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Ball RL, Bogue MA, Liang H, Srivastava A, Ashbrook DG, Lamoureux A, Gerring MW, Hatoum AS, Kim MJ, He H, Emerson J, Berger AK, Walton DO, Sheppard K, El Kassaby B, Castellanos F, Kunde-Ramamoorthy G, Lu L, Bluis J, Desai S, Sundberg BA, Peltz G, Fang Z, Churchill GA, Williams RW, Agrawal A, Bult CJ, Philip VM, Chesler EJ. GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis. Genome Res 2024; 34:145-159. [PMID: 38290977 PMCID: PMC10903950 DOI: 10.1101/gr.278157.123] [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: 08/08/2023] [Accepted: 01/10/2024] [Indexed: 02/01/2024]
Abstract
Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of genetic variants that contribute to disease. Thousands of disease-relevant traits have been characterized in mice and made publicly available. New strains and populations including consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome sequences of inbred strains, along with dense genotypes from others, enable integrated analysis of trait-variant associations across populations, but these analyses are hampered by the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense variant resource by harmonizing multiple data sets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extendable to other model organisms. The result is a web- and programmatically accessible data service called GenomeMUSter, comprising single-nucleotide variants covering 657 strains at 106.8 million segregating sites. Interoperation with phenotype databases, analytic tools, and other resources enable a wealth of applications, including multitrait, multipopulation meta-analysis. We show this in cross-species comparisons of type 2 diabetes and substance use disorder meta-analyses, leveraging mouse data to characterize the likely role of human variant effects in disease. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.
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Affiliation(s)
- Robyn L Ball
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA;
| | - Molly A Bogue
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | | | - Anuj Srivastava
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA
| | - David G Ashbrook
- University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | | | | | - Alexander S Hatoum
- Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri 63130, USA
- Artificial Intelligence and the Internet of Things Institute, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Matthew J Kim
- University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Hao He
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | - Jake Emerson
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | | | | | | | | | | | | | - Lu Lu
- University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | - John Bluis
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | - Sejal Desai
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | | | - Gary Peltz
- Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Zhuoqing Fang
- Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
| | | | - Robert W Williams
- University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Carol J Bult
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
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Price TR, Emfinger CH, Schueler KL, King S, Nicholson R, Beck T, Yandell BS, Summers SA, Holland WL, Krauss RM, Keller MP, Attie AD. Identification of genetic drivers of plasma lipoprotein size in the Diversity Outbred mouse population. J Lipid Res 2023; 64:100471. [PMID: 37944753 PMCID: PMC10750189 DOI: 10.1016/j.jlr.2023.100471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/28/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023] Open
Abstract
Despite great progress in understanding lipoprotein physiology, there is still much to be learned about the genetic drivers of lipoprotein abundance, composition, and function. We used ion mobility spectrometry to survey 16 plasma lipoprotein subfractions in 500 Diversity Outbred mice maintained on a Western-style diet. We identified 21 quantitative trait loci (QTL) affecting lipoprotein abundance. To refine the QTL and link them to disease risk in humans, we asked if the human homologs of genes located at each QTL were associated with lipid traits in human genome-wide association studies. Integration of mouse QTL with human genome-wide association studies yielded candidate gene drivers for 18 of the 21 QTL. This approach enabled us to nominate the gene encoding the neutral ceramidase, Asah2, as a novel candidate driver at a QTL on chromosome 19 for large HDL particles (HDL-2b). To experimentally validate Asah2, we surveyed lipoproteins in Asah2-/- mice. Compared to wild-type mice, female Asah2-/- mice showed an increase in several lipoproteins, including HDL. Our results provide insights into the genetic regulation of circulating lipoproteins, as well as mechanisms by which lipoprotein subfractions may affect cardiovascular disease risk in humans.
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Affiliation(s)
- Tara R Price
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Kathryn L Schueler
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Sarah King
- School of Medicine, University of California-San Francisco, San Francisco, CA, USA
| | - Rebekah Nicholson
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT, USA
| | - Tim Beck
- Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Brian S Yandell
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | - Scott A Summers
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT, USA
| | - William L Holland
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT, USA
| | - Ronald M Krauss
- School of Medicine, University of California-San Francisco, San Francisco, CA, USA
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Alan D Attie
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.
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3
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Jurrjens AW, Seldin MM, Giles C, Meikle PJ, Drew BG, Calkin AC. The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases. eLife 2023; 12:e86139. [PMID: 37000167 PMCID: PMC10065800 DOI: 10.7554/elife.86139] [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: 01/16/2023] [Accepted: 03/15/2023] [Indexed: 04/01/2023] Open
Abstract
Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.
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Affiliation(s)
- Aaron W Jurrjens
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
| | - Marcus M Seldin
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, United States
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Brian G Drew
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - Anna C Calkin
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
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4
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Cao Y, Aquino-Martinez R, Hutchison E, Allayee H, Lusis AJ, Rey FE. Role of gut microbe-derived metabolites in cardiometabolic diseases: Systems based approach. Mol Metab 2022; 64:101557. [PMID: 35870705 PMCID: PMC9399267 DOI: 10.1016/j.molmet.2022.101557] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/30/2022] [Accepted: 07/18/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The gut microbiome influences host physiology and cardiometabolic diseases by interacting directly with intestinal cells or by producing molecules that enter the host circulation. Given the large number of microbial species present in the gut and the numerous factors that influence gut bacterial composition, it has been challenging to understand the underlying biological mechanisms that modulate risk of cardiometabolic disease. SCOPE OF THE REVIEW Here we discuss a systems-based approach that involves simultaneously examining individuals in populations for gut microbiome composition, molecular traits using "omics" technologies, such as circulating metabolites quantified by mass spectrometry, and clinical traits. We summarize findings from landmark studies using this approach and discuss future applications. MAJOR CONCLUSIONS Population-based integrative approaches have identified a large number of microbe-derived or microbe-modified metabolites that are associated with cardiometabolic traits. The knowledge gained from these studies provide new opportunities for understanding the mechanisms involved in gut microbiome-host interactions and may have potentially important implications for developing novel therapeutic approaches.
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Affiliation(s)
- Yang Cao
- Departments of Medicine, Human Genetics, and Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095, USA
| | - Ruben Aquino-Martinez
- Department of Bacteriology, University of Wisconsin, Madison, Madison, WI 53706, USA
| | - Evan Hutchison
- Department of Bacteriology, University of Wisconsin, Madison, Madison, WI 53706, USA
| | - Hooman Allayee
- Departments of Population & Public Health Sciences and Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Aldons J Lusis
- Departments of Medicine, Human Genetics, and Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095, USA.
| | - Federico E Rey
- Department of Bacteriology, University of Wisconsin, Madison, Madison, WI 53706, USA
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5
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Byun J, Han Y, Li Y, Xia J, Long E, Choi J, Xiao X, Zhu M, Zhou W, Sun R, Bossé Y, Song Z, Schwartz A, Lusk C, Rafnar T, Stefansson K, Zhang T, Zhao W, Pettit RW, Liu Y, Li X, Zhou H, Walsh KM, Gorlov I, Gorlova O, Zhu D, Rosenberg SM, Pinney S, Bailey-Wilson JE, Mandal D, de Andrade M, Gaba C, Willey JC, You M, Anderson M, Wiencke JK, Albanes D, Lam S, Tardon A, Chen C, Goodman G, Bojeson S, Brenner H, Landi MT, Chanock SJ, Johansson M, Muley T, Risch A, Wichmann HE, Bickeböller H, Christiani DC, Rennert G, Arnold S, Field JK, Shete S, Le Marchand L, Melander O, Brunnstrom H, Liu G, Andrew AS, Kiemeney LA, Shen H, Zienolddiny S, Grankvist K, Johansson M, Caporaso N, Cox A, Hong YC, Yuan JM, Lazarus P, Schabath MB, Aldrich MC, Patel A, Lan Q, Rothman N, Taylor F, Kachuri L, Witte JS, Sakoda LC, Spitz M, Brennan P, Lin X, McKay J, Hung RJ, Amos CI. Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer. Nat Genet 2022; 54:1167-1177. [PMID: 35915169 PMCID: PMC9373844 DOI: 10.1038/s41588-022-01115-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 05/27/2022] [Indexed: 02/03/2023]
Abstract
To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.
