Economics at your fingertips  

A random forest approach to capture genetic effects in the presence of population structure

Johannes Stephan, Oliver Stegle () and Andreas Beyer ()
Additional contact information
Oliver Stegle: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI)
Andreas Beyer: Cellular Networks and Systems Biology, University of Cologne, CECAD

Nature Communications, 2015, vol. 6, issue 1, 1-10

Abstract: Abstract The accurate mapping of causal variants in genome-wide association studies requires the consideration of both, confounding factors (for example, population structure) and nonlinear interactions between individual genetic variants. Here, we propose a method termed ‘mixed random forest’ that simultaneously accounts for population structure and captures nonlinear genetic effects. We test the model in simulation experiments and show that the mixed random forest approach improves detection power compared with established approaches. In an application to data from an outbred mouse population, we find that mixed random forest identifies associations that are more consistent with prior knowledge than competing methods. Further, our approach allows predicting phenotypes from genotypes with greater accuracy than any of the other methods that we tested. Our results show that approaches that simultaneously account for both, confounding due to population structure and epistatic interactions, are important to fully explain the heritable component of complex quantitative traits.

Date: 2015
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1038/ncomms8432

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

Page updated 2023-06-15
Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8432