Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle

Abstract

Test-day traits are important for genetic evaluation in dairy cattle and are better modeled by multiple-trait random regression models (RRM). The reliability and bias of genomic estimated breeding values (GEBV) predicted using multiple-trait RRM via single-step genomic best linear unbiased prediction (ssGBLUP) were investigated in the 3 major dairy cattle breeds in Canada (i.e., Ayrshire, Holstein, and Jersey). Individual additive genomic random regression coefficients for the test-day traits were predicted using 2 multiple-trait RRM: (1) one for milk, fat, and protein yields in the first, second, and third lactations, and (2) one for somatic cell score in the first, second, and third lactations. The predicted coefficients were used to derive GEBV for each lactation day and, subsequently, the daily GEBV were compared with traditional daily parent averages obtained by BLUP. To ensure compatibility between pedigree and genomic information for genotyped animals, different scaling factors for combining the inverse of genomic (G −1 ) and pedigree A 22 1 − ( ) relationship matrices were tested. In addition, the inclusion of only genotypes from animals with accurate breeding values (defined in preliminary analysis) was compared with the inclusion of all available genotypes in the analyzes. The ssGBLUP model led to considerably larger validation reliabilities than the BLUP model without genomic information. In general, scaling factors used to combine the G −1 and A 22 1 − matrices had small influence on the validation reliabilities. However, a greater effect was observed in the inflation of GEBV. Less inflated GEBV were obtained by the ssGBLUP compared with the parent average from traditional BLUP when using optimal scaling factors to combine the G −1 and A 22 1 − matrices. Similar results were observed when including either all available genotypes or only genotypes from animals with accurate breeding values. These findings indicate that ssGBLUP using multiple-trait RRM increases reliability and reduces bias of breeding values of young animals when compared with parent average from traditional BLUP in the Canadian Ayrshire, Holstein, and Jersey breeds.

Comparing Alternative Single-Step GBLUP Approaches and Training Population Designs for Genomic Evaluation of Crossbred Animals

“…As genomic datasets were simulated, all individuals included in the pedigree also had genotypes. In order to make G −1 and A 22 −1 matrices compatible (Misztal et al, 2017;Oliveira et al, 2019), different values for the τ (from 0.9 to 2.5; defined at every 0.1) and ω (from 0.5 to 1.2; defined at every 0.1) parameters were tested. These ranges were chosen based on the literature (Misztal et al, 2017;Oliveira et al, 2019).…”

“…In order to make G −1 and A 22 −1 matrices compatible (Misztal et al, 2017;Oliveira et al, 2019), different values for the τ (from 0.9 to 2.5; defined at every 0.1) and ω (from 0.5 to 1.2; defined at every 0.1) parameters were tested. These ranges were chosen based on the literature (Misztal et al, 2017;Oliveira et al, 2019). As G −1 and A 22 −1 matrices were basically the same in all scenarios (i.e., the A 22 −1 matrix was the same in all scenarios, and G −1 matrix was the same in SC1, SC2, and SC3; and training crossbred animals were excluded from SC4 and SC5, but the validation crossbred animals remained on all SCs), τ and ω parameters were only tested using SC1.…”

“…σ 2 u and σ 2 e are the additive genetic and residual variances, respectively. Variance components were independently estimated for each scenario using the AIREMLF90 software and the A matrix, since it has been currently recommended in several ssGBLUP and WssGBLUP studies (Ali et al, 2019;Oliveira et al, 2019;Pocrnic et al, 2019). The multiple-trait model used in SC2 can be described as:…”

“…The ssGBLUP and WssGBLUP methods assume that the statistical model is correct and that allelic frequencies come from the base population (Oliveira et al, 2019). However, these assumptions usually do not hold in practice, which can result in prediction bias (Vitezica et al, 2011).…”

“…−1 matrices are usually not on the same scale (Misztal et al, 2017;Oliveira et al, 2019). In order to obtain better prediction accuracies and reduce the bias, Tsuruta et al (2011) and Misztal et al (2013) reported that scaling factors should be used when combining G −1 and A 22 −1 matrices to create the H matrix.…”