Genomic selection: marker assisted selection on a genome wide scale.

Genomic selection (GS) has become a very intense field of research during recent years. GS may be defined as the simultaneous selection for many (tens or hundreds of thousands of) markers, which cover the entire genome in a dense manner so that all genes are expected to be in linkage disequilibrium with at least some of the markers. In a sense, GS is marker assisted selection on a genome wide scale. Methodology for GS was first presented by Meuwissen et al. (Genetics 157:1819–1829), but the term GS was actually first introduced by Haley and Visscher at Armidale WCGALP in 1998 (as pointed by Sijne van der Beek, personal communication). Fortunately, Haley and Visscher used the term in exactly the same way as we did in the 2001 paper. GS sounded like a crazy idea in 2001, when a major aim in marker assisted selection research was to reduce the costs of genotyping. However, the development in the technology for single nucleotide polymorphism (SNP) genotyping has been tremendous, and in humans 500 000 SNPs are genotyped routinely nowadays. It is this incredible development of the genotyping technology that makes GS feasible. This issue of Journal of Animal Breeding and Genetics contains a series of papers (the references without journal name) presenting the state of the art on genomic selection. I will not discuss every paper separately here, but will consider some issues that are across the papers. One issue that is mentioned by Goddard and Hayes and was also described this year by Guillaume et al. (Dublin EAAP) is the equivalence between the BLUP-GS model, where the marker effects are estimated by BLUP (see Kolbehdari et al. for details), and the traditional BLUP model, where the usual pedigree based relationship matrix is replaced by a relationship matrix estimated by the markers. Basically, if X denotes the design matrix of the marker effects, then XX¢ is used as a marker estimated relationship matrix. Replacing the pedigree relationship matrix by a marker estimated relationship matrix has been tested before in non-GS marker assisted selection schemes and the results may be summarized as (e.g. Villanueva et al., J. Anim. Sci. 83:1747–1752): (1) the extra genetic gains are quite small, (2) they decrease with increasing genome size and (3) the pedigree should be used in addition to the markers to infer the relationship matrix. Result (3) suggests that we should perhaps use more sophisticated marker estimated kinship matrices in GS than simply XX¢. Results (1) and (2) suggest that it is perhaps really needed to use more sophisticated GS models than BLUP-GS, such as the Bayesian model ‘BayesB’ where a priori many marker effects are assumed to be zero. In effect, the latter reduces the genome size by concentrating on those parts of the genome where there are quantitative trait loci (QTL). A variety of methods have been suggested for the calculation of GS-estimated breeding values (EBV), ranging from BLUP (Kolbehdari et al.), Bayesian methods such as BayesB (Meuwissen et al., Genetics 157:1819–1829), and machine learning techniques (Long et al.). These methods differ in their assumptions about the underlying genetic model: BLUP assumes the infinitesimal model, i.e. a large number of genes each with small effects scattered along the chromosomes; the machine learning technique assumes that there are a limited number of genes, and thus also a limited number of SNPs, worthwhile to be fitted; and BayesB is in between, i.e. it assumes few genes with large effects and many genes with small effect. The method that reflects the biological nature of the gene effects closest is expected to yield the most accurate GS-EBV. Thus, more research into the distribution of gene effects is warranted. The paper of Muir shows that selection has a large decreasing effect on the accuracy of GS-EBV. The question arises: is the reduction of the accuracy of selection due to the Bulmer effect larger for GS-EBV than for conventional EBV? Dekkers’ paper describes a general framework for answering this question. If we assume a heritability of 0.5 and only phenotypic and pedigree information are available, the pseudo-BLUP accuracy of selection is 0.77 when the Bulmer effect was ignored. This is reduced to 0.72 when accounting for the Bulmer effect (assuming selection reduced the variance of the EBV by 80% in both parents). If we assume that GS-EBV J. Anim. Breed. Genet. ISSN 0931-2668