Effect of breed composition in genomic prediction using crossbred pig reference population
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초록
In contrast to conventional genomic prediction, which typically targets a single breed and circumvents the necessity for population structure adjustments, multi-breed genomic prediction necessitates accounting for population structure to mitigate potential bias. The presence of this structure in multi-breed datasets can influence prediction accuracy, rendering proper modeling crucial for achieving unbiased results. This study aimed to address the effect of population structure on multi-breed genomic prediction, particularly focusing on crossbred reference populations. The prediction accuracy of genomic models was assessed by incorporating genomic breed composition (GBC) or principal component analysis (PCA) into the genomic best linear unbiased prediction (GBLUP) model. The accuracy of five different genomic prediction models was evaluated using data from 354 Duroc × Korean native pig crossbreds, 1,105 Landrace × Korean native pig crossbreds, and 1,107 Landrace × Yorkshire × Duroc crossbreds. The models tested were GBLUP without population structure adjustment, GBLUP with PCA as a fixed effect, GBLUP with GBC as a fixed effect, GBLUP with PCA as a random effect, and GBLUP with GBC as a random effect. The highest prediction accuracies for backfat thickness (0.59) and carcass weight (0.50) were observed in Models 1, 4, and 5. In contrast, Models 2 and 3, which included population structure as a fixed effect, exhibited lower accuracies, with backfat thickness accuracies of 0.40 and 0.53 and carcass weight accuracies of 0.34 and 0.38, respectively. These findings suggest that in multi-breed genomic prediction, the most efficient and accurate approach is either to forgo adjusting for population structure or, if adjustments are necessary, to model it as a random effect. This study provides a robust framework for multi-breed genomic prediction, highlighting the critical role of appropriately accounting for population structure. Moreover, our findings have important implications for improving genomic selection efficiency, ultimately enhancing commercial production by optimizing prediction accuracy in crossbred populations.
Abstract
In contrast to conventional genomic prediction, which typically targets a single breed and circumvents the necessity for population structure adjustments, multi-breed genomic prediction necessitates accounting for population structure to mitigate potential bias. The presence of this structure in multi-breed datasets can influence prediction accuracy, rendering proper modeling crucial for achieving unbiased results. This study aimed to address the effect of population structure on multi-breed genomic prediction, particularly focusing on crossbred reference populations. The prediction accuracy of genomic models was assessed by incorporating genomic breed composition (GBC) or principal component analysis (PCA) into the genomic best linear unbiased prediction (GBLUP) model. The accuracy of five different genomic prediction models was evaluated using data from 354 Duroc × Korean native pig crossbreds, 1,105 Landrace × Korean native pig crossbreds, and 1,107 Landrace × Yorkshire × Duroc crossbreds. The models tested were GBLUP without population structure adjustment, GBLUP with PCA as a fixed effect, GBLUP with GBC as a fixed effect, GBLUP with PCA as a random effect, and GBLUP with GBC as a random effect. The highest prediction accuracies for backfat thickness (0.59) and carcass weight (0.50) were observed in Models 1, 4, and 5. In contrast, Models 2 and 3, which included population structure as a fixed effect, exhibited lower accuracies, with backfat thickness accuracies of 0.40 and 0.53 and carcass weight accuracies of 0.34 and 0.38, respectively. These findings suggest that in multi-breed genomic prediction, the most efficient and accurate approach is either to forgo adjusting for population structure or, if adjustments are necessary, to model it as a random effect. This study provides a robust framework for multi-breed genomic prediction, highlighting the critical role of appropriately accounting for population structure. Moreover, our findings have important implications for improving genomic selection efficiency, ultimately enhancing commercial production by optimizing prediction accuracy in crossbred populations.