A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures
McMahan Christopher (),
Kacamarga Muhamad Fitra,
Pardamean Carissa and
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Lund Robert: Department of Mathematical Sciences, Clemson University, Clemson, SC, USA
Pardamean Bens: Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
Statistical Applications in Genetics and Molecular Biology, 2017, vol. 16, issue 5-6, 407-419
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.
Keywords: Bayesian hierarchical models; EM algorithm; genomic studies; MAP estimator; rice science; shrinkage prior (search for similar items in EconPapers)
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