Whole genome marker-assisted selection.

Abstract

Abstract Most applications of genomic selection (GS) have so far been in animals, especially dairy cattle, although theoretical studies have also been conducted for maize. For the last 50 years, commercial breeding programmes for dairy cattle have been based on the progeny test scheme, which results in an average generation interval of approximately 7 years for the sire to dam path. It should be possible to double rates of genetic gain by the application of GS, if generation intervals are reduced to close to the biological minimum. During the last decade, methods were developed for high-throughput genotyping of thousands of single nucleotide polymorphisms per individuals. The number of potential polymorphic markers per species was increased from about 1000 to tens of thousands, and costs per individual marker genotype were reduced from several dollars to less than one cent. The application of marker-assisted selection based on genome-wide association studies requires solutions to new statistical problems. Specifically how should information from pedigree, phenotypic records and genotypes be combined to optimally rank candidates for selection? Various linear and Bayesian methods have been proposed and tested to compute genomic estimated breeding values. Linear models require significantly less computing time, and perform nearly as well as Bayesian methodologies. Methods are generally evaluated by comparing genomic evaluations based only on pedigree and genotype to genetic evaluations based on daughter records of the same bulls. Genomic selection programmes for dairy cattle have been implemented in the USA, Canada, Australia, New Zealand and the Netherlands. With declining genotyping costs it becomes economically viable to genotype more individuals, including candidate bull dams. More emphasis can be placed on low heritability traits, such as fertility and disease traits, and it will be easier to control the increase in inbreeding in commercial animal populations.