Bayesian model selection for characterizing genomic imprinting effects and patterns

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 quantitative trait loci (QTL) and Bayesian method for simultaneously modeling multiple QTLs have been adopted in various studies.

Results: In a widely used F(2) 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.

Fig. 1.

The genome-wide 2logBFs profiles of scanning loci (A) and genetic effects (B) obtained with Bayesian model selection for body weights in mice. The horizontal reference line is the critical 2lnBF value of 2.1 for the significance test. The genome consists of 19 linkage groups that are separated by the vertical dotted lines. The 19 linkage groups are drawn to scale and proportional to their chromosomal lengths. Positions of the markers are indicated by the ticks on the horizontal axis. The thick solid, dashed and thin solid lines represent additive, dominance and imprinting effects, respectively.

Fig. 2.

The profile of LR test statistics from maximum likelihood interval mapping for body weights in mice. The horizontal reference line is the empirical critical value at the significant level of 0.05 for the LR test statistic generated from permutation tests. Linkage groups are separated by the vertical dotted lines and marker positions are indicated by the ticks on the horizontal axis.

Fig. 3.

The genome-wide 2logBFs profile of scanning loci (above) and genetic effects (below) obtained with Bayesian model selection for HALI survival time in mice. The horizontal reference line is the critical 2lnBF value of 2.1 for the significance test. The genome consists of 20 linkage groups that are separated by the vertical dotted lines. The 20 linkage groups are drawn to scale and proportional to their chromosomal lengths. Positions of the markers are indicated by the ticks on the horizontal axis. The thick solid, dashed and thin solid lines represent additive, dominance and imprinting effects, respectively.

Fig. 4.

The profile of LR test statistics obtained with maximum likelihood interval mapping for HALI survival time in mice. The horizontal reference line is the empirical critical value at the significant level of 0.05 for the LR test statistic generated from permutation tests. Linkage groups are separated by the vertical dotted lines and marker positions are indicated by the ticks on the horizontal axis.