Using genomic selection to improve the accuracy of genomic prediction for multi-populations in pigs

The size of the reference group is among the most critical determinants of genomic estimated breeding values (GEBVs) accuracy. However, small- and medium-sized pig farms often need help accumulating adequate reference data, posing significant challenges to breeding programs. To solve this problem, exploring the potential benefits of combining reference groups of different sizes is necessary to improve GEBV accuracy. The primary objective of this investigation was to assess a more effective statistical model for combined multi-populations and its potential to enhance the accuracy of GEBVs for small and medium populations. Three populations were simulated using the QMSim software, each consisting of different sizes (300, 600, and 1 500, respectively). To assess the impact of heritability on the accuracy of GEBVs, four different levels of heritability (0.05, 0.15, 0.35, and 0.5) were simulated. Simultaneously, to investigate the impact of kinship on multi-populations, the study created four distinct scenarios for the three sizes of populations. These scenarios included: (1) the three groups are all independent, (2) the large group and the small group with a familial connection (n = 1 800), a middle group (n = 600) acting independently with no kinship, (3) the large group with a familial connection to the middle group (n = 2 100) but no connection to the small group (n = 300), and (4) the small group with a familial connection to the middle group (n = 900), while the large group (n = 1 500) acted independently with no kinship. This study evaluates and compares the accuracy of predicting breeding values using four different methods, including genomic best linear unbiased prediction (GBLUP), single-stepGBLUP (ssGBLUP), and two Bayesian models (Bayes A and Bayes B), with varying sizes of reference groups. In each scenario, three different prediction strategies were compared: (1) Merging all three different sizes of populations for predicting, (2) predicting each independent population separately, and (3) the other two populations predict the population. Our findings reveal that combining populations enhances the Bayesian models, with Bayes B yielding the highest accuracy. In independent populations, the best linear unbiased prediction (BLUP) models demonstrated the highest accuracy. However, in cases where populations were related and the heritability was high, the Bayes B model exhibited the highest overall accuracy (slightly higher than BLUP models) in the independent population. Our results underscore the importance of considering population combinations when using genetic models to predict breeding values, particularly for pig farmers with limited resources.