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Bocher O, Marenne G, Génin E, Perdry H. Ravages: An R package for the simulation and analysis of rare variants in multicategory phenotypes. Genet Epidemiol 2023; 47:450-460. [PMID: 37158367 DOI: 10.1002/gepi.22529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/27/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023]
Abstract
Current software packages for the analysis and the simulations of rare variants are only available for binary and continuous traits. Ravages provides solutions in a single R package to perform rare variant association tests for multicategory, binary and continuous phenotypes, to simulate datasets under different scenarios and to compute statistical power. Association tests can be run in the whole genome thanks to C++ implementation of most of the functions, using either RAVA-FIRST, a recently developed strategy to filter and analyse genome-wide rare variants, or user-defined candidate regions. Ravages also includes a simulation module that generates genetic data for cases who can be stratified into several subgroups and for controls. Through comparisons with existing programmes, we show that Ravages complements existing tools and will be useful to study the genetic architecture of complex diseases. Ravages is available on the CRAN at https://cran.r-project.org/web/packages/Ravages/ and maintained on Github at https://github.com/genostats/Ravages.
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Affiliation(s)
- Ozvan Bocher
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France
- Institute of Translational Genomics, Helmholtz Zentrum München, Munich, Germany
| | | | | | - Hervé Perdry
- CESP Inserm, U1018, UFR Médecine, Univ Paris-Sud, Université Paris-Saclay, Villejuif, France
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2
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Jang SK, Evans L, Fialkowski A, Arnett DK, Ashley-Koch AE, Barnes KC, Becker DM, Bis JC, Blangero J, Bleecker ER, Boorgula MP, Bowden DW, Brody JA, Cade BE, Jenkins BWC, Carson AP, Chavan S, Cupples LA, Custer B, Damrauer SM, David SP, de Andrade M, Dinardo CL, Fingerlin TE, Fornage M, Freedman BI, Garrett ME, Gharib SA, Glahn DC, Haessler J, Heckbert SR, Hokanson JE, Hou L, Hwang SJ, Hyman MC, Judy R, Justice AE, Kaplan RC, Kardia SLR, Kelly S, Kim W, Kooperberg C, Levy D, Lloyd-Jones DM, Loos RJF, Manichaikul AW, Gladwin MT, Martin LW, Nouraie M, Melander O, Meyers DA, Montgomery CG, North KE, Oelsner EC, Palmer ND, Payton M, Peljto AL, Peyser PA, Preuss M, Psaty BM, Qiao D, Rader DJ, Rafaels N, Redline S, Reed RM, Reiner AP, Rich SS, Rotter JI, Schwartz DA, Shadyab AH, Silverman EK, Smith NL, Smith JG, Smith AV, Smith JA, Tang W, Taylor KD, Telen MJ, Vasan RS, Gordeuk VR, Wang Z, Wiggins KL, Yanek LR, Yang IV, Young KA, Young KL, Zhang Y, Liu DJ, Keller MC, Vrieze S. Rare genetic variants explain missing heritability in smoking. Nat Hum Behav 2022; 6:1577-1586. [PMID: 35927319 PMCID: PMC9985486 DOI: 10.1038/s41562-022-01408-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/10/2022] [Indexed: 12/11/2022]
Abstract
Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.
