PIBLUP: High-Performance Software for Large-Scale Genetic Evaluation of Animals and Plants

MAGE: metafounders-assisted genomic estimation of breeding value, a novel additive-dominance single-step model in crossbreeding systems

MOTIVATION

Utilizing both purebred and crossbred data in animal genetics is widely recognized as an optimal strategy for enhancing the predictive accuracy of breeding values. Practically, the different genetic background among several purebred populations and their crossbred offspring populations limits the application of traditional prediction methods. Several studies endeavor to predict the crossbred performance via the partial relationship, which divides the data into distinct sub-populations based on the common genetic background, such as one single purebred population and its corresponding crossbred descendant. However, this strategy makes prediction inaccurate due to ignoring half of the parental information of crossbreed animals. Furthermore, dominance effects, although playing a significant role in crossbreeding systems, cannot be modeled under such a prediction model.

RESULTS

To overcome this weakness, we developed a novel multi-breed single-step model using metafounders to assess ancestral relationships across diverse breeds under a unified framework. We proposed to use multi-breed dominance combined relationship matrices to model additive and dominance effects simultaneously. Our method provides a straightforward way to evaluate the heterosis of crossbreeds and the breeding values of purebred parents efficiently and accurately. We performed simulation and real data analyses to verify the potential of our proposed method. Our proposed model improved prediction accuracy under all scenarios considered compared to commonly used methods.

AVAILABILITY AND IMPLEMENTATION

The software for implementing our method is available at https://github.com/CAU-TeamLiuJF/MAGE.

Improving genomic prediction for two Yorkshire populations with a limited size using the single-step method

In this study, we conducted genomic prediction for two Yorkshire purebred populations (Yichun and Chifeng) from two different provinces of China that both had a limited population size. Two growth traits (age adjusted to 100 kg weight, AGE; back-fat thickness adjusted to 100 kg weight, BF) and one reproduction trait (total number of piglets born, TNB) were analyzed with four prediction strategies: one-population BLUP, joint two-population BLUP, one-population single-step BLUP (SSBLUP) and joint two-population SSBLUP. Our results illustrate that accuracies of genomic estimated breeding values were improved for BF and TNB for the Yichun population and for BF for the Chifeng population by genomic prediction (one-population SSBLUP and joint two-population SSBLUP). The accuracy of TNB for the Yichun population was increased two fold when comparing the one-population SSBLUP to the one-population BLUP prediction. Meanwhile, prediction biases were dramatically reduced for AGE for the Yichun population and for TNB for the Chifeng population. The conclusions of this study are as follows: first, genomic prediction is useful for improving prediction accuracy for purebred pig breeding farms with a limited population size; second, joint genomic prediction for different populations of the same breed with certain genetic links has the trend to further improve prediction accuracy.