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

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http://hdl.handle.net/10138/164645

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Benner , C , Spencer , C C A , Havulinna , A S , Salomaa , V , Ripatti , S & Pirinen , M 2016 , ' FINEMAP : efficient variable selection using summary data from genome-wide association studies ' , Bioinformatics , vol. 32 , no. 10 , pp. 1493-1501 . https://doi.org/10.1093/bioinformatics/btw018

Title: FINEMAP : efficient variable selection using summary data from genome-wide association studies
Author: Benner, Christian; Spencer, Chris C. A.; Havulinna, Aki S.; Salomaa, Veikko; Ripatti, Samuli; Pirinen, Matti
Contributor: University of Helsinki, Clinicum
University of Helsinki, Clinicum
University of Helsinki, Institute for Molecular Medicine Finland
Date: 2016-05-15
Language: eng
Number of pages: 9
Belongs to series: Bioinformatics
ISSN: 1367-4803
URI: http://hdl.handle.net/10138/164645
Abstract: 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.
Subject: CAUSAL VARIANTS
STATISTICS
IMPUTATION
RISK
3142 Public health care science, environmental and occupational health
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