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Wendt FR, Pathak GA, Tylee DS, Goswami A, Polimanti R. Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective. ACTA ACUST UNITED AC 2020; 4:2470547020924844. [PMID: 32518889 PMCID: PMC7254587 DOI: 10.1177/2470547020924844] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 04/17/2020] [Indexed: 12/15/2022]
Abstract
Genome-wide association studies (GWAS) have been performed for many psychiatric disorders and revealed a complex polygenic architecture linking mental and physical health phenotypes. Psychiatric diagnoses are often heterogeneous, and several layers of trait heterogeneity may contribute to detection of genetic risks per disorder or across multiple disorders. In this review, we discuss these heterogeneities and their consequences on the discovery of risk loci using large-scale genetic data. We primarily highlight the ways in which sex and diagnostic complexity contribute to risk locus discovery in schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, major depressive disorder, obsessive-compulsive disorder, Tourette’s syndrome and chronic tic disorder, anxiety disorders, suicidality, feeding and eating disorders, and substance use disorders. Genetic data also have facilitated discovery of clinically relevant subphenotypes also described here. Collectively, GWAS of psychiatric disorders revealed that the understanding of heterogeneity, polygenicity, and pleiotropy is critical to translate genetic findings into treatment strategies.
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Affiliation(s)
- Frank R Wendt
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Daniel S Tylee
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Aranyak Goswami
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
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152
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Hujoel MLA, Gazal S, Loh PR, Patterson N, Price AL. Liability threshold modeling of case-control status and family history of disease increases association power. Nat Genet 2020; 52:541-547. [PMID: 32313248 PMCID: PMC7210076 DOI: 10.1038/s41588-020-0613-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 03/12/2020] [Indexed: 12/22/2022]
Abstract
Family history of disease can provide valuable information in case-control association studies, but it is currently unclear how to best combine case-control status and family history of disease. We developed an association method based on posterior mean genetic liabilities under a liability threshold model, conditional on case-control status and family history (LT-FH). Analyzing 12 diseases from the UK Biobank (average N = 350,000) we compared LT-FH to genome-wide association without using family history (GWAS) and a previous proxy-based method incorporating family history (GWAX). LT-FH was 63% (standard error (s.e.) 6%) more powerful than GWAS and 36% (s.e. 4%) more powerful than the trait-specific maximum of GWAS and GWAX, based on the number of independent genome-wide-significant loci across all diseases (for example, 690 loci for LT-FH versus 423 for GWAS); relative improvements were similar when applying BOLT-LMM to GWAS, GWAX and LT-FH phenotypes. Thus, LT-FH greatly increases association power when family history of disease is available.
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Affiliation(s)
- Margaux L A Hujoel
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Po-Ru Loh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA
| | | | - Alkes L Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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153
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Holland D, Frei O, Desikan R, Fan CC, Shadrin AA, Smeland OB, Sundar VS, Thompson P, Andreassen OA, Dale AM. Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model. PLoS Genet 2020; 16:e1008612. [PMID: 32427991 PMCID: PMC7272101 DOI: 10.1371/journal.pgen.1008612] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 06/04/2020] [Accepted: 01/15/2020] [Indexed: 12/27/2022] Open
Abstract
Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.
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Affiliation(s)
- Dominic Holland
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America
- Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
| | - Oleksandr Frei
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rahul Desikan
- Department of Radiology, University of California, San Francisco, San Francisco, California, United States of America
| | - Chun-Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America
- Department of Radiology, University of California, San Diego, La Jolla, California, United States of America
- Department of Cognitive Sciences, University of California at San Diego, La Jolla, California, United States of America
| | - Alexey A. Shadrin
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B. Smeland
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - V. S. Sundar
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America
- Department of Radiology, University of California, San Diego, La Jolla, California, United States of America
| | - Paul Thompson
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Ole A. Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America
- Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
- Department of Radiology, University of California, San Diego, La Jolla, California, United States of America
- Department of Psychiatry, University of California, San Diego, La Jolla, California, United States of America
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154
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van de Vegte YJ, Said MA, Rienstra M, van der Harst P, Verweij N. Genome-wide association studies and Mendelian randomization analyses for leisure sedentary behaviours. Nat Commun 2020; 11:1770. [PMID: 32317632 PMCID: PMC7174427 DOI: 10.1038/s41467-020-15553-w] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 03/11/2020] [Indexed: 01/02/2023] Open
Abstract
Leisure sedentary behaviours are associated with increased risk of cardiovascular disease, but whether this relationship is causal is unknown. The aim of this study is to identify genetic determinants associated with leisure sedentary behaviours and to estimate the potential causal effect on coronary artery disease (CAD). Genome wide association analyses of leisure television watching, leisure computer use and driving behaviour in the UK Biobank identify 145, 36 and 4 genetic loci (P < 1×10-8), respectively. High genetic correlations are observed between sedentary behaviours and neurological traits, including education and body mass index (BMI). Two-sample Mendelian randomization (MR) analysis estimates a causal effect between 1.5 hour increase in television watching and CAD (OR 1.44, 95%CI 1.25-1.66, P = 5.63 × 10-07), that is partially independent of education and BMI in multivariable MR analyses. This study finds independent observational and genetic support for the hypothesis that increased sedentary behaviour by leisure television watching is a risk factor for CAD.
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Affiliation(s)
- Yordi J van de Vegte
- Department of Cardiology, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, The Netherlands
| | - M Abdullah Said
- Department of Cardiology, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, The Netherlands
| | - Michiel Rienstra
- Department of Cardiology, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, The Netherlands.
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, The Netherlands.
- Durrer Center for Cardiogenetic Research, Netherlands Heart Institute, 3511GC, Utrecht, The Netherlands.
- Department of Cardiology, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands.
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, The Netherlands.
- Genomics plc, OX1 1JD, Oxford, UK.
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155
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Cai N, Revez JA, Adams MJ, Andlauer TFM, Breen G, Byrne EM, Clarke TK, Forstner AJ, Grabe HJ, Hamilton SP, Levinson DF, Lewis CM, Lewis G, Martin NG, Milaneschi Y, Mors O, Müller-Myhsok B, Penninx BWJH, Perlis RH, Pistis G, Potash JB, Preisig M, Shi J, Smoller JW, Streit F, Tiemeier H, Uher R, Van der Auwera S, Viktorin A, Weissman MM, Kendler KS, Flint J. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat Genet 2020; 52:437-447. [PMID: 32231276 PMCID: PMC7906795 DOI: 10.1038/s41588-020-0594-5] [Citation(s) in RCA: 145] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 02/19/2020] [Indexed: 12/18/2022]
Abstract
Minimal phenotyping refers to the reliance on the use of a small number of self-reported items for disease case identification, increasingly used in genome-wide association studies (GWAS). Here we report differences in genetic architecture between depression defined by minimal phenotyping and strictly defined major depressive disorder (MDD): the former has a lower genotype-derived heritability that cannot be explained by inclusion of milder cases and a higher proportion of the genome contributing to this shared genetic liability with other conditions than for strictly defined MDD. GWAS based on minimal phenotyping definitions preferentially identifies loci that are not specific to MDD, and, although it generates highly predictive polygenic risk scores, the predictive power can be explained entirely by large sample sizes rather than by specificity for MDD. Our results show that reliance on results from minimal phenotyping may bias views of the genetic architecture of MDD and impede the ability to identify pathways specific to MDD.
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Affiliation(s)
- Na Cai
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany.
| | - Joana A Revez
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Till F M Andlauer
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Gerome Breen
- NIHR Maudsley Biomedical Research Centre, King's College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Toni-Kim Clarke
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Andreas J Forstner
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Centre for Human Genetics, University of Marburg, Marburg, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Steven P Hamilton
- Department of Psychiatry, Kaiser Permanente Northern California, San Francisco, CA, USA
| | - Douglas F Levinson
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, UK
| | - Nicholas G Martin
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit and GGZinGeest, Amsterdam, the Netherlands
| | - Ole Mors
- Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Bertram Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit and GGZinGeest, Amsterdam, the Netherlands
| | - Roy H Perlis
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Giorgio Pistis
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - James B Potash
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Martin Preisig
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Fabien Streit
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Henning Tiemeier
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Sandra Van der Auwera
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Alexander Viktorin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Myrna M Weissman
- Department of Psychiatry, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
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156
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Manousaki D, Mitchell R, Dudding T, Haworth S, Harroud A, Forgetta V, Shah RL, Luan J, Langenberg C, Timpson NJ, Richards JB. Genome-wide Association Study for Vitamin D Levels Reveals 69 Independent Loci. Am J Hum Genet 2020; 106:327-337. [PMID: 32059762 PMCID: PMC7058824 DOI: 10.1016/j.ajhg.2020.01.017] [Citation(s) in RCA: 123] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 01/22/2020] [Indexed: 12/13/2022] Open
Abstract
We aimed to increase our understanding of the genetic determinants of vitamin D levels by undertaking a large-scale genome-wide association study (GWAS) of serum 25 hydroxyvitamin D (25OHD). To do so, we used imputed genotypes from 401,460 white British UK Biobank participants with available 25OHD levels, retaining single-nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) > 0.1% and imputation quality score > 0.3. We performed a linear mixed model GWAS on standardized log-transformed 25OHD, adjusting for age, sex, season of measurement, and vitamin D supplementation. These results were combined with those from a previous GWAS including 42,274 Europeans. In silico functional follow-up of the GWAS results was undertaken to identify enrichment in gene sets, pathways, and expression in tissues, and to investigate the partitioned heritability of 25OHD and its shared heritability with other traits. Using this approach, the SNP heritability of 25OHD was estimated to 16.1%. 138 conditionally independent SNPs were detected (p value < 6.6 × 10-9) among which 53 had MAF < 5%. Single variant association signals mapped to 69 distinct loci, among which 63 were previously unreported. We identified enrichment in hepatic and lipid metabolism gene pathways and enriched expression of the 25OHD genes in liver, skin, and gastrointestinal tissues. We observed partially shared heritability between 25OHD and socio-economic traits, a feature which may be mediated through time spent outdoors. Therefore, through a large 25OHD GWAS, we identified 63 loci that underline the contribution of genes outside the vitamin D canonical metabolic pathway to the genetic architecture of 25OHD.
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Affiliation(s)
- Despoina Manousaki
- Department of Human Genetics, McGill University, Montreal, QC H3A 1B1, Canada; Centre for Clinical Epidemiology, Department of Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
| | - Ruth Mitchell
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Tom Dudding
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK; Bristol Dental School, University of Bristol, Bristol BS8 2BN, UK
| | - Simon Haworth
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK; Bristol Dental School, University of Bristol, Bristol BS8 2BN, UK
| | - Adil Harroud
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Department of Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
| | - Rupal L Shah
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | | | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - J Brent Richards
- Department of Human Genetics, McGill University, Montreal, QC H3A 1B1, Canada; Centre for Clinical Epidemiology, Department of Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada; Department of Medicine, McGill University Montreal, QC H3G 1Y6, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada; Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK.
