Genetic gain & Inbreeding

Validation of predicted Mendelian sampling variance for optimizing selection: An application in pigs

---

Abstract

In modern animal breeding programs, a key goal is to optimize selection while maintaining genetic diversity. One approach to address this issue is to account for the genetic variance of future descendants in selection decisions by valuing diversity through the expected genetic gain. This can be achieved using predicted gametic Mendelian Sampling Variances (pred_MSVgam), which represent an individual's contribution to the genetic variance of the next generation. Information on gametic Mendelian Sampling Variance selection candidates can be used to improve the chances of producing superior animals. Before applying new selection methods, the accuracy of predicted gametic Mendelian Sampling Variances (pred_MSVgam) must be evaluated. Thus, this study aimed to validate how accurately the predicted gametic MSV of sires reflects the variation observed among their offspring for four pig finishing traits: Average daily gain prior to testing (YDG), Average daily gain during testing (TDG), Backfat thickness at off-testing (BFE), Loin Depth at off-testing (LDE), and one pseudo-selection index that incorporated these four traits. The complete data set included 997,380 phenotypic records, 410,877 genotyped animals with 20,958 SNPs, and pedigree information from 1,050,924 individuals. To validate predicted variances, the training subset was filtered to include animals born before 2023. EBVs and SNP effects were estimated using a multi-trait ssGTBLUP(T-factoring) model applied to both the full and the training datasets. Young, genotyped sires from the training set were selected as validation individuals, and their pred_MSVgam was estimated using their phased genotypes, a genetic map, and SNP effects estimated from the training set. Validation was performed using progeny born between January 2023 and December 2024, by comparing the pred_MSVgam values calculated for the sires with the observed variances of offspring MS term from their EBVs (obs_MSVoff) estimated from the entire dataset. The validation was performed on pigs from two terminal sire-line populations (P1 and P2) and included only sires with at least 100 phenotyped offspring per trait. Results showed that sires with higher pred_MSVgam produced greater obs_MSVoff. The correlation between these two measurements ranged from 0.20 to 0.68 at the trait level and from 0.55 to 0.60 at the index level, indicating overall good predictive ability of pred_MSVgam in both cases. These results were consistent across both populations. The differences in correlations among traits and populations likely indicate variation in marker effects and family structure, with larger families showing higher correlations. They were therefore less impacted by random sampling errors. In conclusion, pred_MSVgam could be a valuable tool for identifying selection candidates with a higher potential to produce offspring with greater genetic variability and, as a result, more extreme breeding values. This may improve genetic gain. Additionally, this tool may help reduce diversity loss in pig populations under high selection pressure.

Keywords: 2026

How to Cite:

Londoño-Gil, M., Leite, N., Niehoff, T., Hanenberg, E. & Grindflek, E., (2026) “Validation of predicted Mendelian sampling variance for optimizing selection: An application in pigs”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2272066. doi: https://doi.org/10.31274/wcgalp.23410

Rights: 1