» Articles » PMID: 38136964

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

Overview
Journal Genes (Basel)
Publisher MDPI
Date 2023 Dec 23
PMID 38136964
Authors
Affiliations
Soon will be listed here.
Abstract

The aim of this study was to assess the impact of incorporating genomic data using the single-step genomic best linear unbiased prediction (ssGBLUP) method compared to the best linear unbiased prediction (BLUP) method on the reliability of breeding values for age at first calving, calving interval, and productive longevity at 78 months in Charolais cattle. The study included 48,590 purebred Charolais individuals classified into four subgroups based on genotyping and performance records. The results showed that considering genotypes significantly improved genomic estimated breeding values (GEBV) reliability across all categories except nongenotyped individuals. For young genotyped individuals, the increase in reliability was up to 27% for both sexes. The highest average reliability was achieved for genotyped proven bulls and cows with performance records, and the inclusion of genomic data further improved the reliability by up to 22% and 21% for cows and bulls, respectively. The gain in reliability was observed mainly during the first three calvings, and then the differences decreased. The imported individuals showed lower estimated breeding values (EBV) and GEBV reliabilities than the domestic population, probably due to the weak genetic connection with the domestic population. However, when the progeny of imported heifers were sired by domestic bulls, the reliability increased by up to 24%. For nongenotyped individuals, only a slight increase in reliability was observed; however, the number of genotyped individuals in the population was still relatively small.

References
1.
Nwogwugwu C, Kim Y, Choi H, Lee J, Lee S . Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population. Asian-Australas J Anim Sci. 2020; 33(12):1912-1921. PMC: 7649411. DOI: 10.5713/ajas.20.0217. View

2.
Montesinos-Lopez A, Runcie D, Ibba M, Perez-Rodriguez P, Montesinos-Lopez O, Crespo L . Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials. G3 (Bethesda). 2021; 11(10). PMC: 8496321. DOI: 10.1093/g3journal/jkab270. View

3.
Song H, Zhang J, Zhang Q, Ding X . Using Different Single-Step Strategies to Improve the Efficiency of Genomic Prediction on Body Measurement Traits in Pig. Front Genet. 2019; 9:730. PMC: 6340005. DOI: 10.3389/fgene.2018.00730. View

4.
Esrafili Taze Kand Mohammaddiyeh M, Rafat S, Shodja J, Javanmard A, Esfandyari H . Selective genotyping to implement genomic selection in beef cattle breeding. Front Genet. 2023; 14:1083106. PMC: 10064214. DOI: 10.3389/fgene.2023.1083106. View

5.
Lourenco D, Misztal I, Tsuruta S, Aguilar I, Ezra E, Ron M . Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. J Dairy Sci. 2014; 97(3):1742-52. DOI: 10.3168/jds.2013-6916. View