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157
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Sinclair-Waters M, Ødegård J, Korsvoll SA, Moen T, Lien S, Primmer CR, Barson NJ. Beyond large-effect loci: large-scale GWAS reveals a mixed large-effect and polygenic architecture for age at maturity of Atlantic salmon. Genet Sel Evol 2020; 52:9. [PMID: 32050893 PMCID: PMC7017552 DOI: 10.1186/s12711-020-0529-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Accepted: 01/28/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Understanding genetic architecture is essential for determining how traits will change in response to evolutionary processes such as selection, genetic drift and/or gene flow. In Atlantic salmon, age at maturity is an important life history trait that affects factors such as survival, reproductive success, and growth. Furthermore, age at maturity can seriously impact aquaculture production. Therefore, characterizing the genetic architecture that underlies variation in age at maturity is of key interest. RESULTS Here, we refine our understanding of the genetic architecture for age at maturity of male Atlantic salmon using a genome-wide association study of 11,166 males from a single aquaculture strain, using imputed genotypes at 512,397 single nucleotide polymorphisms (SNPs). All individuals were genotyped with a 50K SNP array and imputed to higher density using parents genotyped with a 930K SNP array and pedigree information. We found significant association signals on 28 of 29 chromosomes (P-values: 8.7 × 10-133-9.8 × 10-8), including two very strong signals spanning the six6 and vgll3 gene regions on chromosomes 9 and 25, respectively. Furthermore, we identified 116 independent signals that tagged 120 candidate genes with varying effect sizes. Five of the candidate genes found here were previously associated with age at maturity in other vertebrates, including humans. DISCUSSION These results reveal a mixed architecture of large-effect loci and a polygenic component that consists of multiple smaller-effect loci, suggesting a more complex genetic architecture of Atlantic salmon age at maturity than previously thought. This more complex architecture will have implications for selection on this key trait in aquaculture and for management of wild salmon populations.
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Affiliation(s)
- Marion Sinclair-Waters
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland. .,Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
| | - Jørgen Ødegård
- AquaGen, Trondheim, Norway.,Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | | | | | - Sigbjørn Lien
- Centre for Integrative Genetics, Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Craig R Primmer
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland.,Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Nicola J Barson
- Centre for Integrative Genetics, Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
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158
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Multiple rare inherited variants in a four generation schizophrenia family offer leads for complex mode of disease inheritance. Schizophr Res 2020; 216:288-294. [PMID: 31813803 PMCID: PMC8958857 DOI: 10.1016/j.schres.2019.11.041] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 11/23/2019] [Accepted: 11/24/2019] [Indexed: 02/01/2023]
Abstract
Schizophrenia is a clinically and genetically heterogeneous neuropsychiatric disorder, with a polygenic basis but identification of the specific determinants is a continuing challenge. In this study, we analyzed a multigenerational family, with all healthy individuals in the first two generations, and four progeny affected with schizophrenia in the subsequent two generations, using whole exome sequencing. We identified five rare protein sequence altering heterozygous variants, in five different genes namely SMARCA5, PDE1B, TNIK, SMARCA2 and FLRT shared among all affected members and predicted to be damaging. Variants in SMARCA5 and PDE1B were inherited from the unaffected father whereas variants in TNIK, SMARCA2 and FLRT1 were inherited from the unaffected mother in all the three affected individuals in the third generation; and notably all these five variants were transmitted by an affected mother to her affected son. Microsatellite based analysis lent a modest linkage support (LOD score of 1.2; θ=0.0 at each variant). Of note, analysis of exome data of an ancestry matched unrelated schizophrenia cohort (n = 350), revealed a total of 16 rare variants (MAF < 0.01) in these five genes. Interestingly, these five genes involved in neurodevelopmental and/or neurotransmitter signaling processes are implicated in the etiology of schizophrenia previously. This study provides good evidence for a likely cumulative contribution of multiple rare variants from disease relevant genes with a threshold effect in disease development and seems to explain the unusual disease transmission pattern generally witnessed in such conditions, but warrants extensive replication efforts in families with similar complex disease inheritance profiles.
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159
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Kloosterman M, Santema BT, Roselli C, Nelson CP, Koekemoer A, Romaine SPR, Van Gelder IC, Lam CSP, Artola VA, Lang CC, Ng LL, Metra M, Anker S, Filippatos G, Dickstein K, Ponikowski P, van der Harst P, van der Meer P, van Veldhuisen DJ, Benjamin EJ, Voors AA, Samani NJ, Rienstra M. Genetic risk and atrial fibrillation in patients with heart failure. Eur J Heart Fail 2020; 22:519-527. [PMID: 31919934 PMCID: PMC7319410 DOI: 10.1002/ejhf.1735] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 09/28/2019] [Accepted: 11/29/2019] [Indexed: 12/13/2022] Open
Abstract
AIMS To study the association between an atrial fibrillation (AF) genetic risk score with prevalent AF and all-cause mortality in patients with heart failure. METHODS AND RESULTS An AF genetic risk score was calculated in 3759 European ancestry individuals (1783 with sinus rhythm, 1976 with AF) from the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) by summing 97 single nucleotide polymorphism (SNP) alleles (ranging from 0-2) weighted by the natural logarithm of the relative SNP risk from the latest AF genome-wide association study. Further, we assessed AF risk variance explained by additive SNP variation, and performance of clinical or genetic risk factors, and the combination in classifying AF prevalence. AF was classified as AF or atrial flutter (AFL) at baseline electrocardiogram and/or a history of AF or AFL. The genetic risk score was associated with AF after multivariable adjustment. Odds ratio for AF prevalence per 1-unit increase genetic risk score was 2.12 (95% confidence interval 1.84-2.45, P = 2.15 × 10-24 ) in the total cohort, 2.08 (1.72-2.50, P = 1.30 × 10-14 ) in heart failure with reduced ejection fraction (HFrEF) and 2.02 (1.37-2.99, P = 4.37 × 10-4 ) in heart failure with preserved ejection fraction (HFpEF). AF-associated loci explained 22.9% of overall AF SNP heritability. Addition of the genetic risk score to clinical risk factors increased the C-index by 2.2% to 0.721. CONCLUSIONS The AF genetic risk score was associated with increased AF prevalence in HFrEF and HFpEF. Genetic variation accounted for 22.9% of overall AF SNP heritability. Addition of genetic risk to clinical risk improved model performance in classifying AF prevalence.
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Affiliation(s)
- Mariëlle Kloosterman
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Bernadet T Santema
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Carolina Roselli
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Andrea Koekemoer
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Simon P R Romaine
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Isabelle C Van Gelder
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Carolyn S P Lam
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,National Heart Centre, Singapore, Singapore
| | - Vicente A Artola
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Chim C Lang
- School of Medicine Centre for Cardiovascular and Lung Biology, Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital & Medical School, Dundee, UK
| | - Leon L Ng
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Marco Metra
- Institute of Cardiology, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Stefan Anker
- Department of Cardiology (CVK), Berlin Institute of Health Center for Regenerative Therapies (BCRT); German Centre for Cardiovascular Research (DZHK) partner site Berlin; Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | | | - Piotr Ponikowski
- Department for Heart Disease, Centre for Heart Disease, University Hospital, Medical University, Wroclaw, Poland
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Peter van der Meer
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Dirk J van Veldhuisen
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Emelia J Benjamin
- Department of Medicine, Boston University School of Medicine, Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Adriaan A Voors
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Michiel Rienstra
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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160
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Damena D, Chimusa ER. Genome-wide heritability analysis of severe malaria resistance reveals evidence of polygenic inheritance. Hum Mol Genet 2020; 29:168-176. [PMID: 31691794 PMCID: PMC7416678 DOI: 10.1093/hmg/ddz258] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 10/14/2019] [Accepted: 10/23/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Estimating single nucleotide polymorphism (SNP)-heritability (h2g) of severe malaria resistance and its distribution across the genome might shed new light in to the underlying biology. METHOD We investigated h2g of severe malaria resistance from a genome-wide association study (GWAS) dataset (sample size = 11 657). We estimated the h2g and partitioned in to chromosomes, allele frequencies and annotations using the genetic relationship-matrix restricted maximum likelihood approach. We further examined non-cell type-specific and cell type-specific enrichments from GWAS-summary statistics. RESULTS The h2g of severe malaria resistance was estimated at 0.21 (se = 0.05, P = 2.7 × 10-5), 0.20 (se = 0.05, P = 7.5 × 10-5) and 0.17 (se = 0.05, P = 7.2 × 10-4) in Gambian, Kenyan and Malawi populations, respectively. A comparable range of h2g [0.21 (se = 0.02, P < 1 × 10-5)] was estimated from GWAS-summary statistics meta-analysed across the three populations. Partitioning analysis from raw genotype data showed significant enrichment of h2g in genic SNPs while summary statistics analysis suggests evidences of enrichment in multiple categories. Supporting the polygenic inheritance, the h2g of severe malaria resistance is distributed across the chromosomes and allelic frequency spectrum. However, the h2g is disproportionately concentrated on three chromosomes (chr 5, 11 and 20), suggesting cost-effectiveness of targeting these chromosomes in future malaria genomic sequencing studies. CONCLUSION We report for the first time that the heritability of malaria resistance is largely ascribed by common SNPs and the causal variants are overrepresented in protein coding regions of the genome. Further studies with larger sample sizes are needed to better understand the underpinning genetics of severe malaria resistance.
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Affiliation(s)
- Delesa Damena
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine University of Cape Town, Private Bag, Rondebosch, 7700 Cape Town, South Africa
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine University of Cape Town, Private Bag, Rondebosch, 7700 Cape Town, South Africa
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161
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Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet 2019; 20:467-484. [PMID: 31068683 DOI: 10.1038/s41576-019-0127-1] [Citation(s) in RCA: 897] [Impact Index Per Article: 179.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.
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Affiliation(s)
- Vivian Tam
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Nikunj Patel
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Michelle Turcotte
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Yohan Bossé
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval, Québec City, Québec, Canada.,Department of Molecular Medicine, Laval University, Québec City, Quebec, Canada
| | - Guillaume Paré
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. .,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada. .,Inserm UMRS 954 N-GERE (Nutrition-Genetics-Environmental Risks), University of Lorraine, Faculty of Medicine, Nancy, France.
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162
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Abstract
The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.
