Ming Fang | bioRxiv Science

bioRxiv2016

BayesFM: a software program to fine-map multiple causative variants in GWAS identified risk loci.

AbsractWe herein describe a new method to fine-map GWAS-identified risk loci based on the Bayesian Least Absolute Shrinkage Selection Operator (LASSO) combined with a Monte Carlo Markov Chain (MCMC) approach, and corresponding software package (BayesFM). We characterize the performances of BayesFM using simulated data, showing that it outperforms standard forward selection both in terms of sensitivity and specificity. We apply the method to the NOD2 locus, a well-established risk locus for Crohns disease, in which we identify 13 putative independent signals.

Bioinformatics↗

bioRxiv2015

Association mapping of inflammatory bowel disease loci to single variant resolution

Inflammatory bowel disease (IBD) is a chronic gastrointestinal inflammatory disorder that affects millions worldwide. Genome-wide association studies (GWAS) have identified 200 IBD-associated loci, but few have been conclusively resolved to specific functional variants. Here we report fine-mapping of 94 IBD loci using high-density genotyping in 67,852 individuals. Of the 139 independent associations identified in these regions, 18 were pinpointed to a single causal variant with >95% certainty, and an additional 27 associations to a single variant with >50% certainty. These 45 variants are significantly enriched for protein-coding changes (n=13), direct disruption of transcription factor binding sites (n=3) and tissue specific epigenetic marks (n=10), with the latter category showing enrichment in specific immune cells among associations stronger in CD and gut mucosa among associations stronger in UC. The results of this study suggest that high-resolution, fine-mapping in large samples can convert many GWAS discoveries into statistically convincing causal variants, providing a powerful substrate for experimental elucidation of disease mechanisms.

Genetics↗