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Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies
- The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e.The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.…
Author details: | Bettina Mieth, Marius Kloft, Juan Antonio Rodriguez, Soren Sonnenburg, Robin Vobruba, Carlos Morcillo-Suarez, Xavier Farre, Urko M. Marigorta, Ernst Fehr, Thorsten Dickhaus, Gilles BlanchardGND, Daniel Schunk, Arcadi Navarro, Klaus-Robert Müller |
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DOI: | https://doi.org/10.1038/srep36671 |
ISSN: | 2045-2322 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/27892471 |
Title of parent work (English): | Scientific reports |
Publisher: | Nature Publ. Group |
Place of publishing: | London |
Publication type: | Article |
Language: | English |
Year of first publication: | 2016 |
Publication year: | 2016 |
Release date: | 2020/03/22 |
Volume: | 6 |
Number of pages: | 14 |
Funding institution: | ERC [ERC-2011-AdG 295642-FEP]; German National Science Foundation (DFG) [MU 987/6-1, RA 1894/1-1, DI 1723/3-1, SCHU 2828/2-1, FOR 1735]; FP7-ICT Programme of the European Community, under the PASCAL2 Network of Excellence; German Research Foundation (DFG) [KL 2698/2-1]; Federal Ministry of Science and Education (BMBF) [031L0023A, 031B0187B]; Spanish Multiple Sclerosis Network (REEM), of the Instituto de Salud Carlos III [RD12/0032/0011]; Spanish National Institute for Bioinformatics [PT13/0001/0026]; Spanish Government [BFU2012-38236]; FEDER; European Union [634143]; Ministry of Education, Science, and Technology, through the National Research Foundation of Korea [R31-10008] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
Peer review: | Referiert |