Polydactyl inheritance in the pig.

Two pigs were identified having "extra feet" (preaxial polydactyly) within a purebred population of Yorkshire pigs. Polydactyly is an inherited disorder in many species that may be controlled by either recessive or dominant genes. Experimental matings were conducted using pigs that had produced affected offspring with the result of 12 polydactyl offspring out of 95 piglets.

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

Background: Marginal posterior genotype probabilities need to be computed for genetic analyses such as geneticcounseling in humans and selective breeding in animal and plant species.

Methods: 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.

A nonsense mutation in cGMP-dependent type II protein kinase (PRKG2) causes dwarfism in American Angus cattle.

Historically, dwarfism was the major genetic defect in U.S. beef cattle.

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.

A study on the minimum number of loci required for genetic evaluation using a finite locus model.

For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model.

The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP.

Under additive inheritance, the Henderson mixed model equations (HMME) provide an efficient approach to obtaining genetic evaluations by marker assisted best linear unbiased prediction (MABLUP) given pedigree relationships, trait and marker data. For large pedigrees with many missing markers, however, it is not feasible to calculate the exact gametic variance covariance matrix required to construct HMME. The objective of this study was to investigate the consequences of using approximate gametic variance covariance matrices on response to selection by MABLUP.

A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models.

An increased availability of genotypes at marker loci has prompted the development of models that include the effect of individual genes. Selection based on these models is known as marker-assisted selection (MAS). MAS is known to be efficient especially for traits that have low heritability and non-additive gene action.