Bayesian inference for genomic imprinting underlying developmental characteristics.
The identification of imprinted genes is becoming a standard procedure in searching for quantitative trait loci (QTL) underlying complex traits. When a developmental characteristic such as growth or drug response is observed at multiple time points, understanding the dynamics of gene function governing the underlying feature should provide more biological information regarding the genetic control of an organism. Recognizing that differential imprinting can be development-specific, mapping imprinted genes considering the dynamic imprinting effect can provide additional biological insights into the epigenetic control of a complex trait. In this study, we proposed a Bayesian imprinted QTL (iQTL) mapping framework considering the dynamics of imprinting effects and model multiple iQTLs with an efficient Bayesian model selection procedure. The method overcomes the limitation of likelihood-based mapping procedure, and can simultaneously identify multiple iQTLs with different gene action modes across the whole genome with high computational efficiency. An inference procedure using Bayes factors to distinguish different imprinting patterns of iQTL was proposed. Monte Carlo simulations were conducted to evaluate the performance of the method. The utility of the approach was illustrated through an analysis of a body weight growth data set in an F(2) family derived from LG/J and SM/J mouse stains. The proposed Bayesian mapping method provides an efficient and computationally feasible framework for genome-wide multiple iQTL inference with complex developmental traits.
School of Agriculture and Biology, Shanghai Jiaotong University, China.
Runqing
R
Yang
Xin
X
Wang
Yuehua
Y
Cui
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001835:Body Weight / Q000235:genetics; D016678:Genome; D018392:Genomic Imprinting; D051379:Mice; D008815:Mice, Inbred Strains; D008957:Models, Genetic; D009010:Monte Carlo Method; D040641:Quantitative Trait Loci