FINEMAP: efficient variable selection using summary data from genome-wide association studies

Bioinformatics. 2016 May 15;32(10):1493-501. doi: 10.1093/bioinformatics/btw018. Epub 2016 Jan 14.


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 and emerging sequencing projects.

Availability and implementation: FINEMAP v1.0 is freely available for Mac OS X and Linux at

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MeSH terms

  • Algorithms
  • Genome
  • Genome-Wide Association Study*
  • Genomics
  • Polymorphism, Single Nucleotide
  • Software