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Bioinformatics Advance Access published online on October 30, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp620
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Bayesian Model Selection for Characterizing Genomic Imprinting Effects and Patterns

Runqing Yang 1,*, Xin Wang 1, Zeyuan Wu 1, Daniel R. Prows 2 and Min Lin 3,*

1 School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 200240, China.
2 Division of Human Genetics, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA.
3 Department of Biostatistics and Bioinformatics, School of Medicine, Duke University, Durham, NC 27710, USA

*To whom correspondence should be addressed. Runqing Yang, Min Lin E-mail: runqingyang{at}sjtu.edu.cn, annie.lin{at}duke.edu


  Abstract

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 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 data sets. Our approach can be generally used to identify inheritance modes and determine the contribution of major genes for quantitative variations.

Contact: annie.lin{at}duke.edu; runqingyang{at}sjtu.edu.cn

Associate Editor: Dr. Jeffrey Barrett

Received on September 3, 2009; revised on October 16, 2009; accepted on October 27, 2009

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