Non-additive genetic effects for fertility traits in Canadian Holstein cattle (Open Access publication )

Abstract

-The effects of additive, dominance, additive by dominance, additive by additive and dominance by dominance genetic effects on age at first service, non-return rates and interval from calving to first service were estimated. Practical considerations of computing additive and dominance relationships using the genomic relationship matrix are discussed. The final strategy utilized several groups of 1000 animals (heifers or cows) in which all animals had a non-zero dominance relationship with at least one other animal in the group. Direct inversion of relationship matrices was possible within the 1000 animal subsets. Estimates of variances were obtained using Bayesian methodology via Gibbs sampling. Estimated non-additive genetic variances were generally as large as or larger than the additive genetic variance in most cases, except for non-return rates and interval from calving to first service for cows. Non-additive genetic effects appear to be of sizeable magnitude for fertility traits and should be included in models intended for estimating additive genetic merit. However, computing additive and dominance relationships for all possible pairs of individuals is very time consuming in populations of more than 200 000 animals. fertility / gene interactions / dairy cattle / non-additive models

Marker-based estimates reveal significant non-additive effects in clonally propagated cassava (Manihot esculenta): implications for the prediction of total genetic value and the selection of varieties

“…The importance of nonadditive genetic variance in evolution by natural and artificial selection is controversial Crow 2010;Hansen 2013). Nevertheless, numerous studies have found and exploited dominance and epistasis in animal breeding, including dairy (Ahlborn-Breier and Hohenboken 1991;Fuerst and Sölkner 1994;Varona et al 1998;Van Tassell et al 2000;Palucci et al 2007) and beef (Rodriguezalmeida et al 1995) cattle. Diallelic studies have indicated significant SCA for maize grain yield (Doerksen et al 2003;Wardyn et al 2007).…”

“…Even for low h 2 traits and without inbred cassava, using the kinds of models presented in this paper, good parents can be selected based on additive predictions and total genetic value can be predicted for the identification of potential commercial varieties, all based on the combination of marker and preliminary field trial data (Heslot et al 2015). This approach has been previously advocated for plant breeding (Oakey et al 2007;Heslot et al 2015) and has proven effective in animal breeding (e.g., Ahlborn-Breier and Hohenboken 1991; Palucci et al 2007;Su et al 2012;Nishio and Satoh 2014). Nonadditive models using GRMs can thus improve the efficiency and productivity of variety selection pipelines that are the most labor-and time-intensive part of selecting good cassava clones after crossing.…”

Unraveling Additive from Nonadditive Effects Using Genomic Relationship Matrices

“…The proportion of additive variance attributable to interactions of alleles largely depends on the distribution of allele frequencies at causal loci (Lu et al 1999;Hill et al 2008;Zuk et al 2012). This affects the estimation of variance components and breeding value (BV) predictions (Vanderwerf and Deboer 1989;Palucci et al 2007), as well as the ability to dissect the genetic architecture of the trait at the causal level. Understanding the genetic architecture of a trait is also useful for defining breeding strategies and for maximizing genetic gains.…”

An overview on quantitative and genomic tools for utilising dominance genetic variation in improving animal production

“…Recently, Palucci et al (2007) circumvented the size problem by dividing the population data into subsets and presented the variance components as weighted averages of the subset estimates. They found that for computing the additive and dominance relationships among some 600 000 animals in the pedigree file with the algorithm of Smith and Mäki-Tanila (1990) would require computing 3.56 × 10 11 elements.…”