurven
- Department of Anthropology, University of California Santa Barbara, Santa Barbara, California, United States of America
| | - Daniel E. Lieberman
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Julien F. Ayroles
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
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25
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Liu Z, Ye T, Sun B, Schooling M, Tchetgen ET. Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference. Biometrics 2023; 79:2208-2219. [PMID: 35950778 DOI: 10.1111/biom.13735] [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] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/02/2022] [Indexed: 11/28/2022]
Abstract
Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.
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Affiliation(s)
- Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, New York, USA
| | - Ting Ye
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Baoluo Sun
- Department of Statistics and Data Science, National University of Singapore, Singapore
| | - Mary Schooling
- CUNY Graduate School of Public Health and Health Policy, New York, New York, USA
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Eric Tchetgen Tchetgen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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26
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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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27
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Khramtsova EA, Wilson MA, Martin J, Winham SJ, He KY, Davis LK, Stranger BE. Quality control and analytic best practices for testing genetic models of sex differences in large populations. Cell 2023; 186:2044-2061. [PMID: 37172561 PMCID: PMC10266536 DOI: 10.1016/j.cell.2023.04.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 01/31/2023] [Accepted: 04/07/2023] [Indexed: 05/15/2023]
Abstract
Phenotypic sex-based differences exist for many complex traits. In other cases, phenotypes may be similar, but underlying biology may vary. Thus, sex-aware genetic analyses are becoming increasingly important for understanding the mechanisms driving these differences. To this end, we provide a guide outlining the current best practices for testing various models of sex-dependent genetic effects in complex traits and disease conditions, noting that this is an evolving field. Insights from sex-aware analyses will not only teach us about the biology of complex traits but also aid in achieving the goals of precision medicine and health equity for all.
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Affiliation(s)
- Ekaterina A Khramtsova
- Population Analytics and Insights, Data Science Analytics & Insights, Janssen R&D, Lower Gwynedd Township, PA, USA.
| | - Melissa A Wilson
- School of Life Sciences, Center for Evolution and Medicine, Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85282, USA
| | - Joanna Martin
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Stacey J Winham
- Department of Quantitative Health Sciences, Division of Computational Biology, Mayo Clinic, Rochester, MN, USA
| | - Karen Y He
- Population Analytics and Insights, Data Science Analytics & Insights, Janssen R&D, Lower Gwynedd Township, PA, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Barbara E Stranger
- Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Chicago, IL, USA.
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28
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Glunk V, Laber S, Sinnott-Armstrong N, Sobreira DR, Strobel SM, Batista TM, Kubitz P, Moud BN, Ebert H, Huang Y, Brandl B, Garbo G, Honecker J, Stirling DR, Abdennur N, Calabuig-Navarro V, Skurk T, Ocvirk S, Stemmer K, Cimini BA, Carpenter AE, Dankel SN, Lindgren CM, Hauner H, Nobrega MA, Claussnitzer M. A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes. Nat Metab 2023; 5:861-879. [PMID: 37253881 DOI: 10.1038/s42255-023-00807-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 04/12/2023] [Indexed: 06/01/2023]
Abstract
Recent large-scale genomic association studies found evidence for a genetic link between increased risk of type 2 diabetes and decreased risk for adiposity-related traits, reminiscent of metabolically obese normal weight (MONW) association signatures. However, the target genes and cellular mechanisms driving such MONW associations remain to be identified. Here, we systematically identify the cellular programmes of one of the top-scoring MONW risk loci, the 2q24.3 risk locus, in subcutaneous adipocytes. We identify a causal genetic variant, rs6712203, an intronic single-nucleotide polymorphism in the COBLL1 gene, which changes the conserved transcription factor motif of POU domain, class 2, transcription factor 2, and leads to differential COBLL1 gene expression by altering the enhancer activity at the locus in subcutaneous adipocytes. We then establish the cellular programme under the genetic control of the 2q24.3 MONW risk locus and the effector gene COBLL1, which is characterized by impaired actin cytoskeleton remodelling in differentiating subcutaneous adipocytes and subsequent failure of these cells to accumulate lipids and develop into metabolically active and insulin-sensitive adipocytes. Finally, we show that perturbations of the effector gene Cobll1 in a mouse model result in organismal phenotypes matching the MONW association signature, including decreased subcutaneous body fat mass and body weight along with impaired glucose tolerance. Taken together, our results provide a mechanistic link between the genetic risk for insulin resistance and low adiposity, providing a potential therapeutic hypothesis and a framework for future identification of causal relationships between genome associations and cellular programmes in other disorders.
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Affiliation(s)
- Viktoria Glunk
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Samantha Laber
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
| | - Nasa Sinnott-Armstrong
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Herbold Computational Biology Program, Publich Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Debora R Sobreira
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Sophie M Strobel
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
| | - Thiago M Batista
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Phil Kubitz
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bahareh Nemati Moud
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hannah Ebert
- Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Yi Huang
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Beate Brandl
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Garrett Garbo
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
| | - Julius Honecker
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - David R Stirling
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nezar Abdennur
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Virtu Calabuig-Navarro
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Thomas Skurk
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Soeren Ocvirk
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Intestinal Microbiology Research Group, Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Kerstin Stemmer
- Molecular Cell Biology, Institute for Theoretical Medicine, University of Augsburg, Augsburg, Germany
- Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Simon N Dankel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Cecilia M Lindgren
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Hans Hauner
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Marcelo A Nobrega
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA.
