Exploiting pleiotropy to enhance variant discovery with functional false discovery rates

The cost of acquiring participants for genome-wide association studies (GWAS) can limit sample sizes and inhibit discovery of genetic variants. We introduce the surrogate functional false discovery rate (sfFDR) framework which integrates summary statistics of related traits to increase power. The sfFDR framework provides estimates of FDR quantities such as the functional local FDR and q-value, and uses these estimates to derive a functional p-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization). Compared to a standard analysis, sfFDR substantially increased power (equivalent to a 60% increase in sample size) in a study of obesity-related traits from the UK Biobank, and discovered eight additional lead SNPs near genes linked to immune-related responses in a rare disease GWAS of eosinophilic granulomatosis with polyangiitis. Collectively, these results highlight the utility of exploiting related traits in both small and large studies.