Combining Genomic and Genealogical Information in a Reproducing Kernel Hilbert Spaces Regression Model for Genome-Enabled Predictions in Dairy Cattle

Silvia Teresa Rodríguez-Ramilo,Luis Alberto García-Cortés,Óscar González-Recio

doi:10.1371/journal.pone.0093424

Silvia Teresa Rodríguez-Ramilo, Luis Alberto García-Cortés + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0093424

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Abstract

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.

Highlights

Genomic selection refers to artificial selection decisions made using breeding values predicted from dense marker data [1]
The emphasis placed on genomic information was larger in production traits than in type traits, suggesting that most of the additive genetic variability in production traits was captured by genomic relationships
When more weight was given to genealogical information, differences between the mean squared error for N = 7,000 and N = 14,487 were smaller than when more weight was given to genomic information

Summary

Introduction

Genomic selection refers to artificial selection decisions made using breeding values predicted from dense marker data [1]. Different approaches may be used as genome-enhanced prediction methods for predicting DGV These can be based on regularized linear regression in marker models [1,3] or on relationship matrices between individuals calculated using genomic information [4,5,6]. A straightforward reason is that allele frequencies in the base population are unknown in livestock or humans, and the adjustments proposed by VanRaden [5], Yang et al [6] or Vitezica et al [13] do not translate G and A to the same base and scale [14] These problems can be overcome by combining G and A matrices with weights estimated properly. Concluding remarks are provided in the final section of the article

Materials and Methods

Results and Discussion

Conclusions