title: Genomic prediction in French Charolais beef cattle using high-density single nucleotide polymorphism markers.
abstract: The objective of the study was to develop a genomic evaluation for French beef cattle breeds and assess accuracy and bias of prediction for different genomic selection strategies. Based on a reference population of 2,682 Charolais bulls and cows, genotyped or imputed to a high-density SNP panel (777K SNP), we tested the influence of different statistical methods, marker densities (50K versus 777K), and training population sizes and structures on the quality of predictions. Four different training sets containing up to 1,979 animals and a unique validation set of 703 young bulls only known on their individual performances were formed. BayesC method had the largest average accuracy compared to genomic BLUP or pedigree-based BLUP. No gain of accuracy was observed when increasing the density of markers from 50K to 777K. For a BayesC model and 777K SNP panels, the accuracy calculated as the correlation between genomic predictions and deregressed EBV (DEBV) divided by the square root of heritability was 0.42 for birth weight, 0.34 for calving ease, 0.45 for weaning weight, 0.52 for muscular development, and 0.27 for skeletal development. Half of the training set constituted animals having only their own performance recorded, whose contribution only represented 5% of the accuracy. Using DEBV as a response brought greater accuracy than using EBV (+5% on average). Considering a residual polygenic component strongly reduced bias for most of the traits. The optimal percentage of polygenic variance varied across traits. Among the methodologies tested to implement genomic selection in the French Charolais beef cattle population, the most accurate and less biased methodology was to analyze DEBV under a BayesC strategy and a residual polygenic component approach. With this approach, a 50K SNP panel performed as well as a 777K panel.
authors.affiliation: INRA, UMR 1313 Génétique Animale et Biologie Intégrative, 78350 Jouy-en-Josas, France AgroParisTech, UMR 1313 Génétique Animale et Biologie Intégrative, 75231 Paris, France.
authors.forename: M
authors.initials: M
authors.lastname: Gunia
authors.affiliation: Union Nationale des Coopératives agricoles d'Elevage et d'Insémination Animale, 149 rue de Bercy, 75595 Paris Cedex 12, France.
authors.forename: R
authors.initials: R
authors.lastname: Saintilan
authors.affiliation: INRA, UMR 1313 Génétique Animale et Biologie Intégrative, 78350 Jouy-en-Josas, France AgroParisTech, UMR 1313 Génétique Animale et Biologie Intégrative, 75231 Paris, France.
authors.forename: E
authors.initials: E
authors.lastname: Venot
authors.affiliation: INRA, UMR 1313 Génétique Animale et Biologie Intégrative, 78350 Jouy-en-Josas, France AgroParisTech, UMR 1313 Génétique Animale et Biologie Intégrative, 75231 Paris, France Union Nationale des Coopératives agricoles d'Elevage et d'Insémination Animale, 149 rue de Bercy, 75595 Paris Cedex 12, France.
authors.forename: C
authors.initials: C
authors.lastname: Hozé
authors.affiliation: Institut de l'Elevage, 149 rue de Bercy, 75595 Paris Cedex 12, France.
authors.forename: M N
authors.initials: MN
authors.lastname: Fouilloux
authors.affiliation: INRA, UMR 1313 Génétique Animale et Biologie Intégrative, 78350 Jouy-en-Josas, France AgroParisTech, UMR 1313 Génétique Animale et Biologie Intégrative, 75231 Paris, France florence.phocas@jouy.inra.fr.
authors.forename: F
authors.initials: F
authors.lastname: Phocas
mesh_terms: D000818:Animals; D001947:Breeding / Q000379:methods*; D002417:Cattle / Q000235:genetics*; D005260:Female; D005602:France; D016678:Genome / Q000235:genetics*; D023281:Genomics / Q000379:methods; D005838:Genotype; D008297:Male; D008460:Meat; D015233:Models, Statistical; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011156:Population Density; D012641:Selection, Genetic / Q000235:genetics*; D013045:Species Specificity
keywords: accuracy; beef cattle; bias; genomic selection; high-density chip