Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

Robert Maier; Gerhard Moser; Guo-Bo Chen; Stephan Ripke; William Coryell; James B Potash; William A Scheftner; Jianxin Shi; Myrna M Weissman; Christina M Hultman; Mikael Landén; Douglas F Levinson; Kenneth S Kendler; Jordan W Smoller; Naomi R Wray; S Hong Lee; Cross-Disorder Working Group of the Psychiatric Genomics Consortium; Jonna Kuntsi; Peter McGuffin; Cathryn Lewis; Anne Farmer; Ian Craig; Gerome Breen; Philip Asherson; Allan Young; Edmund Sonuga-Barke

doi:10.1016/j.ajhg.2014.12.006

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

Robert Maier, Gerhard Moser, Guo-Bo Chen, Stephan Ripke, William Coryell, James B Potash, William A Scheftner, Jianxin Shi, Myrna M Weissman, Christina M Hultman, Mikael Landén, Douglas F Levinson, Kenneth S Kendler, Jordan W Smoller, Naomi R Wray, S Hong Lee, Cross-Disorder Working Group of the Psychiatric Genomics Consortium, Jonna Kuntsi, Peter McGuffin, Cathryn LewisAnne Farmer, Ian Craig, Gerome Breen, Philip Asherson, Allan Young, Edmund Sonuga-Barke

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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.

Original language	English
Pages (from-to)	283-94
Number of pages	12
Journal	American Journal of Human Genetics
Volume	96
Issue number	2
Early online date	29 Jan 2015
DOIs	https://doi.org/10.1016/j.ajhg.2014.12.006
Publication status	Published - 5 Feb 2015

Keywords

Bipolar Disorder
Depressive Disorder, Major
Genetic Testing
Genetics, Medical
Humans
Linear Models
Mental Disorders
Multifactorial Inheritance
Multivariate Analysis
Polymorphism, Single Nucleotide
Risk Assessment
Schizophrenia

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10.1016/j.ajhg.2014.12.006

Joint Analysis of Psychiatric Disorders_MAIER_Accepted8December2014Publishedonline29January2015_GOLD VoR (CC BY)Final published version, 271 KBLicence: CC BY

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Maier, R., Moser, G., Chen, G.-B., Ripke, S., Coryell, W., Potash, J. B., Scheftner, W. A., Shi, J., Weissman, M. M., Hultman, C. M., Landén, M., Levinson, D. F., Kendler, K. S., Smoller, J. W., Wray, N. R., Lee, S. H., Cross-Disorder Working Group of the Psychiatric Genomics Consortium, Kuntsi, J., McGuffin, P., ... Sonuga-Barke, E. (2015). Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. American Journal of Human Genetics, 96(2), 283-94. https://doi.org/10.1016/j.ajhg.2014.12.006

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title = "Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder",

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.",

keywords = "Bipolar Disorder, Depressive Disorder, Major, Genetic Testing, Genetics, Medical, Humans, Linear Models, Mental Disorders, Multifactorial Inheritance, Multivariate Analysis, Polymorphism, Single Nucleotide, Risk Assessment, Schizophrenia",

author = "Robert Maier and Gerhard Moser and Guo-Bo Chen and Stephan Ripke and William Coryell and Potash, {James B} and Scheftner, {William A} and Jianxin Shi and Weissman, {Myrna M} and Hultman, {Christina M} and Mikael Land{\'e}n and Levinson, {Douglas F} and Kendler, {Kenneth S} and Smoller, {Jordan W} and Wray, {Naomi R} and Lee, {S Hong} and {Cross-Disorder Working Group of the Psychiatric Genomics Consortium} and Jonna Kuntsi and Peter McGuffin and Cathryn Lewis and Anne Farmer and Ian Craig and Gerome Breen and Philip Asherson and Allan Young and Edmund Sonuga-Barke",

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doi = "10.1016/j.ajhg.2014.12.006",

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Maier, R, Moser, G, Chen, G-B, Ripke, S, Coryell, W, Potash, JB, Scheftner, WA, Shi, J, Weissman, MM, Hultman, CM, Landén, M, Levinson, DF, Kendler, KS, Smoller, JW, Wray, NR, Lee, SH, Cross-Disorder Working Group of the Psychiatric Genomics Consortium, Kuntsi, J , McGuffin, P , Lewis, C , Farmer, A , Craig, I , Breen, G , Asherson, P , Young, A & Sonuga-Barke, E 2015, 'Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder', American Journal of Human Genetics, vol. 96, no. 2, pp. 283-94. https://doi.org/10.1016/j.ajhg.2014.12.006

TY - JOUR

T1 - Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

AU - Maier, Robert

AU - Moser, Gerhard

AU - Chen, Guo-Bo

AU - Ripke, Stephan

AU - Coryell, William

AU - Potash, James B

AU - Scheftner, William A

AU - Shi, Jianxin

AU - Weissman, Myrna M

AU - Hultman, Christina M

AU - Landén, Mikael

AU - Levinson, Douglas F

AU - Kendler, Kenneth S

AU - Smoller, Jordan W

AU - Wray, Naomi R

AU - Lee, S Hong

AU - Cross-Disorder Working Group of the Psychiatric Genomics Consortium

AU - Kuntsi, Jonna

AU - McGuffin, Peter

AU - Lewis, Cathryn

AU - Farmer, Anne

AU - Craig, Ian

AU - Breen, Gerome

AU - Asherson, Philip

AU - Young, Allan

AU - Sonuga-Barke, Edmund

PY - 2015/2/5

Y1 - 2015/2/5

N2 - 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.

AB - 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.

KW - Bipolar Disorder

KW - Depressive Disorder, Major

KW - Genetic Testing

KW - Genetics, Medical

KW - Humans

KW - Linear Models

KW - Mental Disorders

KW - Multifactorial Inheritance

KW - Multivariate Analysis

KW - Polymorphism, Single Nucleotide

KW - Risk Assessment

KW - Schizophrenia

U2 - 10.1016/j.ajhg.2014.12.006

DO - 10.1016/j.ajhg.2014.12.006

M3 - Article

C2 - 25640677

SN - 0002-9297

VL - 96

SP - 283

EP - 294

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

IS - 2

ER -

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

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