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Affiliation(s)
- Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Yafang Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Jun Xia
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xiangjun Xiao
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, P. R. China
| | - Wen Zhou
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Ryan Sun
- Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Department of Molecular Medicine, Laval University, Quebec City, Quebec, Canada
| | - Zhuoyi Song
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ann Schwartz
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | - Christine Lusk
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | | | | | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wei Zhao
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rowland W Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Yanhong Liu
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Xihao Li
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Kyle M Walsh
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
| | - Ivan Gorlov
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Olga Gorlova
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Dakai Zhu
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Susan M Rosenberg
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Susan Pinney
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Diptasri Mandal
- Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | | | - Colette Gaba
- The University of Toledo College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - James C Willey
- The University of Toledo College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Ming You
- Center for Cancer Prevention, Houston Methodist Research Institute, Houston, TX, USA
| | | | - John K Wiencke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephan Lam
- Department of Integrative Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Adonina Tardon
- Public Health Department, University of Oviedo, ISPA and CIBERESP, Asturias, Spain
| | - Chu Chen
- Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Stig Bojeson
- Department of Clinical Biochemistry, Herlev Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mattias Johansson
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Thomas Muley
- Division of Cancer Epigenomics, DKFZ - German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Angela Risch
- Division of Cancer Epigenomics, DKFZ - German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Biosciences and Medical Biology, Allergy-Cancer-BioNano Research Centre, University of Salzburg, Salzburg, Austria
- Cancer Cluster Salzburg, Salzburg, Austria
| | | | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany
| | - David C Christiani
- Department of Epidemiology, Harvard T.H.Chan School of Public Health, Boston, MA, USA
| | - Gad Rennert
- Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Susanne Arnold
- University of Kentucky, Markey Cancer Center, Lexington, KY, USA
| | - John K Field
- Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Sanjay Shete
- Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
- Department of Epidemiology, 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
- University Health Network- The Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Angeline S Andrew
- Departments of Epidemiology and Community and Family Medicine, Dartmouth College, Hanover, NH, USA
| | | | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P. R. China
| | | | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Mikael Johansson
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Neil Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Angela Cox
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jian-Min Yuan
- UPMC Hillman Cancer Center and Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - 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, FL, USA
| | - Melinda C Aldrich
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alpa Patel
- American Cancer Society, Atlanta, GA, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fiona Taylor
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Margaret Spitz
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Paul Brennan
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Xihong Lin
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - James McKay
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
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6
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Olguín V, Durán A, Las Heras M, Rubilar JC, Cubillos FA, Olguín P, Klein AD. Genetic Background Matters: Population-Based Studies in Model Organisms for Translational Research. Int J Mol Sci 2022; 23:ijms23147570. [PMID: 35886916 PMCID: PMC9316598 DOI: 10.3390/ijms23147570] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 02/01/2023] Open
Abstract
We are all similar but a bit different. These differences are partially due to variations in our genomes and are related to the heterogeneity of symptoms and responses to treatments that patients exhibit. Most animal studies are performed in one single strain with one manipulation. However, due to the lack of variability, therapies are not always reproducible when treatments are translated to humans. Panels of already sequenced organisms are valuable tools for mimicking human phenotypic heterogeneities and gene mapping. This review summarizes the current knowledge of mouse, fly, and yeast panels with insightful applications for translational research.
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Affiliation(s)
- Valeria Olguín
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
| | - Anyelo Durán
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
| | - Macarena Las Heras
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
| | - Juan Carlos Rubilar
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
| | - Francisco A. Cubillos
- Departamento de Biología, Santiago, Facultad de Química y Biología, Universidad de Santiago de Chile, Santiago 9170022, Chile;
- Millennium Institute for Integrative Biology (iBio), Santiago 7500565, Chile
| | - Patricio Olguín
- Program in Human Genetics, Institute of Biomedical Sciences, Biomedical Neurosciences Institute, Department of Neuroscience, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile;
| | - Andrés D. Klein
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
- Correspondence:
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7
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Zou J, Gopalakrishnan S, Parker CC, Nicod J, Mott R, Cai N, Lionikas A, Davies RW, Palmer AA, Flint J. Analysis of independent cohorts of outbred CFW mice reveals novel loci for behavioral and physiological traits and identifies factors determining reproducibility. G3 (BETHESDA, MD.) 2022; 12:jkab394. [PMID: 34791208 PMCID: PMC8728023 DOI: 10.1093/g3journal/jkab394] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 10/17/2021] [Indexed: 12/12/2022]
Abstract
Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6, and GM11545 with bone mineral density, and Psmb9 with weight. However, replication at a nominal threshold of 0.05 between the two component studies was low, with less than one-third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner's Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Leveraging these observations, we integrated information about replication rates, study-specific heterogeneity, and Winner's Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study.
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Affiliation(s)
- Jennifer Zou
- Department of Computer Science, University of California, Los Angeles, CA 90024, USA
| | - Shyam Gopalakrishnan
- Faculty of Health and Medical Sciences, GLOBE Institute, University of Copenhagen, Copenhagen DK-1353, Denmark
| | - Clarissa C Parker
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, VT 05753, USA
| | | | - Richard Mott
- UCL Department of Genetics, Evolution & Environment, UCL Genetics Institute, London WC1E 6BT, UK
| | - Na Cai
- Helmholtz Zentrum Muenchen, Helmoltz Pioneer Campus, Neuherberg 85764, Germany
| | - Arimantas Lionikas
- School of Medicine, Medical Sciences and Nutrition, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Robert W Davies
- Department of Statistics, University of Oxford, Oxford OX1 2JD, UK
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Jonathan Flint
- Department of Biobehavioral Sciences, University of California, Los Angeles, CA 90024, USA
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8
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Xie F, Wang S, Beavis WD, Xu S. Estimation of genetic variance contributed by a quantitative trait locus: correcting the bias associated with significance tests. Genetics 2021; 219:6363798. [PMID: 34740243 DOI: 10.1093/genetics/iyab115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/12/2021] [Indexed: 11/12/2022] Open
Abstract
The Beavis effect in quantitative trait locus (QTL) mapping describes a phenomenon that the estimated effect size of a statistically significant QTL (measured by the QTL variance) is greater than the true effect size of the QTL if the sample size is not sufficiently large. This is a typical example of the Winners' curse applied to molecular quantitative genetics. Theoretical evaluation and correction for the Winners' curse have been studied for interval mapping. However, similar technologies have not been available for current models of QTL mapping and genome-wide association studies where a polygene is often included in the linear mixed models to control the genetic background effect. In this study, we developed the theory of the Beavis effect in a linear mixed model using a truncated noncentral Chi-square distribution. We equated the observed Wald test statistic of a significant QTL to the expectation of a truncated noncentral Chi-square distribution to obtain a bias-corrected estimate of the QTL variance. The results are validated from replicated Monte Carlo simulation experiments. We applied the new method to the grain width (GW) trait of a rice population consisting of 524 homozygous varieties with over 300 k single nucleotide polymorphism markers. Two loci were identified and the estimated QTL heritability were corrected for the Beavis effect. Bias correction for the larger QTL on chromosome 5 (GW5) with an estimated heritability of 12% did not change the QTL heritability due to the extremely large test score and estimated QTL effect. The smaller QTL on chromosome 9 (GW9) had an estimated QTL heritability of 9% reduced to 6% after the bias-correction.