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163
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Hernandez Cordero AI, Gonzales NM, Parker CC, Sokolof G, Vandenbergh DJ, Cheng R, Abney M, Sko A, Douglas A, Palmer AA, Gregory JS, Lionikas A. Genome-wide Associations Reveal Human-Mouse Genetic Convergence and Modifiers of Myogenesis, CPNE1 and STC2. Am J Hum Genet 2019; 105:1222-1236. [PMID: 31761296 PMCID: PMC6904802 DOI: 10.1016/j.ajhg.2019.10.014] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 10/28/2019] [Indexed: 12/11/2022] Open
Abstract
Muscle bulk in adult healthy humans is highly variable even after height, age, and sex are accounted for. Low muscle mass, due to fewer and/or smaller constituent muscle fibers, would exacerbate the impact of muscle loss occurring in aging or disease. Genetic variability substantially influences muscle mass differences, but causative genes remain largely unknown. In a genome-wide association study (GWAS) on appendicular lean mass (ALM) in a population of 85,750 middle-aged (aged 38-49 years) individuals from the UK Biobank (UKB), we found 182 loci associated with ALM (p < 5 × 10-8). We replicated associations for 78% of these loci (p < 5 × 10-8) with ALM in a population of 181,862 elderly (aged 60-74 years) individuals from UKB. We also conducted a GWAS on hindlimb skeletal muscle mass of 1,867 mice from an advanced intercross between two inbred strains (LG/J and SM/J); this GWAS identified 23 quantitative trait loci. Thirty-eight positional candidates distributed across five loci overlapped between the two species. In vitro studies of positional candidates confirmed CPNE1 and STC2 as modifiers of myogenesis. Collectively, these findings shed light on the genetics of muscle mass variability in humans and identify targets for the development of interventions for treatment of muscle loss. The overlapping results between humans and the mouse model GWAS point to shared genetic mechanisms across species.
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Affiliation(s)
- Ana I Hernandez Cordero
- School of Medicine, Medical Sciences, and Nutrition, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen, UK AB24 3FX, UK
| | - Natalia M Gonzales
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Clarissa C Parker
- Department of Psychology, Middlebury College, Middlebury, VT 05753, USA; Program in Neuroscience, Middlebury College, Middlebury, VT, 05753, USA
| | - Greta Sokolof
- Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, IA 52242, USA
| | - David J Vandenbergh
- Department of Biobehavioral Health, Penn State Institute for the Neurosciences, and Molecular, Cellular, and Integrative Sciences Program, Pennsylvania State University, University Park, PA 16802, USA
| | - Riyan Cheng
- Department of Health Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Mark Abney
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Andrew Sko
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Alex Douglas
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 3FX, 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
| | - Jennifer S Gregory
- School of Medicine, Medical Sciences, and Nutrition, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen, UK AB24 3FX, UK
| | - Arimantas Lionikas
- School of Medicine, Medical Sciences, and Nutrition, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen, UK AB24 3FX, UK.
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164
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A resource-efficient tool for mixed model association analysis of large-scale data. Nat Genet 2019; 51:1749-1755. [DOI: 10.1038/s41588-019-0530-8] [Citation(s) in RCA: 162] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 10/16/2019] [Indexed: 12/13/2022]
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165
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Kruempel JC, Howington MB, Leiser SF. Computational tools for geroscience. TRANSLATIONAL MEDICINE OF AGING 2019; 3:132-143. [PMID: 33241167 PMCID: PMC7685266 DOI: 10.1016/j.tma.2019.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The rapid progress of the past three decades has led the geroscience field near a point where human interventions in aging are plausible. Advances across scientific areas, such as high throughput "-omics" approaches, have led to an exponentially increasing quantity of data available for biogerontologists. To best translate the lifespan and healthspan extending interventions discovered by basic scientists into preventative medicine, it is imperative that the current data are comprehensively utilized to generate testable hypotheses about translational interventions. Building a translational pipeline for geroscience will require both systematic efforts to identify interventions that extend healthspan across taxa and diagnostics that can identify patients who may benefit from interventions prior to the onset of an age-related morbidity. Databases and computational tools that organize and analyze both the wealth of information available on basic biogerontology research and clinical data on aging populations will be critical in developing such a pipeline. Here, we review the current landscape of databases and computational resources available for translational aging research. We discuss key platforms and tools available for aging research, with a focus on how each tool can be used in concert with hypothesis driven experiments to move closer to human interventions in aging.
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Affiliation(s)
- Joseph C.P. Kruempel
- Molecular & Integrative Physiology Department, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Marshall B. Howington
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Scott F. Leiser
- Molecular & Integrative Physiology Department, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
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166
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Lyons PA, Peters JE, Alberici F, Liley J, Coulson RMR, Astle W, Baldini C, Bonatti F, Cid MC, Elding H, Emmi G, Epplen J, Guillevin L, Jayne DRW, Jiang T, Gunnarsson I, Lamprecht P, Leslie S, Little MA, Martorana D, Moosig F, Neumann T, Ohlsson S, Quickert S, Ramirez GA, Rewerska B, Schett G, Sinico RA, Szczeklik W, Tesar V, Vukcevic D, Terrier B, Watts RA, Vaglio A, Holle JU, Wallace C, Smith KGC. Genome-wide association study of eosinophilic granulomatosis with polyangiitis reveals genomic loci stratified by ANCA status. Nat Commun 2019; 10:5120. [PMID: 31719529 PMCID: PMC6851141 DOI: 10.1038/s41467-019-12515-9] [Citation(s) in RCA: 137] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 07/01/2019] [Indexed: 02/02/2023] Open
Abstract
Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare inflammatory disease of unknown cause. 30% of patients have anti-neutrophil cytoplasmic antibodies (ANCA) specific for myeloperoxidase (MPO). Here, we describe a genome-wide association study in 676 EGPA cases and 6809 controls, that identifies 4 EGPA-associated loci through conventional case-control analysis, and 4 additional associations through a conditional false discovery rate approach. Many variants are also associated with asthma and six are associated with eosinophil count in the general population. Through Mendelian randomisation, we show that a primary tendency to eosinophilia contributes to EGPA susceptibility. Stratification by ANCA reveals that EGPA comprises two genetically and clinically distinct syndromes. MPO+ ANCA EGPA is an eosinophilic autoimmune disease sharing certain clinical features and an HLA-DQ association with MPO+ ANCA-associated vasculitis, while ANCA-negative EGPA may instead have a mucosal/barrier dysfunction origin. Four candidate genes are targets of therapies in development, supporting their exploration in EGPA.
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Affiliation(s)
- Paul A Lyons
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre University of Cambridge, Cambridge, CB2 0AW, UK
| | - James E Peters
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- Health Data Research UK, Cambridge, UK
| | - Federico Alberici
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Nephrology and Immunopathology Unit-ASST Santi Paolo e Carlo, San Carlo Borromeo Hospital, Milan, Italy
- Dipartimento di Scienze della Salute, University of Milano, Milano, Italy
| | - James Liley
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
| | - Richard M R Coulson
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - William Astle
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
- NHS Blood and Transplant, Long Road, Cambridge Biomedical Campus, Cambridge, UK
| | | | - Francesco Bonatti
- Unit of Molecular Genetics, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy
| | - Maria C Cid
- Department of Autoimmune Diseases, Hospital Clínic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CRB-CELLEX, Barcelona, Spain
| | - Heather Elding
- The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
- Department of Human Genetics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK
| | - Giacomo Emmi
- Department of Experimental and Clinical Medicine, University of Firenze, Firenze, Italy
| | - Jörg Epplen
- Human Genetics, Ruhr University Bochum, Bochum, Germany
| | - Loïc Guillevin
- Service de Médecine Interne, Hôpital Cochin, 75679, Paris Cedex 14, France
| | - David R W Jayne
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Tao Jiang
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
| | - Iva Gunnarsson
- Division of Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Peter Lamprecht
- Department of Rheumatology and Clinical Immunology, University of Lübeck, 23562, Lübeck, Germany
| | - Stephen Leslie
- Schools of Mathematics and Statistics, and BioSciences, and Melbourne Integrative Genomics, University of Melbourne, Parkville, VIC, 3010, Australia
- Data Science, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
| | - Mark A Little
- Trinity Health Kidney Centre, Trinity Translational Medicine Institute, Tallaght Hospital, Dublin, Ireland
| | - Davide Martorana
- Unit of Molecular Genetics, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy
| | - Frank Moosig
- Rheumazentrum Schleswig-Holstein Mitte, Neumünster, Germany
| | - Thomas Neumann
- Department of Internal Medicine 3, Jena University Hospital, Jena, Germany
- Department of Rheumatology, Immunology and Rehabilitation, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Sophie Ohlsson
- Department of Nephrology, Division of Clinical Sciences, Lund University, Lund, Sweden
| | - Stefanie Quickert
- Department of Internal Medicine 3, Jena University Hospital, Jena, Germany
- Department of Internal Medicine 4 (Gastroenterology, Hepatology, and Infectious Diseases), Jena University Hospital, Jena, Germany
| | - Giuseppe A Ramirez
- Unit of Immunology, Rheumatology, Allergy and Rare Diseases, Università Vita Salute San Raffaele and IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Georg Schett
- Department of Internal Medicine 3, Rheumatology and Immunology, Friedrich Alexander University Erlangen-Nuremberg and Universitatsklinikum Erlangen, Erlangen, Germany
| | - Renato A Sinico
- Department of Medicine and Surgery, Università degli Studi di Milano-Bicocca (School of Medicine and Surgery), via Cadore, 48, 20900, Monza, Italy
| | | | - Vladimir Tesar
- Department of Nephrology, 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | - Damjan Vukcevic
- Schools of Mathematics and Statistics, and BioSciences, and Melbourne Integrative Genomics, University of Melbourne, Parkville, VIC, 3010, Australia
- Data Science, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
| | - Benjamin Terrier
- Service de Médecine Interne, Hôpital Cochin, 75679, Paris Cedex 14, France
| | - Richard A Watts
- Department of Rheumatology, Ipswich Hospital, Heath Road, Ipswich, Suffolk, IP4 5PD, UK
- Norwich Medical School, University of East Anglia, Norwich, NR7 4TJ, UK
| | - Augusto Vaglio
- Department of Biomedical Experimental and Clinical Sciences "Mario Serio", University of Firenze, and Meyer Children's Hospital, Firenze, Italy
| | - Julia U Holle
- Rheumazentrum Schleswig-Holstein Mitte, Neumünster, Germany
| | - Chris Wallace
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre University of Cambridge, Cambridge, CB2 0AW, UK
- Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
| | - Kenneth G C Smith
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre University of Cambridge, Cambridge, CB2 0AW, UK.
