MAIER, R. Moser, G., Chen, G-B., Ripke, S., Cross-Disorder Group of the Psychiatric Genomics Consortium (PGC-CDG), Coryell, W., Potash, J. B., Scheftner, W. A., Shi, J., Weissman, M. M., Hultman, C. M., Landen, M., Levinson, D..F,; Kendler, K. S., Smoller, J. W., Wray, N. R., Lee, S. H.,

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. American Journal of Human Genetics, 96, 283-294. .(request a copy) open access - 364 collaborators including Oades,R.D

Introduction: 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.

Method: 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 andsimultaneously evaluates individual risk for each disorder.


1/ We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder & major depressive disorder in the discovery as well as in independent validation datasets.

2/ By grouping SNPs based on genome annotation & fitting multiple random effects, we show that the prediction accuracy could be further improved.

3/ 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, & 76% for major depressive disorders using single trait models.


a) Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible & powerful tool to maximize prediction accuracy.

b) 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.