Performance of pedigree and various forms of marker-derived relationship coefficients in genomic prediction and their correlations

Abstract

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 ( $\mathrm{GK}$ ) 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 $K=\lambda G+\left(1-\lambda \right)A$ , where the weight ( $\lambda$ ) 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.

Keywords: Bayesian multiple-trait genome-enabled prediction; genetic parameters; marker-based relationship matrix.