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167
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Aung N, Vargas JD, Yang C, Cabrera CP, Warren HR, Fung K, Tzanis E, Barnes MR, Rotter JI, Taylor KD, Manichaikul AW, Lima JA, Bluemke DA, Piechnik SK, Neubauer S, Munroe PB, Petersen SE. Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development. Circulation 2019; 140:1318-1330. [PMID: 31554410 PMCID: PMC6791514 DOI: 10.1161/circulationaha.119.041161] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND The genetic basis of left ventricular (LV) image-derived phenotypes, which play a vital role in the diagnosis, management, and risk stratification of cardiovascular diseases, is unclear at present. METHODS The LV parameters were measured from the cardiovascular magnetic resonance studies of the UK Biobank. Genotyping was done using Affymetrix arrays, augmented by imputation. We performed genome-wide association studies of 6 LV traits-LV end-diastolic volume, LV end-systolic volume, LV stroke volume, LV ejection fraction, LV mass, and LV mass to end-diastolic volume ratio. The replication analysis was performed in the MESA study (Multi-Ethnic Study of Atherosclerosis). We identified the candidate genes at genome-wide significant loci based on the evidence from extensive bioinformatic analyses. Polygenic risk scores were constructed from the summary statistics of LV genome-wide association studies to predict the heart failure events. RESULTS The study comprised 16 923 European UK Biobank participants (mean age 62.5 years; 45.8% men) without prevalent myocardial infarction or heart failure. We discovered 14 genome-wide significant loci (3 loci each for LV end-diastolic volume, LV end-systolic volume, and LV mass to end-diastolic volume ratio; 4 loci for LV ejection fraction, and 1 locus for LV mass) at a stringent P<1×10-8. Three loci were replicated at Bonferroni significance and 7 loci at nominal significance (P<0.05 with concordant direction of effect) in the MESA study (n=4383). Follow-up bioinformatic analyses identified 28 candidate genes that were enriched in the cardiac developmental pathways and regulation of the LV contractile mechanism. Eight genes (TTN, BAG3, GRK5, HSPB7, MTSS1, ALPK3, NMB, and MMP11) supported by at least 2 independent lines of in silico evidence were implicated in the cardiac morphogenesis and heart failure development. The polygenic risk scores of LV phenotypes were predictive of heart failure in a holdout UK Biobank sample of 3106 cases and 224 134 controls (odds ratio 1.41, 95% CI 1.26 - 1.58, for the top quintile versus the bottom quintile of the LV end-systolic volume risk score). CONCLUSIONS We report 14 genetic loci and indicate several candidate genes that not only enhance our understanding of the genetic architecture of prognostically important LV phenotypes but also shed light on potential novel therapeutic targets for LV remodeling.
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Affiliation(s)
- Nay Aung
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
- National Institute for Health Research, Barts Cardiovascular Biomedical Research Centre (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health National Health Service Trust, West Smithfield, London, United Kingdom (N.A., K.F., S.E.P.)
| | - Jose D. Vargas
- Medstar Heart and Vascular Institute, Medstar Georgetown University Hospital, Washington, DC (J.D.V.)
| | - Chaojie Yang
- Center for Public Health Genomics, University of Virginia, Charlottesville (C.Y., A.W.M.)
| | - Claudia P. Cabrera
- Centre for Translational Bioinformatics (C.P.C., E.T., M.R.B.), Queen Mary University of London, United Kingdom
| | - Helen R. Warren
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
- National Institute for Health Research, Barts Cardiovascular Biomedical Research Centre (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
| | - Kenneth Fung
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
- National Institute for Health Research, Barts Cardiovascular Biomedical Research Centre (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health National Health Service Trust, West Smithfield, London, United Kingdom (N.A., K.F., S.E.P.)
| | - Evan Tzanis
- Centre for Translational Bioinformatics (C.P.C., E.T., M.R.B.), Queen Mary University of London, United Kingdom
| | - Michael R. Barnes
- Centre for Translational Bioinformatics (C.P.C., E.T., M.R.B.), Queen Mary University of London, United Kingdom
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Division of Genomics Outcomes, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-University of California, Los Angeles, Medical Center, Torrance, CA (J.I.R., K.D.T.)
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Division of Genomics Outcomes, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-University of California, Los Angeles, Medical Center, Torrance, CA (J.I.R., K.D.T.)
| | - Ani W. Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville (C.Y., A.W.M.)
| | - Joao A.C. Lima
- Division of Cardiology, Johns Hopkins University, Baltimore, MD (J.AC.L.)
| | - David A. Bluemke
- Department of Radiology, University of Wisconsin, Madison (D.A.B.)
| | - Stefan K. Piechnik
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (S.K.P., S.N.)
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom (S.K.P., S.N.)
| | - Patricia B. Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
- National Institute for Health Research, Barts Cardiovascular Biomedical Research Centre (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
| | - Steffen E. Petersen
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
- National Institute for Health Research, Barts Cardiovascular Biomedical Research Centre (N.A., H.R.W., K.F., P.B.M., S.E.P.), Queen Mary University of London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health National Health Service Trust, West Smithfield, London, United Kingdom (N.A., K.F., S.E.P.)
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168
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Ramírez J, van Duijvenboden S, Aung N, Laguna P, Pueyo E, Tinker A, Lambiase PD, Orini M, Munroe PB. Cardiovascular Predictive Value and Genetic Basis of Ventricular Repolarization Dynamics. Circ Arrhythm Electrophysiol 2019; 12:e007549. [PMID: 31607149 DOI: 10.1161/circep.119.007549] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Early prediction of cardiovascular risk in the general population remains an important issue. The T-wave morphology restitution (TMR), an ECG marker quantifying ventricular repolarization dynamics, is strongly associated with cardiovascular mortality in patients with heart failure. Our aim was to evaluate the cardiovascular prognostic value of TMR in a UK middle-aged population and identify any genetic contribution. METHODS We analyzed ECG recordings from 55 222 individuals from a UK middle-aged population undergoing an exercise stress test in UK Biobank (UKB). TMR was used to measure ventricular repolarization dynamics, exposed in this cohort by exercise (TMR during exercise, TMRex) and recovery from exercise (TMR during recovery, TMRrec). The primary end point was cardiovascular events; secondary end points were all-cause mortality, ventricular arrhythmias, and atrial fibrillation with median follow-up of 7 years. Genome-wide association studies for TMRex and TMRrec were performed, and genetic risk scores were derived and tested for association in independent samples from the full UKB cohort (N=360 631). RESULTS A total of 1743 (3.2%) individuals in UKB who underwent the exercise stress test had a cardiovascular event, and TMRrec was significantly associated with cardiovascular events (hazard ratio, 1.11; P=5×10-7), independent of clinical variables and other ECG markers. TMRrec was also associated with all-cause mortality (hazard ratio, 1.10) and ventricular arrhythmias (hazard ratio, 1.16). We identified 12 genetic loci in total for TMRex and TMRrec, of which 9 are associated with another ECG marker. Individuals in the top 20% of the TMRrec genetic risk score were significantly more likely to have a cardiovascular event in the full UKB cohort (18 997, 5.3%) than individuals in the bottom 20% (hazard ratio, 1.07; P=6×10-3). CONCLUSIONS TMR and TMR genetic risk scores are significantly associated with cardiovascular risk in a UK middle-aged population, supporting the hypothesis that increased spatio-temporal heterogeneity of ventricular repolarization is a substrate for cardiovascular risk and the validity of TMR as a cardiovascular risk predictor.
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Affiliation(s)
- Julia Ramírez
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,Institute of Cardiovascular Science, University College London, United Kingdom (J.R., S.v.D., P.D.L., M.O.)
| | - Stefan van Duijvenboden
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,Institute of Cardiovascular Science, University College London, United Kingdom (J.R., S.v.D., P.D.L., M.O.)
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute (N.A.), Queen Mary University of London, United Kingdom.,Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom (N.A., P.D.L.)
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragón Institute of Engineering Research, IIS Aragón, University of Zaragoza, Spain (P.L., E.P.).,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain (P.L., E.P.)
| | - Esther Pueyo
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragón Institute of Engineering Research, IIS Aragón, University of Zaragoza, Spain (P.L., E.P.).,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain (P.L., E.P.)
| | - Andrew Tinker
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,National Institute of Health Research Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry (A.T., P.B.M.), Queen Mary University of London, United Kingdom
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, United Kingdom (J.R., S.v.D., P.D.L., M.O.).,Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom (N.A., P.D.L.)
| | - Michele Orini
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,Institute of Cardiovascular Science, University College London, United Kingdom (J.R., S.v.D., P.D.L., M.O.)
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,National Institute of Health Research Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry (A.T., P.B.M.), Queen Mary University of London, United Kingdom
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169
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Torrey EF. Second Chance. Schizophr Bull 2019; 45:1379-1380. [PMID: 28338753 PMCID: PMC6811813 DOI: 10.1093/schbul/sbw188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
My second career as a schizophrenia researcher will focus on infectious agents as a cause. It will include the collection of serial sera, cerebrospinal fluid, functional magnetic resonance imaging, and diffusion tensor imaging on a cohort of affected individuals over 20 years. Since I believe that the initial transmission of these agents occurs in childhood, I will also follow a cohort of children from birth to age 20. Additional projects will focus on rheumatoid arthritis, geographic case clusters, immigrants, and epidemiology.
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Affiliation(s)
- E Fuller Torrey
- Stanley Medical Research Institute, Kensington, MD,To whom correspondence should be addressed; Stanley Medical Research Institute, 10605 Concord Street, Suite 206, Kensington, MD 20895, US; tel: 301-571-2078, e-mail:
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170
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Bernstein MR, Zdraljevic S, Andersen EC, Rockman MV. Tightly linked antagonistic-effect loci underlie polygenic phenotypic variation in C. elegans. Evol Lett 2019; 3:462-473. [PMID: 31636939 PMCID: PMC6791183 DOI: 10.1002/evl3.139] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 08/23/2019] [Indexed: 12/31/2022] Open
Abstract
Recent work has provided strong empirical support for the classic polygenic model for trait variation. Population-based findings suggest that most regions of genome harbor variation affecting most traits. Here, we use the approach of experimental genetics to show that, indeed, most genomic regions carry variants with detectable effects on growth and reproduction in Caenorhabditis elegans populations sensitized by nickel stress. Nine of 15 adjacent intervals on the X chromosome, each encompassing ∼0.001 of the genome, have significant effects when tested individually in near-isogenic lines (NILs). These intervals have effects that are similar in magnitude to those of genome-wide significant loci that we mapped in a panel of recombinant inbred advanced intercross lines (RIAILs). If NIL-like effects were randomly distributed across the genome, the RIAILs would exhibit phenotypic variance that far exceeds the observed variance. However, the NIL intervals are arranged in a pattern that significantly reduces phenotypic variance relative to a random arrangement; adjacent intervals antagonize one another, cancelling each other's effects. Contrary to the expectation of small additive effects, our findings point to large-effect variants whose effects are masked by epistasis or linkage disequilibrium between alleles of opposing effect.