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Affiliation(s)
- Seon-Kyeong Jang
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Luke Evans
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Ecology & Evolution, University of Colorado Boulder, Boulder, CO, USA
| | | | - Donna K Arnett
- Dean's Office, University of Kentucky College of Public Health, Lexington, KY, USA
| | | | - Kathleen C Barnes
- Division of Biomedical Informatics & Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Diane M Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | | | - Meher Preethi Boorgula
- Division of Biomedical Informatics & Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E Cade
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Brenda W Campbell Jenkins
- Jackson Heart Study Graduate Training and Education Center, Jackson State University School of Public Health, Jackson, MS, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Sameer Chavan
- Division of Biomedical Informatics & Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Brian Custer
- Vitalant Research Institute, San Francisco, CA, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Surgery, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Sean P David
- Department of Family Medicine, Prtizker School of Medicine, University of Chicago, Chicago, IL, USA
- NorthShore University HealthSystem, Evanston, IL, USA
| | - Mariza de Andrade
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Tasha E Fingerlin
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Center for Genes Environment and Health, National Jewish Health, Denver, CO, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Barry I Freedman
- Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Melanie E Garrett
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Sina A Gharib
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Center for Lung Biology, Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hosptial and Harvard Medical School, Boston, MA, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Susan R Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - John E Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Shih-Jen Hwang
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Matthew C Hyman
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Renae Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health System, Danville, PA, USA
| | - Robert C Kaplan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Shannon Kelly
- Department of Pediatrics, UCSF Benioff Children's Hospital Oakland, Oakland, CA, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | | | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Mark T Gladwin
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Mehdi Nouraie
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | | | - Courtney G Montgomery
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elizabeth C Oelsner
- Division of General Medicine, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Marinelle Payton
- Department of Epidemiology and Biostatistics, Jackson Heart Study Graduate Training and Education Center, Jackson State University School of Public Health, Jackson, MS, USA
| | - Anna L Peljto
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Michael Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Dandi Qiao
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Daniel J Rader
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas Rafaels
- Division of Biomedical Informatics & Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert M Reed
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - David A Schwartz
- Department of Medicine, School of Medicine, University of Colorado Denver, Aurora, CO, USA
- Department of Immunology, School of Medicine, University of Colorado Denver, Aurora, CO, USA
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicholas L Smith
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - J Gustav Smith
- Wallenberg Laboratory/Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University, Gothenburg, Sweden
- Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Albert V Smith
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marilyn J Telen
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Ramachandran S Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Victor R Gordeuk
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Zhe Wang
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kerri L Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ivana V Yang
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kendra A Young
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Kristin L Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yingze Zhang
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Dajiang J Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
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3
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Whole Exome Sequencing Study Identifies Novel Rare Risk Variants for Habitual Coffee Consumption Involved in Olfactory Receptor and Hyperphagia. Nutrients 2022; 14:nu14204330. [PMID: 36297015 PMCID: PMC9607528 DOI: 10.3390/nu14204330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
Habitual coffee consumption is an addictive behavior with unknown genetic variations and has raised public health issues about its potential health-related outcomes. We performed exome-wide association studies to identify rare risk variants contributing to habitual coffee consumption utilizing the newly released UK Biobank exome dataset (n = 200,643). A total of 34,761 qualifying variants were imported into SKAT to conduct gene-based burden and robust tests with minor allele frequency <0.01, adjusting the polygenic risk scores (PRS) of coffee intake to exclude the effect of common coffee-related polygenic risk. The gene-based burden and robust test of the exonic variants found seven exome-wide significant associations, such as OR2G2 (PSKAT = 1.88 × 10−9, PSKAT-Robust = 2.91 × 10−17), VEZT1 (PSKAT = 3.72 × 10−7, PSKAT-Robust = 1.41 × 10−7), and IRGC (PSKAT = 2.92 × 10−5, PSKAT-Robust = 1.07 × 10−7). These candidate genes were verified in the GWAS summary data of coffee intake, such as rs12737801 (p = 0.002) in OR2G2, and rs34439296 (p = 0.008) in IRGC. This study could help to extend genetic insights into the pathogenesis of coffee addiction, and may point to molecular mechanisms underlying health effects of habitual coffee consumption.
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4
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Vali-Pour M, Park S, Espinosa-Carrasco J, Ortiz-Martínez D, Lehner B, Supek F. The impact of rare germline variants on human somatic mutation processes. Nat Commun 2022; 13:3724. [PMID: 35764656 PMCID: PMC9240060 DOI: 10.1038/s41467-022-31483-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 06/17/2022] [Indexed: 02/07/2023] Open
Abstract
Somatic mutations are an inevitable component of ageing and the most important cause of cancer. The rates and types of somatic mutation vary across individuals, but relatively few inherited influences on mutation processes are known. We perform a gene-based rare variant association study with diverse mutational processes, using human cancer genomes from over 11,000 individuals of European ancestry. By combining burden and variance tests, we identify 207 associations involving 15 somatic mutational phenotypes and 42 genes that replicated in an independent data set at a false discovery rate of 1%. We associate rare inherited deleterious variants in genes such as MSH3, EXO1, SETD2, and MTOR with two phenotypically different forms of DNA mismatch repair deficiency, and variants in genes such as EXO1, PAXIP1, RIF1, and WRN with deficiency in homologous recombination repair. In addition, we identify associations with other mutational processes, such as APEX1 with APOBEC-signature mutagenesis. Many of the genes interact with each other and with known mutator genes within cellular sub-networks. Considered collectively, damaging variants in the identified genes are prevalent in the population. We suggest that rare germline variation in diverse genes commonly impacts mutational processes in somatic cells.
