A deep learning strategy for accurate identification of purebred and hybrid pigs across SNP chips.

Background: Breed identification plays an important role in conserving indigenous breeds, managing genetic resources, and developing effective breeding strategies. However, researches on breed identification in livestock mainly focused on purebreds, and they yielded lower predict accuracy in hybrid. In this study, we presented a Multi-Layer Perceptron (MLP) model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs.

Results: We utilized a total of 8,199 pigs from breeding farms in eight provinces in China, comprising Yorkshire, Landrace, Duroc and hybrids of Yorkshire × Landrace. All the animals were genotyped with 1K, 50K and 100K SNP chips. Comparing with random forest (RF), support vector regression (SVR) and Admixture, our results from five replicates of fivefold cross validation demonstrated that MLP achieved a breed identification accuracy of 100% for both hybrid and purebreds in 50K and 100K SNP chips, SVR performed comparable with MLP, they both outperformed RF and Admixture. In the independent testing, MLP yielded accuracy of 100% for all three pure breeds and hybrid across all SNP chips and panel, while SVR yielded 0.026%-0.121% lower accuracy than MLP. Compared with classification-based framework, the new strategy of multi-output regression framework in this study was helpful to improve the predict accuracy. MLP, RF and SVR, achieved consistent improvements across all six SNP chips/panel, especially in hybrid identification. Our results showed the determination threshold for purebred had different effects, SVR, RF and Admixture were very sensitive to threshold values, their optimal threshold fluctuated in different scenarios, while MLP kept optimal threshold 0.75 in all cases. The threshold of 0.65-0.75 is ideal for accurate breed identification. Among different density of SNP chips, the 1K SNP chip was most cost-effective as yielding 100% accuracy with enlarging training set. Hybrid individuals in the training set were useful for both purebred and hybrid identification.

Conclusions: Our new MLP strategy demonstrated its high accuracy and robust applicability across low-, medium-, and high-density SNP chips. Multi-output regression framework could universally enhance prediction accuracy for ML methods. Our new strategy is also helpful for breed identification in other livestock.