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Affiliation(s)
- Max R. Bernstein
- Department of Biology and Center for Genomics & Systems BiologyNew York UniversityNew YorkNew York10003
| | - Stefan Zdraljevic
- Molecular Biosciences and Interdisciplinary Biological Sciences ProgramNorthwestern UniversityEvanstonIllinois60208
| | - Erik C. Andersen
- Molecular Biosciences and Interdisciplinary Biological Sciences ProgramNorthwestern UniversityEvanstonIllinois60208
| | - Matthew V. Rockman
- Department of Biology and Center for Genomics & Systems BiologyNew York UniversityNew YorkNew York10003
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171
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O'Connor LJ, Schoech AP, Hormozdiari F, Gazal S, Patterson N, Price AL. Extreme Polygenicity of Complex Traits Is Explained by Negative Selection. Am J Hum Genet 2019; 105:456-476. [PMID: 31402091 PMCID: PMC6732528 DOI: 10.1016/j.ajhg.2019.07.003] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 07/03/2019] [Indexed: 12/16/2022] Open
Abstract
Complex traits and common diseases are extremely polygenic, their heritability spread across thousands of loci. One possible explanation is that thousands of genes and loci have similarly important biological effects when mutated. However, we hypothesize that for most complex traits, relatively few genes and loci are critical, and negative selection-purging large-effect mutations in these regions-leaves behind common-variant associations in thousands of less critical regions instead. We refer to this phenomenon as flattening. To quantify its effects, we introduce a mathematical definition of polygenicity, the effective number of independently associated SNPs (Me), which describes how evenly the heritability of a trait is spread across the genome. We developed a method, stratified LD fourth moments regression (S-LD4M), to estimate Me, validating that it produces robust estimates in simulations. Analyzing 33 complex traits (average N = 361k), we determined that heritability is spread ∼4× more evenly among common SNPs than among low-frequency SNPs. This difference, together with evolutionary modeling of new mutations, suggests that complex traits would be orders of magnitude less polygenic if not for the influence of negative selection. We also determined that heritability is spread more evenly within functionally important regions in proportion to their heritability enrichment; functionally important regions do not harbor common SNPs with greatly increased causal effect sizes, due to selective constraint. Our results suggest that for most complex traits, the genes and loci with the most critical biological effects often differ from those with the strongest common-variant associations.
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Affiliation(s)
- Luke J O'Connor
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Bioinformatics and Integrative Genomics, Harvard Graduate School of Arts and Sciences, Boston, MA 02115, USA.
| | - Armin P Schoech
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Nick Patterson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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172
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Sella G, Barton NH. Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies. Annu Rev Genomics Hum Genet 2019; 20:461-493. [DOI: 10.1146/annurev-genom-083115-022316] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Many traits of interest are highly heritable and genetically complex, meaning that much of the variation they exhibit arises from differences at numerous loci in the genome. Complex traits and their evolution have been studied for more than a century, but only in the last decade have genome-wide association studies (GWASs) in humans begun to reveal their genetic basis. Here, we bring these threads of research together to ask how findings from GWASs can further our understanding of the processes that give rise to heritable variation in complex traits and of the genetic basis of complex trait evolution in response to changing selection pressures (i.e., of polygenic adaptation). Conversely, we ask how evolutionary thinking helps us to interpret findings from GWASs and informs related efforts of practical importance.
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Affiliation(s)
- Guy Sella
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
- Program for Mathematical Genomics, Columbia University, New York, NY 10032, USA
| | - Nicholas H. Barton
- Institute of Science and Technology Austria, 3400 Klosterneuburg, Austria
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173
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Abstract
Motivation Heritability, the proportion of variation in a trait that can be explained by genetic variation, is an important parameter in efforts to understand the genetic architecture of complex phenotypes as well as in the design and interpretation of genome-wide association studies. Attempts to understand the heritability of complex phenotypes attributable to genome-wide single nucleotide polymorphism (SNP) variation data has motivated the analysis of large datasets as well as the development of sophisticated tools to estimate heritability in these datasets. Linear mixed models (LMMs) have emerged as a key tool for heritability estimation where the parameters of the LMMs, i.e. the variance components, are related to the heritability attributable to the SNPs analyzed. Likelihood-based inference in LMMs, however, poses serious computational burdens. Results We propose a scalable randomized algorithm for estimating variance components in LMMs. Our method is based on a method-of-moment estimator that has a runtime complexity O(NMB) for N individuals and M SNPs (where B is a parameter that controls the number of random matrix-vector multiplications). Further, by leveraging the structure of the genotype matrix, we can reduce the time complexity to O(NMBmax( log3N, log3M)). We demonstrate the scalability and accuracy of our method on simulated as well as on empirical data. On standard hardware, our method computes heritability on a dataset of 500 000 individuals and 100 000 SNPs in 38 min. Availability and implementation The RHE-reg software is made freely available to the research community at: https://github.com/sriramlab/RHE-reg.
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Affiliation(s)
- Yue Wu
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Sriram Sankararaman
- 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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174
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Hou K, Burch KS, Majumdar A, Shi H, Mancuso N, Wu Y, Sankararaman S, Pasaniuc B. Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture. Nat Genet 2019; 51:1244-1251. [PMID: 31358995 PMCID: PMC6686906 DOI: 10.1038/s41588-019-0465-0] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 06/13/2019] [Indexed: 12/14/2022]
Abstract
SNP-heritability is a fundamental quantity in the study of complex traits. Recent studies have shown that existing methods to estimate genome-wide SNP-heritability can yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and linkage disequilibrium (LD)-dependent genetic architectures, it remains unclear which estimates reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of genetic architecture, without specifying a heritability model or partitioning SNPs by allele frequency and/or LD. We show analytically and through extensive simulations starting from real genotypes (UK Biobank, N = 337 K) that, unlike existing methods, our closed-form estimator is robust across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.
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Affiliation(s)
- Kangcheng Hou
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Arunabha Majumdar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Huwenbo Shi
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Nicholas Mancuso
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Biostatistics Division, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yue Wu
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sriram Sankararaman
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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175
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Torrey EF, Yolken RH. Schizophrenia as a pseudogenetic disease: A call for more gene-environmental studies. Psychiatry Res 2019; 278:146-150. [PMID: 31200193 DOI: 10.1016/j.psychres.2019.06.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 05/29/2019] [Accepted: 06/03/2019] [Indexed: 01/22/2023]
Abstract
In recent years schizophrenia has been assumed to be largely a genetic disease with heritability estimates, derived primarily from family and twin studies, of 80%-85%. However, the results of genetic research on schizophrenia have not yielded results consistent with that estimate of heritability. In particular, extensive genetic studies have not led to new methods for diagnosis and treatment. An examination of the twin studies on which heritability is based shows why such studies exaggerate the genetic component of schizophrenia. In addition, the effects of infectious agents such as Toxoplasma gondii and the composition of the microbiome can produce a clinical picture that would also appear to be largely genetic due to familial aggregation and a role for a partial genetic contribution to the immune system. It is concluded that the genetic component of schizophrenia may have been overestimated and an increased focus on gene-environmental interactions is likely to accelerate research progress on this disease.
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Affiliation(s)
- E Fuller Torrey
- Stanley Medical Research Institute, 301-571-2078, 10605 Concord St, Suite 206, Kensington, MD20895, USA.
| | - Robert H Yolken
- Stanley Laboratory of Developmental Neurovirology, Johns Hopkins Medical Center, Baltimore, MD, USA
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176
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Border R, Becker S. Stochastic Lanczos estimation of genomic variance components for linear mixed-effects models. BMC Bioinformatics 2019; 20:411. [PMID: 31362713 PMCID: PMC6668092 DOI: 10.1186/s12859-019-2978-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 06/30/2019] [Indexed: 01/14/2023] Open
Abstract
Background Linear mixed-effects models (LMM) are a leading method in conducting genome-wide association studies (GWAS) but require residual maximum likelihood (REML) estimation of variance components, which is computationally demanding. Previous work has reduced the computational burden of variance component estimation by replacing direct matrix operations with iterative and stochastic methods and by employing loose tolerances to limit the number of iterations in the REML optimization procedure. Here, we introduce two novel algorithms, stochastic Lanczos derivative-free REML (SLDF_REML) and Lanczos first-order Monte Carlo REML (L_FOMC_REML), that exploit problem structure via the principle of Krylov subspace shift-invariance to speed computation beyond existing methods. Both novel algorithms only require a single round of computation involving iterative matrix operations, after which their respective objectives can be repeatedly evaluated using vector operations. Further, in contrast to existing stochastic methods, SLDF_REML can exploit precomputed genomic relatedness matrices (GRMs), when available, to further speed computation. Results Results of numerical experiments are congruent with theory and demonstrate that interpreted-language implementations of both algorithms match or exceed existing compiled-language software packages in speed, accuracy, and flexibility. Conclusions Both the SLDF_REML and L_FOMC_REML algorithms outperform existing methods for REML estimation of variance components for LMM and are suitable for incorporation into existing GWAS LMM software implementations. Electronic supplementary material The online version of this article (10.1186/s12859-019-2978-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Richard Border
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, 80309, CO, USA. .,Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, 80309, CO, USA.
| | - Stephen Becker
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, 80309, CO, USA
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177
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Psychiatric Polygenic Risk Scores as Predictor for Attention Deficit/Hyperactivity Disorder and Autism Spectrum Disorder in a Clinical Child and Adolescent Sample. Behav Genet 2019; 50:203-212. [PMID: 31346826 PMCID: PMC7355275 DOI: 10.1007/s10519-019-09965-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 07/10/2019] [Indexed: 12/31/2022]
Abstract
Neurodevelopmental disorders such as attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable and influenced by many single nucleotide polymorphisms (SNPs). SNPs can be used to calculate individual polygenic risk scores (PRS) for a disorder. We aim to explore the association between the PRS for ADHD, ASD and for Schizophrenia (SCZ), and ADHD and ASD diagnoses in a clinical child and adolescent population. Based on the most recent genome wide association studies of ADHD, ASD and SCZ, PRS of each disorder were calculated for individuals of a clinical child and adolescent target sample (N = 688) and for adult controls (N = 943). We tested with logistic regression analyses for an association with (1) a single diagnosis of ADHD (N = 280), (2) a single diagnosis of ASD (N = 295), and (3) combining the two diagnoses, thus subjects with either ASD, ADHD or both (N = 688). Our results showed a significant association of the ADHD PRS with ADHD status (OR 1.6, P = 1.39 × 10−07) and with the combined ADHD/ASD status (OR 1.36, P = 1.211 × 10−05), but not with ASD status (OR 1.14, P = 1). No associations for the ASD and SCZ PRS were observed. In sum, the PRS of ADHD is significantly associated with the combined ADHD/ASD status. Yet, this association is primarily driven by ADHD status, suggesting disorder specific genetic effects of the ADHD PRS.
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178
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Kim W, Kwak SH, Won S. Heritability estimation of dichotomous phenotypes using a liability threshold model on ascertained family-based samples. Genet Epidemiol 2019; 43:761-775. [PMID: 31298783 DOI: 10.1002/gepi.22244] [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: 12/04/2018] [Revised: 05/07/2019] [Accepted: 05/30/2019] [Indexed: 11/05/2022]
Abstract
Numerous methods for estimating heritability have been proposed; however, unlike quantitative phenotypes, heritability estimation for dichotomous phenotypes is computationally and statistically complex, and the use of heritability is infrequent. In this study, we developed a statistical method to estimate heritability of dichotomous phenotypes using a liability threshold model in the context of ascertained family-based samples. This model assumes that dichotomous phenotypes are determined by unobserved latent variables that are normally distributed and can be applied to general pedigree data. The proposed methods were applied to simulated data and Korean type-2 diabetes family-based samples, and the accuracy of the estimates provided by the experimental methods was compared with that of the established methods.
