Selection schemes using genomic information

It has been stated that most traits of economic interest behave as if there were many loci influencing their expression and therefore their variation. This assumption leads to the idea of polygenic inheritance and the infinitesimal model (Garrick & Snell 2005; Hayes & Goddard 2001). In reality, a small number of genes seem to affect a large proportion of the variation in phenotypic characteristics; accompanied by a large number of genes each having small effects (Garrick & Snell 2005; Georges & Massey 1991; Hayes & Goddard 2001). The inclusion of genomic information into selection programmes to improve animal production rely on the expectation that information at the DNA level will lead to faster genetic gain than the one achieved based only on phenotypic information (Meuwissen et al. 2001). Therefore accurate appraisal about the implications of introducing genomic data into a breeding scheme concerning the impact that this inclusion will have in the phenotypic response, is needed before developing an accurate breeding programme.

Lande & Thompson (1990) presented a deterministic simulation model that included molecular genetic information using selection index theory. This was further developed by Dekkers (2007) where he presented equations to include marker information as a correlated trait (with heritability equal to 1). Several assumptions were made to develop the model, highlighting the multivariate normality of the marker information, which the author states that it can be considered "approximately valid" if markers breeding values are based on a large number of markers allowing to be represented as a polygenic trait. Another consideration is that selection index theory does not account for changes in the genetic variance due to changes in gene (allele) frequencies (Bulmer effect). This issue was solved using a selection index software package that adjusts for the Bulmer effect. Based on Dekker's (2007) publication, Janssen-Tapken et al. (2010) compared different selection strategies for improved productivity and marker assisted selection (MAS) for disease traits; Togashi & Lin (2010) analysed different selection methods for genetic improvement of net merit for two traits with the inclusion of marker information; Pryce et al. (2010) presented a deterministic model using the four pathways of selection of Rendel & Robertson (1950) while also accounting for the rate of inbreeding per generation. In sheep production Sise & Amer (2009) presented a deterministic approach using selection

index theory to predict the response to genomic selection in dual purpose sheep flocks. Besides the methodology used the results of these studies suggest that a large amount of information is required to a estimate genomic breeding values (GBV). Better genetic gains may be obtained because genomic information explains a larger amount of the phenotypic variance, and/or a reduction of the generation interval.

Even though deterministic models have the advantage of being less demanding in computing time than stochastic models, most of the work evaluating the impact of information on individual genes or markers has been done using stochastic simulations. This is because deterministic prediction of the selection response by selection index theory needs multivariate normality, which does not occur when only a limited number genes are used in the selection simulation, and also because selection index predictions ignore Bulmer effects (Dekkers 2007).

Stochastic simulation models using BLUP with the inclusion of genetic markers have been compared with conventional BLUP to assess the effectiveness of MAS (Fernando & Grossman 1989; Zhang & Smith 1992), even though it was never commercially applied. The great change in the perception of MAS was when Meuwissen et al. (2001) published their work in which they presented the methodology for genomic selection, a term that was first introduced by Haley and Visscher (1998) (Meuwissen 2007). The methodology is a form of MAS that uses SNPs as a dense marker map combined as “haplotypes”, which represents the total genetic variation of the trait under study, and phenotypes in a training population to estimate the effect of each haplotype in the breeding objective traits. This allows the estimation of breeding values for animals that have no phenotypic record of their own and no progeny (Meuwissen et al. 2001). These predictions of animal genetic merit became known as genomic estimated breeding values (GBV). The statistical methods used by Meuwissen et al. (2001) will be explained in the next section.

Since the appearance of genomic selection, several methodologies have arisen to estimate the GBVs and also for analysing the accuracy of different methods in different scenarios (Amer & Payne 2009; Hayes et al. 2006; Hayes & Goddard 2010; Luan et al. 2009; Pérez-Rodríguez et al. 2013; Schaeffer 2006; Toosi et al. 2010; Weigel et al. 2010). Regarding sheep, studies have evaluated the accuracy of genomic selection for production traits (Sise & Amer 2009; Slack-Smith et al. 2010), genetic

gains in selection indices for dual purpose animals or wool related traits (Pickering et al. 2013; Swan & Brown 2013), or for the detection of faulty genes by identifying animals carrying the undesirable SNP allele (Sise et al. 2008).

In general, the literature concludes that the inclusion of genomic data into selection schemes produces an increase in the rate of genetic gain for the traits or index under simulation. All studies concur that large datasets are required to achieve high accuracies but there is no agreement on a unique methodology for estimating the GBVs, with small differences shown by different statistical analyses.