We introduce FINEMAP, a novel software package to improve the performance of GWAS summary data based fine-mapping. The statistical model of FINEMAP is similar to CAVIAR and CAVIARBF while the important difference is the computational algorithm. FINEMAP uses a Shotgun Stochastic Search (SSS) algorithm (Hans et al., 2007) that explores the vast space of causal configurations by concentrating efforts on the configurations with non-negligible probability. We compare FINEMAP with the exhaustive search algorithm implemented in CAVIARBF. The comparisons to two other GWAS summary data based fine-mapping methods CAVIAR and PAINTOR are not shown in this paper since CAVIARBF is more efficient but equally accurate as CAVIAR and more accurate than PAINTOR without annotation information (Chen et al., 2015). In this paper we show that FINEMAP is thousands of times faster than CAVIARBF while still providing similar accuracy in the examples where CAVIARBF can be applied.FINEMAP is more accurate than CAVIARBF when the number of causal variants in CAVIARBF needs to be restricted for computational reasons.
