Detecting epistasis via Markov bases

TL;DR

This paper introduces a novel two-stage method for detecting epistasis in genome-wide data by extending Fisher's exact test with Markov bases, successfully identifying interacting loci in simulated and real dog data.

Contribution

It presents an innovative approach combining single-locus and multiway interaction searches using Markov bases, outperforming existing methods in detecting epistasis.

Findings

Successfully detects epistasis in simulated data.

Identifies four pairs of SNPs associated with hair length in dogs.

Outperforms logistic regression and Bayesian methods in certain scenarios.

Abstract

Rapid research progress in genotyping techniques have allowed large genome-wide association studies. Existing methods often focus on determining associations between single loci and a specific phenotype. However, a particular phenotype is usually the result of complex relationships between multiple loci and the environment. In this paper, we describe a two-stage method for detecting epistasis by combining the traditionally used single-locus search with a search for multiway interactions. Our method is based on an extended version of Fisher's exact test. To perform this test, a Markov chain is constructed on the space of multidimensional contingency tables using the elements of a Markov basis as moves. We test our method on simulated data and compare it to a two-stage logistic regression method and to a fully Bayesian method, showing that we are able to detect the interacting loci when other methods fail to do so. Finally, we apply our method to a genome-wide data set consisting of 685 dogs and identify epistasis associated with canine hair length for four pairs of SNPs.