Bayesian mapping of genome-wide epistatic imprinted loci for quantitative traits |
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Authors: | Shize Li Xin Wang Jiahan Li Tianfu Yang Lingjiang Min Yang Liu Min Lin Runqing Yang |
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Institution: | (1) College of Animal Science and Veterinary Medicine, Heilongjiang Bayi Agricultural University, Daqing, 163319, People’s Republic of China;(2) School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, 200240, People’s Republic of China;(3) Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA;(4) College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, 266109, People’s Republic of China;(5) Department of Biostatistics and Bioinformatics, School of Medicine, Duke University, Durham, NC 27710, USA; |
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Abstract: | Genomic imprinting, an epigenetic phenomenon of parent-of-origin-specific gene expression, has been widely observed in plants,
animals, and humans. To detect imprinting genes influencing quantitative traits, the least squares and maximum likelihood
approaches for fitting a single quantitative trait locus (QTL) and Bayesian methods for simultaneously modeling multiple QTL
have been adopted, respectively, in various studies. However, most of these studies have only estimated imprinting main effects
and thus ignored imprinting epistatic effects. In the presence of extremely complex genomic imprinting architectures, we introduce
a Bayesian model selection method to analyze the multiple interacting imprinted QTL (iQTL) model. This approach will greatly
enhance the computational efficiency through setting the upper bound of the number of QTLs and performing selective sampling
for QTL parameters. The imprinting types of detected main-effect QTLs can be estimated from the Bayes factor statistic formulated
by the posterior probabilities for the genetic effects being compared. The performance of the proposed method is demonstrated
by several simulation experiments. Moreover, this method is applied to dissect the imprinting genetic architecture for body
weight in mouse and fruit weight in tomato. Matlab code for implementing this approach will be available from the authors
upon request. |
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