Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder
Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk...
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| Published in | American journal of human genetics Vol. 96; no. 2; pp. 283 - 294 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
05.02.2015
Cell Press Elsevier (Cell Press) Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk. |
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| AbstractList | Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk. Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk. |
| Author | Caesar, Sian Holsboer, Florian Meier, Sandra Craig, David W. Duan, Jubao Konte, Bettina Strohmaier, Jana Scheftner, William A. Henders, Anjali K. Wray, Naomi R. Flickinger, Matthew Breuer, René Bloss, Cinnamon S. Montgomery, Grant W. Maier, Robert Puri, Vinay Smoller, Jordan W. Anney, Richard Gurling, Hugh Hoefels, Susanne Middleton, Lefkos Collier, David A. Murtha, Michael Myin-Germeys, Inez Långström, Niklas Datta, Susmita De Haan, Lieuwe Medland, Sarah E. Scott, Laura J. Edenberg, Howard J. Asherson, Philip Kendler, Kenneth S. Herms, Stefan Loo, Sandra K. Potash, James B. Hallmayer, Joachim Berrettini, Wade Quinn, Emma M. Mattheisen, Manuel Klei, Lambertus Propping, Peter Andreassen, Ole A. Hoogendijk, Witte J. Degenhardt, Franziska Lathrop, Mark Malhotra, Anil K. Quested, Digby J. Leboyer, Marion Pericak-Vance, Margaret A. Craddock, Nicholas Ebstein, Richard P. Nimgaonkar, Vishwajit Pato, Carlos N. McMahon, Francis J. Young, Allan H. Sklar, Pamela Smalley, Susan L. Hultman, Christina M. Lichtenstein, Paul Nolen, Willem A. Ledbetter, David H |
| AuthorAffiliation | 4 Department of Psychiatry, Rush University Medical Center, Chicago, IL 60612, USA 11 Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA 2 Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA 8 Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the Gothenburg University, 413 45 Gothenburg, Sweden 3 Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA 6 Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, NY 10032, USA 9 Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA 7 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 77, Sweden 5 Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA 1 The Queensland Brain Institute, The University of Queensland, Brisb |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25640677$$D View this record in MEDLINE/PubMed https://hal.science/hal-01541309$$DView record in HAL https://gup.ub.gu.se/publication/213844$$DView record from Swedish Publication Index http://kipublications.ki.se/Default.aspx?queryparsed=id:130658959$$DView record from Swedish Publication Index |
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| SubjectTerms | AVERAGE Bipolar disorder Bipolar Disorder - genetics Clinical Medicine Comparative analysis Correlation analysis Depressive Disorder, Major - genetics GENETIC RISK Genetic Testing - methods Genetics Genetics & Heredity Genetics, Medical - methods GENOME-WIDE ASSOCIATION Humans IDENTIFICATION INFORMATION Klinisk medicin Life Sciences Linear Models LOCI Medicin och hälsovetenskap Mental depression Mental Disorders - genetics Multifactorial Inheritance - genetics Multivariate Analysis Neurons and Cognition Polymorphism, Single Nucleotide - genetics POPULATION RESTRICTED MAXIMUM-LIKELIHOOD Risk Assessment - methods Risk factors Schizophrenia Schizophrenia - genetics SELECTION TRAITS |
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| Title | Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder |
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