genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs
Recent results indicate that genome-wide association studies (GWAS) have the potential to explain much of the heritability of common complex phenotypes, but methods are lacking to reliably identify the remaining associated single nucleotide polymorphisms (SNPs). We applied stratified False Discovery Rate (sFDR) methods to leverage genic enrichment in GWAS summary statistics data to uncover new loci likely to replicate in independent samples. Specifically, we use linkage disequilibrium-weighted annotations for each SNP in combination with nominal p-values to estimate the True Discovery Rate (TDR = 1-FDR) for strata determined by different genic categories. We show a consistent pattern of enrichment of polygenic effects in specific annotation categories across diverse phenotypes, with the greatest enrichment for SNPs tagging regulatory and coding genic elements, little enrichment in introns, and negative enrichment for intergenic SNPs. Stratified enrichment directly leads to increased TDR for a given p-value, mirrored by increased replication rates in independent samples. We show this in independent Crohn's disease GWAS, where we find a hundredfold variation in replication rate across genic categories. Applying a well-established sFDR methodology we demonstrate the utility of stratification for improving power of GWAS in complex phenotypes, with increased rejection rates from 20% in height to 300% in schizophrenia with traditional FDR and sFDR both fixed at 0.05. Our analyses demonstrate an inherent stratification among GWAS SNPs with important conceptual implications that can be leveraged by statistical methods to improve the discovery of loci.
P L O S Genetics (online), 2013, Vol 9, Issue 4
Biostatistics; Biology; Human genetics; Genomics; Genome-wide association studies; Genome scans; Genome analysis tools; Genetics; Genetic association studies; Mathematics; Research Article; Statistical methods; Statistics; Functional genomics; Computational biology; Population Genetics; GC; readnow; snp; GWAS; genome_wide_association_studies; snps; prior; Methodology; annotation; 9-TOP; Biostats; gwas; GO; SNP; snp_variation; AID; inflation; genomics; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't