The Effect of Reference Population Factors on the Quality of Genomic Selection in Holstein Cattle of Iran

Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The success of genomic selection depends on the potential to predict genomic breeding values (GEBVs) with high accuracy. Genomic selection relies on relationships between individuals to accurately predict genetic value. Accuracy of genomic prediction is highly dependent on the size and type of the reference population (RP) used to estimate marker effects. For small populations, including information from other populations could improve this reliability. A usual strategy is to pool data from other populations. Genome consists of 3 chromosomes each 100cM including 7500 markers with 0.04 cM space and 75 random distributed QTL were simulated. Genomic estimated breeding values of Iranian Holstein cattle were predicted using BayesB based on several reference dataset. Simulation was used to establish Iranian Holstein population and compare the accuracies of GEBVs under a range of different sizes and types of RP. The importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values and effect of pooling data from other populations were also examined to construct the best RP for genomic selection in Iran. The relationship between the animals in the test and reference data sets had high effect on the accuracy of genomic predictions. The increase of accuracy of GEBV by adding bulls in the RP was more than of adding dams indicating a direct relationship between the accuracy of predictions and the number of animals of reference population. whatever the relative relationship between the reference population was reduced by selecting animals, the accuracy is also reduced that in addition to showing the importance of the relationship between the two populations, suggest that Estimates should be repeated over time. The extent of linkage disequilibrium was similar in the Iranian and foreign Holstein populations and linkage disequilibrium between the two populations was very consistent and using the joint versus the Iranian reference dataset increased accuracy of genomic prediction. The makeup of reference data sets is an important factor for the design of genomic evaluation systems to enable additional genetic gain from genomic selection at the lowest cost. An animal’s relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. Our results suggest that the most accurate genomic predictions are achieved when data of dams and other population are combined by data of sires in RP.