Genomic evaluation of both purebred and crossbred performances

Abstract

BackgroundFor a two-breed crossbreeding system, Wei and van der Werf presented a model for genetic evaluation using information from both purebred and crossbred animals. The model provides breeding values for both purebred and crossbred performances. Genomic evaluation incorporates marker genotypes into a genetic evaluation system. Among popular methods are the so-called single-step methods, in which marker genotypes are incorporated into a traditional animal model by using a combined relationship matrix that extends the marker-based relationship matrix to non-genotyped animals. However, a single-step method for genomic evaluation of both purebred and crossbred performances has not been developed yet.ResultsAn extension of the Wei and van der Werf model that incorporates genomic information is presented. The extension consists of four steps: (1) the Wei van der Werf model is reformulated using two partial relationship matrices for the two breeds; (2) marker-based partial relationship matrices are constructed; (3) marker-based partial relationship matrices are adjusted to be compatible to pedigree-based partial relationship matrices and (4) combined partial relationship matrices are constructed using information from both pedigree and marker genotypes. The extension of the Wei van der Werf model can be implemented using software that allows inverse covariance matrices in sparse format as input.ConclusionsA method for genomic evaluation of both purebred and crossbred performances was developed for a two-breed crossbreeding system. The method allows information from crossbred animals to be incorporated in a coherent manner for such crossbreeding systems.

Genomic evaluation by including dominance effects and inbreeding depression for purebred and crossbred performance with an application in pigs

“…Although predictive abilities were not significantly different according to the Hotelling-Williams t-test, the results from 1000 bootstrap samples still showed that the predictive abilities of about 90% of the crossbred animals would be higher when genomic information was available (894 of 1000 bootstrap samples showed higher predictive abilities in scenarios that included genomic information than those in the Nogen scenario; results not shown). Comparison of the predictive abilities that were estimated in the current study with those from a previous study [17] indicated that the single-step model [16] might be more robust than the Vitezica model [22] used in this paper in terms of both predictive ability and unbiasedness for the crossbred performance. Our results suggested that using a small set of genotyped animals and pre-corrected data to implement genetic evaluation for crossbred performance was less powerful than using the whole dataset, which is similar to the conclusions for purebred performance [43].…”

Genomic selection in pigs: state of the art and perspectives

“…Recently, by using field data, Veroneze et al (2015) reported accuracy of 0.25-0.29 in crossbreds by using purebred data, and Hidalgo et al (2015b) reported that the use of crossbred training data would outperform those of purebred training data in the prediction of crossbred merits. Several methodological aspects related to the use of pure breed and crossbreeding information were further developed by Christensen et al (2014) who presented a single-step method for genomic evaluation of both purebred and crossbred performances in a twobreed crossbreeding system by extending a model proposed by Wei and van der Werf (1994). The method included two partial relationship matrices for the two breeds and constructed marker-based partial relationship matrices that were adjusted to be compatible to pedigree-based partial relationship matrices to combine marker and pedigree-based source of information.…”

Genomic selection in multi-breed dairy cattle populations

“…Misztal et al (2013) demonstrated that unknown-parent groups can be used in ssGBLUP to accommodate crossbred animals because unknown parents of different breeds are assigned to different groups. Christensen et al (2014) extended the model of Wei and van der Werf (1994), which models two purebred lines and their F1 crosses to ssGBLUP using partial relationship matrices.…”

“…Recently, Christensen et al (2014) extended the twobreed model of Wei and van der Werf (1994) to include genomic information, using partial relationship matrices to combine pedigree and marker information. Their results showed promising results, and could be readily applied to a population such as the Girolando breed because the method allows information from crossbred animals to be incorporated in a coherent manner for such a crossbreeding system, but additional validation of their approach is desirable.…”

“…Lund et al (2014) recently provided a comprehensive review of genomic selection in multiple breed populations. Research conducted to date has focused on several problems, including use of information from one breed to improve predictions in one or more other breeds (Boerner et al, 2014;Porto-Neto et al, 2015), use of data from crossbreds to improve purebred predictions (Grevenhof and Werf, 2015), use of data from purebreds to improve crossbred predictions (Esfandyari et al, 2015a), use of data from crossbreds to improve crossbred predictions (Esfandyari et al, 2015b), and use of genomic information to estimate genomic predicted transmitting ability (PTA) for both purebred and crossbred animals (Christensen et al, 2014;Hozé et al, 2014). The objective of this review is to describe how genomic selection can be applied to dairy cattle populations that include purebred and crossbred animals.…”