Mailund on the Internet

We just got another paper out — well, out in “online access” — about association mapping for interacting genes.

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

M. Emily et al, European Journal of Human Genetics

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.

This was work we did in the PolyGene project, where one of the problems we considered was detecting disease association when the association is caused by interacting genes.  Testing all combinations of markers is computationally infeasible, to say nothing of multiple testing correction, so we consider only pair-wise interaction.

Even when only considering pairs, handling all of them can be a problem.  If you have half a million markers, you have about 125 billion pairs.  Reducing the number of pairs to consider thus might be worth doing.

One option is to check only pairs where one or both of the individual markers show some association signal.  Another option is to use knowledge about interactions and only consider pairs of markers that a priori are known to interact.

The latter is what we do in this paper.  We use an interaction network to decide which markers are candidates for interaction, and then we test only those.

–
Emily, M., Mailund, T., Hein, J., Schauser, L., & Schierup, M. (2009). Using biological networks to search for interacting loci in genome-wide association studies European Journal of Human Genetics DOI: 10.1038/ejhg.2009.15

74-92=-18