Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population.
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.
Institut National de la Recherche Agronomique (INRA), UMR 1313, Génétique Animale et Biologie Intégrative (GABI), 78350 Jouy-en-Josas, France; Union Nationales des Coopératives d'Élevages et d'Insémination Animales (UNCEIA), 149 rue de Bercy, 75012 Paris, France. Electronic address: chris.hoze@jouy.inra.fr.
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Hozé
Union Nationales des Coopératives d'Élevages et d'Insémination Animales (UNCEIA), 149 rue de Bercy, 75012 Paris, France.
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Fritz
Institut National de la Recherche Agronomique (INRA), UMR 1313, Génétique Animale et Biologie Intégrative (GABI), 78350 Jouy-en-Josas, France.
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Phocas
Institut National de la Recherche Agronomique (INRA), UMR 1313, Génétique Animale et Biologie Intégrative (GABI), 78350 Jouy-en-Josas, France.
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Boichard
Institut National de la Recherche Agronomique (INRA), UMR 1313, Génétique Animale et Biologie Intégrative (GABI), 78350 Jouy-en-Josas, France.
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Ducrocq
Institut National de la Recherche Agronomique (INRA), UMR 1313, Génétique Animale et Biologie Intégrative (GABI), 78350 Jouy-en-Josas, France.
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Croiseau
D000818:Animals; D049628:Body Size; D001947:Breeding; D002417:Cattle / Q000235:genetics* / Q000502:physiology; D016678:Genome; D023281:Genomics / Q000379:methods*; D015810:Linkage Disequilibrium; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis / Q000662:veterinary*; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D011156:Population Density; D012641:Selection, Genetic
dairy cattle; high-density chip; multi-breed genomic selection