--- res: bibo_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 specic 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 single nucleotide polymorphisms (SNPs).@eng bibo_authorlist: - foaf_Person: foaf_givenName: Anna foaf_name: 'Malaspinas, Anna-Sapfo ' foaf_surname: Malaspinas - foaf_Person: foaf_givenName: Caroline foaf_name: Caroline Uhler foaf_surname: Uhler foaf_workInfoHomepage: http://www.librecat.org/personId=49ADD78E-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-7008-0216 bibo_doi: http://dx.doi.org/10.18409/jas.v2i1.27 bibo_issue: '1' bibo_volume: 2 dct_date: 2011^xs_gYear dct_publisher: Public Knowledge Project@ dct_title: Detecting epistasis via Markov bases@ ...