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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29
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Wu H, Eckhardt CM, Baccarelli AA. Molecular mechanisms of environmental exposures and human disease. Nat Rev Genet 2023; 24:332-344. [PMID: 36717624 PMCID: PMC10562207 DOI: 10.1038/s41576-022-00569-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2022] [Indexed: 02/01/2023]
Abstract
A substantial proportion of disease risk for common complex disorders is attributable to environmental exposures and pollutants. An appreciation of how environmental pollutants act on our cells to produce deleterious health effects has led to advances in our understanding of the molecular mechanisms underlying the pathogenesis of chronic diseases, including cancer and cardiovascular, neurodegenerative and respiratory diseases. Here, we discuss emerging research on the interplay of environmental pollutants with the human genome and epigenome. We review evidence showing the environmental impact on gene expression through epigenetic modifications, including DNA methylation, histone modification and non-coding RNAs. We also highlight recent studies that evaluate recently discovered molecular processes through which the environment can exert its effects, including extracellular vesicles, the epitranscriptome and the mitochondrial genome. Finally, we discuss current challenges when studying the exposome - the cumulative measure of environmental influences over the lifespan - and its integration into future environmental health research.
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Affiliation(s)
- Haotian Wu
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Christina M Eckhardt
- Department of Pulmonary, Allergy and Critical Care Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
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30
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Jung HU, Kim DJ, Baek EJ, Chung JY, Ha TW, Kim HK, Kang JO, Lim JE, Oh B. Gene-environment interaction explains a part of missing heritability in human body mass index. Commun Biol 2023; 6:324. [PMID: 36966243 PMCID: PMC10039928 DOI: 10.1038/s42003-023-04679-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 03/07/2023] [Indexed: 03/27/2023] Open
Abstract
Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships-software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.
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Affiliation(s)
- Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Dong Jun Kim
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Eun Ju Baek
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Tae Woong Ha
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Han-Kyul Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea.
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea.
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea.
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31
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Lea AJ, Clark AG, Dahl AW, Devinsky O, Garcia AR, Golden CD, Kamau J, Kraft TS, Lim YAL, Martins D, Mogoi D, Pajukanta P, Perry G, Pontzer H, Trumble BC, Urlacher SS, Venkataraman VV, Wallace IJ, Gurven M, Lieberman D, Ayroles JF. Evolutionary mismatch and the role of GxE interactions in human disease. ARXIV 2023:arXiv:2301.05255v2. [PMID: 36713247 PMCID: PMC9882586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Globally, we are witnessing the rise of complex, non-communicable diseases (NCDs) related to changes in our daily environments. Obesity, asthma, cardiovascular disease, and type 2 diabetes are part of a long list of "lifestyle" diseases that were rare throughout human history but are now common. A key idea from anthropology and evolutionary biology-the evolutionary mismatch hypothesis-seeks to explain this phenomenon. It posits that humans evolved in environments that radically differ from the ones experienced by most people today, and thus traits that were advantageous in past environments may now be "mismatched" and disease-causing. This hypothesis is, at its core, a genetic one: it predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions and have differential health effects in ancestral versus modern environments. Here, we discuss how this concept could be leveraged to uncover the genetic architecture of NCDs in a principled way. Specifically, we advocate for partnering with small-scale, subsistence-level groups that are currently transitioning from environments that are arguably more "matched" with their recent evolutionary history to those that are more "mismatched". These populations provide diverse genetic backgrounds as well as the needed levels and types of environmental variation necessary for mapping GxE interactions in an explicit mismatch framework. Such work would make important contributions to our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and sociocultural contexts.
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Affiliation(s)
- Amanda J. Lea
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
- Child and Brain Development, Canadian Institute for Advanced Research, Toronto, Canada
| | - Andrew G. Clark
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Andrew W. Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Orrin Devinsky
- Department of Neurology, NYU Langone Medical Center, New York, NY, USA
- Comprehensive Epilepsy Center, NYU Langone Medical Center, New York, NY, USA
| | - Angela R. Garcia
- Center for Evolution and Medicine, Arizona State University, Tempe, United States
| | | | - Joseph Kamau
- Department of Biochemistry, School of Medicine, University of Nairobi, Nairobi, Kenya
- Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya
| | - Thomas S. Kraft
- Department of Anthropology, University of Utah, Salt Lake City, USA
| | - Yvonne A. L. Lim
- Department of Parasitology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Dino Martins
- Turkana Basin Research Institute, Turkana, Kenya
- Department of Ecology and Evolution, Princeton University, Princeton, NJ, USA
| | - Donald Mogoi
- Director at County Government of Laikipia, Nanyuki, Kenya
| | - Paivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - George Perry
- Department of Anthropology, Pennsylvania State University, University Park, PA, USA
- Department of Biology, Pennsylvania State University, University Park, PA, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA
| | - Herman Pontzer
- Evolutionary Anthropology, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Benjamin C. Trumble
- School of Human Evolution and Social Change, Arizona State University, Tempe, US
- Center for Evolution and Medicine, Arizona State University, Tempe, United States
| | - Samuel S. Urlacher
- Department of Anthropology, Baylor University, Waco, TX, USA
- Child and Brain Development, Canadian Institute for Advanced Research, Toronto, Canada
| | | | - Ian J. Wallace
- Department of Anthropology, University of New Mexico, Albuquerque, USA
| | - Michael Gurven
- Department of Anthropology, University of California: Santa Barbara, Santa Barbara, CA, USA
| | - Daniel Lieberman
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Julien F. Ayroles
- Department of Ecology and Evolution, Princeton University, Princeton, NJ, USA
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
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32
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Virolainen SJ, VonHandorf A, Viel KCMF, Weirauch MT, Kottyan LC. Gene-environment interactions and their impact on human health. Genes Immun 2023; 24:1-11. [PMID: 36585519 PMCID: PMC9801363 DOI: 10.1038/s41435-022-00192-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 12/31/2022]
Abstract
The molecular processes underlying human health and disease are highly complex. Often, genetic and environmental factors contribute to a given disease or phenotype in a non-additive manner, yielding a gene-environment (G × E) interaction. In this work, we broadly review current knowledge on the impact of gene-environment interactions on human health. We first explain the independent impact of genetic variation and the environment. We next detail well-established G × E interactions that impact human health involving environmental toxicants, pollution, viruses, and sex chromosome composition. We conclude with possibilities and challenges for studying G × E interactions.
