1351
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Martin AR, Daly MJ, Robinson EB, Hyman SE, Neale BM. Predicting Polygenic Risk of Psychiatric Disorders. Biol Psychiatry 2019; 86:97-109. [PMID: 30737014 PMCID: PMC6599546 DOI: 10.1016/j.biopsych.2018.12.015] [Citation(s) in RCA: 142] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2018] [Revised: 11/18/2018] [Accepted: 12/08/2018] [Indexed: 12/27/2022]
Abstract
Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.
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Affiliation(s)
- Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Elise B Robinson
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Steven E Hyman
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
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1352
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Wetherill L, Lai D, Johnson EC, Anokhin A, Bauer L, Bucholz KK, Dick DM, Hariri AR, Hesselbrock V, Kamarajan C, Kramer J, Kuperman S, Meyers JL, Nurnberger JI, Schuckit M, Scott DM, Taylor RE, Tischfield J, Porjesz B, Goate AM, Edenberg HJ, Foroud T, Bogdan R, Agrawal A. Genome-wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward-related ventral striatum activity in African- and European-Americans. GENES, BRAIN, AND BEHAVIOR 2019; 18:e12580. [PMID: 31099175 PMCID: PMC6726116 DOI: 10.1111/gbb.12580] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/19/2019] [Accepted: 05/11/2019] [Indexed: 02/07/2023]
Abstract
Genetic influences on alcohol and drug dependence partially overlap, however, specific loci underlying this overlap remain unclear. We conducted a genome-wide association study (GWAS) of a phenotype representing alcohol or illicit drug dependence (ANYDEP) among 7291 European-Americans (EA; 2927 cases) and 3132 African-Americans (AA: 1315 cases) participating in the family-based Collaborative Study on the Genetics of Alcoholism. ANYDEP was heritable (h 2 in EA = 0.60, AA = 0.37). The AA GWAS identified three regions with genome-wide significant (GWS; P < 5E-08) single nucleotide polymorphisms (SNPs) on chromosomes 3 (rs34066662, rs58801820) and 13 (rs75168521, rs78886294), and an insertion-deletion on chromosome 5 (chr5:141988181). No polymorphisms reached GWS in the EA. One GWS region (chromosome 1: rs1890881) emerged from a trans-ancestral meta-analysis (EA + AA) of ANYDEP, and was attributable to alcohol dependence in both samples. Four genes (AA: CRKL, DZIP3, SBK3; EA: P2RX6) and four sets of genes were significantly enriched within biological pathways for hemostasis and signal transduction. GWS signals did not replicate in two independent samples but there was weak evidence for association between rs1890881 and alcohol intake in the UK Biobank. Among 118 AA and 481 EA individuals from the Duke Neurogenetics Study, rs75168521 and rs1890881 genotypes were associated with variability in reward-related ventral striatum activation. This study identified novel loci for substance dependence and provides preliminary evidence that these variants are also associated with individual differences in neural reward reactivity. Gene discovery efforts in non-European samples with distinct patterns of substance use may lead to the identification of novel ancestry-specific genetic markers of risk.
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Affiliation(s)
- Leah Wetherill
- Indiana University. Department of Medical and Molecular Genetics, Indiana University School of Medicine. Indianapolis, IN
| | - Dongbing Lai
- Indiana University. Department of Medical and Molecular Genetics, Indiana University School of Medicine. Indianapolis, IN
| | - Emma C. Johnson
- Washington University. Washington University School of Medicine, Department of Psychiatry. Saint Louis, MO. USA
| | - Andrey Anokhin
- Washington University. Washington University School of Medicine, Department of Psychiatry. Saint Louis, MO. USA
| | - Lance Bauer
- University of Connecticut. University of Connecticut School of Medicine, Department of Psychiatry. Farmington, CT
| | - Kathleen K. Bucholz
- Washington University. Washington University School of Medicine, Department of Psychiatry. Saint Louis, MO. USA
| | - Danielle M. Dick
- Virginia Commonwealth University. Department of Psychology & College Behavioral and Emotional Health Institute, Virginia Commonwealth University. Richmond, VA
| | - Ahmad R. Hariri
- Duke Institute for Brain Sciences, Dept. of Psychology, Duke University, Durham, NC
| | - Victor Hesselbrock
- University of Connecticut. University of Connecticut School of Medicine, Department of Psychiatry. Farmington, CT
| | - Chella Kamarajan
- SUNY. Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center. Brooklyn, NY
| | - John Kramer
- University of Iowa. University of Iowa Roy J and Lucille A Carver College of Medicine, Department of Psychiatry. Iowa City, IA
| | - Samuel Kuperman
- University of Iowa. University of Iowa Roy J and Lucille A Carver College of Medicine, Department of Psychiatry. Iowa City, IA
| | - Jacquelyn L. Meyers
- SUNY. Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center. Brooklyn, NY
| | - John I. Nurnberger
- Indiana University. Department of Psychiatry, Indiana University School of Medicine. Indianapolis, IN
| | - Marc Schuckit
- University of California San Diego. University of California San Diego, Department of Psychiatry. San Diego, CA
| | - Denise M. Scott
- Howard University, Departments of Pediatrics and Human Genetics, Washington, DC
| | | | | | - Bernice Porjesz
- SUNY. Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center. Brooklyn, NY
| | - Alison M. Goate
- Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY
| | - Howard J. Edenberg
- Indiana University. Department of Medical and Molecular Genetics, Indiana University School of Medicine. Indianapolis, IN
- Indiana University. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine. Indianapolis, IN
| | - Tatiana Foroud
- Indiana University. Department of Medical and Molecular Genetics, Indiana University School of Medicine. Indianapolis, IN
| | - Ryan Bogdan
- Washington University in Saint Louis, Department of Psychological and Brain Sciences, Saint Louis, MO, USA
| | - Arpana Agrawal
- Washington University. Washington University School of Medicine, Department of Psychiatry. Saint Louis, MO. USA
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1353
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McInnes G, Daneshjou R, Katsonis P, Lichtarge O, Srinivasan R, Rana S, Radivojac P, Mooney SD, Pagel KA, Stamboulian M, Jiang Y, Capriotti E, Wang Y, Bromberg Y, Bovo S, Savojardo C, Martelli PL, Casadio R, Pal LR, Moult J, Brenner SE, Altman R. Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges. Hum Mutat 2019; 40:1314-1320. [PMID: 31140652 DOI: 10.1002/humu.23825] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 05/07/2019] [Accepted: 05/27/2019] [Indexed: 01/14/2023]
Abstract
Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent because of the differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an area under the ROC curve of 0.65. Here, we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. In addition, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans.
