Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed

Granado-Tajada, I. ; Varona, L. (Universidad de Zaragoza) ; Ugarte, E.

Resumen: Genomic selection has been implemented over the years in several livestock species, due to the achievable higher genetic progress. The use of genomic information in evaluations provides better prediction accuracy than do pedigree-based evaluations, and the makeup of the genotyped population is a decisive point. The aim of this work is to compare the effect of different genotyping strategies (number and type of animals) on the prediction accuracy for dairy sheep Latxa breeds. A simulation study was designed based on the real data structure of each population, and the phenotypic and genotypic data obtained were used in genetic (BLUP) and genomic (single-step genomic BLUP) evaluations of different genotyping strategies. The genotyping of males was beneficial when they were genetically connected individuals and if they had daughters with phenotypic records. Genotyping females with their own lactation records increased prediction accuracy, and the connection level has less relevance. The differences in genotyping females were independent of their estimated breeding value. The combined genotyping of males and females provided intermediate accuracy results regardless of the female selection strategy. Therefore, assuming that genotyping rams is interesting, the incorporation of genotyped females would be beneficial and worthwhile. The benefits of genotyping individuals from various generations were highlighted, although it was also possible to gain prediction accuracy when historic individuals were not considered. Greater genotyped population sizes resulted in more accuracy, even if the increase seems to reach a plateau.

Idioma: Inglés

DOI: 10.3168/jds.2020-19978

Año: 2021

Publicado en: Journal of Dairy Science 104, 6 (2021), P6861-6872
ISSN: 0022-0302

Factor impacto JCR: 4.225 (2021)

Categ. JCR: AGRICULTURE, DAIRY & ANIMAL SCIENCE rank: 6 / 63 = 0.095 (2021) - Q1 - T1
Categ. JCR: FOOD SCIENCE & TECHNOLOGY rank: 48 / 144 = 0.333 (2021) - Q2 - T2

Factor impacto CITESCORE: 7.1 - Agricultural and Biological Sciences (Q1) - Biochemistry, Genetics and Molecular Biology (Q2)

Factor impacto SCIMAGO: 1.215 - Food Science (Q1) - Animal Science and Zoology (Q1)

Financiación: info:eu-repo/grantAgreement/EUR/INTERREG-POCTEFA/ARDI-EFA208/16

Tipo y forma: Article (Published version)

Área (Departamento): Área Genética (Dpto. Anatom.,Embri.Genét.Ani.)