Genetic analysis of within-litter variation in piglets' birth weight using genomic or pedigree relationship matrices1

Abstract

The objective of this study was to estimate the genetic variance for within-litter variation of birth weight (BW0) using genomic (GRM) or pedigree relationship matrices (PRM) and to compare the accuracy of estimated breeding values (EBV) for within-litter variation of BW0 using GRM and PRM. The BW0 and residual variance of BW0 were modeled by the double hierarchical generalized linear model using GRM or PRM. Data came from 2 dam lines: Landrace and Large White. After editing, the data set in Landrace consisted of 748 sows with 1,938 litters and 29,430 piglets and in Large White of 989 sows with 3,320 litters and 51,818 piglets. To construct GRM, 46,466 (Landrace) and 44,826 (Large White) single nucleotide polymorphisms were used, whereas to construct PRM, 5 generations of pedigree were used. The accuracy of EBV with GRM was estimated with 8-fold cross-validation and compared to PRM. Estimated variance components were highly similar for GRM and PRM. The maternal genetic variance in residual variance of BW0 in Landrace was 0.05 with GRM and 0.06 with PRM. In Large White these were 0.04 with GRM and 0.05 with PRM. The genetic coefficient of variation (GCV SDe) was about 0.10 in both dam lines. This indicates a change of 10% in residual SD of BW0 when achieving a genetic response of 1 genetic standard deviation. The genetic correlation between birth weight and its residual variance was about 0.6 in both dam lines. The accuracies of selection for within-litter variation of birth weight were 0.35 with GRM and 0.23 with PRM in Landrace and 0.29 with GRM and 0.34 with PRM in Large White. In this case, using GRM did not significantly increase accuracies of selection. Results, however, show good opportunities to select for reduced within-litter variation of BW0. Genomic selection can increase accuracy of selection when reference populations contain at least 2,000 sows.

Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluation

“…In contrast, genomic information did not influence the for uniformity of lnWT (29.8%). Since estimates of genetic parameters obtained with and were similar, the for body weight remained similar, which is in agreement with the previous comparison between ( = 11.0 to 12.0%) and ( = 10.0 to 11.0%) for uniformity of birth weight of piglets using a dam model [42]. …”

“…Estimates of for stdWT and lnWT using the sire-dam DHGLM with did not differ from those with , which is in line with estimates of for uniformity of piglet birth weight obtained using either or only the genomic relationship matrix () [42], while lower estimates were reported for environmental variance of somatic cell score in dairy cattle when using compared to [43]. The similarity of the estimates of obtained by using or in this study can be explained by the very similar estimated residuals (proxy of uniformity) between non-genotyped and genotyped animals when using the sire-dam model with and .…”

“…This result is in the upper range of previous findings in fish species (17.4 to 64.0%) [4, 8, 14–16, 19] and in terrestrial animals (10.0 to 58.0%) [6, 9–13, 42]. After accounting for scale effects by logarithm transformations, the for uniformity was reduced but still substantial (29.5 to 29.9%), which was also reported in previous studies on Atlantic salmon [14], rainbow trout [8], rabbit, and pig [45].…”

“…The accuracy of genomic selection is expected to increase when the number of genotyped animals in the reference population increases for any trait [46] but in particular for lowly heritable traits, such as uniformity as shown by Sell-Kubiak et al [42] and somatic cell score by Mulder et al [43]. In this study, uniformity of lnWT had an even lower than uniformity of stdWT.…”

A review of genotype‐by‐environment interaction and micro‐environmental sensitivity in aquaculture species

“…; Sell‐Kubiak et al . ). In general, reducing variance of a trait by selection or mating is likely to be more difficult than changing the mean of the trait (Hohenboken ).…”