Using biological networks to search for interacting loci in genome-wide association studies.
Genome-wide association studies have identified a large number of single-nucleotide polymorphisms (SNPs) that individually predispose to diseases. However, many genetic risk factors remain unaccounted for. Proteins coded by genes interact in the cell, and it is most likely that certain variants mainly affect the phenotype in combination with other variants, termed epistasis. An exhaustive search for epistatic effects is computationally demanding, as several billions of SNP pairs exist for typical genotyping chips. In this study, the experimental knowledge on biological networks is used to narrow the search for two-locus epistasis. We provide evidence that this approach is computationally feasible and statistically powerful. By applying this method to the Wellcome Trust Case-Control Consortium data sets, we report four significant cases of epistasis between unlinked loci, in susceptibility to Crohn's disease, bipolar disorder, hypertension and rheumatoid arthritis.
Emily
Mathieu
M
Bioinformatics Research Center, University of Aarhus, C. F. Møllers Alle, Aarhus C, Denmark. mathieu.emily@uhb.fr
Mailund
Thomas
T
Hein
Jotun
J
Schauser
Leif
L
Schierup
Mikkel Heide
MH
D000483:Alleles; D001172:Arthritis, Rheumatoid / Q000235:genetics; D001714:Bipolar Disorder / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D003424:Crohn Disease / Q000235:genetics; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D006973:Hypertension / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D025941:Protein Interaction Mapping; D012307:Risk Factors