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Affiliation(s)
- Fangjie Xie
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Shibo Wang
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - William D Beavis
- Department of Agronomy, Iowa State University, Ames, IW 50011, USA
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
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9
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Zheng A, Li H, Feng Z, Liu J. Integrative Analyses Reveal Tstd1 as a Potential Modulator of HDL Cholesterol and Mitochondrial Function in Mice. Cells 2021; 10:2976. [PMID: 34831199 PMCID: PMC8616306 DOI: 10.3390/cells10112976] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/25/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022] Open
Abstract
High-density lipoprotein (HDL) cholesterol levels are closely associated with human health and diseases. To identify genes modulating plasma HDL levels, we integrated HDL measurements and multi-omics data collected from diverse mouse cohorts and combined a list of systems genetics methods, including quantitative trait loci (QTL) mapping analysis, mediation analysis, transcriptome-wide association analysis (TWAS), and correlation analysis. We confirmed a significant and conserved QTL for plasma HDL on chromosome 1 and identified that Tstd1 liver transcript correlates with plasma HDL in several independent mouse cohorts, suggesting Tstd1 may be a potential modulator of plasma HDL levels. Correlation analysis using over 70 transcriptomics datasets in humans and mice revealed consistent correlations between Tstd1 and genes known to be involved in cholesterol and HDL regulation. Consistent with strong enrichment in gene sets related to cholesterol and lipoproteins in the liver, mouse strains with high Tstd1 exhibited higher plasma levels of HDL, total cholesterol and other lipid markers. GeneBridge using large-scale expression datasets identified conserved and positive associations between TSTD1/Tstd1 and mitochondrial pathways, as well as cholesterol and lipid pathways in human, mouse and rat. In summary, we identified Tstd1 as a new modulator of plasma HDL and mitochondrial function through integrative systems analyses, and proposed a new mechanism of HDL modulation and a potential therapeutic target for relevant diseases. This study highlights the value of such integrative approaches in revealing molecular mechanisms of complex traits or diseases.
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Affiliation(s)
- Adi Zheng
- Department of Biomedical Sciences, University of Lausanne, Bugnon 7, 1005 Lausanne, Switzerland;
| | - Hao Li
- Center for Mitochondrial Biology and Medicine, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China;
| | - Zhihui Feng
- Center for Mitochondrial Biology and Medicine, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China;
| | - Jiankang Liu
- Center for Mitochondrial Biology and Medicine, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China;
- University of Health and Rehabilitation Sciences, Qingdao 266071, China
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10
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Braz CU, Rowan TN, Schnabel RD, Decker JE. Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle. Sci Rep 2021; 11:13335. [PMID: 34172761 PMCID: PMC8233360 DOI: 10.1038/s41598-021-92455-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
Understanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.
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Affiliation(s)
- Camila U Braz
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Troy N Rowan
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
- Genetics Area Program, University of Missouri, Columbia, MO, 65211, USA
| | - Robert D Schnabel
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
- Genetics Area Program, University of Missouri, Columbia, MO, 65211, USA
- Informatics Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Jared E Decker
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.
- Genetics Area Program, University of Missouri, Columbia, MO, 65211, USA.
- Informatics Institute, University of Missouri, Columbia, MO, 65211, USA.
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11
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Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential. Sci Rep 2021; 11:6894. [PMID: 33767346 PMCID: PMC7994632 DOI: 10.1038/s41598-021-86389-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 03/11/2021] [Indexed: 01/31/2023] Open
Abstract
Rice yield per plant has a complex genetic architecture, which is mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW), and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. To understand the genetic basis of the relationship between rice yield and component traits, we investigated the four traits of two rice hybrid populations (575 + 1495 F1) in different environments and conducted meta-analyses of genome-wide association study (meta-GWAS). In total, 3589 significant loci for three components traits were detected, while only 3 loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus/gene affected component traits to further enhance yield is recommended. Mendelian randomization design is adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction effects (positive effect or negative effect). Additionally, TP (Beta = 1.865) has a greater effect on yield than KGW (Beta = 1.016) and GPP (Beta = 0.086). Five significant loci for component traits that had an indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice. Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield and provide valuable information for improving rice yield potential.
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12
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Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets. BMC Bioinformatics 2021; 22:50. [PMID: 33546598 PMCID: PMC7866684 DOI: 10.1186/s12859-021-03959-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 01/06/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many follow-up investigations involve joint analysis of multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge. RESULTS In this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratification, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimer's disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases. The software is available at https://github.com/HaohanWang/CMM .
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13
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Jin Q, Shi G. Meta-analysis of SNP-environment interaction with heterogeneity for overlapping data. Sci Rep 2021; 11:2590. [PMID: 33510406 PMCID: PMC7844041 DOI: 10.1038/s41598-021-82336-8] [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: 08/24/2020] [Accepted: 01/18/2021] [Indexed: 11/09/2022] Open
Abstract
Meta-analysis is a popular method used in genome-wide association studies, by which the results of multiple studies are combined to identify associations. This process generates heterogeneity. Recently, we proposed a random effect model meta-regression method (MR) to study the effect of single nucleotide polymorphism (SNP)-environment interactions. This method takes heterogeneity into account and produces high power. We also proposed a fixed effect model overlapping MR in which the overlapping data is taken into account. In the present study, a random effect model overlapping MR that simultaneously considers heterogeneity and overlapping data is proposed. This method is based on the random effect model MR and the fixed effect model overlapping MR. A new way of solving the logarithm of the determinant of covariance matrices in likelihood functions is also provided. Tests for the likelihood ratio statistic of the SNP-environment interaction effect and the SNP and SNP-environment joint effects are given. In our simulations, null distributions and type I error rates were proposed to verify the suitability of our method, and powers were applied to evaluate the superiority of our method. Our findings indicate that this method is effective in cases of overlapping data with a high heterogeneity.
