Bayesian analysis for genetic architecture of dynamic traits

Abstract

The dissection of the genetic architecture of quantitative traits, including the number and locations of quantitative trait loci (QTL) and their main and epistatic effects, has been an important topic in current QTL mapping. We extend the Bayesian model selection framework for mapping multiple epistatic QTL affecting continuous traits to dynamic traits in experimental crosses. The extension inherits the efficiency of Bayesian model selection and the flexibility of the Legendre polynomial model fitting to the change in genetic and environmental effects with time. We illustrate the proposed method by simultaneously detecting the main and epistatic QTLs for the growth of leaf age in a doubled-haploid population of rice. The behavior and performance of the method are also shown by computer simulation experiments. The results show that our method can more quickly identify interacting QTLs for dynamic traits in the models with many numbers of genetic effects, enhancing our understanding of genetic architecture for dynamic traits. Our proposed method can be treated as a general form of mapping QTL for continuous quantitative traits, being easier to extend to multiple traits and to a single trait with repeat records.

A Bayesian Nonparametric Approach for Mapping Dynamic Quantitative Traits

“…In this article, we have not considered yet the possible impact of some environmental covariates such as temperature and age variables and their interactions with QTL, as well as the QTL-QTL interactions on the target dynamic traits. Following works such as Zhang and Xu (2005), Yi and Banerjee (2009), Min et al (2011), and Li and Sillanpää (2012), it is not difficult to extend our current marker set by including the environmental covariates or the pairwise markerenvironment or pairwise marker-marker interaction terms as new "marker" variables for variable selection.…”

“…These approaches typically construct a test statistic (e.g., log-likelihood ratio or Wald statistic) to screen the important variables (QTL) through a multiple-testing procedure (e.g., adjusting the P-value by permutation or by Bonferroni correction). In some Bayesian approaches (Yang and Xu 2007;Min et al 2011;Yang et al 2011;Xing et al 2012), the multilocus QTL analysis is performed by assigning shrinkage-inducing priors or spike and slab priors to the marker effects. Wald tests, credible intervals ), or Bayes factors can then be used to justify the QTL.…”

Genetic effects and correlations between production and fertility traits and their dependency on the lactation-stage in Holstein Friesians

“…Just like the phenotype, underlying genetic effects follow a dynamic expression over time. To account for the dynamic effects of genotypes, functional mapping has been introduced for the detection of QTLs, but has been applied mainly in human and plant genetics [1-4]. In livestock however, time dependency of traits is often accounted for when modeling genetic effects, but reported results are static in the sense of that cumulated 305-day breeding values are made public or that gene effects are given for a whole lactation.…”

Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling

“…Even if several methods have been proposed, most of the approaches are limited to single or two-QTL model. Exceptions to this include the methods of Yang and Xu (2007), Min et al, 2011, andHeuven andJanss (2010).…”

“…Functional QTL mapping methods commonly model average curve behaviour with time-specific QTL and environmental effects Xu, 2007 andMin et al, 2011). Individual-specific variations in these methods are described as deviations from the mean curve behaviour, and these deviations are dependent at neighbouring time points.…”