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Affiliation(s)
- Mischan Vali-Pour
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Solip Park
- Centro Nacional de Investigaciones Oncológicas (CNIO), Madrid, Spain
| | - Jose Espinosa-Carrasco
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Daniel Ortiz-Martínez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Ben Lehner
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
| | - Fran Supek
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
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5
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Martínez-Magaña JJ, Genis-Mendoza AD, González-Covarrubias V, Juárez-Rojop IE, Tovilla-Zárate CA, Soberón X, Lanzagorta N, Nicolini H. Association of FAAH p.Pro129Thr and COMT p.Ala72Ser with schizophrenia and comorbid substance use through next-generation sequencing: an exploratory analysis. BRAZILIAN JOURNAL OF PSYCHIATRY 2021; 44:164-170. [PMID: 34037083 PMCID: PMC9041971 DOI: 10.1590/1516-4446-2020-1546] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 03/11/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Individuals with schizophrenia and substance use disorders have a poor prognosis and increased psychiatric symptoms. The present study aimed to explore the association of 106 genes in individuals with schizophrenia and comorbid substance use through a next-generation sequencing (NGS) analysis and different in silico algorithms. METHODS We included 105 individuals diagnosed with schizophrenia and a family history of schizophrenia, of whom 49 (46.67%) presented comorbid substance use. Using NGS, we sequenced 106 genes previously associated with schizophrenia. Logistic regression models were used to assess differences in allele frequencies, and a generalized gene-set analysis was performed at the gene level. Functional annotations were performed using different algorithms and databases. RESULTS We identified a total of 3,109 variants, of which 25 were associated with schizophrenia and comorbid substance use and were located in regulatory and coding regions. We found low-frequency variants in COMT p.Ala72Ser, independently of p.Val158Met, that were associated with substance use. The endocannabinoid functional variant FAAH p.Pro129Thr was also associated with substance use. CONCLUSIONS Genetic variants of genes related to dopaminergic and cannabinoid neurotransmitter systems were associated with comorbid substance use in schizophrenia. Nevertheless, more studies with larger sample sizes are needed to confirm our findings.
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Affiliation(s)
- José J Martínez-Magaña
- División Académica de Ciencias de la Salud, Universidad Juárez Autónoma de Tabasco, Villahermosa, Mexico.,Laboratorio de Genómica de Enfermedades Psiquiátricas y Neurodegenerativas, Instituto Nacional de Medicina Genómica, Ciudad de México, Mexico
| | - Alma D Genis-Mendoza
- Laboratorio de Genómica de Enfermedades Psiquiátricas y Neurodegenerativas, Instituto Nacional de Medicina Genómica, Ciudad de México, Mexico.,Servicios de Atención Psiquiátrica, Hospital Psiquiátrico Infantil Juan N. Navarro, Ciudad de México, Mexico
| | | | - Isela E Juárez-Rojop
- División Académica de Ciencias de la Salud, Universidad Juárez Autónoma de Tabasco, Villahermosa, Mexico
| | - Carlos A Tovilla-Zárate
- División Multidisciplinaria de Comalcalco, Universidad Juárez Autónoma de Tabasco, Comalcalco, Mexico
| | - Xavier Soberón
- Laboratorio de Farmacogenómica, Instituto Nacional de Medicina Genómica, Ciudad de México, Mexico
| | - Nuria Lanzagorta
- Grupo de Estudios Médicos y Familiares Carracci, Ciudad de México, Mexico
| | - Humberto Nicolini
