Andreassen, Ole A3; Djurovic, Srdjan3; Thompson, Wesley K3; Schork, Andrew J3; Kendler, Kenneth S3; O'Donovan, Michael C3; Rujescu, Dan3; Werge, Thomas4; van de Bunt, Martijn3; Morris, Andrew P3; McCarthy, Mark I3; Roddey, J Cooper3; McEvoy, Linda K3; Desikan, Rahul S3; Dale, Anders M3
1 Graduate School of Health and Medical Sciences, Faculty of Health and Medical Sciences, Københavns Universitet2 Section of Neurology, Psychiatry and Sensory Sciences, Department of Clinical Medicine, Faculty of Health and Medical Sciences, Københavns Universitet3 unknown4 Section of Neurology, Psychiatry and Sensory Sciences, Department of Clinical Medicine, Faculty of Health and Medical Sciences, Københavns Universitet
Several lines of evidence suggest that genome-wide association studies (GWASs) have the potential to explain more of the "missing heritability" of common complex phenotypes. However, reliable methods for identifying a larger proportion of SNPs are currently lacking. Here, we present a genetic-pleiotropy-informed method for improving gene discovery with the use of GWAS summary-statistics data. We applied this methodology to identify additional loci associated with schizophrenia (SCZ), a highly heritable disorder with significant missing heritability. Epidemiological and clinical studies suggest comorbidity between SCZ and cardiovascular-disease (CVD) risk factors, including systolic blood pressure, triglycerides, low- and high-density lipoprotein, body mass index, waist-to-hip ratio, and type 2 diabetes. Using stratified quantile-quantile plots, we show enrichment of SNPs associated with SCZ as a function of the association with several CVD risk factors and a corresponding reduction in false discovery rate (FDR). We validate this "pleiotropic enrichment" by demonstrating increased replication rate across independent SCZ substudies. Applying the stratified FDR method, we identified 25 loci associated with SCZ at a conditional FDR level of 0.01. Of these, ten loci are associated with both SCZ and CVD risk factors, mainly triglycerides and low- and high-density lipoproteins but also waist-to-hip ratio, systolic blood pressure, and body mass index. Together, these findings suggest the feasibility of using genetic-pleiotropy-informed methods for improving gene discovery in SCZ and identifying potential mechanistic relationships with various CVD risk factors.
American Journal of Human Genetics, 2013, Vol 92, Issue 2, p. 197-209