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Affiliation(s)
- Samuel J Virolainen
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA
- Immunology Graduate Program, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA
| | - Andrew VonHandorf
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA
| | - Kenyatta C M F Viel
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA
| | - Matthew T Weirauch
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.
- Immunology Graduate Program, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA.
- Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA.
| | - Leah C Kottyan
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.
- Immunology Graduate Program, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA.
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 15012, Cincinnati, OH, 45229, USA.
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33
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Zhong W, Chhibber A, Luo L, Mehrotra DV, Shen J. A fast and powerful linear mixed model approach for genotype-environment interaction tests in large-scale GWAS. Brief Bioinform 2023; 24:6955097. [PMID: 36545787 DOI: 10.1093/bib/bbac547] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/26/2022] [Accepted: 11/12/2022] [Indexed: 12/24/2022] Open
Abstract
Genotype-by-environment interaction (GEI or GxE) plays an important role in understanding complex human traits. However, it is usually challenging to detect GEI signals efficiently and accurately while adjusting for population stratification and sample relatedness in large-scale genome-wide association studies (GWAS). Here we propose a fast and powerful linear mixed model-based approach, fastGWA-GE, to test for GEI effect and G + GxE joint effect. Our extensive simulations show that fastGWA-GE outperforms other existing GEI test methods by controlling genomic inflation better, providing larger power and running hundreds to thousands of times faster. We performed a fastGWA-GE analysis of ~7.27 million variants on 452 249 individuals of European ancestry for 13 quantitative traits and five environment variables in the UK Biobank GWAS data and identified 96 significant signals (72 variants across 57 loci) with GEI test P-values < 1 × 10-9, including 27 novel GEI associations, which highlights the effectiveness of fastGWA-GE in GEI signal discovery in large-scale GWAS.
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Affiliation(s)
- Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Aparna Chhibber
- Translational Bioinformatics, Bristol Myers Squibb, Lawrenceville, NJ 08540, USA
| | - Lan Luo
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA 19454, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA 19454, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
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Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, Cerani A, Liang KYH, Yoshiji S, Willett JDS, Su CY, Raina P, Greenwood CMT, Farjoun Y, Forgetta V, Langenberg C, Zhou S, Ohlsson C, Richards JB. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet 2023; 55:44-53. [PMID: 36635386 PMCID: PMC7614162 DOI: 10.1038/s41588-022-01270-1] [Citation(s) in RCA: 84] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/18/2022] [Indexed: 01/14/2023]
Abstract
Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.
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Affiliation(s)
- Yiheng Chen
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | | | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Guillaume Butler-Laporte
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Tomoko Nakanishi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Agustin Cerani
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Kevin Y H Liang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Satoshi Yoshiji
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Julian Daniel Sunday Willett
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- McGill Genome Centre, McGill University, Montreal, Quebec, Canada
| | - Chen-Yang Su
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Parminder Raina
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada
| | - Yossi Farjoun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Fulcrum Genomics, Boulder, CO, USA
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Claes Ohlsson
- Sahlgrenska Osteoporosis Centre, Centre of 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, Region Västra Götaland, Gothenburg, Sweden
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- 5 Prime Sciences Inc, Montreal, Quebec, Canada.
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Twin Research, King's College London, London, UK.
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Yam P, VerHague M, Albright J, Gertz E, Pardo-Manuel de Villena F, Bennett BJ. Altered macronutrient composition and genetics influence the complex transcriptional network associated with adiposity in the Collaborative Cross. GENES & NUTRITION 2022; 17:13. [PMID: 35945490 PMCID: PMC9364539 DOI: 10.1186/s12263-022-00714-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 07/04/2022] [Indexed: 11/10/2022]
Abstract
Background Obesity is a serious disease with a complex etiology characterized by overaccumulation of adiposity resulting in detrimental health outcomes. Given the liver’s critical role in the biological processes that attenuate adiposity accumulation, elucidating the influence of genetics and dietary patterns on hepatic gene expression is fundamental for improving methods of obesity prevention and treatment. To determine how genetics and diet impact obesity development, mice from 22 strains of the genetically diverse recombinant inbred Collaborative Cross (CC) mouse panel were challenged to either a high-protein or high-fat high-sucrose diet, followed by extensive phenotyping and analysis of hepatic gene expression. Results Over 1000 genes differentially expressed by perturbed dietary macronutrient composition were enriched for biological processes related to metabolic pathways. Additionally, over 9000 genes were differentially expressed by strain and enriched for biological process involved in cell adhesion and signaling. Weighted gene co-expression network analysis identified multiple gene clusters (modules) associated with body fat % whose average expression levels were influenced by both dietary macronutrient composition and genetics. Each module was enriched for distinct types of biological functions. Conclusions Genetic background affected hepatic gene expression in the CC overall, but diet macronutrient differences also altered expression of a specific subset of genes. Changes in macronutrient composition altered gene expression related to metabolic processes, while genetic background heavily influenced a broad range of cellular functions and processes irrespective of adiposity. Understanding the individual role of macronutrient composition, genetics, and their interaction is critical to developing therapeutic strategies and policy recommendations for precision nutrition. Supplementary Information The online version contains supplementary material available at 10.1186/s12263-022-00714-x.
