Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population

Abstract

Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed for 6 traits and using the different prediction approaches. Compared with pedigree-based BLUP, the average gain in accuracy with GS in small populations was 0.057 for the single-breed and 0.086 for multi-breed approach. This gain was up to 0.193 and 0.209, respectively, with the large reference population. Improvement of EBV prediction due to the multi-breed evaluation was higher for animals not closely related to the reference population. In the case of a breed with a small reference population size, the increase in correlation due to multi-breed GS was 0.141 for bulls without their sire in reference population compared with 0.016 for bulls with their sire in reference population. These results demonstrate that multi-breed GS can contribute to increase genomic evaluation accuracy in small breeds.

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

“…The findings of studies applying multi-breed reference sets agree that for the population with the fewest observations in the reference, the gain in reliability is highest (Erbe et al, 2012;Karoui et al, 2012;Olson et al, 2012). Often, only the smaller population benefits, while it is hardly useful for the other breeds in the reference populations Hozé et al, 2014).…”

“…If there is already plenty of information available, an increase in genomic prediction accuracies by adding another breed will hardly be feasible (Simeone et al, 2012). Thus, the amount of information that comes from an Opportunities and challenges for small populations additional population strongly influences the benefit from multi-breed genomic predictions Hozé et al, 2014). Additionally, the characteristics of the respective trait play an important role for the gain in reliability.…”

“…With the non-linear models more emphasis can be given on information of animals that are closer to each other . Penalized methods have been widely applied in multi-population contexts (Erbe et al, 2012;Olson et al, 2012;Hozé et al, 2014;Wiggans et al, 2015). Erbe et al (2012) analyzed within and across breed calculations.…”

Genomic selection in multi-breed dairy cattle populations

“…In a follow-up study, there was no increase in prediction accuracy when the breed origin of alleles in an admixed population of Nordic cattle was accounted for in the model (Makgahlela et al, 2014). Hozé et al (2014) recently showed that multi-breed genomic evaluation can be very helpful for breeds with small (<500 animals) reference populations, but that the gains decrease as the size of the reference population increases. VanRaden and Cooper (2015) showed that genomic evaluations for crossbreds can be computed by taking a weighted sum of purebred marker effects based on the breed composition of the admixed animals.…”

“…Lund et al (2014) recently provided a comprehensive review of genomic selection in multiple breed populations. Research conducted to date has focused on several problems, including use of information from one breed to improve predictions in one or more other breeds (Boerner et al, 2014;Porto-Neto et al, 2015), use of data from crossbreds to improve purebred predictions (Grevenhof and Werf, 2015), use of data from purebreds to improve crossbred predictions (Esfandyari et al, 2015a), use of data from crossbreds to improve crossbred predictions (Esfandyari et al, 2015b), and use of genomic information to estimate genomic predicted transmitting ability (PTA) for both purebred and crossbred animals (Christensen et al, 2014;Hozé et al, 2014). The objective of this review is to describe how genomic selection can be applied to dairy cattle populations that include purebred and crossbred animals.…”