GWAS have linked thousands of genomic regions to complex diseases and traits in humans and in model organisms. Fine-mapping causal variants in these regions is a high-dimensional variable selection problem complicated by strong correlations between the variables. We introduced a software package FINEMAP that implements an important solution to the problem: a stochastic search algorithm to circumvent computationally expensive exhaustive search. In all datasets we have tested, FINEMAP achieves similar accuracy as the exhaustive search but uses only a fraction of processing time. For example, fine-mapping a genomic region with 8612 SNPs allowing for at most five causal variants completes in less than 30 s using FINEMAP while the exhaustive search implemented in CAVIARBF is estimated to run over 300 years. Computationally efficient algorithms are a key to handle the ever-increasing amount of genetic variation captured by emerging sequencing studies as well as to scale up the analyses to whole chromosomes or even to whole genomes.