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Genetic determinants of polygenic prediction accuracy within a population. Genetics 2022; 222:6762086. [PMID: 36250789 PMCID: PMC9713421 DOI: 10.1093/genetics/iyac158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/10/2022] [Indexed: 11/15/2022] Open
Abstract
Genomic risk prediction is on the emerging path toward personalized medicine. However, the accuracy of polygenic prediction varies strongly in different individuals. Based on up to 352,277 European ancestry participants in the UK Biobank, we constructed polygenic risk scores for 15 physiological and biochemical quantitative traits. We identified a total of 185 polygenic prediction variability quantitative trait loci for 11 traits by Levene's test among 254,376 unrelated individuals. We validated the effects of prediction variability quantitative trait loci using an independent test set of 58,927 individuals. For instance, a score aggregating 51 prediction variability quantitative trait locus variants for triglycerides had the strongest Spearman correlation of 0.185 (P-value <1.0 × 10-300) with the squared prediction errors. We found a strong enrichment of complex genetic effects conferred by prediction variability quantitative trait loci compared to risk loci identified in genome-wide association studies, including 89 prediction variability quantitative trait loci exhibiting dominance effects. Incorporation of dominance effects into polygenic risk scores significantly improved polygenic prediction for triglycerides, low-density lipoprotein cholesterol, vitamin D, and platelet. In conclusion, we have discovered and profiled genetic determinants of polygenic prediction variability for 11 quantitative biomarkers. These findings may assist interpretation of genomic risk prediction in various contexts and encourage novel approaches for constructing polygenic risk scores with complex genetic effects.
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Affiliation(s)
- Tianyuan Lu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada.,Quantitative Life Sciences Program, McGill University, Montreal, QC H3A 0G4, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
| | - John Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada.,Department of Human Genetics, McGill University, Montreal, QC H3A 0G4, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK
| | - Celia M T Greenwood
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada.,Department of Human Genetics, McGill University, Montreal, QC H3A 0G4, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 0G4, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H3A 0G4, Canada
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Tang Q, Yang Q, Yu X, Liu Y, Tong Z, Li B, Chen Y, Yu EY, Li W. Association of demographic and clinical factors with risk of acute pancreatitis: An exposure-wide Mendelian randomization study. Mol Genet Genomic Med 2022; 11:e2091. [PMID: 36345251 PMCID: PMC9834139 DOI: 10.1002/mgg3.2091] [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: 12/25/2021] [Revised: 05/27/2022] [Accepted: 10/27/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND The incidence of acute pancreatitis (AP) is increasing over years, which brings enormous economy and health burden. However, the aetiologies of AP and underlying mechanisms are still unclear. Here, we performed a two-sample Mendelian randomization (MR) analysis to investigate the associations between all reported possible risk factors and AP using publicly available genome-wide association study summary statistics. METHODS A series of quality control steps were taken in our analysis to select eligible instrumental single nucleotide polymorphisms which were strongly associated with exposures. To make the conclusions more robust and reliable, we utilized several analytical methods (inverse-variance weighting, MR-PRESSO method, weighted median, MR-Egger regression) that are based on different assumptions of two-sample MR analysis. The MR-Egger intercept test, radial regression and leave-one-out sensitivity analysis were performed to evaluate the horizontal pleiotropy, heterogeneities, and stability of these genetic variants on each exposure. A two-step MR method was applied to explore mediators in significant associations. RESULTS Genetic predisposition to cholelithiasis (effect estimate: 17.30, 95% CI: 12.25-22.36, p = 1.95 E-11), body mass index (0.32, 95% CI: 0.13-0.51, p < 0.001), body fat percentage (0.57, 95% CI: 0.31-0.83, p = 1.31 E-05), trunk fat percentage (0.36, 95% CI: 0.14-0.59, p < 0.005), ever smoked (1.61, 95% CI: 0.45-2.77, p = 0.007), and limbs fat percentage (0.55, 95% CI: 0.41-0.69, p < 0.001) were associated with an increased risk of AP. In addition, whole-body fat-free mass (-0.32, 95% CI: -0.55 to -0.10, p = 0.004) was associated with a decrease risk of AP. CONCLUSION Genetic predisposition to cholelithiasis, obesity and smoking could be causally associated with an increased risk of AP, and whole body fat-free mass could be associated with a decreased risk of AP.
