Including nonadditive genetic effects in mating programs to maximize dairy farm profitability

Abstract

We compared the outcome of mating programs based on different evaluation models that included nonadditive genetic effects (dominance and heterozygosity) in addition to additive effects. The additive and dominance marker effects and the values of regression on average heterozygosity were estimated using 632,003 single nucleotide polymorphisms from 7,902 and 7,510 Holstein cows with calving interval and production (milk, fat, and protein yields) records, respectively. Expected progeny values were computed based on the estimated genetic effects and genotype probabilities of hypothetical progeny from matings between the available genotyped cows and the top 50 young genomic bulls. An index combining the traits based on their economic values was developed and used to evaluate the performance of different mating scenarios in terms of dollar profit. We observed that mating programs with nonadditive genetic effects performed better than a model with only additive effects. Mating programs with dominance and heterozygosity effects increased milk, fat, and protein yields by up to 38, 1.57, and 1.21 kg, respectively. The inclusion of dominance and heterozygosity effects decreased calving interval by up to 0.70 d compared with random mating. The average reduction in progeny inbreeding by the inclusion of nonadditive genetic effects in matings compared with random mating was between 0.25 to 1.57 and 0.64 to 1.57 percentage points for calving interval and production traits, respectively. The reduction in inbreeding was accompanied by an average of A$8.42 (Australian dollars) more profit per mating for a model with additive, dominance, and heterozygosity effects compared with random mating. Mate allocations that benefit from nonadditive genetic effects can improve progeny performance only in the generation where it is being implemented, and the gain from specific combining abilities cannot be accumulated over generations. Continuous updating of genomic predictions and mate allocation programs are required to benefit from nonadditive genetic effects in the long term.

Non-additive Effects in Genomic Selection

“…In a simulated example, Toro and Varona ( 2010 ) compared random mating vs. mate selection with a model including dominance and found advantages that ranged between 6 and 22% of the expected response. Sun et al ( 2013 ), Ertl et al ( 2014 ), and Aliloo et al ( 2017 ) have confirmed these improvements with dairy cattle data. However, its implementation in livestock populations is limited because it must be taken into account that the accuracy of the prediction of a potential mate will be low and the advantage will be only relevant when traits have a large amount of non-additive genetic variance.…”

“…In any case, only additive values (substitution effects) contribute to breeding values and are therefore expressed in the next generation. However, estimates of non-additive genetic effects may be of relevance because: (i) they may contribute to increasing the accuracy of prediction of breeding values and the response to selection (Toro and Varona, 2010 ; Aliloo et al, 2016 ; Duenk et al, 2017 ); (ii) they allow the definition of mate allocation procedures between candidates for selection (Maki-Tanila, 2007 ; Toro and Varona, 2010 ; Aliloo et al, 2017 ); and (iii) they can be used to benefit from non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes (Maki-Tanila, 2007 ; Zeng et al, 2013 ).…”

Estimation of additive and non‐additive genetic effects for fertility and reproduction traits in North American Holstein cattle using genomic information

“…This method is useful as it makes the genomic relationship matrices analogous to the pedigree-based numerator relationship matrices, allowing for a direct comparison of genomic-based estimates of variance components with the pedigree-based counterparts. It has been suggested that the dominance variance could be over-estimated if the average effects of inbreeding depression are not accounted for in the models (Aliloo et al, 2017). Therefore, we also investigated the effect of fitting animal's inbreeding coefficient as a covariate when estimating variance components.…”

Genomic Model with Correlation Between Additive and Dominance Effects

“…The inclusion of dominance in genomic evaluation models has been proposed by several authors (Su et al 2012;Vitezica et al 2013Vitezica et al , 2016Ertl et al 2014;Muñoz et al 2014;Aliloo et al 2016Aliloo et al , 2017Xiang et al 2016). In those studies, additive and dominant marker effects ða and dÞ are considered uncorrelated.…”