- División Académica de Ciencias de la Salud, Universidad Juárez Autónoma de Tabasco, Villahermosa, Mexico.,Grupo de Estudios Médicos y Familiares Carracci, Ciudad de México, Mexico
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6
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Estrada K, Froelich S, Wuster A, Bauer CR, Sterling T, Clark WT, Ru Y, Trinidad M, Nguyen HP, Luu AR, Wendt DJ, Yogalingam G, Yu GK, LeBowitz JH, Cardon LR. Identifying therapeutic drug targets using bidirectional effect genes. Nat Commun 2021; 12:2224. [PMID: 33850126 PMCID: PMC8044152 DOI: 10.1038/s41467-021-21843-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 02/12/2021] [Indexed: 01/15/2023] Open
Abstract
Prioritizing genes for translation to therapeutics for common diseases has been challenging. Here, we propose an approach to identify drug targets with high probability of success by focusing on genes with both gain of function (GoF) and loss of function (LoF) mutations associated with opposing effects on phenotype (Bidirectional Effect Selected Targets, BEST). We find 98 BEST genes for a variety of indications. Drugs targeting those genes are 3.8-fold more likely to be approved than non-BEST genes. We focus on five genes (IGF1R, NPPC, NPR2, FGFR3, and SHOX) with evidence for bidirectional effects on stature. Rare protein-altering variants in those genes result in significantly increased risk for idiopathic short stature (ISS) (OR = 2.75, p = 3.99 × 10-8). Finally, using functional experiments, we demonstrate that adding an exogenous CNP analog (encoded by NPPC) rescues the phenotype, thus validating its potential as a therapeutic treatment for ISS. Our results show the value of looking for bidirectional effects to identify and validate drug targets.
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Affiliation(s)
| | | | | | | | | | | | - Yuanbin Ru
- BioMarin Pharmaceutical Inc., Novato, CA, USA
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7
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Basu MK, Massicano F, Yu L, Halkidis K, Pillai V, Cao W, Zheng L, Zheng XL. Exome Sequencing Identifies Abnormalities in Glycosylation and ANKRD36C in Patients with Immune-Mediated Thrombotic Thrombocytopenic Purpura. Thromb Haemost 2020; 121:506-517. [PMID: 33184803 DOI: 10.1055/s-0040-1719030] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Immune-mediated thrombotic thrombocytopenic purpura (iTTP) is a potentially fatal blood disorder, resulting from autoantibodies against ADAMTS13 (a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13). However, the mechanism underlying anti-ADAMTS13 autoantibody formation is not known, nor it is known how genetic aberrations contribute to the pathogenesis of iTTP. METHODS Here we performed whole exome sequencing (WES) of DNA samples from 40 adult patients with iTTP and 15 local healthy subjects with no history of iTTP and other hematological disorders. RESULTS WES revealed variations in the genes involved in protein glycosylation, including O-linked glycosylation, to be a major pathway affected in patients with iTTP. Moreover, variations in the ANKRD gene family, particularly ANKRD36C and its paralogs, were also more prevalent in patients with iTTP than in the healthy controls. The ANKRD36 family of proteins have been implicated in inflammation. Mass spectrometry revealed a dramatic alternation in plasma glycoprotein profile in patients with iTTP compared with the healthy controls. CONCLUSION Altered glycosylation may affect the disease onset and progression in various ways: it may predispose patients to produce ADAMTS13 autoantibodies or affect their binding properties; it may also alter clearance kinetics of hemostatic and inflammatory proteins. Together, our findings provide novel insights into plausible mechanisms underlying the pathogenesis of iTTP.