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Affiliation(s)
- Qiu‐Yi Tang
- Medical School of Southeast UniversityNanjingChina
| | - Qi Yang
- Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
| | | | - Yu‐Xiu Liu
- Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
| | - Zhi‐Hui Tong
- Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
| | - Bai‐Qiang Li
- Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
| | - Ya‐Ting Chen
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology & Biostatistics, School of Public HealthSoutheast UniversityNanjingChina
| | - Evan Yi‐Wen Yu
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology & Biostatistics, School of Public HealthSoutheast UniversityNanjingChina,CAPHRI Care and Public Health Research Institute, School of Nutrition and Translational Research in MetabolismMaastricht UniversityMaastrichtthe Netherlands
| | - Wei‐Qin Li
- Medical School of Southeast UniversityNanjingChina,Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
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Baek EJ, Jung HU, Chung JY, Jung HI, Kwon SY, Lim JE, Kim HK, Kang JO, Oh B. The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index. Front Genet 2022; 13:1025568. [PMID: 36419825 PMCID: PMC9676478 DOI: 10.3389/fgene.2022.1025568] [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: 08/24/2022] [Accepted: 10/25/2022] [Indexed: 11/09/2022] Open
Abstract
Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction.
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Affiliation(s)
- Eun Ju Baek
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Hye In Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Shin Young Kwon
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Han Kyul Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
- *Correspondence: Ji-One Kang, ; Bermseok Oh,
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
- *Correspondence: Ji-One Kang, ; Bermseok Oh,
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Clark R, Pozarickij A, Hysi PG, Ohno-Matsui K, Williams C, Guggenheim JA. Education interacts with genetic variants near GJD2, RBFOX1, LAMA2, KCNQ5 and LRRC4C to confer susceptibility to myopia. PLoS Genet 2022; 18:e1010478. [PMID: 36395078 PMCID: PMC9671369 DOI: 10.1371/journal.pgen.1010478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 10/14/2022] [Indexed: 11/19/2022] Open
Abstract
Myopia most often develops during school age, with the highest incidence in countries with intensive education systems. Interactions between genetic variants and educational exposure are hypothesized to confer susceptibility to myopia, but few such interactions have been identified. Here, we aimed to identify genetic variants that interact with education level to confer susceptibility to myopia. Two groups of unrelated participants of European ancestry from UK Biobank were studied. A 'Stage-I' sample of 88,334 participants whose refractive error (avMSE) was measured by autorefraction and a 'Stage-II' sample of 252,838 participants who self-reported their age-of-onset of spectacle wear (AOSW) but who did not undergo autorefraction. Genetic variants were prioritized via a 2-step screening process in the Stage-I sample: Step 1 was a genome-wide association study for avMSE; Step 2 was a variance heterogeneity analysis for avMSE. Genotype-by-education interaction tests were performed in the Stage-II sample, with University education coded as a binary exposure. On average, participants were 58 years-old and left full-time education when they were 18 years-old; 35% reported University level education. The 2-step screening strategy in the Stage-I sample prioritized 25 genetic variants (GWAS P < 1e-04; variance heterogeneity P < 5e-05). In the Stage-II sample, 19 of the 25 (76%) genetic variants demonstrated evidence of variance heterogeneity, suggesting the majority were true positives. Five genetic variants located near GJD2, RBFOX1, LAMA2, KCNQ5 and LRRC4C had evidence of a genotype-by-education interaction in the Stage-II sample (P < 0.002) and consistent evidence of a genotype-by-education interaction in the Stage-I sample. For all 5 variants, University-level education was associated with an increased effect of the risk allele. In this cohort, additional years of education were associated with an enhanced effect of genetic variants that have roles including axon guidance and the development of neuronal synapses and neural circuits.
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Affiliation(s)
- Rosie Clark
- School of Optometry & Vision Sciences, Cardiff University, Cardiff, United Kingdom
| | - Alfred Pozarickij
- School of Optometry & Vision Sciences, Cardiff University, Cardiff, United Kingdom
| | - Pirro G. Hysi
- Section of Ophthalmology, School of Life Course Sciences, King’s College London, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King’s College London, London, United Kingdom
| | - Kyoko Ohno-Matsui
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Cathy Williams
- Centre for Academic Child Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Jeremy A. Guggenheim
- School of Optometry & Vision Sciences, Cardiff University, Cardiff, United Kingdom
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Cheesman R, Borgen NT, Lyngstad TH, Eilertsen EM, Ayorech Z, Torvik FA, Andreassen OA, Zachrisson HD, Ystrom E. A population-wide gene-environment interaction study on how genes, schools, and residential areas shape achievement. NPJ SCIENCE OF LEARNING 2022; 7:29. [PMID: 36302785 PMCID: PMC9613652 DOI: 10.1038/s41539-022-00145-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
A child's environment is thought to be composed of different levels that interact with their individual genetic propensities. However, studies have not tested this theory comprehensively across multiple environmental levels. Here, we quantify the contributions of child, parent, school, neighbourhood, district, and municipality factors to achievement, and investigate interactions between polygenic indices for educational attainment (EA-PGI) and environmental levels. We link population-wide administrative data on children's standardised test results, schools and residential identifiers to the Norwegian Mother, Father, and Child Cohort Study (MoBa), which includes >23,000 genotyped parent-child trios. We test for gene-environment interactions using multilevel models with interactions between EA-PGI and random effects for school and residential environments (thus remaining agnostic to specific features of environments). We use parent EA-PGI to control for gene-environment correlation. We found an interaction between students' EA-PGI and schools suggesting compensation: higher-performing schools can raise overall achievement without leaving children with lower EA-PGI behind. Differences between schools matter more for students with lower EA-PGI, explaining 4 versus 2% of the variance in achievement for students 2 SD below versus 2 SD above the mean EA-PGI. Neighbourhood, district, and municipality variation contribute little to achievement (<2% of the variance collectively), and do not interact with children's individual EA-PGI. Policy to reduce social inequality in achievement in Norway should focus on tackling unequal support across schools for children with difficulties.
