RT Journal Article
SR Electronic
T1 BayesFM: a software program to fine-map multiple causative variants in GWAS identified risk loci
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 067801
DO 10.1101/067801
A1 Ming Fang
A1 Michel Georges
YR 2016
UL http://biorxiv.org/content/early/2016/08/04/067801.abstract
AB We 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 Crohn’s disease, in which we identify 13 putative independent signals.