All SNPs Are Not Created Equal: Genome-Wide Association Studies Reveal a Consistent Pattern of Enrichment among Functionally Annotated SNPs

Abstract

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.

Recently evolved human-specific methylated regions are enriched in schizophrenia signals

“…We thus examined if these evolutionary DMRs are enriched for association with schizophrenia or other human traits. Using previously published methodologies (18,29,30) and publicly available GWAS datasets we systematically analyzed twelve diverse phenotypes to investigate the potential role of regions susceptible to epigenetic variation in the emergence of specific traits in the human lineage.…”

“…2, 2017; negative log 10 of a normal distribution. Leftwards deflections from the null line represent enrichment (29).…”

“…Enrichment was determined for genes located within 100kb of human DMRs, HARs, PARs and pHARs as described previously (17) Pathway analysis was performed using Ingenuity Pathway Analysis (IPA) from QIAGEN (www.qiagen.com/ingenuity, last accessed 26 th August 2016). The DMR SNPs driving the enrichment were determined from QQ plots using the method of Schork et al (29). These SNPs were then mapped to genes using LDsnpR (39 …”

“…We determined enrichment using QQ plots as described by Schork et al (29). In such plots the baseline is the null line of no difference between expected distribution of p-values and observed pvalues.…”

Joint analysis of functional genomic data and genome-wide association studies of 18 human traits

“…In this type of analysis, the researcher examines the most strongly associated SNPs in a GWAS and tests whether they fall disproportionately in specific types of genomic regions. These studies have found, for example, that SNPs identified in GWASs are enriched in protein-coding exons, in promoters, in UTRs, 2,4 and among those that influence gene expression. 5,6 Further, in some cases, SNPs associated with a trait are enriched in gene regulatory regions in specific cell types [7][8][9][10][11][12][13][14][15][16][17][18] or near genes expressed in specific cell types.…”