Multi-line ssGBLUP evaluation using preselected markers from whole-genome sequence data in pigs

Abstract

Genomic evaluations in pigs could benefit from using multi-line data along with whole-genome sequencing (WGS) if the data are large enough to represent the variability across populations. The objective of this study was to investigate strategies to combine large-scale data from different terminal pig lines in a multi-line genomic evaluation (MLE) through single-step GBLUP (ssGBLUP) models while including variants preselected from whole-genome sequence (WGS) data. We investigated single-line and multi-line evaluations for five traits recorded in three terminal lines. The number of sequenced animals in each line ranged from 731 to 1,865, with 60k to 104k imputed to WGS. Unknown parent groups (UPG) and metafounders (MF) were explored to account for genetic differences among the lines and improve the compatibility between pedigree and genomic relationships in the MLE. Sequence variants were preselected based on multi-line genome-wide association studies (GWAS) or linkage disequilibrium (LD) pruning. These preselected variant sets were used for ssGBLUP predictions without and with weights from BayesR, and the performances were compared to that of a commercial porcine single-nucleotide polymorphisms (SNP) chip. Using UPG and MF in MLE showed small to no gain in prediction accuracy (up to 0.02), depending on the lines and traits, compared to the single-line genomic evaluation (SLE). Likewise, adding selected variants from the GWAS to the commercial SNP chip resulted in a maximum increase of 0.02 in the prediction accuracy, only for average daily feed intake in the most numerous lines. In addition, no benefits were observed when using preselected sequence variants in multi-line genomic predictions. Weights from BayesR did not help improve the performance of ssGBLUP. This study revealed limited benefits of using preselected whole-genome sequence variants for multi-line genomic predictions, even when tens of thousands of animals had imputed sequence data. Correctly accounting for line differences with UPG or MF in MLE is essential to obtain predictions similar to SLE; however, the only observed benefit of an MLE is to have comparable predictions across lines. Further investigation into the amount of data and novel methods to preselect whole-genome causative variants in combined populations would be of significant interest.

Genomic prediction and genome‐wide association studies for additive and dominance effects for body composition traits using 50 K and imputed high‐density SNP genotypes in Yunong‐black pigs

“…For CC, LBC and BF, the genomic accuracy of the imputed data was lower than that of the 50 K data. Moreover, there are no benefits in using WGS data for genomic prediction without variant preselection (Jang et al., 2023; Van Binsbergen et al., 2015) because of variant redundancy in WGS. However, this hypothesis requires further investigation.…”

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs

“…In certain instances, this strategy has resulted in improved accuracy of genomic prediction for growth and carcass traits in pigs, with improvements ranging from 0.9 to 46% for multi-breed populations [21][22][23]. However, it should be noted that this strategy did not result in improved prediction accuracy in all cases [21,24,25]. Fine mapping of causal variants was still challenging, and the advantages for genomic predictions were limited.…”