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Affiliation(s)
- Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Sungho Won
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Department of Public Health Sciences, Seoul National University, Seoul, Korea.,Institute of Health and Environment, Seoul National University, Seoul, Korea
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179
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Mendizabal I, Berto S, Usui N, Toriumi K, Chatterjee P, Douglas C, Huh I, Jeong H, Layman T, Tamminga CA, Preuss TM, Konopka G, Yi SV. Cell type-specific epigenetic links to schizophrenia risk in the brain. Genome Biol 2019; 20:135. [PMID: 31288836 PMCID: PMC6617737 DOI: 10.1186/s13059-019-1747-7] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 06/25/2019] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The importance of cell type-specific epigenetic variation of non-coding regions in neuropsychiatric disorders is increasingly appreciated, yet data from disease brains are conspicuously lacking. We generate cell type-specific whole-genome methylomes (N = 95) and transcriptomes (N = 89) from neurons and oligodendrocytes obtained from brain tissue of patients with schizophrenia and matched controls. RESULTS The methylomes of the two cell types are highly distinct, with the majority of differential DNA methylation occurring in non-coding regions. DNA methylation differences between cases and controls are subtle compared to cell type differences, yet robust against permuted data and validated in targeted deep-sequencing analyses. Differential DNA methylation between control and schizophrenia tends to occur in cell type differentially methylated sites, highlighting the significance of cell type-specific epigenetic dysregulation in a complex neuropsychiatric disorder. CONCLUSIONS Our results provide novel and comprehensive methylome and transcriptome data from distinct cell populations within patient-derived brain tissues. This data clearly demonstrate that cell type epigenetic-differentiated sites are preferentially targeted by disease-associated epigenetic dysregulation. We further show reduced cell type epigenetic distinction in schizophrenia.
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Affiliation(s)
- Isabel Mendizabal
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Stefano Berto
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Noriyoshi Usui
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, USA
- Center for Medical Research and Education, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
- Department of Neuroscience and Cell Biology, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Kazuya Toriumi
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, USA
- Schizophrenia Research Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, 156-8506, Japan
| | - Paramita Chatterjee
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Connor Douglas
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Iksoo Huh
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
- College of Nursing, The Research Institute of Nursing Science, Seoul National University, Seoul, 03080, South Korea
| | - Hyeonsoo Jeong
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Thomas Layman
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Todd M Preuss
- Division of Neuropharmacology and Neurologic Diseases, Department of Pathology, Yerkes National Primate Research Center, Emory University School of Medicine, Emory University, Atlanta, GA, 30329, USA
| | - Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
| | - Soojin V Yi
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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180
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Gao XR, Huang H, Kim H. Genome-wide association analyses identify 139 loci associated with macular thickness in the UK Biobank cohort. Hum Mol Genet 2019; 28:1162-1172. [PMID: 30535121 DOI: 10.1093/hmg/ddy422] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 11/26/2018] [Accepted: 11/30/2018] [Indexed: 11/13/2022] Open
Abstract
The macula, located near the center of the retina in the human eye, is responsible for providing critical functions, such as central, sharp vision. Structural changes in the macula are associated with many ocular diseases, including age-related macular degeneration (AMD) and glaucoma. Although macular thickness is a highly heritable trait, there are no prior reported genome-wide association studies (GWASs) of it. Here we describe the first GWAS of macular thickness, which was measured by spectral-domain optical coherence tomography using 68 423 participants from the UK Biobank cohort. We identified 139 genetic loci associated with macular thickness at genome-wide significance (P < 5 × 10-8). The most significant loci were LINC00461 (P = 5.1 × 10-120), TSPAN10 (P = 1.2 × 10-118), RDH5 (P = 9.2 × 10-105) and SLC6A20 (P = 1.4 × 10-71). Results from gene expression demonstrated that these genes are highly expressed in the retina. Other hits included many previously reported AMD genes, such as NPLOC4 (P = 1.7 × 10-103), RAD51B (P = 9.1 × 10-14) and SLC16A8 (P = 1.7 × 10-8), further providing functional significance of the identified loci. Through cross-phenotype analysis, these genetic loci also exhibited pleiotropic effects with myopia, neurodegenerative diseases (e.g. Parkinson's disease, schizophrenia and Alzheimer's disease), cancer (e.g. breast, ovarian and lung cancers) and metabolic traits (e.g. body mass index, waist circumference and type 2 diabetes). Our findings provide the first insight into the genetic architecture of macular thickness and may further elucidate the pathogenesis of related ocular diseases, such as AMD.
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Affiliation(s)
- X Raymond Gao
- Departments of Ophthalmology and Visual Science and Biomedical Informatics, Division of Human Genetics, The Ohio State University, Columbus, OH, USA
| | - Hua Huang
- Departments of Ophthalmology and Visual Science and Biomedical Informatics, Division of Human Genetics, The Ohio State University, Columbus, OH, USA
| | - Heejin Kim
- Departments of Ophthalmology and Visual Science and Biomedical Informatics, Division of Human Genetics, The Ohio State University, Columbus, OH, USA
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181
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Gilmour AR. Average information residual maximum likelihood in practice. J Anim Breed Genet 2019; 136:262-272. [PMID: 31247685 DOI: 10.1111/jbg.12398] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 04/01/2019] [Accepted: 04/02/2019] [Indexed: 11/29/2022]
Abstract
Gilmour, Thompson, and Cullis (Biometrics, 1995, 51, 1440) presented the average information residual maximum likelihood (REML) algorithm for efficient variance parameter estimation in the linear mixed model. That paper dealt specifically with traditional variance component models, but the algorithm was quickly applied to more general models and implemented in several REML packages including ASReml (Gilmour et al., Biometrics, 2015, 51, 1440). This paper outlines the theory with respect to these more general models, describes the main issues encountered in fitting these models and how they have been addressed in the ASReml software. The issues covered are the basics steps in the implementation of the algorithm, keeping parameters within the parameter space, maximizing sparsity, avoiding issues associated with unstructured variance matrices by using the factor-analytic structure and handling singularities in marker-based relationship matrices and current work.
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182
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Thompson R. Desert island papers-A life in variance parameter and quantitative genetic parameter estimation reviewed using 16 papers. J Anim Breed Genet 2019; 136:230-242. [PMID: 31247681 DOI: 10.1111/jbg.12400] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 04/01/2019] [Accepted: 04/03/2019] [Indexed: 12/01/2022]
Abstract
I review my scientific career in terms of eight areas and 16 papers. The first two areas are associated with childhood. The other six are associated with residual maximum likelihood (REML), canonical transformation, inbreeding in selected populations, average information residual maximum likelihood (AIREML), the computer program ASReml and sampling-based estimation.
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183
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Matilainen K, Mäntysaari EA, Strandén I. Efficient Monte Carlo algorithm for restricted maximum likelihood estimation of genetic parameters. J Anim Breed Genet 2019; 136:252-261. [PMID: 31247679 DOI: 10.1111/jbg.12375] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 11/21/2018] [Accepted: 11/26/2018] [Indexed: 11/30/2022]
Abstract
Monte Carlo (MC) methods have been found useful in estimation of variance parameters for large data and complex models with many variance components (VC), with respect to both computer memory and computing time. A disadvantage has been a fluctuation in round-to-round values of estimates that makes the estimation of convergence challenging. Furthermore, with Newton-type algorithms, the approximate Hessian matrix might have sufficient accuracy, but the inaccuracy in the gradient vector exaggerates the round-to-round fluctuation to intolerable. In this study, the reuse of the same random numbers within each MC sample was used to remove the MC fluctuation. Simulated data with six VC parameters were analysed by four different MC REML methods: expectation-maximization (EM), Newton-Raphson (NR), average information (AI) and Broyden's method (BM). In addition, field data with 96 VC parameters were analysed by MC EM REML. In all the analyses with reused samples, the MC fluctuations disappeared, but the final estimates by the MC REML methods differed from the analytically calculated values more than expected especially when the number of MC samples was small. The difference depended on the random numbers generated, and based on repeated MC AI REML analyses, the VC estimates were on average non-biased. The advantage of reusing MC samples is more apparent in the NR-type algorithms. Smooth convergence opens the possibility to use the fast converging Newton-type algorithms. However, a disadvantage from reusing MC samples is a possible "bias" in the estimates. To attain acceptable accuracy, sufficient number of MC samples need to be generated.
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Affiliation(s)
| | | | - Ismo Strandén
- Natural Resources Institute Finland (Luke), Jokioinen, Finland
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184
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Wei J, Xie W, Li R, Wang S, Qu H, Ma R, Zhou X, Jia Z. Analysis of trait heritability in functionally partitioned rice genomes. Heredity (Edinb) 2019; 124:485-498. [PMID: 31253955 DOI: 10.1038/s41437-019-0244-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 06/05/2019] [Accepted: 06/08/2019] [Indexed: 01/10/2023] Open
Abstract
Knowledge of the genetic architecture of importantly agronomical traits can speed up genetic improvement in cultivated rice (Oryza sativa L.). Many recent investigations have leveraged genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs), associated with agronomic traits in various rice populations. The reported trait-relevant SNPs appear to be arbitrarily distributed along the genome, including genic and nongenic regions. Whether the SNPs in different genomic regions play different roles in trait heritability and which region is more responsible for phenotypic variation remains opaque. We analyzed a natural rice population of 524 accessions with 3,616,597 SNPs to compare the genetic contributions of functionally distinct genomic regions for five agronomic traits, i.e., yield, heading date, plant height, grain length, and grain width. An analysis of heritability in the functionally partitioned rice genome showed that regulatory or intergenic regions account for the most trait heritability. A close look at the trait-associated SNPs (TASs) indicated that the majority of the TASs are located in nongenic regions, and the genetic effects of the TASs in nongenic regions are generally greater than those in genic regions. We further compared the predictabilities using the genetic variants from genic regions with those using nongenic regions. The results revealed that nongenic regions play a more important role than genic regions in trait heritability in rice, which is consistent with findings in humans and maize. This conclusion not only offers clues for basic research to disclose genetics behind these agronomic traits, but also provides a new perspective to facilitate genomic selection in rice.
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Affiliation(s)
- Julong Wei
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, China.,Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Weibo Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Ruidong Li
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA
| | - Shibo Wang
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA
| | - Han Qu
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA
| | - Renyuan Ma
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA.,Department of Mathematics, Bowdoin College, Brunswick, ME, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Zhenyu Jia
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA.