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Affiliation(s)
- Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway.
| | - Nicolai T Borgen
- Department of Special Needs Education, Faculty of Educational Sciences, University of Oslo, Oslo, Norway
| | - Torkild H Lyngstad
- Department of Sociology & Human Geography, University of Oslo, Oslo, Norway
| | - Espen M Eilertsen
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ziada Ayorech
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Fartein A Torvik
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Henrik D Zachrisson
- Department of Special Needs Education, Faculty of Educational Sciences, University of Oslo, Oslo, Norway
| | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
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41
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St.-Pierre J, Zhang X, Lu T, Jiang L, Loffree X, Wang L, Bhatnagar S, Greenwood CMT. Considering strategies for SNP selection in genetic and polygenic risk scores. Front Genet 2022; 13:900595. [PMID: 36819922 PMCID: PMC9930898 DOI: 10.3389/fgene.2022.900595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 10/05/2022] [Indexed: 02/04/2023] Open
Abstract
Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors.
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Affiliation(s)
- Julien St.-Pierre
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Xinyi Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada,Quantitative Life Sciences, McGill University, Montréal, QC, Canada
| | - Lai Jiang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada
| | - Xavier Loffree
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada,Department of Statistics and Actuarial Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Linbo Wang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sahir Bhatnagar
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Celia M. T. Greenwood
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada,Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada,Quantitative Life Sciences, McGill University, Montréal, QC, Canada,Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada,*Correspondence: Celia M. T. Greenwood,
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42
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Chung JY, Jung HU, Kim DJ, Baek EJ, Kim HK, Kang JO, Lim JE, Oh B. Identification of five genetic variants with differential effects on obesity-related traits based on age. Front Genet 2022; 13:970657. [PMID: 36276968 PMCID: PMC9585212 DOI: 10.3389/fgene.2022.970657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/13/2022] [Indexed: 11/13/2022] Open
Abstract
Obesity is a major public health concern, and its prevalence generally increases with age. As the number of elderly people is increasing in the aging population, the age-dependent increase in obesity has raised interest in the underlying mechanism. To understand the genetic basis of age-related increase in obesity, we identified genetic variants showing age-dependent differential effects on obesity. We conducted stratified analyses between young and old groups using genome-wide association studies of 355,335 United Kingom Biobank participants for five obesity-related phenotypes, including body mass index, body fat percentage, waist-hip ratio, waist circumference, and hip circumference. Using t-statistic, we identified five significant lead single nucleotide polymorphisms: rs2258461 with body mass index, rs9861311 and rs429358 with body fat percentage, rs2870099 with waist-hip ratio, and rs145500243 with waist circumference. Among these single nucleotide polymorphisms, rs429358, located in APOE gene was associated with diverse age-related diseases, such as Alzheimer’s disease, coronary artery disease, age-related degenerative macular diseases, and cognitive decline. The C allele of rs429358 gradually decreases body fat percentage as one grows older in the range of 40–69 years. In conclusion, we identified five genetic variants with differential effects on obesity-related phenotypes based on age using a stratified analysis between young and old groups, which may help to elucidate the mechanisms by which age influences the development of obesity.
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Affiliation(s)
- Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Dong Jun Kim
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Eun Ju Baek
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Han Kyul Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
- *Correspondence: Ji Eun Lim, ; Bermseok Oh,
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
- *Correspondence: Ji Eun Lim, ; Bermseok Oh,
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43
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Lea AJ, Peng J, Ayroles JF. Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels. Genome Res 2022; 32:1826-1839. [PMID: 36229124 PMCID: PMC9712631 DOI: 10.1101/gr.276430.121] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 09/07/2022] [Indexed: 01/18/2023]
Abstract
There is increasing appreciation that, in addition to being shaped by an individual's genotype and environment, most complex traits are also determined by poorly understood interactions between these two factors. So-called "genotype × environment" (G×E) interactions remain difficult to map at the organismal level but can be uncovered using molecular phenotypes. To do so at large scale, we used TM3'seq to profile transcriptomes across 12 cellular environments in 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression levels across environments and revealed a context-dependent genetic architecture: The average heritability of gene expression levels increased in treatment relative to control conditions, and on average, each treatment revealed new expression quantitative trait loci (eQTLs) at 11% of genes. Across our experiments, 22% of all identified eQTLs were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses suggested that positive selection has shaped G×E loci involved in responding to immune challenges and hormones but not to man-made chemicals. We hypothesize that this reflects a reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting G×E interactions, and provides new insight into the evolutionary mechanisms that maintain G×E loci in human populations.