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Affiliation(s)
- Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, California
| | - Roxana Daneshjou
- Department of Dermatology, Stanford School of Medicine, Stanford, California
| | - Panagiostis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.,Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, Texas.,Department of Pharmacology, Baylor College of Medicine, Houston, Texas.,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas
| | | | - Sadhna Rana
- Innovations Labs, Tata Consultancy Services, Hyderabad, India
| | - Predrag Radivojac
- Khoury College of Computer and Information Sciences, Northeastern University, Boston, Massachusetts
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington
| | - Kymberleigh A Pagel
- Department of Computer Science and Informatics, Indiana University, Bloomington, Indiana
| | - Moses Stamboulian
- Department of Computer Science and Informatics, Indiana University, Bloomington, Indiana
| | - Yuxiang Jiang
- Department of Computer Science and Informatics, Indiana University, Bloomington, Indiana
| | - Emidio Capriotti
- BioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
| | - Yanran Wang
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
| | - Samuele Bovo
- Department of Pharmacy and Biotechnology, Bologna Biocomputing Group, University of Bologna, Italy
| | - Castrense Savojardo
- Department of Pharmacy and Biotechnology, Bologna Biocomputing Group, University of Bologna, Italy
| | - Pier Luigi Martelli
- Department of Pharmacy and Biotechnology, Bologna Biocomputing Group, University of Bologna, Italy
| | - Rita Casadio
- Department of Pharmacy and Biotechnology, Bologna Biocomputing Group, University of Bologna, Italy.,Institute of Biomembrane and Bioenergetics, Consiglio Nazionale delle Ricerche, Bari, Italy
| | - Lipika R Pal
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland
| | - John Moult
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.,Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Steven E Brenner
- Department of Plant and Microbial biology, University of California Berkeley, Berkeley, California
| | - Russ Altman
- Departments of Bioengineering, Biomedical Data Science, Genetics, and Medicine, Stanford University, Stanford, California
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1354
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Kerminen S, Martin AR, Koskela J, Ruotsalainen SE, Havulinna AS, Surakka I, Palotie A, Perola M, Salomaa V, Daly MJ, Ripatti S, Pirinen M. Geographic Variation and Bias in the Polygenic Scores of Complex Diseases and Traits in Finland. Am J Hum Genet 2019; 104:1169-1181. [PMID: 31155286 PMCID: PMC6562021 DOI: 10.1016/j.ajhg.2019.05.001] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 04/29/2019] [Indexed: 12/12/2022] Open
Abstract
Polygenic scores (PSs) are becoming a useful tool to identify individuals with high genetic risk for complex diseases, and several projects are currently testing their utility for translational applications. It is also tempting to use PSs to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how the population genetic properties of the training and target samples affect the geographic distribution of PSs. Here, we evaluate geographic differences, and related biases, of PSs in Finland in a geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PSs for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waist-hip ratio (WHR), body-mass index (BMI), and height, but not for Crohn disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PSs and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulations of geographic differences for CAD, WHR, BMI, and height, suggesting bias arising from the population's genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PSs are for small biases even within relatively homogeneous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PSs is essential for translational applications of PSs.
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Affiliation(s)
- Sini Kerminen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA
| | - Jukka Koskela
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland
| | - Sanni E Ruotsalainen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland; National Institute of Health and Welfare, Helsinki 00271, Finland
| | - Ida Surakka
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland; Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Markus Perola
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland; National Institute of Health and Welfare, Helsinki 00271, Finland
| | - Veikko Salomaa
- National Institute of Health and Welfare, Helsinki 00271, Finland
| | - Mark J Daly
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Helsinki Institute for Information Technology and Department of Mathematics and Statistics, University of Helsinki, Helsinki 00014, Finland.
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1355
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1356
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Abstract
Externalizing problems generally refer to a constellation of behaviors and/or disorders characterized by impulsive action and behavioral disinhibition. Phenotypes on the externalizing spectrum include psychiatric disorders, nonclinical behaviors, and personality characteristics (e.g. alcohol use disorders, other illicit substance use, antisocial behaviors, risky sex, sensation seeking, among others). Research using genetic designs including latent designs from twin and family data and more recent designs using genome-wide data reveal that these behaviors and problems are genetically influenced and largely share a common genetic etiology. Large-scale gene-identification efforts have started to identify robust associations between genetic variants and these phenotypes. However, there is still considerable work to be done. This chapter provides an overview of the current state of research into the genetics of behaviors and disorders on the externalizing spectrum.
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Affiliation(s)
- Peter B Barr
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA. .,Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA. .,College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA.
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