A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures

McMahan Christopher (), Baurley James, Bridges William, Joyner Chase, Kacamarga Muhamad Fitra, Lund Robert, Pardamean Carissa and Pardamean Bens
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McMahan Christopher: Department of Mathematical Sciences, Clemson University, Clemson, SC, USA
Baurley James: Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
Bridges William: Department of Mathematical Sciences, Clemson University, Clemson, SC, USA
Joyner Chase: Department of Mathematical Sciences, Clemson University, Clemson, SC, USA
Kacamarga Muhamad Fitra: Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
Lund Robert: Department of Mathematical Sciences, Clemson University, Clemson, SC, USA
Pardamean Carissa: Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
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

Abstract: 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)
Date: 2017
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DOI: 10.1515/sagmb-2017-0044

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