Comput Biol Chem - Fast detection of high-order epistatic interactions in genome-wide association studies using information theoretic measure.

Tópicos

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Resumo

There are many algorithms for detecting epistatic interactions in GWAS. However, most of these algorithms are applicable only for detecting two-locus interactions. Some algorithms are designed to detect only two-locus interactions from the beginning. Others do not have limits to the order of interactions, but in practice take very long time to detect higher order interactions in real data of GWAS. Even the better ones take days to detect higher order interactions in WTCCC data. We propose a fast algorithm for detection of high order epistatic interactions in GWAS. It runs k-means clustering algorithm on the set of all SNPs. Then candidates are selected from each cluster. These candidates are examined to find the causative SNPs of k-locus interactions. We use mutual information from information theory as the measure of association between genotypes and phenotypes. We tested the power and speed of our method on extensive sets of simulated data. The results show that our method has more or equal power, and runs much faster than previously reported methods. We also applied our algorithm on each of seven diseases in WTCCC data to analyze up to 5-locus interactions. It takes only a few hours to analyze 5-locus interactions in one dataset. From the results we make some interesting and meaningful observations on each disease in WTCCC data. In this study, a simple yet powerful two-step approach is proposed for fast detection of high order epistatic interaction. Our algorithm makes it possible to detect high order epistatic interactions in GWAS in a matter of hours on a PC.

Resumo Limpo

mani algorithm detect epistat interact gwas howev algorithm applic detect twolocus interact algorithm design detect twolocus interact begin other limit order interact practic take long time detect higher order interact real data gwas even better one take day detect higher order interact wtccc data propos fast algorithm detect high order epistat interact gwas run kmean cluster algorithm set snps candid select cluster candid examin find causat snps klocus interact use mutual inform inform theori measur associ genotyp phenotyp test power speed method extens set simul data result show method equal power run much faster previous report method also appli algorithm seven diseas wtccc data analyz locus interact take hour analyz locus interact one dataset result make interest meaning observ diseas wtccc data studi simpl yet power twostep approach propos fast detect high order epistat interact algorithm make possibl detect high order epistat interact gwas matter hour pc

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