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Affiliation(s)
- Qinqin Jin
- State Key Laboratory of Integrated Services Networks, Xidian University, 2 South Taibai Road, Xi'an, 710071, Shaanxi, China. .,Applied Science College, Taiyuan University of Science and Technology, Taiyuan, 030024, Shanxi, China.
| | - Gang Shi
- State Key Laboratory of Integrated Services Networks, Xidian University, 2 South Taibai Road, Xi'an, 710071, Shaanxi, China
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14
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Lo S, Muñoz-Amatriaín M, Hokin SA, Cisse N, Roberts PA, Farmer AD, Xu S, Close TJ. A genome-wide association and meta-analysis reveal regions associated with seed size in cowpea [Vigna unguiculata (L.) Walp]. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:3079-3087. [PMID: 31367839 PMCID: PMC6791911 DOI: 10.1007/s00122-019-03407-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 07/24/2019] [Indexed: 05/19/2023]
Abstract
This paper combined GWAS, meta-analysis and sequence homology comparison with common bean to identify regions associated with seed size variation in domesticated cowpea. Seed size is an important trait for yield and commercial value in dry-grain cowpea. Seed size varies widely among different cowpea accessions, and the genetic basis of such variation is not yet well understood. To better decipher the genetic basis of seed size, a genome-wide association study (GWAS) and meta-analysis were conducted on a panel of 368 cowpea diverse accessions from 51 countries. Four traits, including seed weight, length, width and density were evaluated across three locations. Using 51,128 single nucleotide polymorphisms covering the cowpea genome, 17 loci were identified for these traits. One locus was common to weight, width and length, suggesting pleiotropy. By integrating synteny-based analysis with common bean, six candidate genes (Vigun05g036000, Vigun05g039600, Vigun05g204200, Vigun08g217000, Vigun11g187000, and Vigun11g191300) which are implicated in multiple functional categories related to seed size such as endosperm development, embryo development, and cell elongation were identified. These results suggest that a combination of GWAS meta-analysis with synteny comparison in a related plant is an efficient approach to identify candidate gene (s) for complex traits in cowpea. The identified loci and candidate genes provide useful information for improving cowpea varieties and for molecular investigation of seed size.
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Affiliation(s)
- Sassoum Lo
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA.
| | - María Muñoz-Amatriaín
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - Samuel A Hokin
- National Center for Genome Resources, Santa Fe, NM, 87505, USA
| | - Ndiaga Cisse
- Centre d'Etude Régional pour l'Amélioration de l'Adaptation à la Sècheresse, ISRA/CERAAS, Thies, Senegal
| | - Philip A Roberts
- Department of Nematology, University of California, Riverside, CA, 92521, USA
| | - Andrew D Farmer
- National Center for Genome Resources, Santa Fe, NM, 87505, USA
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
| | - Timothy J Close
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
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15
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Kraaijenga VJC, van Munster JJCM, van Zanten GA. Association of Behavior With Noise-Induced Hearing Loss Among Attendees of an Outdoor Music Festival: A Secondary Analysis of a Randomized Clinical Trial. JAMA Otolaryngol Head Neck Surg 2019; 144:490-497. [PMID: 29710132 DOI: 10.1001/jamaoto.2018.0272] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance To date, factors associated with noise-induced hearing loss at music festivals have not yet been analyzed in a single comprehensive data set. In addition, little is known about the hearing loss-associated behavior of music festival attendees. Objectives To assess which factors are associated with the occurrence of a temporary threshold shift (TTS) after music exposure and to investigate the behavior of music festival attendees. Design, Setting, and Participants This prospective post hoc analysis gathered data from a randomized, single-blind clinical trial conducted on September 5, 2015, at an outdoor music festival in Amsterdam, the Netherlands. Adult volunteers with normal hearing were recruited via social media from August 26 through September 3, 2015. Intention to use earplugs was an exclusion criterion. Of 86 volunteers assessed, 51 were included. This post hoc analysis was performed from October 3, 2016, through February 27, 2017. Interventions Music festival visit for 4.5 hours. Main Outcomes and Measures The primary outcome was a TTS on a standard audiogram for the frequencies 3.0- and 4.0-kHz. Multivariable linear regression was performed to determine which factors are associated with a TTS. A questionnaire on behavior, hearing, and tinnitus was distributed to the participants before and after the festival visit. Results A total of 51 participants were included (18 men [35%] and 33 women [65%]) with a mean (SD) age of 27 (6) years. Mean (SD) threshold change across 3.0 and 4.0 kHz was 5.4 (5.7) dB for the right ear and 4.0 (6.1) dB for the left ear. Earplug use (absolute difference in the left ear, -6.0 dB [95% CI, -8.7 to -3.2 dB]; in the right ear, -6.4 dB [95% CI, -8.8 to -4.1 dB]), quantity of alcohol use (absolute difference per unit in the left ear, 1.1 dB [95% CI, 0.5 to 1.7 dB]; in the right ear, 0.7 dB [95% CI, 0.1 to 1.4 dB]), drug use (absolute difference in the right ear, 6.0 dB [95% CI, 0.9 to 11.1 dB]), and male sex (absolute difference in the right ear, 4.1 dB [95% CI, 0.3 to 5.9 dB]) were independently associated with hearing loss, with earplug use being the most important factor. Unprotected participants reported significantly worse subjective hearing performance and tinnitus after the festival visit than did participants using earplugs (Cramer V, 0.62 [95% CI, 0.47-0.79] and 0.39 [95% CI, 0.16-0.62], respectively). In the earplug group, the perceived loudness (r = -0.72; 95% CI, -1.00 to -0.43) and appreciation (r = 0.53; 95% CI, 0.29 to 0.78) of music and speech perception (r = 0.21; 95% CI, 0.09 to 0.35) were correlated with the duration of earplug use. Conclusions and Relevance The present study identified nonuse of earplugs, use of alcohol and drugs, and male sex as associated with a TTS at an outdoor music festival. Physicians should consider these factors to raise awareness about the combined risk of attending music festivals without using earplugs while consuming alcohol and/or drugs. The intention to use earplugs was correlated with the loudness and appreciation of music with earplugs, which may advocate for the use of personalized earplugs. Trial Registration trialregister.nl Identifier: NTR5401.
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Affiliation(s)
- Véronique J C Kraaijenga
- Department of Otorhinolaryngology-Head and Neck Surgery, University Medical Center Utrecht, Utrecht, the Netherlands.,Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - J J C M van Munster
- Department of Otorhinolaryngology-Head and Neck Surgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - G A van Zanten
- Department of Otorhinolaryngology-Head and Neck Surgery, University Medical Center Utrecht, Utrecht, the Netherlands.,Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
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16
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Peterson RE, Cai N, Dahl AW, Bigdeli TB, Edwards AC, Webb BT, Bacanu SA, Zaitlen N, Flint J, Kendler KS. Molecular Genetic Analysis Subdivided by Adversity Exposure Suggests Etiologic Heterogeneity in Major Depression. Am J Psychiatry 2018; 175:545-554. [PMID: 29495898 PMCID: PMC5988935 DOI: 10.1176/appi.ajp.2017.17060621] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE The extent to which major depression is the outcome of a single biological mechanism or represents a final common pathway of multiple disease processes remains uncertain. Genetic approaches can potentially identify etiologic heterogeneity in major depression by classifying patients on the basis of their experience of major adverse events. METHOD Data are from the China, Oxford, and VCU Experimental Research on Genetic Epidemiology (CONVERGE) project, a study of Han Chinese women with recurrent major depression aimed at identifying genetic risk factors for major depression in a rigorously ascertained cohort carefully assessed for key environmental risk factors (N=9,599). To detect etiologic heterogeneity, genome-wide association studies, heritability analyses, and gene-by-environment interaction analyses were performed. RESULTS Genome-wide association studies stratified by exposure to adversity revealed three novel loci associated with major depression only in study participants with no history of adversity. Significant gene-by-environment interactions were seen between adversity and genotype at all three loci, and 13.2% of major depression liability can be attributed to genome-wide interaction with adversity exposure. The genetic risk in major depression for participants who reported major adverse life events (27%) was partially shared with that in participants who did not (73%; genetic correlation=+0.64). Together with results from simulation studies, these findings suggest etiologic heterogeneity within major depression as a function of environmental exposures. CONCLUSIONS The genetic contributions to major depression may differ between women with and those without major adverse life events. These results have implications for the molecular dissection of major depression and other complex psychiatric and biomedical diseases.
