Detecting higher-order epistatic interactions in Genome-Wide Association Studies (GWAS) remains a challenging task in the fields of genetic epidemiology and computer science. A number of algorithms have recently been proposed for epistasis discovery. However, they suffer from a high computational cost since statistical measures have to be evaluated for each possible combination of markers. Hence, many algorithms use additional filtering stages discarding potentially non-interacting markers in order to reduce the overall number of combinations to be examined. Among others, Mutual Information Clustering (MIC) is a common pre-processing filter for grouping markers into partitions using K-Means clustering. Potentially interacting candidates for high-order epistasis are then examined exhaustively in a subsequent phase. However, analyzing real-world datasets of moderate size can still take several hours when performing analysis on a single CPU. In this work we propose a massively parallel computation scheme for the MIC algorithm targeting CUDA-enabled accelerators. Our implementation is able to perform epistasis discovery using more than 500,000 markers in just a couple of seconds in contrast to several hours when using the sequential MIC implementation. This runtime reduction by two orders-of-magnitude enables fast exploration of higher-order epistatic interactions even in large-scale GWAS datasets.<\/p>\n","protected":false},"excerpt":{"rendered":"
Detecting higher-order epistatic interactions in Genome-Wide Association Studies (GWAS) remains a challenging task in the fields of genetic epidemiology and computer science. A number of algorithms have recently been proposed for epistasis discovery. However, they suffer from a high computational cost since statistical measures have to be evaluated for each possible combination of markers. Hence, […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,123,468,1782,14,841,20,1767],"class_list":["post-16477","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-bioinformatics","tag-clustering","tag-computer-science","tag-cuda","tag-filtering","tag-nvidia","tag-nvidia-geforce-gtx-titan-x"],"views":2326,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16477","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=16477"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16477\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16477"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16477"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16477"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}