Sire evaluation for total number born in pigs using a genomic reaction norms approach1

Abstract

In the era of genome-wide selection (GWS), genotype-by-environment (G×E) interactions can be studied using genomic information, thus enabling the estimation of SNP marker effects and the prediction of genomic estimated breeding values (GEBV) for young candidates for selection in different environments. Although G×E studies in pigs are scarce, the use of artificial insemination has enabled the distribution of genetic material from sires across multiple environments. Given the relevance of reproductive traits, such as the total number born (TNB) and the variation in environmental conditions encountered by commercial dams, understanding G×E interactions can be essential for choosing the best sires for different environments. The present work proposes a two-step reaction norm approach for G×E analysis using genomic information. The first step provided estimates of environmental effects (herd-year-season, HYS), and the second step provided estimates of the intercept and slope for the TNB across different HYS levels, obtained from the first step, using a random regression model. In both steps, pedigree ( A: ) and genomic ( G: ) relationship matrices were considered. The genetic parameters (variance components, h(2) and genetic correlations) were very similar when estimated using the A: and G: relationship matrices. The reaction norm graphs showed considerable differences in environmental sensitivity between sires, indicating a reranking of sires in terms of genetic merit across the HYS levels. Based on the G: matrix analysis, SNP by environment interactions were observed. For some SNP, the effects increased at increasing HYS levels, while for others, the effects decreased at increasing HYS levels or showed no changes between HYS levels. Cross-validation analysis demonstrated better performance of the genomic approach with respect to traditional pedigrees for both the G×E and standard models. The genomic reaction norm model resulted in an accuracy of GEBV for "juvenile" boars varying from 0.14 to 0.44 across different HYS levels, while the accuracy of the standard genomic prediction model, without reaction norms, varied from 0.09 to 0.28. These results show that it is important and feasible to consider G×E interactions in evaluations of sires using genomic prediction models and that genomic information can increase the accuracy of selection across environments.

Genomic reaction norm models exploiting genotype × environment interaction on sexual precocity indicator traits in Nellore cattle

“…more accurate predictions were achieved. These results are in agreement with previous studies in pigs (Silva et al ) and cattle (Mota et al ). According to Forni et al () and Yu et al (), using the H matrix instead of the A matrix for genetic evaluation will reduce the error of prediction owing to the better estimation of the kinship coefficient.…”

“…The inclusion of genomic information in the RNM increased the genetic variance of intercept and slope around 18.55 and 23.00% respectively on average (Table ). The increase in genetic variance estimates using genomic information has been described previously (Silva et al ). In addition to the resulting higher variance components estimates, the inclusion of the H matrix in reaction norm models showed lower standard deviations (SD), i.e.…”

Association of single nucleotide polymorphisms with form traits in three New Zealand populations of radiata pine in the presence of genotype by environment interactions

“…Similar results were reported in a study estimating SNP effects on litter size in pigs, where the 100 largest absolute SNP effects ranged from 0 to 0.0026 across different environments (herdyear-season), 86 of which had effects in the opposite directions for different herd-year-season environments (Silva et al 2014). These differences in SNP effects imply that either (1) a SNP associated with a trait in one environment might not be associated with the trait in another environment or that (2) the beneficial effect of a SNP allele on a trait might be detrimental in another environment.…”

Improving production efficiency in the presence of genotype by environment interactions in pig genomic selection breeding programmes

“…A better estimation of genotype by environment estimate was observed with genomic selection as compared to conventional selection methods (Silva et al . ). In addition, genomic selection may help to improve feed efficiency traits in the future.…”