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Affiliation(s)
- Roseann E. Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Na Cai
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, CB10 1SA Hinxton, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, UK
| | - Andy W. Dahl
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Tim B. Bigdeli
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
- State University of New York Downstate Medical Center, Brooklyn, New York
| | - Alexis C. Edwards
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Bradley T. Webb
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Silviu-Alin Bacanu
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Noah Zaitlen
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California
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17
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Wang X, Miao J, Xia J, Chang T, E G, Bao J, Jin S, Xu L, Zhang L, Zhu B, Gao X, Chen Y, Li J, Gao H. Identifying novel genes for carcass traits by testing G × E interaction through genome-wide meta-analysis in Chinese Simmental beef cattle. Livest Sci 2018. [DOI: 10.1016/j.livsci.2018.04.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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18
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An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures. Genetics 2018; 209:685-698. [PMID: 29752291 DOI: 10.1534/genetics.117.300501] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 04/12/2018] [Indexed: 01/29/2023] Open
Abstract
Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. However, current association testing approaches for dealing with sex, such as including sex as a covariate, cannot fully account for these phenomena and can be suboptimal in statistical power. We present a novel association mapping framework, MetaSex, that can comprehensively account for the genetic architecture differences between the sexes. Through simulations and applications to real data, we show that our framework has superior performance than previous approaches in association mapping.
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19
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Lee CH, Eskin E, Han B. Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects. Bioinformatics 2018; 33:i379-i388. [PMID: 28881976 PMCID: PMC5870848 DOI: 10.1093/bioinformatics/btx242] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Motivation Meta-analysis is essential to combine the results of genome-wide association studies (GWASs). Recent large-scale meta-analyses have combined studies of different ethnicities, environments and even studies of different related phenotypes. These differences between studies can manifest as effect size heterogeneity. We previously developed a modified random effects model (RE2) that can achieve higher power to detect heterogeneous effects than the commonly used fixed effects model (FE). However, RE2 cannot perform meta-analysis of correlated statistics, which are found in recent research designs, and the identified variants often overlap with those found by FE. Results Here, we propose RE2C, which increases the power of RE2 in two ways. First, we generalized the likelihood model to account for correlations of statistics to achieve optimal power, using an optimization technique based on spectral decomposition for efficient parameter estimation. Second, we designed a novel statistic to focus on the heterogeneous effects that FE cannot detect, thereby, increasing the power to identify new associations. We developed an efficient and accurate p-value approximation procedure using analytical decomposition of the statistic. In simulations, RE2C achieved a dramatic increase in power compared with the decoupling approach (71% vs. 21%) when the statistics were correlated. Even when the statistics are uncorrelated, RE2C achieves a modest increase in power. Applications to real genetic data supported the utility of RE2C. RE2C is highly efficient and can meta-analyze one hundred GWASs in one day. Availability and implementation The software is freely available at http://software.buhmhan.com/RE2C. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- C H Lee
- Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea
| | - E Eskin
- Department of Computer Science, University of California, Los Angeles, CA, USA.,Department of Human Genetics, University of California, Los Angeles, CA, USA
| | - B Han
- Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea
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Duong D, Gai L, Snir S, Kang EY, Han B, Sul JH, Eskin E. Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes. Bioinformatics 2018; 33:i67-i74. [PMID: 28881962 PMCID: PMC5870567 DOI: 10.1093/bioinformatics/btx227] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Motivation There is recent interest in using gene expression data to contextualize findings from traditional genome-wide association studies (GWAS). Conditioned on a tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression, and eGenes are genes whose expression levels are associated with genetic variants. eQTLs and eGenes provide great supporting evidence for GWAS hits and important insights into the regulatory pathways involved in many diseases. When a significant variant or a candidate gene identified by GWAS is also an eQTL or eGene, there is strong evidence to further study this variant or gene. Multi-tissue gene expression datasets like the Gene Tissue Expression (GTEx) data are used to find eQTLs and eGenes. Unfortunately, these datasets often have small sample sizes in some tissues. For this reason, there have been many meta-analysis methods designed to combine gene expression data across many tissues to increase power for finding eQTLs and eGenes. However, these existing techniques are not scalable to datasets containing many tissues, like the GTEx data. Furthermore, these methods ignore a biological insight that the same variant may be associated with the same gene across similar tissues. Results We introduce a meta-analysis model that addresses these problems in existing methods. We focus on the problem of finding eGenes in gene expression data from many tissues, and show that our model is better than other types of meta-analyses. Availability and Implementation Source code is at https://github.com/datduong/RECOV. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dat Duong
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Lisa Gai
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Sagi Snir
- Institute of Evolution, University of Haifa, Haifa, Israel.,Department of Evolutionary and Environmental Biology, University of Haifa, Haifa, Israel
| | - Eun Yong Kang
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Buhm Han
- Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea.,Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Jae Hoon Sul
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, CA, USA.,Department of Human Genetics, University of California, Los Angeles, CA, USA
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21
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Park DS, Eskin I, Kang EY, Gamazon ER, Eng C, Gignoux CR, Galanter JM, Burchard E, Ye CJ, Aschard H, Eskin E, Halperin E, Zaitlen N. An ancestry-based approach for detecting interactions. Genet Epidemiol 2018; 42:49-63. [PMID: 29114909 PMCID: PMC6065511 DOI: 10.1002/gepi.22087] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 09/06/2017] [Accepted: 09/08/2017] [Indexed: 12/31/2022]
Abstract
BACKGROUND Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies. RESULTS In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at P<5×10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8×10-6). CONCLUSION We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.
