• Animal Bioscience
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• v.37 no.4
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• pp.555-566
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• 2024

Objective: This study aimed to assess the genetic parameters and accuracy of genomic predictions for twenty-four linear body conformation traits and overall conformation scores in Korean Holstein dairy cows. Methods: A dataset of 2,206 Korean Holsteins was collected, and genotyping was performed using the Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The traits investigated included body traits (stature, height at front end, chest width, body depth, angularity, body condition score, and locomotion), rump traits (rump angle, rump width, and loin strength), feet and leg traits (rear leg set, rear leg rear view, foot angle, heel depth, and bone quality), udder traits (udder depth, udder texture, udder support, fore udder attachment, front teat placement, front teat length, rear udder height, rear udder width, and rear teat placement), and overall conformation score. Accuracy of genomic predictions was assessed using the single-trait animal model genomic best linear unbiased prediction method implemented in the ASReml-SA v4.2 software. Results: Heritability estimates ranged from 0.10 to 0.50 for body traits, 0.21 to 0.35 for rump traits, 0.13 to 0.29 for feet and leg traits, and 0.05 to 0.46 for udder traits. Rump traits exhibited the highest average heritability (0.29), while feet and leg traits had the lowest estimates (0.21). Accuracy of genomic predictions varied among the twenty-four linear body conformation traits, ranging from 0.26 to 0.49. The heritability and prediction accuracy of genomic estimated breeding value (GEBV) for the overall conformation score were 0.45 and 0.46, respectively. The GEBVs for body conformation traits in Korean Holstein cows had low accuracy, falling below the 50% threshold. Conclusion: The limited response to selection for body conformation traits in Korean Holsteins may be attributed to both the low heritability of these traits and the lower accuracy estimates for GEBVs. Further research is needed to enhance the accuracy of GEBVs and improve the selection response for these traits.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.6
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• pp.784-788
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• 2008

Studies to detect genes responsible for economic traits in farm animals have been performed using parametric linear models. A non-parametric, model-free approach using the 'expanded multifactor-dimensionality reduction (MDR) method' considering high dimensionalities of interaction effects between multiple single nucleotide polymorphisms (SNPs), was applied to identify interaction effects of SNPs responsible for carcass traits in a Hanwoo beef cattle population. Data were obtained from the Hanwoo Improvement Center, National Agricultural Cooperation Federation, Korea, and comprised 299 steers from 16 paternal half-sib proven sires that were delivered in Namwon or Daegwanryong livestock testing stations between spring of 2002 and fall of 2003. For each steer at approximately 722 days of age, the Longssimus dorsi muscle area (LMA) was measured after slaughter. Three functional SNPs (19_1, 18_4, 28_2) near the microsatellite marker ILSTS035 on BTA6, around which the QTL for meat quality were previously detected, were assessed. Application of the expanded MDR method revealed the best model with an interaction effect between the SNPs 19_1 and 28_2, while only one main effect of SNP19_1 was statistically significant for LMA (p<0.01) under a general linear mixed model. Our results suggest that the expanded MDR method better identifies interaction effects between multiple genes that are related to polygenic traits, and that the method is an alternative to the current model choices to find associations of multiple functional SNPs and/or their interaction effects with economic traits in livestock populations.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.7
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• pp.907-911
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• 2017

Objective: Intramuscular fat is one of the meat quality traits that is considered in the selection strategies for Hanwoo (Korean cattle). Different methods are used to estimate the breeding value of selection candidates. In the present work we focused on accuracy of different genotype relationship matrices as described by forni and pedigree based relationship matrix. Methods: The data set included a total of 778 animals that were genotyped for BovineSNP50 BeadChip. Among these 778 animals, 72 animals were sires for 706 reference animals and were used as a validation dataset. Single trait animal model (best linear unbiased prediction and genomic best linear unbiased prediction) was used to estimate the breeding values from genomic and pedigree information. Results: The diagonal elements for the pedigree based coefficients were slightly higher for the genomic relationship matrices (GRM) based coefficients while off diagonal elements were considerably low for GRM based coefficients. The accuracy of breeding value for the pedigree based relationship matrix (A) was 13% while for GRM (GOF, G05, and Yang) it was 0.37, 0.45, and 0.38, respectively. Conclusion: Accuracy of GRM was 1.5 times higher than A in this study. Therefore, genomic information will be more beneficial than pedigree information in the Hanwoo breeding program.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.2
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• pp.157-162
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• 2018

