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

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...

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Bibliographic Details
Published in:Bioinformatics Vol. 32; no. 10; pp. 1493 - 1501
Main Authors: Benner, Christian, Spencer, Chris C.A., Havulinna, Aki S., Salomaa, Veikko, Ripatti, Samuli, Pirinen, Matti
Format: Journal Article
Language:English
Published: England Oxford University Press 15.05.2016
Subjects:
Algorithms
Bioinformatics
Computational efficiency
Genome
Genome-Wide Association Study
Genomics
Mathematical models
Original Papers
Polymorphism, Single Nucleotide
Searching
Shotguns
Software
Software packages
Summaries
UNIX (operating system)
ISSN:1367-4803, 1367-4811, 1460-2059
Online Access:Get full text
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Summary: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 http://www.christianbenner.com. Contact: christian.benner@helsinki.fi or matti.pirinen@helsinki.fi
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Associate Editor: Oliver Stegle
ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btw018