{"volume":2,"page":"36 - 53","month":"01","date_updated":"2021-01-12T07:40:05Z","publication":"Journal of Algebraic Statistics","date_created":"2018-12-11T12:00:34Z","_id":"2961","quality_controlled":0,"title":"Detecting epistasis via Markov bases","main_file_link":[{"url":"http://arxiv.org/abs/1006.4929","open_access":"1"}],"date_published":"2011-01-01T00:00:00Z","author":[{"last_name":"Malaspinas","full_name":"Malaspinas, Anna-Sapfo ","first_name":"Anna"},{"last_name":"Uhler","full_name":"Caroline Uhler","first_name":"Caroline","id":"49ADD78E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-7008-0216"}],"status":"public","day":"01","year":"2011","type":"journal_article","citation":{"short":"A. Malaspinas, C. Uhler, Journal of Algebraic Statistics 2 (2011) 36–53.","apa":"Malaspinas, A., &#38; Uhler, C. (2011). Detecting epistasis via Markov bases. <i>Journal of Algebraic Statistics</i>. Public Knowledge Project. <a href=\"http://dx.doi.org/10.18409/jas.v2i1.27\">http://dx.doi.org/10.18409/jas.v2i1.27</a>","mla":"Malaspinas, Anna, and Caroline Uhler. “Detecting Epistasis via Markov Bases.” <i>Journal of Algebraic Statistics</i>, vol. 2, no. 1, Public Knowledge Project, 2011, pp. 36–53, doi:<a href=\"http://dx.doi.org/10.18409/jas.v2i1.27\">http://dx.doi.org/10.18409/jas.v2i1.27</a>.","ista":"Malaspinas A, Uhler C. 2011. Detecting epistasis via Markov bases. Journal of Algebraic Statistics. 2(1), 36–53.","ama":"Malaspinas A, Uhler C. Detecting epistasis via Markov bases. <i>Journal of Algebraic Statistics</i>. 2011;2(1):36-53. doi:<a href=\"http://dx.doi.org/10.18409/jas.v2i1.27\">http://dx.doi.org/10.18409/jas.v2i1.27</a>","ieee":"A. Malaspinas and C. Uhler, “Detecting epistasis via Markov bases,” <i>Journal of Algebraic Statistics</i>, vol. 2, no. 1. Public Knowledge Project, pp. 36–53, 2011.","chicago":"Malaspinas, Anna, and Caroline Uhler. “Detecting Epistasis via Markov Bases.” <i>Journal of Algebraic Statistics</i>. Public Knowledge Project, 2011. <a href=\"http://dx.doi.org/10.18409/jas.v2i1.27\">http://dx.doi.org/10.18409/jas.v2i1.27</a>."},"publication_status":"published","oa":1,"acknowledgement":"Anna-Sapfo Malaspinas is supported by a Janggen-Poehn Fellowship. Caroline Uhler is supported by an International Fulbright Science and Technology Fellowship.","doi":"http://dx.doi.org/10.18409/jas.v2i1.27","intvolume":"         2","issue":"1","extern":1,"publist_id":"3764","publisher":"Public Knowledge Project","abstract":[{"text":"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\nperform 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).","lang":"eng"}]}