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Affiliation(s)
- Amanda J. Lea
- Department of Ecology and Evolution, Princeton University, Princeton, New Jersey 08544, USA;,Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Julie Peng
- Department of Ecology and Evolution, Princeton University, Princeton, New Jersey 08544, USA;,Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Julien F. Ayroles
- Department of Ecology and Evolution, Princeton University, Princeton, New Jersey 08544, USA;,Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
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A quantile integral linear model to quantify genetic effects on phenotypic variability. Proc Natl Acad Sci U S A 2022; 119:e2212959119. [PMID: 36122202 PMCID: PMC9522331 DOI: 10.1073/pnas.2212959119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (n = 375,791), QUAIL identified 11 vQTLs for body mass index (BMI) that have not been previously reported. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Furthermore, variance polygenic scores (vPGSs) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.
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45
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Song H, Wang X, Guo Y, Ding X. G × EBLUP: A novel method for exploring genotype by environment interactions and genomic prediction. Front Genet 2022; 13:972557. [PMID: 36171888 PMCID: PMC9510768 DOI: 10.3389/fgene.2022.972557] [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: 06/18/2022] [Accepted: 08/01/2022] [Indexed: 11/17/2022] Open
Abstract
Genotype by environment (G × E) interaction is fundamental in the biology of complex traits and diseases. However, most of the existing methods for genomic prediction tend to ignore G × E interaction (GEI). In this study, we proposed the genomic prediction method G × EBLUP by considering GEI. Meanwhile, G × EBLUP can also detect the genome-wide single nucleotide polymorphisms (SNPs) subject to GEI. Using comprehensive simulations and analysis of real data from pigs and maize, we showed that G × EBLUP achieved higher efficiency in mapping GEI SNPs and higher prediction accuracy than the existing methods, and its superiority was more obvious when the GEI variance was large. For pig and maize real data, compared with GBLUP, G × EBLUP showed improvement by 3% in the prediction accuracy for backfat thickness, while our findings indicated that the trait of days to 100 kg of pig was not affected by GEI and G × EBLUP did not improve the accuracy of genomic prediction for the trait. A significant advantage was observed for G × EBLUP in maize; the prediction accuracy was improved by ∼5.0 and 7.7% for grain weight and water content, respectively. Furthermore, G × EBLUP was not influenced by the number of environment levels. It could determine a favourable environment using SNP Bayes factors for each environment, implying that it is a robust and useful method for market-specific animal and plant breeding. We proposed G × EBLUP, a novel method for the estimation of genomic breeding value by considering GEI. This method identified the genome-wide SNPs that were susceptible to GEI and yielded higher genomic prediction accuracies and lower mean squared error compared with the GBLUP method.
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Affiliation(s)
- Hailiang Song
- Beijing Key Laboratory of Fisheries Biotechnology, Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xue Wang
- Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture and Rural Affairs, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yi Guo
- Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture and Rural Affairs, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiangdong Ding
- Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture and Rural Affairs, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
- *Correspondence: Xiangdong Ding, , orcid.org/0000000226842551
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46
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Exploring Lead loci shared between schizophrenia and Cardiometabolic traits. BMC Genomics 2022; 23:617. [PMID: 36008755 PMCID: PMC9414090 DOI: 10.1186/s12864-022-08766-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 07/13/2022] [Indexed: 11/10/2022] Open
Abstract
Individuals with schizophrenia (SCZ) have, on average, a 10- to 20-year shorter expected life span than the rest of the population, primarily due to cardiovascular disease comorbidity. Genome-wide association studies (GWAS) have previously been used to separately identify common variants in SCZ and cardiometabolic traits. However, genetic variants jointly influencing both traits remain to be fully characterised. To assess overlaps (if any) between the genetic architecture of SCZ and cardiometabolic traits, we used conditional false discovery rate (FDR) and local genetic correlation statistical framework analyses. A conjunctional FDR was used to identify shared genetic traits between SCZ and cardiometabolic risk factors. We identified 144 genetic variants which were shared between SCZ and body mass index (BMI), and 15 variants shared between SCZ and triglycerides (TG). Furthermore, we discovered four novel single nucleotide polymorphisms (SNPs) (rs3865350, rs9860913, rs13307 and rs9614186) and four proximate genes (DERL2, SNX4, LY75 and EFCAB6) which were shared by SCZ and BMI. We observed that the novel genetic variant rs13307 and the most proximate gene LY75 exerted potential effects on SCZ and BMI comorbidity. Also, we observed a mixture of concordant and opposite direction associations with shared genetic variants. We demonstrated a moderate to high genetic overlap between SCZ and cardiometabolic traits associated with a pattern of bidirectional associations. Our data suggested a complex interplay between metabolism-related gene pathways in SCZ pathophysiology.
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47
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Nutrigenetic Interaction of Spontaneously Hypertensive Rat Chromosome 20 Segment and High-Sucrose Diet Sensitizes to Metabolic Syndrome. Nutrients 2022; 14:nu14163428. [PMID: 36014934 PMCID: PMC9416443 DOI: 10.3390/nu14163428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Several corresponding regions of human and mammalian genomes have been shown to affect sensitivity to the manifestation of metabolic syndrome via nutrigenetic interactions. In this study, we assessed the effect of sucrose administration in a newly established congenic strain BN.SHR20, in which a limited segment of rat chromosome 20 from a metabolic syndrome model, spontaneously hypertensive rat (SHR), was introgressed into Brown Norway (BN) genomic background. We mapped the extent of the differential segment and compared the genomic sequences of BN vs. SHR within the segment in silico. The differential segment of SHR origin in BN.SHR20 spans about 9 Mb of the telomeric portion of the short arm of chromosome 20. We identified non-synonymous mutations e.g., in ApoM, Notch4, Slc39a7, Smim29 genes and other variations in or near genes associated with metabolic syndrome in human genome-wide association studies. Male rats of BN and BN.SHR20 strains were fed a standard diet for 18 weeks (control groups) or 16 weeks of standard diet followed by 14 days of high-sucrose diet (HSD). We assessed the morphometric and metabolic profiles of all groups. Adiposity significantly increased only in BN.SHR20 after HSD. Fasting glycemia and the glucose levels during the oral glucose tolerance test were higher in BN.SHR20 than in BN groups, while insulin levels were comparable. The fasting levels of triacylglycerols were the highest in sucrose-fed BN.SHR20, both compared to the sucrose-fed BN and the control BN.SHR20. The non-esterified fatty acids and total cholesterol concentrations were higher in BN.SHR20 compared to their respective BN groups, and the HSD elicited an increase in non-esterified fatty acids only in BN.SHR20. In a new genetically defined model, we have isolated a limited genomic region involved in nutrigenetic sensitization to sucrose-induced metabolic disturbances.
