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

Maier, Robert, Moser, Gerhard, Chen, Guo-Bo, Ripke, Stephan, Cross-Disorder Working Group of the Psychiatric Genomics Consortium, Coryell, William, Potash, James B., Scheftner, William A., Shi, Jianxin, Weissman, Myrna M., Hultman, Christina M., Landen, Mikael, Levinson, Douglas F., Kendler, Kenneth S., Smoller, Jordan W., Wray, Naomi R. and Lee, S. Hong (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: 283-294. doi:10.1016/j.ajhg.2014.12.006


Author Maier, Robert
Moser, Gerhard
Chen, Guo-Bo
Ripke, Stephan
Cross-Disorder Working Group of the Psychiatric Genomics Consortium
Coryell, William
Potash, James B.
Scheftner, William A.
Shi, Jianxin
Weissman, Myrna M.
Hultman, Christina M.
Landen, Mikael
Levinson, Douglas F.
Kendler, Kenneth S.
Smoller, Jordan W.
Wray, Naomi R.
Lee, S. Hong
Title Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder
Journal name American Journal of Human Genetics   Check publisher's open access policy
ISSN 0002-9297
1537-6605
Publication date 2015-02-05
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.ajhg.2014.12.006
Open Access Status DOI
Volume 96
Issue 2
Start page 283
End page 294
Total pages 12
Place of publication Cambridge, MA, United States
Publisher Cell Press (Elsevier)
Collection year 2016
Language eng
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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2016 Collection
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 18 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 25 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 17 Feb 2015, 01:17:07 EST by System User on behalf of Queensland Brain Institute