Genomic prediction based on runs of homozygosity

BACKGROUND: Genomic prediction is based on the accurate estimation of the genomic relationships among and between training animals and selection candidates in order to obtain accurate estimates of the genomic estimated breeding values (GEBV). Various methods have been used to predict GEBV based on population-wide linkage disequilibrium relationships (G(IBS)) or sometimes on linkage analysis relationships (G(LA)). Here, we propose a novel method to predict GEBV based on a genomic relationship matrix using runs of homozygosity (G(ROH)). Runs of homozygosity were used to derive probabilities of multi-locus identity by descent chromosome segments. The accuracy and bias of the prediction of GEBV using G(ROH) were compared to those using G(IBS) and G(LA). Comparisons were performed using simulated datasets derived from a random pedigree and a real pedigree of Italian Brown Swiss bulls. The comparison of accuracies of GEBV was also performed on data from 1086 Italian Brown Swiss dairy cattle. RESULTS: Simulations with various thresholds of minor allele frequency for markers and quantitative trait loci showed that G(ROH) achieved consistently more accurate GEBV (0 to 4% points higher) than G(IBS) and G(LA). The bias of GEBV prediction for simulated data was higher based on the real pedigree than based on a random pedigree. In the analyses with real data, G(ROH) and G(LA) had similar accuracies. However, G(LA) achieved a higher accuracy when the prediction was done on the youngest animals. The G(IBS) matrices calculated with and without standardized marker genotypes resulted in similar accuracies. CONCLUSIONS: The present study proposes G(ROH) as a novel method to estimate genomic relationship matrices and predict GEBV based on runs of homozygosity and shows that it can result in higher or similar accuracies of GEBV prediction than G(LA), except for the real data analysis with validation of young animals. Compared to G(IBS), G(ROH) resulted in more accurate GEBV predictions.