PubMed ID:
20505004
Public Release Type:
Journal
Publication Year: 2010
Affiliation: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Maria-Goeppert-Strasse 1, 23562 Lübeck, Germany.
DOI:
https://doi.org/10.1093/bioinformatics/btq257
Authors:
König IR,
Schwarz DF,
Ziegler A
Studies:
Inflammatory Bowel Disease Genetics
Genome-wide association (GWA) studies have proven to be a successful approach for helping unravel the genetic basis of complex genetic diseases. However, the identified associations are not well suited for disease prediction, and only a modest portion of the heritability can be explained for most diseases, such as Type 2 diabetes or Crohn's disease. This may partly be due to the low power of standard statistical approaches to detect gene-gene and gene-environment interactions when small marginal effects are present. A promising alternative is Random Forests, which have already been successfully applied in candidate gene analyses. Important single nucleotide polymorphisms are detected by permutation importance measures. To this day, the application to GWA data was highly cumbersome with existing implementations because of the high computational burden.