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185
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Kim SS, Dai C, Hormozdiari F, van de Geijn B, Gazal S, Park Y, O'Connor L, Amariuta T, Loh PR, Finucane H, Raychaudhuri S, Price AL. Genes with High Network Connectivity Are Enriched for Disease Heritability. Am J Hum Genet 2019; 104:896-913. [PMID: 31051114 PMCID: PMC6506868 DOI: 10.1016/j.ajhg.2019.03.020] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 03/20/2019] [Indexed: 12/13/2022] Open
Abstract
Recent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 diseases and complex traits (average N = 323K) to identify enriched annotations. First, we analyzed 18,119 biological pathways. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and 75 known functional annotations (from the baseline-LD model), a stringent step that greatly reduced the number of pathways detected; most significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, emphasizing the importance of accounting for known annotations.
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Affiliation(s)
- Samuel S Kim
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Chengzhen Dai
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Yongjin Park
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Luke O'Connor
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA 02138, USA
| | - Tiffany Amariuta
- Program in Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA 02138, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Hilary Finucane
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Soumya Raychaudhuri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
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186
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Liu X, Li YI, Pritchard JK. Trans Effects on Gene Expression Can Drive Omnigenic Inheritance. Cell 2019; 177:1022-1034.e6. [PMID: 31051098 PMCID: PMC6553491 DOI: 10.1016/j.cell.2019.04.014] [Citation(s) in RCA: 268] [Impact Index Per Article: 53.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/18/2018] [Accepted: 04/07/2019] [Indexed: 01/02/2023]
Abstract
Early genome-wide association studies (GWASs) led to the surprising discovery that, for typical complex traits, most of the heritability is due to huge numbers of common variants with tiny effect sizes. Previously, we argued that new models are needed to understand these patterns. Here, we provide a formal model in which genetic contributions to complex traits are partitioned into direct effects from core genes and indirect effects from peripheral genes acting in trans. We propose that most heritability is driven by weak trans-eQTL SNPs, whose effects are mediated through peripheral genes to impact the expression of core genes. In particular, if the core genes for a trait tend to be co-regulated, then the effects of peripheral variation can be amplified such that nearly all of the genetic variance is driven by weak trans effects. Thus, our model proposes a framework for understanding key features of the architecture of complex traits.
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Affiliation(s)
- Xuanyao Liu
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
| | - Yang I Li
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
| | - Jonathan K Pritchard
- Departments of Biology and Genetics and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
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187
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Chen J, Liu J, Calhoun VD. The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2019; 107:912-927. [PMID: 32051642 PMCID: PMC7015534 DOI: 10.1109/jproc.2019.2913145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.
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Affiliation(s)
- Jiayu Chen
- The Mind Research Network, Albuquerque, NM 87106 USA
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
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188
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Shor T, Kalka I, Geiger D, Erlich Y, Weissbrod O. Estimating variance components in population scale family trees. PLoS Genet 2019; 15:e1008124. [PMID: 31071088 PMCID: PMC6529016 DOI: 10.1371/journal.pgen.1008124] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 05/21/2019] [Accepted: 04/03/2019] [Indexed: 12/14/2022] Open
Abstract
The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records.
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Affiliation(s)
- Tal Shor
- Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel
- MyHeritage Ltd., Or Yehuda, Israel
| | - Iris Kalka
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Dan Geiger
- Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel
| | - Yaniv Erlich
- MyHeritage Ltd., Or Yehuda, Israel
- The New York Genome Center, New York, NY, United States of America
- Department of Computer Science, Fu School of Engineering, Columbia University, NY, United States of America
| | - Omer Weissbrod
- Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
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189
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Li X, Wu D, Cui Y, Liu B, Walter H, Schumann G, Li C, Jiang T. Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies. BMC Bioinformatics 2019; 20:219. [PMID: 31039742 PMCID: PMC6492418 DOI: 10.1186/s12859-019-2792-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 04/02/2019] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability. RESULTS In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy. CONCLUSIONS The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.
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Affiliation(s)
- Xin Li
- School of Mathematical Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310027 China
| | - Dongya Wu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100049 China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
| | - Bing Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Chong Li
- School of Mathematical Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310027 China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, 4 Section 2 North Jianshe Road, Chengdu, 610054 China
- The Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072 Australia
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100049 China
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190
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Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct. Am J Hum Genet 2019; 104:665-684. [PMID: 30929738 DOI: 10.1016/j.ajhg.2019.02.022] [Citation(s) in RCA: 143] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 02/20/2019] [Indexed: 12/13/2022] Open
Abstract
The extent to which genetic risk factors are shared between childhood-onset (COA) and adult-onset (AOA) asthma has not been estimated. On the basis of data from the UK Biobank study (n = 447,628), we found that the variance in disease liability explained by common variants is higher for COA (onset at ages between 0 and 19 years; h2g = 25.6%) than for AOA (onset at ages between 20 and 60 years; h2g = 10.6%). The genetic correlation (rg) between COA and AOA was 0.67. Variation in age of onset among COA-affected individuals had a low heritability (h2g = 5%), which we confirmed in independent studies and also among AOA-affected individuals. To identify subtype-specific genetic associations, we performed a genome-wide association study (GWAS) in the UK Biobank for COA (13,962 affected individuals) and a separate GWAS for AOA (26,582 affected individuals) by using a common set of 300,671 controls for both studies. We identified 123 independent associations for COA and 56 for AOA (37 overlapped); of these, 98 and 34, respectively, were reproducible in an independent study (n = 262,767). Collectively, 28 associations were not previously reported. For 96 COA-associated variants, including five variants that represent COA-specific risk factors, the risk allele was more common in COA- than in AOA-affected individuals. Conversely, we identified three variants that are stronger risk factors for AOA. Variants associated with obesity and smoking had a stronger contribution to the risk of AOA than to the risk of COA. Lastly, we identified 109 likely target genes of the associated variants, primarily on the basis of correlated expression quantitative trait loci (up to n = 31,684). GWAS informed by age of onset can identify subtype-specific risk variants, which can help us understand differences in pathophysiology between COA and AOA and so can be informative for drug development.
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191
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John J, Kukshal P, Sharma A, Bhatia T, Nimgaonkar VL, Deshpande SN, Thelma BK. Rare variants in Protein tyrosine phosphatase, receptor type A (PTPRA) in schizophrenia: Evidence from a family based study. Schizophr Res 2019; 206:75-81. [PMID: 30594456 PMCID: PMC7321970 DOI: 10.1016/j.schres.2018.12.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Revised: 09/25/2018] [Accepted: 12/08/2018] [Indexed: 12/23/2022]
Abstract
The contribution of both common and rare risk variants to the genetic architecture of schizophrenia (SZ) has been documented in genome-wide association studies, whole exome and whole genome sequencing approaches. As SZ is highly heritable and segregates in families, highly penetrant rare variants are more likely to be identified through analyses of multiply affected families. Further, much of the gene mapping studies in SZ have utilized individuals of Caucasian ancestry. Analysis of other ethnic groups may be informative. In this study, we aimed at identification of rare, penetrant risk variants utilizing whole exome sequencing (WES) in a three-generation Indian family with multiple members affected. Filtered data from WES, combined with in silico analyses revealed a novel heterozygous missense variant (NM_080841:c.1730C>G:p.T577R; exon18) in Protein tyrosine phosphatase, receptor type A (PTPRA 20p13). The variant was located in an evolutionarily conserved position and predicted to be damaging. Screening for variants in this gene in the WES data of an independent SZ cohort (n = 350) of matched ethnicity, identified five additional rare missense variants with MAF < 0.003, which were also predicted to be damaging. In conclusion, the rare missense variants in PTPRA identified in this study could confer risk for SZ. This has also derived support from concordant data from prior linkage and association, as well as animal studies which indicated a role for PTPRA in glutamate function.
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Affiliation(s)
- Jibin John
- Department of Genetics, University of Delhi South Campus, Benito Juarez Road, New Delhi 110 021, India
| | - Prachi Kukshal
- Department of Genetics, University of Delhi South Campus, Benito Juarez Road, New Delhi 110 021, India
| | - Aditya Sharma
- Department of Genetics, University of Delhi South Campus, Benito Juarez Road, New Delhi 110 021, India
| | - Triptish Bhatia
- Department of Psychiatry, PGIMER-Dr. RML Hospital, New Delhi 110 001, India
| | - V L Nimgaonkar
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, 3811 O'Hara Street, Pittsburgh, PA 15213, USA; Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, DeSoto St, Pittsburgh, PA 15213, USA
| | - S N Deshpande
- Department of Psychiatry, PGIMER-Dr. RML Hospital, New Delhi 110 001, India
| | - B K Thelma
- Department of Genetics, University of Delhi South Campus, Benito Juarez Road, New Delhi 110 021, India.
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192
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Harold D, Connolly S, Riley BP, Kendler KS, McCarthy SE, McCombie WR, Richards A, Owen MJ, O'Donovan MC, Walters J, Donohoe G, Gill M, Corvin A, Morris DW. Population-based identity-by-descent mapping combined with exome sequencing to detect rare risk variants for schizophrenia. Am J Med Genet B Neuropsychiatr Genet 2019; 180:223-231. [PMID: 30801977 PMCID: PMC8863274 DOI: 10.1002/ajmg.b.32716] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 10/22/2018] [Accepted: 12/03/2018] [Indexed: 12/30/2022]
Abstract
Genome-wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re-analyze an Irish schizophrenia GWAS dataset (n = 3,473) by performing identity-by-descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNT but did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow-up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes.
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Affiliation(s)
- Denise Harold
- Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine and Discipline of Psychiatry, Trinity College Dublin, Dublin, Ireland
- School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Siobhan Connolly
- Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine and Discipline of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Brien P Riley
- Departments of Psychiatry and Human Genetics, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Kenneth S Kendler
- Departments of Psychiatry and Human Genetics, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Shane E McCarthy
- The Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - William R McCombie
- The Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Alex Richards
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, United Kingdom
| | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, United Kingdom
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, United Kingdom
| | - James Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, United Kingdom
| | - Gary Donohoe
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging, Cognition & Genomics (NICOG) Centre & NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Galway, Ireland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine and Discipline of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine and Discipline of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Derek W Morris
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging, Cognition & Genomics (NICOG) Centre & NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Galway, Ireland
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193
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Visscher PM, Goddard ME. From R.A. Fisher's 1918 Paper to GWAS a Century Later. Genetics 2019; 211:1125-1130. [PMID: 30967441 PMCID: PMC6456325 DOI: 10.1534/genetics.118.301594] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 12/26/2018] [Indexed: 11/18/2022] Open
Abstract
The genetics and evolution of complex traits, including quantitative traits and disease, have been hotly debated ever since Darwin. A century ago, a paper from R.A. Fisher reconciled Mendelian and biometrical genetics in a landmark contribution that is now accepted as the main foundation stone of the field of quantitative genetics. Here, we give our perspective on Fisher's 1918 paper in the context of how and why it is relevant in today's genome era. We mostly focus on human trait variation, in part because Fisher did so too, but the conclusions are general and extend to other natural populations, and to populations undergoing artificial selection.