Objective: The aim of this study is to identify single nucleotide polymorphisms (SNPs) and genes related to pig IMF and estimate the heritability of intramuscular fat content (IMF). Methods: Genome-wide association study (GWAS) on 704 inbred Berkshires was performed for IMF. To consider the inbreeding among samples, associations of the SNPs with IMF were tested as random effects in a mixed linear model using the genetic relationship matrix by GEMMA. Significant genes were compared with reported pig IMF quantitative trait loci (QTL) regions and functional classification of the identified genes were also performed. Heritability of IMF was estimated by GCTA tool. Results: Total 365 SNPs were found to be significant from a cutoff of p-value <0.01 and the 365 significant SNPs were annotated across 120 genes. Twenty five genes were on pig IMF QTL regions. Bone morphogenetic protein-binding endothelial cell precursor-derived regulator, forkhead box protein O1, ectodysplasin A receptor, ring finger protein 149, cluster of differentiation, tyrosine-protein phosphatase non-receptor type 1, SRY (sex determining region Y)-box 9 (SOX9), MYC proto-oncogene, and macrophage migration inhibitory factor were related to mitogen-activated protein kinase pathway, which regulates the differentiation to adipocytes. These genes and the genes mapped on QTLs could be the candidate genes affecting IMF. Heritability of IMF was estimated as 0.52, which was relatively high, suggesting that a considerable portion of the total variance of IMF is explained by the SNP information. Conclusion: Our results can contribute to breeding pigs with better IMF and therefore, producing pork with better sensory qualities.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.6
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• pp.775-781
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• 2016

A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM) considering different order of Legendre polynomial for the additive genetic effect (4th order) and the permanent environmental effect (5th order). Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11) to 0.99 (TD-4 and TD-5). The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.9
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• pp.1219-1227
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• 2014

A whole genome association (WGA) study was carried out to find quantitative trait loci (QTL) for sensory evaluation traits in Hanwoo. Carcass samples of 250 Hanwoo steers were collected from National Agricultural Cooperative Livestock Research Institute, Ansung, Gyeonggi province, Korea, between 2011 and 2012 and genotyped with the Affymetrix Bovine Axiom Array 640K single nucleotide polymorphism (SNP) chip. Among the SNPs in the chip, a total of 322,160 SNPs were chosen after quality control tests. After adjusting for the effects of age, slaughter-year-season, and polygenic effects using genome relationship matrix, the corrected phenotypes for the sensory evaluation measurements were regressed on each SNP using a simple linear regression additive based model. A total of 1,631 SNPs were detected for color, aroma, tenderness, juiciness and palatability at 0.1% comparison-wise level. Among the significant SNPs, the best set of 52 SNP markers were chosen using a forward regression procedure at 0.05 level, among which the sets of 8, 14, 11, 10, and 9 SNPs were determined for the respectively sensory evaluation traits. The sets of significant SNPs explained 18% to 31% of phenotypic variance. Three SNPs were pleiotropic, i.e. AX-26703353 and AX-26742891 that were located at 101 and 110 Mb of BTA6, respectively, influencing tenderness, juiciness and palatability, while AX-18624743 at 3 Mb of BTA10 affected tenderness and palatability. Our results suggest that some QTL for sensory measures are segregating in a Hanwoo steer population. Additional WGA studies on fatty acid and nutritional components as well as the sensory panels are in process to characterize genetic architecture of meat quality and palatability in Hanwoo.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.5
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• pp.598-602
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• 2004

The Tongcheng pig breed is a famous Chinese indigenous breed. The Ministry of Agriculture of China has filed it as 1 of 19 national key conservation breeds selected from more than 100 Chinese indigenous pig breeds in 2000. In order to improve the reproductive performance, it has been intensively selected to increase the litter size for about 10 years. The population randomly sampled from conservation nucleus of eight families in the Tongcheng pigs was genotyped for identification of their estrogen receptor locus polymorphisms with the PCR-RFLPs method. Only AB heterozygotes and BB homozygotes were detected, and $X^2$ test demonstrated that the locus was in disequilibrium at a significant level (p<0.05). In the present paper, the litter sizes in different parities were regarded as different traits. Holistic status of other unspecific and unidentified genes was estimated by using the statistical methods. Coefficients of kurtosis and skewness showed that the litter size still presented segregating characteristic in the 2nd, 5th, 7th, 8th and 9th parities. Analysis of homogeneity of variance between families confirmed the results for the 5th, 7th and 8th parities. The heritability of litter size for the 1st to 10th parities was estimated with paternal half-sib model and individual estimated breeding values (EBVs) were evaluated by a single trait animal model as well. We found that the averages of EBVs for litter size in each parity did not differ significantly between genotypes, despite the significant difference for original phenotype records in the 3rd, 4th and 5th parities (p<0.05 or p<0.01). The results may be explained by the deduction that the polymorphisms of ESR locus are no longer the important genetic base of litter size variation when the frequency of allele B accumulated in the experience of selection procedure, and further conferring that there exist special genes associated with litter size in the recent Tongcheng pigs population can be made.