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48
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Ju D, Hui D, Hammond DA, Wonkam A, Tishkoff SA. Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine. Annu Rev Biomed Data Sci 2022; 5:321-339. [PMID: 35576557 PMCID: PMC9904154 DOI: 10.1146/annurev-biodatasci-122220-112550] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.
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Affiliation(s)
- Dan Ju
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Daniel Hui
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
- Graduate Program in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dorothy A Hammond
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
- Penn Center for Global Genomics & Health Equity, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA;
| | - Sarah A Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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49
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Fitzgerald T, Birney E. CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank. CELL GENOMICS 2022; 2:100167. [PMID: 36779085 PMCID: PMC9903682 DOI: 10.1016/j.xgen.2022.100167] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 04/11/2022] [Accepted: 07/13/2022] [Indexed: 10/15/2022]
Abstract
Copy number variation (CNV) is known to influence human traits, having a rich history of research into common and rare genetic disease, and although CNV is accepted as an important class of genomic variation, progress on copy-number-based genome-wide association studies (GWASs) from next-generation sequencing (NGS) data has been limited. Here we present a novel method for large-scale copy number analysis from NGS data generating robust copy number estimates and allowing copy number GWASs (CN-GWASs) to be performed genome-wide in discovery mode. We provide a detailed analysis in the UK Biobank resource and a specifically designed software package. We use these methods to perform CN-GWAS analysis across 78 human traits, discovering over 800 genetic associations that are likely to contribute strongly to trait distributions. Finally, we compare CNV and SNP association signals across the same traits and samples, defining specific CNV association classes.
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Affiliation(s)
- Tomas Fitzgerald
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
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50
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Maxwell TJ, Franks PW, Kahn SE, Knowler WC, Mather KJ, Florez JC, Jablonski KA. Quantitative trait loci, G×E and G×G for glycemic traits: response to metformin and placebo in the Diabetes Prevention Program (DPP). J Hum Genet 2022; 67:465-473. [PMID: 35260800 PMCID: PMC10102970 DOI: 10.1038/s10038-022-01027-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 11/09/2022]
Abstract
The complex genetic architecture of type-2-diabetes (T2D) includes gene-by-environment (G×E) and gene-by-gene (G×G) interactions. To identify G×E and G×G, we screened markers for patterns indicative of interactions (relationship loci [rQTL] and variance heterogeneity loci [vQTL]). rQTL exist when the correlation between multiple traits varies by genotype and vQTL occur when the variance of a trait differs by genotype (potentially flagging G×G and G×E). In the metformin and placebo arms of the DPP (n = 1762) we screened 280,965 exomic and intergenic SNPs, for rQTL and vQTL patterns in association with year one changes from baseline in glycemia and related traits (insulinogenic index [IGI], insulin sensitivity index [ISI], fasting glucose and fasting insulin). Significant (p < 1.8 × 10-7) rQTL and vQTL generated a priori hypotheses of individual G×E tests for a SNP × metformin treatment interaction and secondarily for G×G screens. Several rQTL and vQTL identified led to 6 nominally significant (p < 0.05) metformin treatment × SNP interactions (4 for IGI, one insulin, and one glucose) and 12G×G interactions (all IGI) that exceeded experiment-wide significance (p < 4.1 × 10-9). Some loci are directly associated with incident diabetes, and others are rQTL and modify a trait's relationship with diabetes (2 diabetes/glucose, 2 diabetes/insulin, 1 diabetes/IGI). rs3197999, an ISI/insulin rQTL, is a possible gene damaging missense mutation in MST1, is associated with ulcerative colitis, sclerosing cholangitis, Crohn's disease, BMI and coronary artery disease. This study demonstrates evidence for context-dependent effects (G×G & G×E) and the complexity of these T2D-related traits.
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Affiliation(s)
- Taylor J Maxwell
- Computational Biology Institute, The George Washington University, Ashburn, VA, USA.
| | - Paul W Franks
- Genetic & Molecular Epidemiology Unit, Lund University Diabetes Center, Lund, Sweden
| | - Steven E Kahn
- VA Puget Sound Health Care System and University of Washington, Seattle, WA, USA
| | - William C Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA
| | - Kieren J Mather
- Center for Diabetes and Metabolic Diseases & Division of Endocrinology & Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kathleen A Jablonski
- The Biostatistics Center, The Milken Institute of Public Health, The George Washington University, Rockville, MD, USA
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