QTL mapping in outbred half-sib families using Bayesian model selection

Bayesian model selection for multiple QTLs mapping combining linkage disequilibrium and linkage

Linkage disequilibrium (LD) mapping is able to localize quantitative trait loci (QTL) within a rather small region (e.g. 2 cM), which is much narrower than linkage analysis (LA, usually 20 cM). The multilocus LD method utilizes haplotype information around putative mutation and takes historical recombination events into account, and thus provides a powerful method for further fine mapping. However, sometimes there are more than one QTLs in the region being studied. In this study, the Bayesian model selection implemented via the Markov chain Monte Carlo (MCMC) method is developed for fine mapping of multiple QTLs using haplotype information in a small region. The method combines LD as well as linkage information. A series of simulation experiments were conducted to investigate the behavior of the method. The results showed that this new multiple QTLs method was more efficient in separating closely linked QTLs than single-marker association studies.

Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.

QTL linkage analysis of connected populations using ancestral marker and pedigree information

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals' genotype probability without significantly increasing computational demand.

Representational Difference Analysis (RDA) reveals differential expression of conserved as well as novel genes during caste-specific development of the honey bee (Apis mellifera L.) ovary

In highly eusocial insects, such as the honey bee, Apis mellifera, the reproductive bias has become embedded in morphological caste differences. These are most expressively denoted in ovary size, with adult queens having large ovaries consisting of 150-200 ovarioles each, while workers typically have only 1-20 ovarioles per ovary. This morphological differentiation is a result of hormonal signals triggered by the diet change in the third larval instar, which eventually generate caste-specific gene expression patterns. To reveal these we produced differential gene expression libraries by Representational Difference Analysis (RDA) for queen and worker ovaries in a developmental stage when cell death is a prominent feature in the ovarioles of workers, whereas all ovarioles are maintained and extend in length in queens. In the queen library, 48% of the gene set represented homologs of known Drosophila genes, whereas in the worker ovary, the largest set (59%) were ESTs evidencing novel genes, not even computationally predicted in the honey bee genome. Differential expression was confirmed by quantitative RT-PCR for a selected gene set, denoting major differences for two queen and two worker library genes. These included two unpredicted genes located in chromosome 11 (Group11.35 and Group11.31, respectively) possibly representing long non-coding RNAs. Being candidates as modulators of ovary development, their expression and functional analysis should be a focal point for future studies.