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Affiliation(s)
- Danny S. Park
- Department of Bioengineering and Therapeutic Sciences. University of California San Francisco. San Francisco, CA
| | - Itamar Eskin
- The Blavatnik School of Computer Science. Tel-Aviv University. Tel Aviv, Israel
| | - Eun Yong Kang
- Department of Computer Science. University of California Los Angeles. Los Angeles, CA
| | - Eric R. Gamazon
- Division of Genetic Medicine, Department of Medicine. Vanderbilt University. Nashville, TN
- Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Celeste Eng
- Department of Medicine. University of California San Francisco. San Francisco, CA
| | - Christopher R. Gignoux
- Department of Bioengineering and Therapeutic Sciences. University of California San Francisco. San Francisco, CA
- Department of Genetics. Stanford University. Palo Alto, CA
| | - Joshua M. Galanter
- Department of Medicine. University of California San Francisco. San Francisco, CA
| | - Esteban Burchard
- Department of Bioengineering and Therapeutic Sciences. University of California San Francisco. San Francisco, CA
- Department of Medicine. University of California San Francisco. San Francisco, CA
| | - Chun J. Ye
- Institute of Human Genetics. University of California San Francisco. San Francisco, CA
| | - Hugues Aschard
- Department of Epidemiology. Harvard School of Public Health. Boston, MA
| | - Eleazar Eskin
- Department of Computer Science. University of California Los Angeles. Los Angeles, CA
| | - Eran Halperin
- The Blavatnik School of Computer Science. Tel-Aviv University. Tel Aviv, Israel
| | - Noah Zaitlen
- Department of Bioengineering and Therapeutic Sciences. University of California San Francisco. San Francisco, CA
- Department of Medicine. University of California San Francisco. San Francisco, CA
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Karunakaran S, Clee SM. Genetics of metabolic syndrome: potential clues from wild-derived inbred mouse strains. Physiol Genomics 2018; 50:35-51. [DOI: 10.1152/physiolgenomics.00059.2017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The metabolic syndrome (MetS) is a complex constellation of metabolic abnormalities including obesity, abnormal glucose metabolism, dyslipidemia, and elevated blood pressure that together substantially increase risk for cardiovascular disease and Type 2 diabetes. Both genetic and environmental factors contribute to the development of MetS, but this process is still far from understood. Human studies have revealed only part of the underlying basis. Studies in mice offer many strengths that can complement human studies to help elucidate the etiology and pathophysiology of MetS. Here we review the ways mice can contribute to MetS research. In particular, we focus on the information that can be obtained from studies of the inbred strains, with specific focus on the phenotypes of the wild-derived inbred strains. These are newly derived inbred strains that were created from wild-caught mice. They contain substantial genetic variation that is not present in the classical inbred strains, have phenotypes of relevance for MetS, and various mouse strain resources have been created to facilitate the mining of this new genetic variation. Thus studies using wild-derived inbred strains hold great promise for increasing our understanding of MetS.
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Affiliation(s)
- Subashini Karunakaran
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | - Susanne M. Clee
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
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23
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Ahmad S, Mora S, Franks PW, Orho-Melander M, Ridker PM, Hu FB, Chasman DI. Adiposity and Genetic Factors in Relation to Triglycerides and Triglyceride-Rich Lipoproteins in the Women's Genome Health Study. Clin Chem 2017; 64:231-241. [PMID: 29097515 DOI: 10.1373/clinchem.2017.280545] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 10/11/2017] [Indexed: 02/07/2023]
Abstract
BACKGROUND Previous results from Scandinavian cohorts have shown that obesity accentuates the effects of common genetic susceptibility variants on increased triglycerides (TG). Whether such interactions are present in the US population and further selective for particular TG-rich lipoprotein subfractions is unknown. METHODS We examined these questions using body mass index (BMI) and waist circumference (WC) among women of European ancestry from the Women's Genome Health Study (WGHS) (n = 21840 for BMI; n = 19313 for WC). A weighted genetic risk score (TG-wGRS) based on 40 published TG-associated single-nucleotide polymorphisms was calculated using published effect estimates. RESULTS Comparing overweight (BMI ≥ 25 kg/m2) and normal weight (BMI < 25 kg/m2) WGHS women, each unit increase of TG-wGRS was associated with TG increases of 1.013% and 1.011%, respectively, and this differential association was significant (Pinteraction = 0.014). Metaanalyses combining results for WGHS BMI with the 4 Scandinavian cohorts (INTER99, HEALTH2006, GLACIER, MDC) (total n = 40026) yielded a more significant interaction (Pinteraction = 0.001). Similarly, we observed differential association of the TG-wGRS with TG (Pinteraction = 0.006) in strata of WC (<80 cm vs ≥80 cm). Metaanalysis with 2 additional cohorts reporting WC (INTER99 and HEALTH2006) (total n = 27834) was significant with consistent effects (Pinteraction = 0.006). We also observed highly significant interactions of the TG-wGRS across the strata of BMI with very large, medium, and small TG-rich lipoprotein subfractions measured by nuclear magnetic resonance spectroscopy (all Pinteractions < 0.0001). The differential effects were strongest for very large TG-rich lipoprotein. CONCLUSIONS Our results support the original findings and suggest that obese individuals may be more susceptible to aggregated genetic risk associated with common TG-raising alleles, with effects accentuated in the large TG-rich lipoprotein subfraction.
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Affiliation(s)
- Shafqat Ahmad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA; .,Preventive Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Samia Mora
- Preventive Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Paul W Franks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Sweden.,Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University, Umeå, Sweden
| | - Marju Orho-Melander
- Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Paul M Ridker
- Preventive Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Daniel I Chasman
- Preventive Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA;
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Harrill AH, McAllister KA. New Rodent Population Models May Inform Human Health Risk Assessment and Identification of Genetic Susceptibility to Environmental Exposures. ENVIRONMENTAL HEALTH PERSPECTIVES 2017; 125:086002. [PMID: 28886592 PMCID: PMC5783628 DOI: 10.1289/ehp1274] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Revised: 04/19/2017] [Accepted: 04/27/2017] [Indexed: 05/13/2023]
Abstract
BACKGROUND This paper provides an introduction for environmental health scientists to emerging population-based rodent resources. Mouse reference populations provide an opportunity to model environmental exposures and gene-environment interactions in human disease and to inform human health risk assessment. OBJECTIVES This review will describe several mouse populations for toxicity assessment, including older models such as the Mouse Diversity Panel (MDP), and newer models that include the Collaborative Cross (CC) and Diversity Outbred (DO) models. METHODS This review will outline the features of the MDP, CC, and DO mouse models and will discuss published case studies investigating the use of these mouse population resources in each step of the risk assessment paradigm. DISCUSSION These unique resources have the potential to be powerful tools for generating hypotheses related to gene-environment interplay in human disease, performing controlled exposure studies to understand the differential responses in humans for susceptibility or resistance to environmental exposures, and identifying gene variants that influence sensitivity to toxicity and disease states. CONCLUSIONS These new resources offer substantial advances to classical toxicity testing paradigms by including genetically sensitive individuals that may inform toxicity risks for sensitive subpopulations. Both in vivo and complementary in vitro resources provide platforms with which to reduce uncertainty by providing population-level data around biological variability. https://doi.org/10.1289/EHP1274.
