PT - JOURNAL ARTICLE AU - Christian Benner AU - Chris C.A. Spencer AU - Samuli Ripatti AU - Matti Pirinen TI - FINEMAP: Efficient variable selection using summary data from genome-wide association studies AID - 10.1101/027342 DP - 2015 Jan 01 TA - bioRxiv PG - 027342 4099 - http://biorxiv.org/content/early/2015/09/22/027342.short 4100 - http://biorxiv.org/content/early/2015/09/22/027342.full AB - Motivation The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive.Results We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies.Availability FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com.Contact: christian.benner{at}helsinki.fi, matti.pirinen{at}helsinki.fi