In recent years, with development and validation of different genotyping panels, several methods have been proposed to build efficient similarity matrices among individuals to be used for genomic selection. Consequently, the estimated genetic parameters from such information may deviate from their counterpart using traditional family information. In this study, we used a pedigree-based numerator relationship matrix (A) and three types of marker-based relationship matrices ( G ) including two identical by descent, that is G K and G M and one identical by state, G V as well as four Gaussian kernel ( GK ) similarity kernels with different smoothing parameters to predict yet to be observed phenotypes. Also, we used different kinship matrices that are a linear combination of marker-derived IBD or IBS matrices with A, constructed as K = λ G + 1 - λ A , where the weight ( λ ) assigned to each source of information varied over a grid of values. A Bayesian multiple-trait Gaussian model was fitted to estimate the genetic parameters and compare the prediction accuracy in terms of predictive correlation, mean square error and unbiasedness. Results show that the estimated genetic parameters (heritability and correlations) are affected by the source of the information used to create kinship or the weight placed on the sources of genomic and pedigree information. The superiority of GK-based model depends on the smoothing parameters (θ) so that with an optimum θ value, the GK-based model statistically yielded better performance (higher predictive correlation, lowest MSE and unbiased estimates) and more stable correlations and heritability than the model with IBD, IBS or A kinship matrices or any of the linear combinations.

中文翻译：

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谱系和各种形式的标记衍生关系系数在基因组预测中的表现及其相关性

近年来，随着不同基因分型面板的开发和验证，已经提出了几种方法来构建用于基因组选择的个体之间的有效相似性矩阵。因此，根据这些信息估计的遗传参数可能与使用传统家庭信息的对应物有所不同。在本研究中，我们使用了一个基于谱系的分子关系矩阵 (A) 和三种基于标记的关系矩阵 ( G )，其中两个是血统相同的，即 GK 和 GM，一个是州相同的，GV 以及四个具有不同平滑参数的高斯核 (GK) 相似性核来预测尚未观察到的表型。此外，我们使用了不同的亲属矩阵，它们是标记衍生的 IBD 或 IBS 矩阵与 A 的线性组合，构造为 K = λ G + 1 - λ A ，其中分配给每个信息源的权重 (λ) 在值网格上变化。拟合贝叶斯多性状高斯模型以估计遗传参数并在预测相关性、均方误差和无偏性方面比较预测准确性。结果表明，估计的遗传参数（遗传力和相关性）受用于建立亲属关系的信息来源或基因组和谱系信息来源的权重影响。基于 GK 的模型的优越性取决于平滑参数 (θ)，因此在最佳 θ 值下，基于 GK 的模型在统计上产生了更好的性能（更高的预测相关性、最低的 MSE 和无偏估计）和更稳定的相关性和遗传力。带有 IBD 的模型，