Biomedical subjects
Publications and source records attributed to B P Kinghorn.
We use linear algebra to calculate an index of information content in genotype probabilities which has previously been calculated using trigonometry. The new method can be generalized allowing the index to be calculated for loci with more than two alleles. Applications of this index include its use in genotyping strategies, strategies to manage genetic disorders and in estimation of genotype effects.
The two key components in a breeding program can be described as the development of an objective function and the implementation of an algorithm for mate selection to target the objective function. However, mate selection is unacceptably slow for objective functions that involve longer term aims, such as engineering a good connection between herds. This paper proposes animal clustering to facilitate group mate selection, plus a step to refine mate selection for individual animals. Cluster analysis is used to form groups of animals within each sex based on nominated factors such as breed, herd, age, and EBV. Mate selection is then performed on groups, reducing the size of the problem considerably. The example used involved a three-breed crossbred population with a simple objective function to maximize predicted progeny merit. Twenty sires and 500 dams were to be selected from 816 and 1784 candidates. Group mate selection achieved 96.9% efficiency (random selection, 0%; full mate selection, 100%) and took 0.28 s to compute (394.2 s for full mate selection). Permitting more candidates to reach the individual mate selection stage increased efficiency to, for example, 98.9% at the cost of an extra 1.15 s. Efficiency was also improved by permitting more clusters.
A genotype probability index (GPI) is proposed to indicate the information content of genotype probabilities derived from a segregation analysis. Typically, some individuals are genotyped at a marker locus or a quantitative trait locus, and segregation analysis is used to make genotype inferences about ungenotyped relatives. Genotype probabilities for a two-allele autosomal locus are plotted on a triangular surface. The GPI has a value of zero at the point corresponding to Hardy-Weinberg frequencies, and a value of 100% at the vertices of the triangle. Trigonometric functions are used to help calculate intermediate index values. It is proposed that such an index can be useful to help identify which ungenotyped individuals or loci should be genotyped to maximize the benefit/cost of genotyping operations.
Genotypes at a marker locus give information on transmission of genes from parents to offspring and that information can be used in predicting the individuals' additive genetic value at a linked quantitative trait locus (MQTL). In this paper a recursive method is presented to build the gametic relationship matrix for an autosomal MQTL which requires knowledge on recombination rate between the marker locus and the MQTL linked to it. A method is also presented to obtain the inverse of the gametic relationship matrix. This information can be used in a mixed linear model for simultaneous evaluation of fixed effects, gametic effects at the MQTL and additive genetic effects due to quantitative trait loci unlinked to the marker locus (polygenes). An equivalent model can be written at the animal level using the numerator relationship matrix for the MQTL and a method for obtaining the inverse of this matrix is presented. Information on several unlinked marker loci, each of them linked to a different locus affecting the trait of interest, can be used by including an effect for each MQTL. The number of equations per animal in this case is 2m + 1 where m is the number of MQTL. A method is presented to reduce the number of equations per animal to one by combining information on all MQTL and polygenes into one numerator relationship matrix. It is illustrated how the method can accommodate individuals with partial or no marker information. Numerical examples are given to illustrate the methods presented. Opportunities to use the presented model in constructing genetic maps are discussed.
This paper describes a method for screening animal populations on an index of calculated probabilities of genotype status at an unknown single locus. Animals selected by such a method might then be candidates in test matings and genetic marker analyses for major gene detection. The method relies on phenotypic measures for a continuous trait plus identification of sire and dam. Some missing phenotypes and missing pedigree information are permitted. The method is an iterative two-step procedure, the first step estimates genotype probabilities and the second step estimates genotypic effects by regressing phenotypes on genotype probabilities, modeled as true genotype status plus error. Prior knowledge or choice of major locus-free heritability for the trait of interest is required, plus initial starting estimates of the effect on phenotype of carrying one and two copies of the unknown gene. Gene frequency can be estimated by this method, but it is demonstrated that the consequences of using an incorrect fixed prior for gene frequency are not particularly adverse where true frequency of the allele with major effect is low. Simulations involving deterministic sampling from the normal distribution lead to convergence for estimates of genotype effects at the true values, for a reasonable range of starting values, illustrating that estimation of major gene effects has a rational basis. In the absence of polygenic effects, stochastic simulations of 600 animals in five generations resulted in estimates of genotypic effects close to the true values. However, stochastic simulations involving generation and fitting of both major genotype and animal polygenic effects showed upward bias in estimates of major genotype effects. This can be partially overcome by not using information from relatives when calculating genotype probabilities-a result which suggests a route to a modified method which is unbiased and yet does use this information.
Polymorphic bands were detected within the DQB and DRB regions of the ovine major histocompatibility complex by probing TaqI digested DNA from three large sheep half-sib families derived from a highly resistant ram. All animals were phenotypically assessed for Haemonchus contortus resistance by faecal egg counts and associations with RFLP bands and haplotypes were estimated using mixed model, best linear unbiased prediction statistical methods. Although the highly resistant sire was homozygous at the MHC, no significant associations were found between any band or haplotype and faecal egg count.