Scaling to account for heterogeneous variances in a Bayesian analysis of broiler quantitative trait loci

Mapping genome-wide QTL of ratio traits with Bayesian shrinkage analysis for its component traits

The ratio trait is defined as a ratio of two regular quantitative traits with normal distribution, which is distinguished from regular quantitative traits in the genetic analysis because it does not follow the normal distribution. On the basis of maximum likelihood method that uses a special linear combination of the two component traits, we develop a Bayesian mapping strategy for ratio traits, which firstly analyzes the two component traits by Bayesian shrinkage method, and then generates a new posterior sample of genetic effects for a ratio trait from ones of population means and genetic effects for the two component traits, finally, infers QTL for the ratio trait via post MCMC analysis for the new posterior sample. A simulation study demonstrates that the new method has higher detecting power of the QTL than maximum likelihood method. An application is illustrated to map genome-wide QTL for relative growth rate of height on soybean.

Mapping quantitative trait loci for traits defined as ratios

Many traits are defined as ratios of two quantitative traits. Methods of QTL mapping for regular quantitative traits are not optimal when applied to ratios due to lack of normality for traits defined as ratios. We develop a new method of QTL mapping for traits defined as ratios. The new method uses a special linear combination of the two component traits, and thus takes advantage of the normal property of the new variable. Simulation study shows that the new method can substantially increase the statistical power of QTL detection relative to the method which treats ratios as regular quantitative traits. The new method also outperforms the method that uses Box-Cox transformed ratio as the phenotype. A real example of QTL mapping for relative growth rate in soybean demonstrates that the new method can detect more QTL than existing methods of QTL mapping for traits defined as ratios.

Bayesian reanalysis of a quantitative trait locus accounting for multiple environments by scaling in broilers1

A Bayesian method was developed to handle QTL analyses of multiple experimental data of outbred populations with heterogeneity of variance between sexes for all random effects. The method employed a scaled reduced animal model with random polygenic and QTL allelic effects. A parsimonious model specification was applied by choosing assumptions regarding the covariance structure to limit the number of parameters to estimate. Markov chain Monte Carlo algorithms were applied to obtain marginal posterior densities. Simulation demonstrated that joint analysis of multiple environments is more powerful than separate single trait analyses of each environment. Measurements on broiler BW obtained from 2 experiments concerning growth efficiency and carcass traits were used to illustrate the method. The population consisted of 10 full-sib families from a cross between 2 broiler lines. Microsatellite genotypes were determined on generations 1 and 2, and phenotypes were collected on groups of generation 3 animals. The model included a polygenic correlation, which had a posterior mean of 0.70 in the analyses. The reanalysis agreed on the presence of a QTL in marker bracket MCW0058-LEI0071 accounting for 34% of the genetic variation in males and 24% in females in the growth efficiency experiment. In the carcass experiment, this QTL accounted for 19% of the genetic variation in males and 6% in females.

Full pedigree quantitative trait locus analysis in commercial pigs using variance components

In commercial livestock populations, QTL detection methods often use existing half-sib family structures and ignore additional relationships within and between families. We reanalyzed the data from a large QTL confirmation experiment with 10 pig lines and 10 chromosome regions using identity-by-descent (IBD) scores and variance component analyses. The IBD scores were obtained using a Monte Carlo Markov Chain method, as implemented in the LOKI software, and were used to model a putative QTL in a mixed animal model. The analyses revealed 61 QTL at a nominal 5% level (out of 650 tests). Twenty-seven QTL mapped to areas where QTL have been reported, and eight of these exceeded the threshold to claim confirmed linkage (P < 0.01). Forty-two of the putative QTL were detected previously using half-sib analyses, whereas 46 QTL previously identified by half-sib analyses could not be confirmed using the variance component approach. Some of the differences could be traced back to the underlying assumptions between the two methods. Using a deterministic approach to estimate IBD scores on a subset of the data gave very similar results to LOKI. We have demonstrated the feasibility of applying variance component QTL analysis to a large amount of data, equivalent to a genome scan. In many situations, the deterministic IBD approach offers a fast alternative to LOKI.