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Affiliation(s)
- Malay Kumar Basu
- Division of Genomic Diagnostics and Bioinformatics, Department of Pathology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Felipe Massicano
- Division of Genomic Diagnostics and Bioinformatics, Department of Pathology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Lijia Yu
- Division of Genomic Diagnostics and Bioinformatics, Department of Pathology, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Konstantine Halkidis
- Division of Hematology/Oncology, Department of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Vikram Pillai
- Department of Pathology & Laboratory Medicine, The University of Kansas Medical Center, Kansas City, Kansas, United States
| | - Wenjing Cao
- Department of Pathology & Laboratory Medicine, The University of Kansas Medical Center, Kansas City, Kansas, United States
| | - Liang Zheng
- Department of Pathology & Laboratory Medicine, The University of Kansas Medical Center, Kansas City, Kansas, United States
| | - X Long Zheng
- Department of Pathology & Laboratory Medicine, The University of Kansas Medical Center, Kansas City, Kansas, United States
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Li X, Li Z, Zhou H, Gaynor SM, Liu Y, Chen H, Sun R, Dey R, Arnett DK, Aslibekyan S, Ballantyne CM, Bielak LF, Blangero J, Boerwinkle E, Bowden DW, Broome JG, Conomos MP, Correa A, Cupples LA, Curran JE, Freedman BI, Guo X, Hindy G, Irvin MR, Kardia SLR, Kathiresan S, Khan AT, Kooperberg CL, Laurie CC, Liu XS, Mahaney MC, Manichaikul AW, Martin LW, Mathias RA, McGarvey ST, Mitchell BD, Montasser ME, Moore JE, Morrison AC, O'Connell JR, Palmer ND, Pampana A, Peralta JM, Peyser PA, Psaty BM, Redline S, Rice KM, Rich SS, Smith JA, Tiwari HK, Tsai MY, Vasan RS, Wang FF, Weeks DE, Weng Z, Wilson JG, Yanek LR, Neale BM, Sunyaev SR, Abecasis GR, Rotter JI, Willer CJ, Peloso GM, Natarajan P, Lin X. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat Genet 2020; 52:969-983. [PMID: 32839606 PMCID: PMC7483769 DOI: 10.1038/s41588-020-0676-4] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 07/02/2020] [Indexed: 12/13/2022]
Abstract
Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.
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Affiliation(s)
- Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sheila M Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yaowu Liu
- School of Statistics, Southwestern University of Finance and Economics, Chengdu, China
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ryan Sun
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rounak Dey
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jai G Broome
- Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Adolfo Correa
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Barry I Freedman
- Department of Internal Medicine, Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - George Hindy
- Department of Population Medicine, Qatar University College of Medicine, QU Health, Doha, Qatar
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sekar Kathiresan
- Verve Therapeutics, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alyna T Khan
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Charles L Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - X Shirley Liu
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Michael C Mahaney
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Lisa W Martin
- Division of Cardiology, George Washington School of Medicine and Health Sciences, Washington, DC, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Stephen T McGarvey
- Department of Epidemiology, International Health Institute, Department of Anthropology, Brown University, Providence, RI, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore VA Medical Center, Baltimore, MD, USA
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jill E Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jeffrey R O'Connell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Akhil Pampana
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Juan M Peralta
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Fei Fei Wang
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel E Weeks
- Department of Human Genetics and Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
- Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Shamil R Sunyaev
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gonçalo R Abecasis
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Cristen J Willer
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Statistics, Harvard University, Cambridge, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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9
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Derkach A, Moore SC, Boca SM, Sampson JN. Group testing in mediation analysis. Stat Med 2020; 39:2423-2436. [DOI: 10.1002/sim.8546] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 11/01/2019] [Accepted: 03/05/2020] [Indexed: 11/09/2022]
Affiliation(s)
- Andriy Derkach
- Biostatistics Branch, Division of Cancer Epidemiology and GeneticsNational Cancer Institute Rockville Maryland USA
| | - Steven C. Moore
- Metabolomics Epidemiology Branch, Division of Cancer Epidemiology and GeneticsNational Cancer Institute Rockville Maryland USA
| | - Simina M. Boca
- Innovation Center for Biomedical Informatics, Department of Oncology and Biostatistics, Bioinformatics and BiomathematicsGeorgetown University Medical Center Washington District of Columbia USA
| | - Joshua N. Sampson
- Biostatistics Branch, Division of Cancer Epidemiology and GeneticsNational Cancer Institute Rockville Maryland USA
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Dapas M, Dunaif A. The contribution of rare genetic variants to the pathogenesis of polycystic ovary syndrome. ACTA ACUST UNITED AC 2020; 12:26-32. [PMID: 32440573 DOI: 10.1016/j.coemr.2020.02.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Polycystic ovary syndrome (PCOS) is a highly heritable disorder, but only a small proportion of the heritability can be accounted for by common genetic risk variants identified to date. It is possible that variants with lower allele frequencies that cannot be detected using genome-wide association study arrays contribute to PCOS. Here, we discuss the challenges inherent to studying rare genetic variants in complex disease and review several recent studies that have used DNA sequencing techniques to investigate whether rare variants play a role in PCOS pathogenesis. We evaluate these findings in the context of the latest literature in PCOS and complex disease genetics.
