Over the past decade, Fourier transform infrared (FTIR) spectroscopy has been used to predict novel milk protein phenotypes. Genomic data might help predict these phenotypes when integrated with milk FTIR spectra. The objective of this study was to investigate prediction accuracy for milk protein phenotypes when heterogeneous on-farm, genomic, and pedigree data were integrated with the spectra. To this end, we used the records of 966 Italian Brown Swiss cows with milk FTIR spectra, on-farm information, medium-density genetic markers, and pedigree data. True and total whey protein, and five casein, and two whey protein traits were analyzed. Multiple kernel learning constructed from spectral and genomic (pedigree) relationship matrices and multilayer BayesB assigning separate priors for FTIR and markers were benchmarked against a baseline partial least squares (PLS) regression. Seven combinations of covariates were considered, and their predictive abilities were evaluated by repeated random sub-sampling and herd cross-validations (CV). Addition of the on-farm effects such as herd, days in milk, and parity to spectral data improved predictions as compared to those obtained using the spectra alone. Integrating genomics and/or the top three markers with a large effect further enhanced the predictions. Pedigree data also improved prediction, but to a lesser extent than genomic data. Multiple kernel learning and multilayer BayesB increased predictive performance, whereas PLS did not. Overall, multilayer BayesB provided better predictions than multiple kernel learning, and lower prediction performance was observed in herd CV compared to repeated random sub-sampling CV. Integration of genomic information with milk FTIR spectral can enhance milk protein trait predictions by 25% and 7% on average for repeated random sub-sampling and herd CV, respectively. Multiple kernel learning and multilayer BayesB outperformed PLS when used to integrate heterogeneous data for phenotypic predictions.

中文翻译：

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整合基因组和红外光谱数据可改善奶牛乳蛋白组成的预测

在过去的十年中，傅里叶变换红外（FTIR）光谱已用于预测新型牛奶蛋白表型。与牛奶FTIR光谱整合后，基因组数据可能有助于预测这些表型。这项研究的目的是研究将异种农场，基因组和家谱数据与光谱结合时牛奶蛋白表型的预测准确性。为此，我们使用了966头意大利棕色瑞士奶牛的记录，这些奶牛具有FTIR光谱，农场信息，中等密度遗传标记和家谱数据。分析了真乳清蛋白和总乳清蛋白，以及五个酪蛋白和两个乳清蛋白性状。从基线和谱系（谱系）关系矩阵和多层BayesB（为FTIR和标记分配单独的先验值）构建的多核学习是针对基线偏最小二乘（PLS）回归进行基准测试的。考虑了协变量的七个组合，并通过重复随机子抽样和牛群交叉验证（CV）评估了它们的预测能力。与单独使用光谱获得的结果相比，在光谱数据中添加农场效应（例如牛群，牛奶天数和奇偶校验）可改善预测结果。将基因组学和/或排名前三的标记物整合在一起，效果显着，进一步增强了预测结果。家谱数据也改善了预测，但程度不及基因组数据。多核学习和多层BayesB可以提高预测性能，而PLS则不能。总体而言，多层BayesB提供的预测优于多核学习，与重复随机子采样CV相比，在牛群CV中观察到的预测性能较低。将基因组信息与牛奶FTIR光谱相集成，可以使重复随机次采样和牛群CV的牛奶蛋白质性状预测平均分别提高25％和7％。当使用多核学习和多层BayesB集成用于表型预测的异构数据时，其性能优于PLS。将基因组信息与牛奶FTIR光谱相集成，可以使重复随机次采样和牛群CV的牛奶蛋白质性状预测平均分别提高25％和7％。当使用多核学习和多层BayesB集成用于表型预测的异构数据时，其性能优于PLS。将基因组信息与牛奶FTIR光谱相集成，可以使重复随机次采样和牛群CV的牛奶蛋白质性状预测平均分别提高25％和7％。当使用多核学习和多层BayesB集成用于表型预测的异构数据时，其性能优于PLS。