Journal Article

Bayesian model selection for characterizing genomic imprinting effects and patterns

Runqing Yang, Xin Wang, Zeyuan Wu, Daniel R. Prows and Min Lin

in Bioinformatics

Volume 26, issue 2, pages 235-241
Published in print January 2010 | ISSN: 1367-4803
Published online October 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp620
Bayesian model selection for characterizing genomic imprinting effects and patterns

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Motivation: Although imprinted genes have been ubiquitously observed in nature, statistical methodology still has not been systematically developed for jointly characterizing genomic imprinting effects and patterns. To detect imprinting genes influencing quantitative traits, the least square and maximum likelihood approaches for fitting a single quantitative trait loci (QTL) and Bayesian method for simultaneously modeling multiple QTLs have been adopted in various studies.

Results: In a widely used F2 reciprocal mating population for mapping imprinting genes, we herein propose a genomic imprinting model which describes additive, dominance and imprinting effects of multiple imprinted quantitative trait loci (iQTL) for traits of interest. Depending upon the estimates of the above genetic effects, we categorized imprinting patterns into seven types, which provides a complete classification scheme for describing imprinting patterns. Bayesian model selection was employed to identify iQTL along with many genetic parameters in a computationally efficient manner. To make statistical inference on the imprinting types of iQTL detected, a set of Bayes factors were formulated using the posterior probabilities for the genetic effects being compared. We demonstrated the performance of the proposed method by computer simulation experiments and then applied this method to two real datasets. Our approach can be generally used to identify inheritance modes and determine the contribution of major genes for quantitative variations.

Contact: annie.lin@duke.edu; runqingyang@sjtu.edu.cn

Journal Article.  5345 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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