The accuracy of genomic prediction for meat quality traits in Hanwoo cattle when using genotypes from different SNP densities and preselected variants from imputed whole genome sequence
Mohammed Bedhane
A * , Julius van der Werf A , Sara de las Heras-Saldana A , Dajeong Lim B , Byoungho Park B , Mi Na Park B , Roh Seung Hee C and Samuel Clark A
+ Author Affiliations
- Author Affiliations
A School of Environmental and Rural Science, University of New England, Armidale, NSW 2350, Australia.
B National Institute of Animal Science, RDA, Seoul, Republic of Korea.
C Hanwoo Genetic Improvement Centre, Nagi, Republic of Korea.
Animal Production Science 62(1) 21-28 https://doi.org/10.1071/AN20659
Submitted: 5 December 2020 Accepted: 1 August 2021 Published: 5 November 2021
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Genomic prediction is the use of genomic data in the estimation of genomic breeding values (GEBV) in animal breeding. In beef cattle breeding programs, genomic prediction increases the rates of genetic gain by increasing the accuracy of selection at earlier ages.
Aims: The objectives of the study were to examine the effect of single-nucleotide polymorphism (SNP) density and to evaluate the effect of using SNPs preselected from imputed whole-genome sequence for genomic prediction.
Methods: Genomic and phenotypic data from 2110 Hanwoo steers were used to predict GEBV for marbling score (MS), meat texture (MT), and meat colour (MC) traits. Three types of SNP densities including 50k, high-density (HD), and whole-genome sequence data and preselected SNPs from genome-wide association study (GWAS) were used for genomic prediction analyses. Two scenarios (independent and dependent discovery populations) were used to select top significant SNPs. The accuracy of GEBV was assessed using random cross-validation. Genomic best linear unbiased prediction (GBLUP) was used to predict the breeding values for each trait.
Key results: Our result showed that very similar prediction accuracies were observed across all SNP densities used in the study. The prediction accuracy among traits ranged from 0.29 ± 0.05 for MC to 0.46 ± 0.04 for MS. Depending on the studied traits, up to 5% of prediction accuracy improvement was obtained when the preselected SNPs from GWAS analysis were included in the prediction analysis.
Conclusions: High SNP density such as HD and the whole-genome sequence data yielded a similar prediction accuracy in Hanwoo beef cattle. Therefore, the 50K SNP chip panel is sufficient to capture the relationships in a breed with a small effective population size such as the Hanwoo cattle population. Preselected variants improved prediction accuracy when they were included in the genomic prediction model.
Implications: The estimated genomic prediction accuracies are moderately accurate in Hanwoo cattle and for searching for SNPs that are more productive could increase the accuracy of estimated breeding values for the studied traits.
Keywords: Bias, genomic selection, GWAS, Korean beef cattle, meat quality, prediction accuracy, preselected SNPs, sequence data.
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