Improved Detection of Common Variants Associated with Schizophrenia by Leveraging Pleiotropy with Cardiovascular-Disease Risk Factors

详细信息    查看全文

• 作者：Ole A. Andreassen¹ ; ² ; ³ ; ^{o.a.andreassen@medisin.uio.no} ; Srdjan Djurovic¹ ; ² ; Wesley K. Thompson³ ; Andrew J. Schork⁴ ; ⁵ ; ⁶ ; Kenneth S. Kendler⁷ ; Michael C. O&rsquo ; Donovan⁸ ; Dan Rujescu⁹ ; Thomas Werge¹⁰ ; Martijn van de Bunt¹¹ ; Andrew P. Morris¹¹ ; Mark I. McCarthy¹¹ ; International Consortium for Blood Pressure GWAS
• 刊名：The American Journal of Human Genetics
• 出版年：2013
• 出版时间：7 February, 2013
• 年：2013
• 卷：92
• 期：2
• 页码：197-209
• 全文大小：1006 K

文摘

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.