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Affiliation(s)
- Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia 4072
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia 4072
| | - Michael E Goddard
- Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia 3083
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Victoria, Australia 3004
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194
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Sharma S, Ressler KJ. Genomic updates in understanding PTSD. Prog Neuropsychopharmacol Biol Psychiatry 2019; 90:197-203. [PMID: 30452941 PMCID: PMC6431237 DOI: 10.1016/j.pnpbp.2018.11.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 11/07/2018] [Accepted: 11/16/2018] [Indexed: 12/14/2022]
Abstract
Twin studies as well as more recent genetics-based heritability analyses demonstrate that up to 40 to 50% of the variance in predicting PTSD following trauma is heritable. However, most of the specific gene pathways and mechanism that mediate risk vs. resilience for PTSD following trauma exposure have yet to be elucidated. This review will examine the latest results from large scale Genome-wide association studies as well as other approaches aimed at understanding mechanisms of development of and recovery from PTSD.
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Affiliation(s)
- Sumeet Sharma
- Neuroscience Program, Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States; McLean Hospital, Division of Depression and Anxiety, Belmont, MA, United States
| | - Kerry J Ressler
- McLean Hospital, Division of Depression and Anxiety, Belmont, MA, United States; Harvard Medical School, Boston, MA, United States; Neuroscience Program, Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States.
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195
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Lawn RB, Sallis HM, Taylor AE, Wootton RE, Smith GD, Davies NM, Hemani G, Fraser A, Penton-Voak IS, Munafò MR. Schizophrenia risk and reproductive success: a Mendelian randomization study. ROYAL SOCIETY OPEN SCIENCE 2019; 6:181049. [PMID: 31031992 PMCID: PMC6458425 DOI: 10.1098/rsos.181049] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 01/07/2019] [Indexed: 06/09/2023]
Abstract
Schizophrenia is a debilitating and heritable mental disorder associated with lower reproductive success. However, the prevalence of schizophrenia is stable over populations and time, resulting in an evolutionary puzzle: how is schizophrenia maintained in the population, given its apparent fitness costs? One possibility is that increased genetic liability for schizophrenia, in the absence of the disorder itself, may confer some reproductive advantage. We assessed the correlation and causal effect of genetic liability for schizophrenia with number of children, age at first birth and number of sexual partners using data from the Psychiatric Genomics Consortium and UK Biobank. Linkage disequilibrium score regression showed little evidence of genetic correlation between genetic liability for schizophrenia and number of children (r g = 0.002, p = 0.84), age at first birth (r g = -0.007, p = 0.45) or number of sexual partners (r g = 0.007, p = 0.42). Mendelian randomization indicated no robust evidence of a causal effect of genetic liability for schizophrenia on number of children (mean difference: 0.003 increase in number of children per doubling in the natural log odds ratio of schizophrenia risk, 95% confidence interval (CI): -0.003 to 0.009, p = 0.39) or age at first birth (-0.004 years lower age at first birth, 95% CI: -0.043 to 0.034, p = 0.82). We find some evidence of a positive effect of genetic liability for schizophrenia on number of sexual partners (0.165 increase in the number of sexual partners, 95% CI: 0.117-0.212, p = 5.30×10-10). These results suggest that increased genetic liability for schizophrenia does not confer a fitness advantage but does increase mating success.
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Affiliation(s)
- Rebecca B. Lawn
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
| | - Hannah M. Sallis
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Amy E. Taylor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
| | - Robyn E. Wootton
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Ian S. Penton-Voak
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
| | - Marcus R. Munafò
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
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196
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Galinsky KJ, Reshef YA, Finucane HK, Loh PR, Zaitlen N, Patterson NJ, Brown BC, Price AL. Estimating cross-population genetic correlations of causal effect sizes. Genet Epidemiol 2019; 43:180-188. [PMID: 30474154 PMCID: PMC6375794 DOI: 10.1002/gepi.22173] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 10/10/2018] [Accepted: 10/10/2018] [Indexed: 01/09/2023]
Abstract
Recent studies have examined the genetic correlations of single-nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated ρ g , the cross-population correlation of joint-fit effect sizes at genotyped SNPs. However, the value of ρ g depends both on the cross-population correlation of true causal effect sizes ( ρ b ) and on the similarity in linkage disequilibrium (LD) patterns in the two populations, which drive tagging effects. Here, we derive the value of the ratio ρ g / ρ b as a function of LD in each population. By applying existing methods to obtain estimates of ρ g , we can use this ratio to estimate ρ b . Our estimates of ρ b were equal to 0.55 ( SE = 0.14) between Europeans and East Asians averaged across nine traits in the Genetic Epidemiology Research on Adult Health and Aging data set, 0.54 ( SE = 0.18) between Europeans and South Asians averaged across 13 traits in the UK Biobank data set, and 0.48 ( SE = 0.06) and 0.65 ( SE = 0.09) between Europeans and East Asians in summary statistic data sets for type 2 diabetes and rheumatoid arthritis, respectively. These results implicate substantially different causal genetic architectures across continental populations.
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Affiliation(s)
- Kevin J. Galinsky
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115
- Takeda Oncology, 40 Landsdowne Street, Cambridge, MA 02139, USA
| | - Yakir A. Reshef
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
| | - Hilary K. Finucane
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Schmidt Fellows Program, Broad Institute of MIT and Harvard
| | - Po-Ru Loh
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Noah Zaitlen
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nick J. Patterson
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | | | - Alkes L. Price
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
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197
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Gao XR, Huang H, Nannini DR, Fan F, Kim H. Genome-wide association analyses identify new loci influencing intraocular pressure. Hum Mol Genet 2019; 27:2205-2213. [PMID: 29617998 DOI: 10.1093/hmg/ddy111] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 03/26/2018] [Indexed: 12/21/2022] Open
Abstract
Elevated intraocular pressure (IOP) is a significant risk factor for glaucoma, the leading cause of irreversible blindness worldwide. While previous studies have identified numerous genetic variants associated with IOP, these loci only explain a fraction of IOP heritability. Recently established of biobank repositories have resulted in large amounts of data, enabling the identification of the remaining heritability for complex traits. Here, we describe the largest genome-wide association study of IOP to date using participants of European ancestry from the UK Biobank. We identified 671 directly genotyped variants that are significantly associated with IOP (P < 5 × 10-8). In addition to 103 novel loci, the top ranked novel IOP genes are LMX1B, NR1H3, MADD and SEPT9. We replicated these findings in an external population and examined the pleiotropic nature of these loci. These discoveries not only further our understanding of the genetic architecture of IOP, but also shed new light on the biological processes underlying glaucoma.
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Affiliation(s)
- X Raymond Gao
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Hua Huang
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Drew R Nannini
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Fangda Fan
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Heejin Kim
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
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198
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Schoech AP, Jordan DM, Loh PR, Gazal S, O'Connor LJ, Balick DJ, Palamara PF, Finucane HK, Sunyaev SR, Price AL. Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection. Nat Commun 2019; 10:790. [PMID: 30770844 PMCID: PMC6377669 DOI: 10.1038/s41467-019-08424-6] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 01/09/2019] [Indexed: 02/06/2023] Open
Abstract
Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1%) explain less than 10% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.
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Affiliation(s)
- Armin P Schoech
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.
| | - Daniel M Jordan
- Charles R. Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, MA, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
| | - Luke J O'Connor
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
| | - Daniel J Balick
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, USA
| | - Pier F Palamara
- Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Hilary K Finucane
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
| | - Shamil R Sunyaev
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.
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199
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Coleman JRI, Bryois J, Gaspar HA, Jansen PR, Savage JE, Skene N, Plomin R, Muñoz-Manchado AB, Linnarsson S, Crawford G, Hjerling-Leffler J, Sullivan PF, Posthuma D, Breen G. Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals. Mol Psychiatry 2019; 24:182-197. [PMID: 29520040 PMCID: PMC6330082 DOI: 10.1038/s41380-018-0040-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 11/13/2017] [Accepted: 01/02/2018] [Indexed: 11/08/2022]
Abstract
Variance in IQ is associated with a wide range of health outcomes, and 1% of the population are affected by intellectual disability. Despite a century of research, the fundamental neural underpinnings of intelligence remain unclear. We integrate results from genome-wide association studies (GWAS) of intelligence with brain tissue and single cell gene expression data to identify tissues and cell types associated with intelligence. GWAS data for IQ (N = 78,308) were meta-analyzed with a study comparing 1247 individuals with mean IQ ~170 to 8185 controls. Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons. Tissue-specific analyses find the most significant enrichment for frontal cortex brain expressed genes. These results suggest specific neuronal cell types and genes may be involved in intelligence and provide new hypotheses for neuroscience experiments using model systems.
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Affiliation(s)
- Jonathan R I Coleman
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
- NIHR Biomedical Research Centre for Mental Health, South London and Maudsley NHS Trust, London, SE5 8AF, UK
| | - Julien Bryois
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Héléna A Gaspar
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Philip R Jansen
- Department of Complex Trait Genetics, VU University, Center for Neurogenomics and Cognitive Research, Amsterdam, 1081 HV, The Netherlands
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, VU University, Center for Neurogenomics and Cognitive Research, Amsterdam, 1081 HV, The Netherlands
| | - Nathan Skene
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Robert Plomin
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Ana B Muñoz-Manchado
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Sten Linnarsson
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Greg Crawford
- Center for Genomic and Computational Biology, Duke University, Durham, NC, 27708, USA
- Department of Pediatrics, Medical Genetics Division, Duke University, Durham, NC, 27708, USA
| | - Jens Hjerling-Leffler
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-17177, Sweden
- Departments of Genetics, University of North Carolina, Chapel Hill, NC, 27599-7264, USA
| | - Danielle Posthuma
- Department of Complex Trait Genetics, VU University, Center for Neurogenomics and Cognitive Research, Amsterdam, 1081 HV, The Netherlands.
- Department of Clinical Genetics, VU University Medical Center (VUMC), Neuroscience Campus Amsterdam, Amsterdam, 1081 HV, The Netherlands.
| | - Gerome Breen
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK.
- NIHR Biomedical Research Centre for Mental Health, South London and Maudsley NHS Trust, London, SE5 8AF, UK.
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200
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A large-scale population study of early life factors influencing left-handedness. Sci Rep 2019; 9:584. [PMID: 30679750 PMCID: PMC6345846 DOI: 10.1038/s41598-018-37423-8] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 11/29/2018] [Indexed: 12/27/2022] Open
Abstract
Hand preference is a conspicuous variation in human behaviour, with a worldwide proportion of around 90% of people preferring to use the right hand for many tasks, and 10% the left hand. We used the large cohort of the UK biobank (~500,000 participants) to study possible relations between early life factors and adult hand preference. The probability of being left-handed was affected by the year and location of birth, likely due to cultural effects. In addition, hand preference was affected by birthweight, being part of a multiple birth, season of birth, breastfeeding, and sex, with each effect remaining significant after accounting for all others. Analysis of genome-wide genotype data showed that left-handedness was very weakly heritable, but shared no genetic basis with birthweight. Although on average left-handers and right-handers differed for a number of early life factors, all together these factors had only a minimal predictive value for individual hand preference.
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