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

Christian Benner, Chris C.A. Spencer, Samuli Ripatti, Matti Pirinen
doi: https://doi.org/10.1101/027342
Christian Benner
1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
2Department of Public Health, University of Helsinki, Helsinki, Finland
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Chris C.A. Spencer
3Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
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Samuli Ripatti
1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
2Department of Public Health, University of Helsinki, Helsinki, Finland
4Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK
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Matti Pirinen
1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
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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.

Availability FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com.

Contact: christian.benner{at}helsinki.fi, matti.pirinen{at}helsinki.fi

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 22, 2015.
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FINEMAP: Efficient variable selection using summary data from genome-wide association studies
Christian Benner, Chris C.A. Spencer, Samuli Ripatti, Matti Pirinen
bioRxiv 027342; doi: https://doi.org/10.1101/027342
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FINEMAP: Efficient variable selection using summary data from genome-wide association studies
Christian Benner, Chris C.A. Spencer, Samuli Ripatti, Matti Pirinen
bioRxiv 027342; doi: https://doi.org/10.1101/027342

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