TY  - JOUR
A1  - Mieth, Bettina
A1  - Kloft, Marius
A1  - Rodriguez, Juan Antonio
A1  - Sonnenburg, Soren
A1  - Vobruba, Robin
A1  - Morcillo-Suarez, Carlos
A1  - Farre, Xavier
A1  - Marigorta, Urko M.
A1  - Fehr, Ernst
A1  - Dickhaus, Thorsten
A1  - Blanchard, Gilles
A1  - Schunk, Daniel
A1  - Navarro, Arcadi
A1  - Müller, Klaus-Robert
T1  - Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies
T2  - Scientific reports
N2  - 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.
Y1  - 2016
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/44735
SN  - 2045-2322
VL  - 6
PB  - Nature Publ. Group
CY  - London
ER  -