English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/150474
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Title

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

AuthorsMieth, Bettina; Rodríguez-Pérez, Juan Antonio ; Morcillo-Suárez, Carlos ; Farré, Xavier; Navarro, Arcadi ; Müller, Klaus-Robert
Issue Date28-Nov-2016
PublisherNature Publishing Group
CitationScientific Reports 6: 36671 (2016)
AbstractThe 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.
DescriptionMieth, Bettina et al.
Publisher version (URL)http://dx.doi.org/10.1038/srep36671
URIhttp://hdl.handle.net/10261/150474
DOI10.1038/srep36671
ISSN2045-2322
Appears in Collections:(IBE) Artículos
Files in This Item:
File Description SizeFormat 
srep36671.pdf1,09 MBAdobe PDFThumbnail
View/Open
Show full item record
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.