Motivation

Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation.

Results

In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution compared to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data.

Availability and implementation

The fastPAINTOR framework is implemented in the PAINTOR v3.0 package which is publicly available to the research community http://bogdan.bioinformatics.ucla.edu/software/paintor CONTACT: gkichaev@ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Recent studies have identified thousands of regions in the genome associated with chromatin modifications, which may in turn be affecting gene expression. Existing works have used heuristic methods to investigate the relationships between genome, epigenome, and gene expression, but, to our knowledge, none have explicitly modeled the chain of causality whereby genetic variants impact chromatin, which impacts gene expression. In this work we introduce a new hierarchical fine-mapping framework that integrates information across all three levels of data to better identify the causal variant and chromatin mark that are concordantly influencing gene expression. In simulations we show that our method is more accurate than existing approaches at identifying the causal mark influencing expression. We analyze empirical genetic, chromatin, and gene expression data from 65 African-ancestry and 47 European-ancestry individuals and show that many of the paths prioritized by our method are consistent with the proposed causal model and often lie in likely functional regions.