Comparison of efficient methods to evaluate genetic markers for use in breeding programs

Abstract

Detection and characterisation of markers associated with unknown QTL is the initial step in marker assisted selection (MAS) or genomic selection (GS) practiced in livestock breeding. Good response to MAS or GS depends on a set of markers that can explain most of the total genetic variation and on estimating their effects with sufficient accuracy. It is generally not economically feasible to genotype a large population of animals to obtain estimates of the marker associated effects, using currently available techniques. Therefore alternative approaches are tested in order to maximise the utility of genotype data, limiting the number of animals genotyped. Partial genotyping is one approach used to reduce genotyping costs, carried out either by randomly selecting a proportion of animals from the population concerned, or selectively genotyping the top and bottom extreme animals in the phenotypic distribution. Selective genotyping (SG) is reported to have more power in detecting QTL associated with a trait. However, this is associated with biased estimates of the effects of the QTL or markers tested. Further, it is possible to get a higher number of false positive detections of markers with this approach, especially when multiple markers are evaluated, as comparisons of animals are made from the phenotypic extremes. This thesis investigates the use of partial genotyping methods in detecting and characterising single nucleotide polymorphism (SNP) markers with the use of a combination of methods: selective and randomised genotyping methods using genotype probabilities for the "missing" genotypes. In addition, a modified method of SG was developed which includes animal relationships as well as phenotypic dispersal, and this was tested in a population with a high degree of animal relationships. The performance of the markers selected with each method was evaluated using MAS and GS.