Improved Detection of Common Variants Associated with Schizophrenia and Bipolar Disorder Using Pleiotropy-Informed Conditional False Discovery Rate

Ole A Andreassen,Andrew J Schork,Wesley K Thompson,The Psychiatric Genomics Consortium (Pgc) ,John R Kelsoe,Anders M Dale,Rahul S Desikan,J Cooper Roddey,Stephan Ripke,Pamela Sklar,Morten Mattingsdal,Chi-Hua Chen,Kenneth S Kendler,Srdjan Djurovic,Thomas Werge,Bipolar Disorder And Schizophrenia Working Groups ,Linda Mcevoy,Michael C O'Donovan,Dan Rujescu

doi:10.1371/journal.pgen.1003455

Abstract

Several lines of evidence suggest that genome-wide association studies (GWAS) have the potential to explain more of the “missing heritability” of common complex phenotypes. However, reliable methods to identify a larger proportion of single nucleotide polymorphisms (SNPs) that impact disease risk are currently lacking. Here, we use a genetic pleiotropy-informed conditional false discovery rate (FDR) method on GWAS summary statistics data to identify new loci associated with schizophrenia (SCZ) and bipolar disorders (BD), two highly heritable disorders with significant missing heritability. Epidemiological and clinical evidence suggest similar disease characteristics and overlapping genes between SCZ and BD. Here, we computed conditional Q–Q curves of data from the Psychiatric Genome Consortium (SCZ; n = 9,379 cases and n = 7,736 controls; BD: n = 6,990 cases and n = 4,820 controls) to show enrichment of SNPs associated with SCZ as a function of association with BD and vice versa with a corresponding reduction in FDR. Applying the conditional FDR method, we identified 58 loci associated with SCZ and 35 loci associated with BD below the conditional FDR level of 0.05. Of these, 14 loci were associated with both SCZ and BD (conjunction FDR). Together, these findings show the feasibility of genetic pleiotropy-informed methods to improve gene discovery in SCZ and BD and indicate overlapping genetic mechanisms between these two disorders.

Highlights

Converging evidence suggests that complex human phenotypes are influenced by numerous genes each explaining a small proportion of the variance [1]
We used a conditional false discovery rate approach for analysis of genomewide association studies (GWAS) data, exploiting ‘‘genetic pleiotropy’’ to increase discovery of common gene variants associated with schizophrenia and bipolar disorders
Leveraging the increased power from combining GWAS of two associated phenotypes, we found a striking overlap in polygenic signals, allowing for the discovery of several new common gene variants associated with bipolar disorder and schizophrenia that were not identified in the original analysis using traditional GWAS methods

Summary

Introduction

Converging evidence suggests that complex human phenotypes are influenced by numerous genes each explaining a small proportion of the variance [1]. Though thousands of single nucleotide polymorphisms (SNPs) have been identified by genomewide association studies (GWAS) [2,3], these SNPs fail to explain a large proportion of the heritability of most complex phenotypes studied. This is commonly referred to as the ‘missing heritability’ problem. Recent findings indicate that GWAS have the potential to explain a greater proportion of the heritability of common complex phenotypes [4,5,6], and more SNPs are likely to be identified in larger samples [7]. New analytical methods are needed to reliably identify a larger proportion of SNPs associated with complex diseases and phenotypes, since recruitment and genotyping of sufficiently large samples for existing methods may be impractical and prohibitively expensive

Methods

Results

Discussion

Conclusion