期刊
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
卷 16, 期 5-6, 页码 407-419出版社
WALTER DE GRUYTER GMBH
DOI: 10.1515/sagmb-2017-0044
关键词
Bayesian hierarchical models; EM algorithm; genomic studies; MAP estimator; rice science; shrinkage prior
资金
- International Treaty on Plant Genetic Resources for Food and Agriculture-Indonesia [W3A-PR-07]
- NIH/NIAID [R01 AI121351]
- NIH/NIDA [R43 DA041211-01A1]
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.
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