87 research outputs found
Comparison of “Maximum A‐Posteriori Estimation” and “Quasi Best Linear Unbiased Prediction” with threshold characters
No full textBayesian QTL mapping using skewed Student-<it>t </it>distributions
No full textAbstract In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.</p
A rapid conditional enumeration haplotyping method in pedigrees
No full textAbstract Haplotyping in pedigrees provides valuable information for genetic studies (e.g., linkage analysis and association study). In order to identify a set of haplotype configurations with the highest likelihoods for a large pedigree with a large number of linked loci, in our previous work, we proposed a conditional enumeration haplotyping method which sets a threshold for the conditional probabilities of the possible ordered genotypes at every unordered individual-marker to delete some ordered genotypes with low conditional probabilities and then eliminate some haplotype configurations with low likelihoods. In this article we present a rapid haplotyping algorithm based on a modification of our previous method by setting an additional threshold for the ratio of the conditional probability of a haplotype configuration to the largest conditional probability of all haplotype configurations in order to eliminate those configurations with relatively low conditional probabilities. The new algorithm is much more efficient than our previous method and the widely used software SimWalk2.</p
Exploitation of nonadditive variance through nonrandom mating
No full textMixed model equations to predict additive and nonadditive genetic values also predict specific combining abilities or combination effects among sire and dams or among sires and maternal grandsires (mgs). Current mating programs, utilizing nonadditive genetic variance only by avoiding mating between close relatives to prevent inbreeding depression, could be improved upon by use of predicted combination effects due to nonadditive variation beyond inbreeding. Simulation was employed to evaluate increase in progeny performance from nonrandom mating based on predicted combination effects among sires and mgs over random mating. Nonrandom mating strategies included mate allocation by linear programming, which is optimum, and two approximations, sequential selection based on progeny merit, and sequential selection based on deviation of progeny merit from mgs average. Genetic parameters were heritability equal to .05, .15, or .25 and ratio of dominance variance to phenotypic variance equal to .05, .10, or .15. These dominance ratios represent the range of recent estimates for yield and type traits. A total of 400 bulls were grouped by .99, .85, and .70 PTA reliability, with the first group being sires and mgs of the others. Using recurrence equations for combination effects, a matrix of true combination effects among the bulls was created. Reliabilites for estimated combination effects were computed for three types of bull populations; one with much information available (.41 to .79 ), one with little information ( .15 to .41 ) and one with an intermediate amount of information available (.15 to .79) and used to form matrices of estimated combination effects. Herds consisted of cows sired by .99 and .85 reliability bulls. Four mating groups of 123 cows, mated to 10 bulls from all bull groups, produced heifers to replace the herd. Herds were replicated 20 times for each type of bull population and each combination of heritability and dominance ratio. The three nonrandom mating strategies yielded means significantly different from random mating (p ≤ .05). When scaled by the standard deviation of milk yield, gains made by linear programming were 12.3 to 40.1 kg for low-reliability populations, 16.4 to 46.4 kg for intermediate reliability populations, and 31.0 to 80.3 kg for high reliability populations. Herds modified to utilize embryo transfer had less gain in progeny merit due to combination effects (20kg) with nonrandom mating compared to non-ET herds with identical heritability and dominance ratio, when donor cows were selected by estimated breeding value. Selection of donor cows based on combination effects yielded large gains (90.72kg) but such selection would only be justified in populations where nonadditive variance was more important than additive. A procedure for routinely approximating reliabilites of combination effects using information from three sources (information on parent subclasses, information on progeny subclasses, and records in subclass of interest) was presented.Master of Scienc
Genetical Research
No full textIn a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses. further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.US Department of Agriculture's National Research Initiative Competitive Grants Program (grant 96-35205-3662)National Science Foundation (grant DBI-9723022)NIH grant GM4534
Bayesian estimation of genetic parameters for multivariate threshold and continuous phenotypes and molecular genetic data in simulated horse populations using Gibbs sampling
No full textAbstract Background Requirements for successful implementation of multivariate animal threshold models including phenotypic and genotypic information are not known yet. Here simulated horse data were used to investigate the properties of multivariate estimators of genetic parameters for categorical, continuous and molecular genetic data in the context of important radiological health traits using mixed linear-threshold animal models via Gibbs sampling. The simulated pedigree comprised 7 generations and 40000 animals per generation. Additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits were simulated, resembling situations encountered in the Warmblood horse. Quantitative trait locus (QTL) effects and genetic marker information were simulated for one of the liabilities. Different scenarios with respect to recombination rate between genetic markers and QTL and polymorphism information content of genetic markers were studied. For each scenario ten replicates were sampled from the simulated population, and within each replicate six different datasets differing in number and distribution of animals with trait records and availability of genetic marker information were generated. (Co)Variance components were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. Residual variances were fixed to zero and a proper prior was used for the genetic covariance matrix. Results Effective sample sizes (ESS) and biases of genetic parameters differed significantly between datasets. Bias of heritability estimates was -6% to +6% for the continuous trait, -6% to +10% for the binary traits of moderate heritability, and -21% to +25% for the binary traits of low heritability. Additive genetic correlations were mostly underestimated between the continuous trait and binary traits of low heritability, under- or overestimated between the continuous trait and binary traits of moderate heritability, and overestimated between two binary traits. Use of trait information on two subsequent generations of animals increased ESS and reduced bias of parameter estimates more than mere increase of the number of informative animals from one generation. Consideration of genotype information as a fixed effect in the model resulted in overestimation of polygenic heritability of the QTL trait, but increased accuracy of estimated additive genetic correlations of the QTL trait. Conclusion Combined use of phenotype and genotype information on parents and offspring will help to identify agonistic and antagonistic genetic correlations between traits of interests, facilitating design of effective multiple trait selection schemes.</p
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