Predicting productive, health, and reproductive traits in Mexican Holstein cattle using single nucleotide polymorphisms, haplotypes, and runs of homozygosity

Abstract

The aim of this study was to evaluate the use of 3 different genomic relationship matrices built from SNPs, haplotypes (HAP), and runs of homozygosity (ROH), on phenotype predictive ability and estimated genetic variance of milk yield, SCS, and days open in Mexican Holstein cattle. The analyses included the use of the genomic relationship matrices as kernel-based models fitting either one or multiple sources of information. The SNPs and HAP matrices were built as linear kernels, and the ROH matrix as a Gaussian kernel. Also, we used as a reference the performance of the single-step GBLUP. Predictive ability was evaluated in 10-fold cross-validation. The highest predictive correlation was obtained using SNPs (0.63 for SCS, 0.57 for milk yield, and 0.20 for days open). The use of multigenomic relationships, including HAP and ROH, did not outperform the use of only SNPs in predictive ability, but the highest genetic variance was estimated using ROH (0.39 for milk yield, 0.26 for SCS, and 0.22 for days open).