FINEMAP: efficient variable selection using summary data from genome-wide association studies
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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 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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Statistical fine-mapping supported the replicated lead variants to be causal at their respective loci (rs10191329 PIP = 0.75, rs149097173 PIP = 0.95).
“Statistical fine-mapping16 supported the replicated lead variants to be causal at their respective loci (rs10191329 posterior inclusion probability (PIP) = 0.75, rs149097173 PIP = 0.95; Supplementary Fig. 5).”
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- Authors
- Benner, C., Spencer, C. C. A., Havulinna, A. S., Salomaa, V., Ripatti, S., & Pirinen, M.
- Affiliations
- University of Helsinki (FI)
- Centre for Human Genetics (GB)
- Finnish Institute for Health and Welfare (FI)
- Corresponding author
- Benner, Christian
- Pirinen, Matti
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-01-14
- Volume
- 32
- Issue
- 10
- Pages
- 1493-1501
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC4866522
- OpenAlex
- W2297334215
- Cited by
- 815
- References
- 1
- Citations by year
- 2026: 118 · 2025: 122 · 2024: 113 · 2023: 94 · 2022: 90 · 2021: 87 · 2020: 73 · 2019: 54 · 2018: 38 · 2017: 13 · 2016: 11 · 2012: 1
- FWCI
- 31.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Työväentutkimus Vuosikirja (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) (submittedVersion)
- Funders
- Foundation for Cardiovascular Research
- Wellcome Trust (139635, EU-FP7-, 261433, 097364/Z/11/Z, /Z/11/Z, 201413, WTCCC2, 097364)
- Academy of Finland (255847, 288509, 251217 and 255847, 257654, 139635, 251217)
- China Scholarship Council
- Sydäntutkimussäätiö
- Sigrid Juséliuksen Säätiö
- Terveyden ja hyvinvoinnin laitos
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- fine mapping, feature selection, summary data, genome-wide association study, causal variants, complex diseases, computational efficiency
- MeSH terms
- Algorithms, Software, Genome, Polymorphism, Single Nucleotide, Genomics, Genome-Wide Association Study
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