Linear models for joint association and linkage QTL mapping

<p>Abstract</p> <p>Background</p> <p>Populational linkage disequilibrium and within-family linkage are commonly used for QTL mapping and marker assisted selection. The combination of both results in more robust and accurate locations of the QTL, but models proposed so far have been either single marker, complex in practice or well fit to a particular family structure.</p> <p>Results</p> <p>We herein present linear model theory to come up with additive effects of the QTL alleles in any member of a general pedigree, conditional to observed markers and pedigree, accounting for possible linkage disequilibrium among QTLs and markers. The model is based on association analysis in the founders; further, the additive effect of the QTLs transmitted to the descendants is a weighted (by the probabilities of transmission) average of the substitution effects of founders' haplotypes. The model allows for non-complete linkage disequilibrium QTL-markers in the founders. Two submodels are presented: a simple and easy to implement Haley-Knott type regression for half-sib families, and a general mixed (variance component) model for general pedigrees. The model can use information from all markers. The performance of the regression method is compared by simulation with a more complex IBD method by Meuwissen and Goddard. Numerical examples are provided.</p> <p>Conclusion</p> <p>The linear model theory provides a useful framework for QTL mapping with dense marker maps. Results show similar accuracies but a bias of the IBD method towards the center of the region. Computations for the linear regression model are extremely simple, in contrast with IBD methods. Extensions of the model to genomic selection and multi-QTL mapping are straightforward.</p

Empirical Progeny Equivalent for Genotyped Animals in Multi-breed Beef Cattle Genetic Evaluations Using Single-step Bayesian Regression Model

The objective of this study was to measure the accuracies of genomic enhanced EPDs of genotyped animals in an international multi-breed beef cattle genetic evaluations using a single-step Bayesian regression model. The average BIF accuracies of genotyped animals with no progeny information were compared to the average BIF accuracies of non-genotyped sires with different number of progenies with observed phenotypes for birth, weaning and yearling weights. The results showed that the BIF accuracy of a genotyped animal is equivalent to the BIF accuracy of a non-genotyped sire with 21, 22 and 10 progenies with observed phenotypes for BW, WW and YW, respectively. These results demonstrate the value of DNA testing of selection candidates for beef cattle breeders across the world

An efficient algorithm to compute marginal posterior genotype probabilities for every member of a pedigree with loops

<p>Abstract</p> <p>Background</p> <p>Marginal posterior genotype probabilities need to be computed for genetic analyses such as geneticcounseling in humans and selective breeding in animal and plant species.</p> <p>Methods</p> <p>In this paper, we describe a peeling based, deterministic, exact algorithm to compute efficiently genotype probabilities for every member of a pedigree with loops without recourse to junction-tree methods from graph theory. The efficiency in computing the likelihood by peeling comes from storing intermediate results in multidimensional tables called cutsets. Computing marginal genotype probabilities for individual <it>i </it>requires recomputing the likelihood for each of the possible genotypes of individual <it>i</it>. This can be done efficiently by storing intermediate results in two types of cutsets called anterior and posterior cutsets and reusing these intermediate results to compute the likelihood.</p> <p>Examples</p> <p>A small example is used to illustrate the theoretical concepts discussed in this paper, and marginal genotype probabilities are computed at a monogenic disease locus for every member in a real cattle pedigree.</p

GenSim: Simulation of Descendants from Sequenced Ancestors Data

High-density real or imputed SNP genotypes are now routinely used for genomic prediction and genome-wide association studies. This is extending to the use of actual or imputed next generation sequence data in these activities. Simulation studies are useful to mimic these complex scenarios and test different analytical methods. We have developed a software tool GenSim to simulate sequence data in descendants. In this software, a crossover position and origin simulation (CPOS) algorithm is implemented to efficiently drop down sequence data from founders in complex pedigrees. Parallel programming techniques are used to reduce computing time

Improved techniques for sampling complex pedigrees with the Gibbs sampler

Markov chain Monte Carlo (MCMC) methods have been widely used to overcome computational problems in linkage and segregation analyses. Many variants of this approach exist and are practiced; among the most popular is the Gibbs sampler. The Gibbs sampler is simple to implement but has (in its simplest form) mixing and reducibility problems; furthermore in order to initiate a Gibbs sampling chain we need a starting genotypic or allelic configuration which is consistent with the marker data in the pedigree and which has suitable weight in the joint distribution. We outline a procedure for finding such a configuration in pedigrees which have too many loci to allow for exact peeling. We also explain how this technique could be used to implement a blocking Gibbs sampler

Bayesian Methods for Genomic Prediction and Genome-Wide Association Studies combining Information on Genotyped and Non-Genotyped Individuals

Genomic prediction involves using high-density marker genotypes to characterize the impact on performance of every region of the genome, and using that information to predict performance of genotyped selection candidates. This is a relatively new technology and is now gaining traction in personalized medicine and in various livestock industries. Our new approach promises to overcome serious limitations with existing techniques for genomic prediction

Accuracy of Genomic Predictions for Birth, Weaning and Yearling Weights in US Simmental Beef Cattle

Direct genomic breeding values (DGV) based on actual or imputed GeneSeek Genomic Profiler HD (GGPHD) genotypes were obtained for birth, weaning and yearling weights using Bayesian regression on about 20,000 US Simmental pure- or cross-bred beef cattle. Accuracies of DGV were quantified using 4-fold cross validation. Accuracies expressed as genetic correlations between DGV and trait ranged from 0.61 to 0.65, and the regressions of phenotype on DGV ranged from 0.61 to 0.66. These results indicate good predictive ability of genomic prediction with GGPHD chips but DGV need to be adjusted for bias