Evaluation of Genome-Enabled Prediction for Carcass Primal Cut Yields Using Single-Step Genomic Best Linear Unbiased Prediction in Hanwoo Cattle

Naserkheil, Masoumeh; Mehrban, Hossein; Lee, Deukhwan; Park, Mi Na

doi:10.3390/genes12121886

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…On the application of genomic selection in Hanwoo beef cattle, various reports have been published in recent years (Lopez, An, et al, 2020; Mehrban et al, 2017, 2021; Naserkheil, Mehrban, et al, 2021; Park et al, 2020; Seo et al, 2022). The accuracy of (G)EBV in JBC, however, has not yet been the subject of any published research.…”

Section: Introductionmentioning

confidence: 99%

Assessment of genomic breeding values and their accuracies for carcass traits in Jeju Black cattle using whole‐genome SNP chip panels

Haque

Iqbal

Bae

et al. 2023

J Animal Breeding Genetics

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The objective of the present study was to evaluate the breeding value and accuracy of genomic estimated breeding values (GEBVs) of carcass traits in Jeju Black cattle (JBC) using Hanwoo steers and JBC as a reference population using the single‐trait animal model. Our research included genotype and phenotype information on 19,154 Hanwoo steers with 1097 JBC acting as the reference population. Likewise, the test population consisted of 418 genotyped JBC individuals with no phenotypic records for those carcass traits. For estimating the accuracy of GEBV, we divided the entire population into three groups. Hanwoo and JBC make up the first group; Hanwoo and JBC, who has both the genotype and phenotypic records, are referred to as the reference (training) population, and JBC, who lacks phenotypic information is referred to as the test (validation) population. The second group consists of the JBC (without phenotype) as the test population and Hanwoo as a reference population with phenotype and genotypic data. The only JBCs in the third group are those who have genotypic and phenotypic data on them as a reference population but no phenotypic data on them as a test population. The single‐trait animal model was used in all three groups for statistical purposes. The reference populations estimated heritabilities for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) as 0.30, 0.26, 0.26, and 0.34 for the Hanwoo steer and 0.42, 0.27, 0.26, and 0.48 for JBC. The average accuracy for carcass traits in Group 1 was 0.80 for the Hanwoo and JBC reference population compared with 0.73 for the JBC test population. Although the average accuracy for carcass traits in Group 2 was 0.80, it was 0.80 for the Hanwoo reference population and only 0.56 for the JBC test population. The average accuracy for the JBC reference and test populations was 0.68 and 0.50, respectively, when they were included in the accuracy comparison without the Hanwoo reference population. Groups 1 and 2 used Hanwoo as reference population, which led to a better average accuracy; however, Group 3 only used the JBC reference and test population, which led to a lower average accuracy. This might be due to the fact that Group 3 used a smaller reference size than the group that came before it and that the genetic makeup of the Hanwoo and JBC breeds differed. The GEBV accuracy for MS was higher than that of other traits across all three analysis groups, followed by CWT, EMA, and BF, which may be partially explained by the MS traits' higher heritability. This study suggests that in order to achieve more accuracy, a large reference population particular to a breed should be established. Therefore, to increase the accuracy of GEBV prediction and the genetic benefit from genomic selection in JBC, individual reference breeds, and large populations are required.

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Section: Introductionmentioning

confidence: 99%

Assessment of genomic breeding values and their accuracies for carcass traits in Jeju Black cattle using whole‐genome SNP chip panels

Haque

Iqbal

Bae

et al. 2023

J Animal Breeding Genetics

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Selecting an appropriate statistical model for estimating genetic parameters: A case study of pig maternal breeds in Czechia

Calta,

Žáková,

Krupa

et al. 2024

Livestock Science

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Influence of variance component estimates on genomic predictions for growth and reproductive‐related traits in Nellore cattle

Cardona‐Cifuentes,

Neira,

Albuquerque

et al. 2024

J Animal Breeding Genetics

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This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, and 0.15 for W120; 0.31, 0.39, and 0.35 for W450; 0.35, 0.47, and 0.41 for SC. For W450 and SC, using the H matrix reduced the heritability (0.33 and 0.32 for W450; 0.41 and 0.38 for SC for H_After and H_All, respectively). For W120, Dataset1 and VCs from A_After showed the highest accuracy for direct and maternal GEBV (0.953 and 0.868). For W450, Dataset 1 and VC from H_After allowed the highest accuracy (0.854) but use Dataset 2 and same VC source yield similar value (0.846). For SC, Dataset 2 with VC from H_After showed the highest accuracy (0.925). To use Dataset 2 does not cause important changes in bias or dispersion with respect to Dataset 1. The VC and genetic parameters changed for W120, W450, and SC450, using records before or after the GS implementation. For W450 and SC450, genetic variance and heritability estimates increased with the use of GS. For W120, genomic predictions were more accurate using A for VC estimation. Accuracy gains were observed for W450 and SC450 using H in VC estimation and/or discarding records before GS. It is possible to discard phenotypic records before GS implementation without generating bias or dispersion in the GEBV of young candidates.

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Evaluation of Genome-Enabled Prediction for Carcass Primal Cut Yields Using Single-Step Genomic Best Linear Unbiased Prediction in Hanwoo Cattle

Cited by 4 publications

References 52 publications

Assessment of genomic breeding values and their accuracies for carcass traits in Jeju Black cattle using whole‐genome SNP chip panels

Assessment of genomic breeding values and their accuracies for carcass traits in Jeju Black cattle using whole‐genome SNP chip panels

Selecting an appropriate statistical model for estimating genetic parameters: A case study of pig maternal breeds in Czechia

Influence of variance component estimates on genomic predictions for growth and reproductive‐related traits in Nellore cattle

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