RT Journal Article SR Electronic T1 Improved methods for multi-trait fine mapping of pleiotropic risk loci JF bioRxiv FD Cold Spring Harbor Laboratory SP 054684 DO 10.1101/054684 A1 Gleb Kichaev A1 Megan Roytman A1 Ruth Johnson A1 Eleazar Eskin A1 Sara Lindstroem A1 Peter Kraft A1 Bogdan Pasaniuc YR 2016 UL http://biorxiv.org/content/early/2016/05/21/054684.1.abstract AB 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 biologicaly 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. 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 relative 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.