Tiezzi Francesco, Fleming Allison, Malchiodi Francesca
Department of Agriculture, Food, Environment and Forestry, University of Florence, 50144 Firenze, Italy.
Department of Animal Science, North Carolina State University, Raleigh, NC 27695, USA.
Animals (Basel). 2022 May 6;12(9):1189. doi: 10.3390/ani12091189.
The purpose of this study was to provide a procedure for the inclusion of milk spectral information into genomic prediction models. Spectral data were considered a set of covariates, in addition to genomic covariates. Milk yield and somatic cell score were used as traits to investigate. A cross-validation was employed, making a distinction for predicting new individuals' performance under known environments, known individuals' performance under new environments, and new individuals' performance under new environments. We found an advantage of including spectral data as environmental covariates when the genomic predictions had to be extrapolated to new environments. This was valid for both observed and, even more, unobserved families (genotypes). Overall, prediction accuracy was larger for milk yield than somatic cell score. Fourier-transformed infrared spectral data can be used as a source of information for the calculation of the 'environmental coordinates' of a given farm in a given time, extrapolating predictions to new environments. This procedure could serve as an example of integration of genomic and phenomic data. This could help using spectral data for traits that present poor predictability at the phenotypic level, such as disease incidence and behavior traits. The strength of the model is the ability to couple genomic with high-throughput phenomic information.
本研究的目的是提供一种将牛奶光谱信息纳入基因组预测模型的方法。除了基因组协变量外，光谱数据被视为一组协变量。使用产奶量和体细胞评分作为研究性状。采用交叉验证，区分在已知环境下预测新个体的表现、在新环境下预测已知个体的表现以及在新环境下预测新个体的表现。我们发现，当基因组预测必须外推到新环境时，将光谱数据作为环境协变量具有优势。这对于观察到的家庭（基因型）以及甚至未观察到的家庭（基因型）都是有效的。总体而言，产奶量的预测准确性高于体细胞评分。傅里叶变换红外光谱数据可作为一种信息来源，用于计算给定农场在给定时间的“环境坐标”，将预测外推到新环境。该方法可作为基因组和表型组数据整合的一个示例。这有助于将光谱数据用于在表型水平上预测性较差的性状，如疾病发病率和行为性状。该模型的优势在于能够将基因组与高通量表型组信息相结合。