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11
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Gao Z, Terhorst J, Van Hout CV, Stoev S. U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies. Bioinformatics 2019; 36:974-975. [DOI: 10.1093/bioinformatics/btz637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/11/2019] [Accepted: 08/08/2019] [Indexed: 01/23/2023] Open
Abstract
Abstract
Summary
Despite the availability of existing calculators for statistical power analysis in genetic association studies, there has not been a model-invariant and test-independent tool that allows for both planning of prospective studies and systematic review of reported findings. In this work, we develop a web-based application U-PASS (Unified Power analysis of ASsociation Studies), implementing a unified framework for the analysis of common association tests for binary qualitative traits. The application quantifies the shared asymptotic power limits of the common association tests, and visualizes the fundamental statistical trade-off between risk allele frequency and odds ratio. The application also addresses the applicability of asymptotics-based power calculations in finite samples, and provides guidelines for single-SNP-based association tests. In addition to designing prospective studies, U-PASS enables researchers to retrospectively assess the statistical validity of previously reported associations.
Availability and implementation
U-PASS is an open-source R Shiny application. A live instance is hosted at https://power.stat.lsa.umich.edu. Source is available on https://github.com/Pill-GZ/U-PASS.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zheng Gao
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jonathan Terhorst
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Stilian Stoev
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
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12
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Rhoades SD, Bastarache L, Denny JC, Hughey JJ. Pulling the covers in electronic health records for an association study with self-reported sleep behaviors. Chronobiol Int 2018; 35:1702-1712. [PMID: 30183400 DOI: 10.1080/07420528.2018.1508152] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The electronic health record (EHR) contains rich histories of clinical care, but has not traditionally been mined for information related to sleep habits. Here, we performed a retrospective EHR study based on a cohort of 3,652 individuals with self-reported sleep behaviors documented from visits to the sleep clinic. These individuals were obese (mean body mass index 33.6 kg/m2) and had a high prevalence of sleep apnea (60.5%), however we found sleep behaviors largely concordant with prior prospective cohort studies. In our cohort, average wake time was 1 hour later and average sleep duration was 40 minutes longer on weekends than on weekdays (p < 10-12). Sleep duration varied considerably as a function of age and tended to be longer in females and in whites. Additionally, through phenome-wide association analyses, we found an association of long weekend sleep with depression, and an unexpectedly large number of associations of long weekday sleep with mental health and neurological disorders (q < 0.05). We then sought to replicate previously published genetic associations with morning/evening preference on a subset of our cohort with extant genotyping data (n = 555). While those findings did not replicate in our cohort, a polymorphism (rs3754214) in high linkage disequilibrium with a previously published polymorphism near TARS2 was associated with long sleep duration (p < 0.01). Collectively, our results highlight the potential of the EHR for uncovering the correlates of human sleep in real-world populations.
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Affiliation(s)
- Seth D Rhoades
- a Department of Biomedical Informatics , Vanderbilt University Medical Center , Nashville , Tennessee , USA
| | - Lisa Bastarache
- a Department of Biomedical Informatics , Vanderbilt University Medical Center , Nashville , Tennessee , USA
| | - Joshua C Denny
- a Department of Biomedical Informatics , Vanderbilt University Medical Center , Nashville , Tennessee , USA.,b Department of Medicine , Vanderbilt University Medical Center , Nashville , Tennessee , USA
| | - Jacob J Hughey
- a Department of Biomedical Informatics , Vanderbilt University Medical Center , Nashville , Tennessee , USA.,c Department of Biological Sciences , Vanderbilt University , Nashville , Tennessee , USA
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13
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Zhang Y, Qi G, Park JH, Chatterjee N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat Genet 2018; 50:1318-1326. [DOI: 10.1038/s41588-018-0193-x] [Citation(s) in RCA: 158] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 05/18/2018] [Indexed: 12/23/2022]
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