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Affiliation(s)
- Alison H Harrill
- Biomolecular Screening Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services , Research Triangle Park, North Carolina, USA
| | - Kimberly A McAllister
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services , Research Triangle Park, North Carolina, USA
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25
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26
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The Genetic Architecture of Noise-Induced Hearing Loss: Evidence for a Gene-by-Environment Interaction. G3-GENES GENOMES GENETICS 2016; 6:3219-3228. [PMID: 27520957 PMCID: PMC5068943 DOI: 10.1534/g3.116.032516] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The discovery of environmentally specific genetic effects is crucial to the understanding of complex traits, such as susceptibility to noise-induced hearing loss (NIHL). We describe the first genome-wide association study (GWAS) for NIHL in a large and well-characterized population of inbred mouse strains, known as the Hybrid Mouse Diversity Panel (HMDP). We recorded auditory brainstem response (ABR) thresholds both pre and post 2-hr exposure to 10-kHz octave band noise at 108 dB sound pressure level in 5–6-wk-old female mice from the HMDP (4–5 mice/strain). From the observation that NIHL susceptibility varied among the strains, we performed a GWAS with correction for population structure and mapped a locus on chromosome 6 that was statistically significantly associated with two adjacent frequencies. We then used a “genetical genomics” approach that included the analysis of cochlear eQTLs to identify candidate genes within the GWAS QTL. In order to validate the gene-by-environment interaction, we compared the effects of the postnoise exposure locus with that from the same unexposed strains. The most significant SNP at chromosome 6 (rs37517079) was associated with noise susceptibility, but was not significant at the same frequencies in our unexposed study. These findings demonstrate that the genetic architecture of NIHL is distinct from that of unexposed hearing levels and provide strong evidence for gene-by-environment interactions in NIHL.
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27
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ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis. G3-GENES GENOMES GENETICS 2016; 6:1793-8. [PMID: 27194809 PMCID: PMC4938634 DOI: 10.1534/g3.116.029439] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses. In this paper, we describe ForestPMPlot, a flexible visualization tool for analyzing studies included in a meta-analysis. The main feature of the tool is visualizing the differences in the effect sizes of the studies to understand why the studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. We show the application of this tool to interpret a meta-analysis of 17 mouse studies, and to interpret a multi-tissue eQTL study.
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28
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Lusis AJ, Seldin MM, Allayee H, Bennett BJ, Civelek M, Davis RC, Eskin E, Farber CR, Hui S, Mehrabian M, Norheim F, Pan C, Parks B, Rau CD, Smith DJ, Vallim T, Wang Y, Wang J. The Hybrid Mouse Diversity Panel: a resource for systems genetics analyses of metabolic and cardiovascular traits. J Lipid Res 2016; 57:925-42. [PMID: 27099397 PMCID: PMC4878195 DOI: 10.1194/jlr.r066944] [Citation(s) in RCA: 112] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 04/12/2016] [Indexed: 02/07/2023] Open
Abstract
The Hybrid Mouse Diversity Panel (HMDP) is a collection of approximately 100 well-characterized inbred strains of mice that can be used to analyze the genetic and environmental factors underlying complex traits. While not nearly as powerful for mapping genetic loci contributing to the traits as human genome-wide association studies, it has some important advantages. First, environmental factors can be controlled. Second, relevant tissues are accessible for global molecular phenotyping. Finally, because inbred strains are renewable, results from separate studies can be integrated. Thus far, the HMDP has been studied for traits relevant to obesity, diabetes, atherosclerosis, osteoporosis, heart failure, immune regulation, fatty liver disease, and host-gut microbiota interactions. High-throughput technologies have been used to examine the genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes of the mice under various environmental conditions. All of the published data are available and can be readily used to formulate hypotheses about genes, pathways and interactions.
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Affiliation(s)
- Aldons J Lusis
- Departments of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA Microbiology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA Human Genetics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA
| | - Marcus M Seldin
- Departments of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA
| | - Hooman Allayee
- Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA
| | - Brian J Bennett
- Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Mete Civelek
- Departments of Biomedical Engineering University of Virginia, Charlottesville, VA
| | - Richard C Davis
- Departments of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA
| | - Eleazar Eskin
- Departments of Computer Science, University of California-Los Angeles, Los Angeles, CA
| | - Charles R Farber
- Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Simon Hui
- Departments of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA
| | - Margarete Mehrabian
- Departments of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA
| | - Frode Norheim
- Departments of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA
| | - Calvin Pan
- Human Genetics, University of California-Los Angeles, Los Angeles, CA
| | - Brian Parks
- Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI
| | - Christoph D Rau
- Anesthesiology, University of California-Los Angeles, Los Angeles, CA
| | - Desmond J Smith
- Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA
| | - Thomas Vallim
- Departments of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA
| | - Yibin Wang
- Anesthesiology, University of California-Los Angeles, Los Angeles, CA
| | - Jessica Wang
- Departments of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA
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Collaborative Cross and Diversity Outbred data resources in the Mouse Phenome Database. Mamm Genome 2015; 26:511-20. [PMID: 26286858 PMCID: PMC4602074 DOI: 10.1007/s00335-015-9595-6] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 08/10/2015] [Indexed: 10/27/2022]
Abstract
The Mouse Phenome Database was originally conceived as a platform for the integration of phenotype data collected on a defined collection of 40 inbred mouse strains--the "phenome panel." This model provided an impetus for community data sharing, and integration was readily achieved through the reproducible genotypes of the phenome panel strains. Advances in the development of mouse populations lead to an expanded role of the Mouse Phenome Database to encompass new strain panels and inbred strain crosses. The recent introduction of the Collaborative Cross and Diversity Outbred mice, which share an extensive pool of genetic variation from eight founder inbred strains, presents new opportunities and challenges for community data resources. A wide variety of molecular and clinical phenotypes are being collected across genotypes, tissues, ages, environmental exposures, interventions, and treatments. The Mouse Phenome Database provides a framework for retrieval, integration, analysis, and display of these data, enabling them to be evaluated in the context of existing data from standard inbred strains. Primary data in the Mouse Phenome Database are supported by extensive metadata on protocols and procedures. These are centrally curated to ensure accuracy and reproducibility and to provide data in consistent formats. The Mouse Phenome Database represents an established and growing community data resource for mouse phenotype data and encourages submissions from new mouse resources, enabling investigators to integrate existing data into their studies of the phenotypic consequences of genetic variation.
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Bubier JA, Phillips CA, Langston MA, Baker EJ, Chesler EJ. GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data. Mamm Genome 2015; 26:556-66. [PMID: 26092690 PMCID: PMC4602068 DOI: 10.1007/s00335-015-9575-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Accepted: 06/03/2015] [Indexed: 01/20/2023]
Abstract
A persistent challenge lies in the interpretation of consensus and discord from functional genomics experimentation. Harmonizing and analyzing this data will enable investigators to discover relations of many genes to many diseases, and from many phenotypes and experimental paradigms to many diseases through their genomic substrates. The GeneWeaver.org system provides a platform for cross-species integration and interrogation of heterogeneous curated and experimentally derived functional genomics data. GeneWeaver enables researchers to store, share, analyze, and compare results of their own genome-wide functional genomics experiments in an environment containing rich companion data obtained from major curated repositories, including the Mouse Genome Database and other model organism databases, along with derived data from highly specialized resources, publications, and user submissions. The data, largely consisting of gene sets and putative biological networks, are mapped onto one another through gene identifiers and homology across species. A versatile suite of interactive tools enables investigators to perform a variety of set analysis operations to find consilience among these often noisy experimental results. Fast algorithms enable real-time analysis of large queries. Specific applications include prioritizing candidate genes for quantitative trait loci, identifying biologically valid mouse models and phenotypic assays for human disease, finding the common biological substrates of related diseases, classifying experiments and the biological concepts they represent from empirical data, and applying patterns of genomic evidence to implicate novel genes in disease. These results illustrate an alternative to strict emphasis on replicability, whereby researchers classify experimental results to identify the conditions that lead to their similarity.
