Zhang Zipeng, Fang Zhengwen, Du Yongwang, He Yilin, Qian Changsong, Ye Weijian, Zhang Ning, Zhang Jianan, Ding Xiangdong
State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
MolBreeding Biotechnology Ltd., Shijiazhuang, 050035, China.
J Anim Sci Biotechnol. 2025 Aug 14;16(1):116. doi: 10.1186/s40104-025-01249-y.
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.
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.
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.
品种鉴定在保护本土品种、管理遗传资源以及制定有效的育种策略方面发挥着重要作用。然而，家畜品种鉴定的研究主要集中在纯种上，在杂种中预测准确率较低。在本研究中，我们提出了一种具有多输出回归框架的多层感知器（MLP）模型，专门用于猪纯种和杂种的基因组品种组成预测。
我们使用了来自中国八个省份养殖场的总共8199头猪，包括约克夏猪、长白猪、杜洛克猪以及约克夏×长白杂种猪。所有动物都用1K、50K和100K SNP芯片进行了基因分型。与随机森林（RF）、支持向量回归（SVR）和混合模型（Admixture）相比，我们五重交叉验证的五次重复结果表明，在50K和100K SNP芯片上，MLP对杂种和纯种的品种鉴定准确率均达到100%，SVR与MLP表现相当，它们均优于RF和Admixture。在独立测试中，MLP在所有SNP芯片和平板上对所有三个纯种和杂种的准确率均达到100%，而SVR的准确率比MLP低0.026%-0.121%。与基于分类的框架相比，本研究中的多输出回归框架新策略有助于提高预测准确率。MLP、RF和SVR在所有六个SNP芯片/平板上均实现了一致的提高，尤其是在杂种鉴定方面。我们的结果表明，纯种的判定阈值有不同影响，SVR、RF和Admixture对阈值非常敏感，它们的最佳阈值在不同情况下波动，而MLP在所有情况下的最佳阈值均保持为0.75。0.65-0.75的阈值对于准确的品种鉴定是理想的。在不同密度的SNP芯片中，1K SNP芯片最具成本效益，通过扩大训练集可达到100%的准确率。训练集中的杂种个体对纯种和杂种鉴定都有用。
我们新的MLP策略在低、中、高密度SNP芯片上均展示了其高精度和强大的适用性。多输出回归框架可以普遍提高ML方法的预测准确率。我们的新策略也有助于其他家畜的品种鉴定。
