College of Animal Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Hainan Institute of Zhejiang University, Building 11, Yongyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya, 572025, Hainan, China.
Genet Sel Evol. 2023 Mar 21;55(1):18. doi: 10.1186/s12711-023-00788-0.
Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration.
We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds.
In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.
经过 9000 多年的自然和人工选择，产生了多种家猪品种。准确识别猪品种对于品种保护、可持续繁殖、猪肉可追溯性和本地资源登记都很重要。
我们使用广泛的猪品种（N=91）评估了 4 种选择器和 6 种分类器的品种识别性能。内部交叉验证和外部独立测试表明，偏最小二乘回归（PLSR）是最有效的选择器，偏最小二乘判别分析（PLS-DA）是品种识别中最有效的分类器。五重交叉验证表明，使用 PLSR 作为选择器，PLS-DA 作为分类器来区分 91 个猪品种，只需 3K 个单核苷酸多态性（SNP），准确率达到 98.4%。我们还构建了一个包含 124 个猪品种的参考数据集，并使用它开发了一个网络工具 iDIGs（http://alphaindex.zju.edu.cn/iDIGs_en/），作为全球猪品种识别的综合应用程序。iDIGs 允许用户（1）在没有参考群体的情况下识别猪品种，（2）设计小面板来区分几个特定的猪品种。
在这项研究中，我们证明了在广泛的猪品种中进行品种识别是可行的，并开发了一个用于这种猪品种识别的网络工具。
