Using biological networks to search for interacting loci in genomewide association studies

TY - JOUR

T1 - Using biological networks to search for interacting loci in genomewide association studies

AU - Emily, Mathieu

AU - Mailund, Thomas

AU - Hein, Jotun

AU - Schauser, Leif

AU - Schierup, Mikkel Heide

N1 - Paper id:: doi: 10.1038/ejhg.2009.15

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - association studies

KW - genome-wide scan

KW - epistasis

KW - biological network

U2 - 10.1038/ejhg.2009.15

DO - 10.1038/ejhg.2009.15

M3 - Journal article

C2 - 19277065

SN - 1018-4813

JO - European Journal of Human Genetics

JF - European Journal of Human Genetics

ER -