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Affiliation(s)
| | - Charles A Phillips
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA
| | - Michael A Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA
| | - Erich J Baker
- Computer Science Department, Baylor University, Waco, TX, 76798, USA
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31
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Buchner DA, Nadeau JH. Contrasting genetic architectures in different mouse reference populations used for studying complex traits. Genome Res 2015; 25:775-91. [PMID: 25953951 PMCID: PMC4448675 DOI: 10.1101/gr.187450.114] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 03/31/2015] [Indexed: 01/14/2023]
Abstract
Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine context-dependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.
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Affiliation(s)
- David A Buchner
- Department of Genetics and Genome Sciences, Department of Biochemistry, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Joseph H Nadeau
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122, USA
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Abstract
PURPOSE OF REVIEW Recent genome-wide association studies have identified numerous common genetic variants associated with plasma lipid traits and have provided new insights into the regulation of lipoprotein metabolism including the identification of novel biological processes. These findings add to a body of existing data on dietary and environmental factors affecting plasma lipids. Here we explore how interactions between genetic risk factors and other phenotypes may explain some of the missing heritability of plasma lipid traits. RECENT FINDINGS Recent studies have identified true statistical interaction between several environmental and genetic risk factors and their effects on plasma lipid fractions. These include interactions between behaviors such as smoking or exercise as well as specific dietary nutrients and the effect size of specific genetic variants on plasma lipid traits risk and modifying effects of measures of adiposity on the cumulative impact of a number of common genetic variants on each of plasma triglycerides and HDL cholesterol. SUMMARY Interactions between genetic risk factors and clinical phenotypes may account for some of the unexplained heritability of plasma lipid traits. Recent studies provide biological insight into specific genetic associations and may aid in the identification of dyslipidemic patients for whom specific lifestyle interventions are likely to be most effective.
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Affiliation(s)
- Christopher B Cole
- aAtherogenomics Laboratory bRuddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
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Li YR, Keating BJ. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med 2014; 6:91. [PMID: 25473427 PMCID: PMC4254423 DOI: 10.1186/s13073-014-0091-5] [Citation(s) in RCA: 124] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWASs) are the method most often used by geneticists to interrogate the human genome, and they provide a cost-effective way to identify the genetic variants underpinning complex traits and diseases. Most initial GWASs have focused on genetically homogeneous cohorts from European populations given the limited availability of ethnic minority samples and so as to limit population stratification effects. Transethnic studies have been invaluable in explaining the heritability of common quantitative traits, such as height, and in examining the genetic architecture of complex diseases, such as type 2 diabetes. They provide an opportunity for large-scale signal replication in independent populations and for cross-population meta-analyses to boost statistical power. In addition, transethnic GWASs enable prioritization of candidate genes, fine-mapping of functional variants, and potentially identification of SNPs associated with disease risk in admixed populations, by taking advantage of natural differences in genomic linkage disequilibrium across ethnically diverse populations. Recent efforts to assess the biological function of variants identified by GWAS have highlighted the need for large-scale replication, meta-analyses and fine-mapping across worldwide populations of ethnically diverse genetic ancestries. Here, we review recent advances and new approaches that are important to consider when performing, designing or interpreting transethnic GWASs, and we highlight existing challenges, such as the limited ability to handle heterogeneity in linkage disequilibrium across populations and limitations in dissecting complex architectures, such as those found in recently admixed populations.
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Affiliation(s)
- Yun R Li
- />The Center for Applied Genomics, 1,016 Abramson Building, The Children’s Hospital of Philadelphia, Philadelphia, 19104 PA USA
- />Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | - Brendan J Keating
- />The Center for Applied Genomics, 1,016 Abramson Building, The Children’s Hospital of Philadelphia, Philadelphia, 19104 PA USA
- />Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
- />Department of Surgery, Division of Transplantation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
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Genome-wide association study for age-related hearing loss (AHL) in the mouse: a meta-analysis. J Assoc Res Otolaryngol 2014; 15:335-52. [PMID: 24570207 DOI: 10.1007/s10162-014-0443-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Accepted: 01/13/2014] [Indexed: 01/04/2023] Open
Abstract
Age-related hearing loss (AHL) is characterized by a symmetric sensorineural hearing loss primarily in high frequencies and individuals have different levels of susceptibility to AHL. Heritability studies have shown that the sources of this variance are both genetic and environmental, with approximately half of the variance attributable to hereditary factors as reported by Huag and Tang (Eur Arch Otorhinolaryngol 267(8):1179-1191, 2010). Only a limited number of large-scale association studies for AHL have been undertaken in humans, to date. An alternate and complementary approach to these human studies is through the use of mouse models. Advantages of mouse models include that the environment can be more carefully controlled, measurements can be replicated in genetically identical animals, and the proportion of the variability explained by genetic variation is increased. Complex traits in mouse strains have been shown to have higher heritability and genetic loci often have stronger effects on the trait compared to humans. Motivated by these advantages, we have performed the first genome-wide association study of its kind in the mouse by combining several data sets in a meta-analysis to identify loci associated with age-related hearing loss. We identified five genome-wide significant loci (<10(-6)). One of these loci confirmed a previously identified locus (ahl8) on distal chromosome 11 and greatly narrowed the candidate region. Specifically, the most significant associated SNP is located 450 kb upstream of Fscn2. These data confirm the utility of this approach and provide new high-resolution mapping information about variation within the mouse genome associated with hearing loss.
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Etges WJ. No boundaries: genomes, organisms, and ecological interactions responsible for divergence and reproductive isolation. J Hered 2014; 105 Suppl 1:756-70. [PMID: 25149252 PMCID: PMC4170711 DOI: 10.1093/jhered/esu039] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Accepted: 05/31/2014] [Indexed: 12/23/2022] Open
Abstract
Revealing the genetic basis of traits that cause reproductive isolation, particularly premating or sexual isolation, usually involves the same challenges as most attempts at genotype-phenotype mapping and so requires knowledge of how these traits are expressed in different individuals, populations, and environments, particularly under natural conditions. Genetic dissection of speciation phenotypes thus requires understanding of the internal and external contexts in which underlying genetic elements are expressed. Gene expression is a product of complex interacting factors internal and external to the organism including developmental programs, the genetic background including nuclear-cytotype interactions, epistatic relationships, interactions among individuals or social effects, stochasticity, and prevailing variation in ecological conditions. Understanding of genomic divergence associated with reproductive isolation will be facilitated by functional expression analysis of annotated genomes in organisms with well-studied evolutionary histories, phylogenetic affinities, and known patterns of ecological variation throughout their life cycles. I review progress and prospects for understanding the pervasive role of host plant use on genetic and phenotypic expression of reproductive isolating mechanisms in cactophilic Drosophila mojavensis and suggest how this system can be used as a model for revealing the genetic basis for species formation in organisms where speciation phenotypes are under the joint influences of genetic and environmental factors.
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Affiliation(s)
- William J Etges
- From Program in Ecology and Evolutionary Biology, Department of Biological Sciences, 1 University of Arkansas, Fayetteville, AR.
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