{"id":335,"date":"2017-12-11T09:05:41","date_gmt":"2017-12-11T02:05:41","guid":{"rendered":"http:\/\/research.binus.ac.id\/airnd\/?p=335"},"modified":"2020-11-12T17:05:04","modified_gmt":"2020-11-12T10:05:04","slug":"a-bayesian-hierarchical-model-for-identifying-significant-polygenic-effects-while-controlling-for-confounding-and-repeated-measures","status":"publish","type":"post","link":"https:\/\/research.binus.ac.id\/airdc\/2017\/12\/a-bayesian-hierarchical-model-for-identifying-significant-polygenic-effects-while-controlling-for-confounding-and-repeated-measures\/","title":{"rendered":"A Bayesian Hierarchical Model for Identifying Significant Polygenic Effects while Controlling for Confounding and Repeated Measures"},"content":{"rendered":"<div><\/div>\n<p style=\"text-align: justify\"><span style=\"font-size: 14px;text-align: justify\">Genomic studies of plants often seek to identify genetic factors associated with desirable traits. The process of evaluating genetic markers one by one (i.e. a marginal analysis) may not identify important polygenic and environmental effects. Further, confounding due to growing conditions\/factors and genetic similarities among plant varieties may influence conclusions. When developing new plant varieties to optimize yield or thrive in future adverse conditions (e.g. flood, drought), scientists seek a complete understanding of how the factors influence desirable traits. Motivated by a study design that measures rice yield across different seasons, fields, and plant varieties in Indonesia, we develop a regression method that identifies significant genomic factors, while simultaneously controlling for field factors and genetic similarities in the plant varieties. Our approach develops a Bayesian maximum a posteriori probability (MAP) estimator under a generalized double Pareto shrinkage prior. Through a hierarchical representation of the proposed model, a novel and computationally efficient expectation-maximization (EM) algorithm is developed for variable selection and estimation. The performance of the proposed approach is demonstrated through simulation and is used to analyze rice yields from a pilot study conducted by the Indonesian Center for Rice Research.<\/span><\/p>\n<div>Statistical Applications in Genetics and Molecular Biology<\/div>\n<div><\/div>\n<div><strong>Jamilatuzzahro, Rezzy Eko Caraka, Riki Herliansyah, Asmawati S.,\u00a0Dian Megah Sari, Bens Pardamean<\/strong><\/div>\n<div id=\"yui_3_14_1_1_1512957888400_1039\" class=\"publication-abstract\">\n<div><\/div>\n<\/div>\n<div><a href=\"https:\/\/www.researchgate.net\/publication\/321090649_A_Bayesian_hierarchical_model_for_identifying_significant_polygenic_effects_while_controlling_for_confounding_and_repeated_measures\">Read Full Paper<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Genomic studies of plants often seek to identify genetic factors associated with desirable traits. The process of evaluating genetic markers one by one (i.e. a marginal analysis) may not identify important polygenic and environmental effects. Further, confounding due to growing conditions\/factors and genetic similarities among plant varieties may influence conclusions. When developing new plant varieties [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":1634,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16,6],"tags":[],"class_list":["post-335","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications","category-research"],"_links":{"self":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/335","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/comments?post=335"}],"version-history":[{"count":2,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/335\/revisions"}],"predecessor-version":[{"id":1635,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/335\/revisions\/1635"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media\/1634"}],"wp:attachment":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media?parent=335"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/categories?post=335"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/tags?post=335"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}