• Journal of Animal Science and Technology
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• v.50 no.2
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• pp.151-156
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• 2008

The study aimed to estimate genetic parameters of body weights in Hanwoo steers using random regression model and compare it with single trait animal model. A total of 1,372 Hanwoo steers that belonged to progeny testing program of the Hanwoo Genetic Improvement conducted at the Livestock Improvement Main Center of the National Agricultural Cooperative Federation (LIMC-NACF) in Rep. of Korea were used. Results of the random regression model fitting quadratic function revealed heritability values from 0.17 to 0.30 for the whole testing days up to 800 days. The results of the animal model showed estimated heritability values ranged from 0.24 to 0.36. Estimates of permanent environmental correlations tended to increase with increasing test in days. Unlike in the direct genetic correlation that at early stage the estimate was slightly negative it was 0.30 then increased to approach unity at later stage of test. Comparing the results between random regression model and the animal model showed not much differences and both followed similar pattern and therefore the use of random regression model for the national genetic evaluation of Hanwoo could be implemented.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.12
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• pp.1863-1870
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• 2018

Objective: The Bayesian first-order antedependence models, which specified single nucleotide polymorphisms (SNP) effects as being spatially correlated in the conventional BayesA/B, had more accurate genomic prediction than their corresponding classical counterparts. Given advantages of $BayesC{\pi}$ over BayesA/B, we have developed hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$, and ante-hyper-$BayesC{\pi}$ to evaluate influences of the antedependence model and hyperparameters for $v_g$ and $s_g^2$ on $BayesC{\pi}$.Methods: Three public data (two simulated data and one mouse data) were used to validate our proposed methods. Genomic prediction performance of proposed methods was compared to traditional $BayesC{\pi}$, ante-BayesA and ante-BayesB. Results: Through both simulation and real data analyses, we found that hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$ and ante-hyper-$BayesC{\pi}$ were comparable with $BayesC{\pi}$, ante-BayesB, and ante-BayesA regarding the prediction accuracy and bias, except the situation in which ante-BayesB performed significantly worse when using a few SNPs and ${\pi}=0.95$. Conclusion: Hyper-$BayesC{\pi}$ is recommended because it avoids pre-estimated total genetic variance of a trait compared with $BayesC{\pi}$ and shortens computing time compared with ante-BayesB. Although the antedependence model in $BayesC{\pi}$ did not show the advantages in our study, larger real data with high density chip may be used to validate it again in the future.

• Journal of Animal Science and Technology
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• v.58 no.10
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• pp.38.1-38.6
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• 2016

Background: Despite the importance of relationships between somatic cell score (SCS) and currently selected traits (milk, fat and protein yield) of Holstein cows, there was a lack of comprehensive literature for it in Iran. Therefore we tried to examine heritabilities and relationships between these traits using a fixed-regression animal model and Bayesian inference. The data set consisted of 1,078,966 test-day observations from 146,765 primiparous daughters of 1930 sires, with calvings from 2002 to 2013. Results: Marginal posterior means of heritability estimates for SCS ($0.03{\pm}0.002$) were distinctly lower than those for milk ($0.204{\pm}0.006$), fat ($0.096{\pm}0.004$) and protein ($0.147{\pm}0.005$) yields. In the case of phenotypic correlations, the relationships between production and SCS were near zero at the beginning of lactation but become increasingly negative as days in milk increased. Although all environmental correlations between production and SCS were negative ($-0.177{\pm}0.007$, $-0.165{\pm}0.008$ and $-0.152{\pm}0.007$ between SCS and milk, fat, and protein yield, respectively), slightly antagonistic genetic correlations were found; with posterior mean of relationships ranging from $0.01{\pm}0.039$ to $0.11{\pm}0.036$. This genetic opposition was distinctly higher for protein than for fat. Conclusion: Although small, the positive genetic correlations suggest some genetic antagonism between desired increased milk production and reduced SCS (i.e., single-trait selection for increased milk production will also increase SCS).