Rodríguez-Ramilo Silvia Teresa, García-Cortés Luis Alberto, González-Recio Oscar
Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain; Departamento Técnico Conafe, Madrid, Spain.
Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain.
PLoS One. 2014 Mar 26;9(3):e93424. doi: 10.1371/journal.pone.0093424. eCollection 2014.
Genome-enhanced genotypic evaluations are becoming popular in several livestock species. For this purpose, the combination of the pedigree-based relationship matrix with a genomic similarities matrix between individuals is a common approach. However, the weight placed on each matrix has been so far established with ad hoc procedures, without formal estimation thereof. In addition, when using marker- and pedigree-based relationship matrices together, the resulting combined relationship matrix needs to be adjusted to the same scale in reference to the base population. This study proposes a semi-parametric Bayesian method for combining marker- and pedigree-based information on genome-enabled predictions. A kernel matrix from a reproducing kernel Hilbert spaces regression model was used to combine genomic and genealogical information in a semi-parametric scenario, avoiding inversion and adjustment complications. In addition, the weights on marker- versus pedigree-based information were inferred from a Bayesian model with Markov chain Monte Carlo. The proposed method was assessed involving a large number of SNPs and a large reference population. Five phenotypes, including production and type traits of dairy cattle were evaluated. The reliability of the genome-based predictions was assessed using the correlation, regression coefficient and mean squared error between the predicted and observed values. The results indicated that when a larger weight was given to the pedigree-based relationship matrix the correlation coefficient was lower than in situations where more weight was given to genomic information. Importantly, the posterior means of the inferred weight were near the maximum of 1. The behavior of the regression coefficient and the mean squared error was similar to the performance of the correlation, that is, more weight to the genomic information provided a regression coefficient closer to one and a smaller mean squared error. Our results also indicated a greater accuracy of genomic predictions when using a large reference population.
基因组增强型基因型评估在几种家畜物种中越来越受欢迎。为此，将基于系谱的亲缘关系矩阵与个体间的基因组相似性矩阵相结合是一种常用方法。然而，到目前为止，每个矩阵的权重是通过临时程序确定的，没有对其进行正式估计。此外，当同时使用基于标记和系谱的亲缘关系矩阵时，所得的组合亲缘关系矩阵需要根据基础群体调整到相同的尺度。本研究提出了一种半参数贝叶斯方法，用于结合基于标记和系谱的信息进行基因组预测。在半参数情况下，使用来自再生核希尔伯特空间回归模型的核矩阵来组合基因组和系谱信息，避免了求逆和调整的复杂性。此外，基于标记和系谱的信息的权重是通过带有马尔可夫链蒙特卡罗的贝叶斯模型推断出来的。所提出的方法通过大量单核苷酸多态性（SNP）和一个大型参考群体进行了评估。评估了包括奶牛生产和类型性状在内的五种表型。使用预测值与观测值之间的相关性、回归系数和均方误差来评估基于基因组预测的可靠性。结果表明，当给予基于系谱的亲缘关系矩阵更大权重时，相关系数低于给予基因组信息更多权重的情况。重要的是，推断权重的后验均值接近最大值1。回归系数和均方误差的表现与相关性的表现相似，即给予基因组信息更多权重会使回归系数更接近1且均方误差更小。我们的结果还表明，使用大型参考群体时基因组预测的准确性更高。
