• Asian-Australasian Journal of Animal Sciences
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• v.19 no.12
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• pp.1691-1695
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• 2006

The identification method that inflects real time ultrasound (RUT) and the potential application of marker assisted selection (MAS) for improvement of a cow population of Hanwoo (Korean Native cattle) was studied. The averages of RUT longissimus muscle area, RUT fat thickness, and RUT marbling score scanned at the 13th rib were 55.78 $cm^2$, 3.70 mm and 3.83 scores, respectively. We investigated the effects of the two SNPs (Kpn2 I and Msp I) in the leptin gene on carcass traits for Hanwoo cows by using ultrasound measurements. Genotype CC of the Kpn2 I had a significantly higher effect on back fat thickness (4.23 mm) and longissimus muscle area (57.57 $cm^2$) than genotype TT (3.14 mm, 53.93 $cm^2$, respectively, p<0.05). Genotype AA of the Msp I had a significantly higher effect only on marbling score (5.37) than genotype AB (3.57, p<0.05) and BB (3.37, p<0.05). Significant effects of SNPs in the leptin gene were found for the ultrasound measures of body composition in live cattle.

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• Journal of Animal Science and Technology
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• v.62 no.3
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• pp.293-305
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• 2020

The difference in the breeding programs and population history may have diversely shaped the genomes of Korean native cattle breeds. In the absence of phenotypic data, comparisons of breeds that have been subjected to different selective pressures can aid to identify genomic regions and genes controlling qualitative and complex traits. In this study to decipher genetic variation and identify evidence of divergent selection, 3 Korean cattle breeds were genotyped using the recently developed high-density GeneSeek Genomic Profiler F250 (GGP-F250) array. The three Korean cattle breeds clustered according to their coat color phenotypes and breeding programs. The Heugu breed reliably showed smaller effective population size at all generations considered. Across the autosomal chromosomes, 113 and 83 annotated genes were identified from Hanwoo-Chikso and Hanwoo-Heugu comparisons, respectively of which 16 genes were shared between the two pairwise comparisons. The most important signals of selection were detected on bovine chromosomes 14 (24.39-25.13 Mb) and 18 (13.34-15.07 Mb), containing genes related to body size, and coat color (XKR4, LYN, PLAG1, SDR16C5, TMEM68, CDH15, MC1R, and GALNS). Some of the candidate genes are also associated with meat quality traits (ACSF3, EIF2B1, BANP, APCDD1, and GALM) and harbor quantitative trait locus (QTL) for beef production traits. Further functional analysis revealed that the candidate genes (DBI, ACSF3, HINT2, GBA2, AGPAT5, SCAP, ELP6, APOB, and RBL1) were involved in gene ontology (GO) terms relevant to meat quality including fatty acid oxidation, biosynthesis, and lipid storage. Candidate genes previously known to affect beef production and quality traits could be used in the beef cattle selection strategies.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.1
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• pp.36-42
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• 2016

Milk-related traits (milk yield, fat and protein) have been crucial to selection of Holstein. It is essential to find the current selection trends of Holstein. Despite this, uncovering the current trends of selection have been ignored in previous studies. We suggest a new formula to detect the current selection trends based on single nucleotide polymorphisms (SNP). This suggestion is based on the best linear unbiased prediction (BLUP) and the Fisher's fundamental theorem of natural selection both of which are trait-dependent. Fisher's theorem links the additive genetic variance to the selection coefficient. For Holstein milk production traits, we estimated the additive genetic variance using SNP effect from BLUP and selection coefficients based on genetic variance to search highly selective SNPs. Through these processes, we identified significantly selective SNPs. The number of genes containing highly selective SNPs with p-value <0.01 (nearly top 1% SNPs) in all traits and p-value <0.001 (nearly top 0.1%) in any traits was 14. They are phosphodiesterase 4B (PDE4B), serine/threonine kinase 40 (STK40), collagen, type XI, alpha 1 (COL11A1), ephrin-A1 (EFNA1), netrin 4 (NTN4), neuron specific gene family member 1 (NSG1), estrogen receptor 1 (ESR1), neurexin 3 (NRXN3), spectrin, beta, non-erythrocytic 1 (SPTBN1), ADP-ribosylation factor interacting protein 1 (ARFIP1), mutL homolog 1 (MLH1), transmembrane channel-like 7 (TMC7), carboxypeptidase X, member 2 (CPXM2) and ADAM metallopeptidase domain 12 (ADAM12). These genes may be important for future artificial selection trends. Also, we found that the SNP effect predicted from BLUP was the key factor to determine the expected current selection coefficient of SNP. Under Hardy-Weinberg equilibrium of SNP markers in current generation, the selection coefficient is equivalent to $2^*SNP$ effect.

• Food Science and Biotechnology
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• v.16 no.1
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• pp.67-70
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• 2007

Mackerel (Scomber japonicus) often causes severe allergic reactions in sensitive people. Food containing undeclared mackerel may pose a risk to such people. The major allergenic protein in fish such as mackerel, codfish, and Alaska pollack has been found to be parvalbumin. In this study, we developed a polymerase chain reaction (PCR) method to detect mackerel DNA using primers corresponding to the parvalbumin gene. We cloned and sequenced 1.5 kb of parvalbumin gene by PCR using mackerel genomic DNA as a template. Nucleotide sequence analysis of genomic parvalbumin gene, composed of 4 exons and 3 introns, allowed the selection of two pairs of oligonucleotide primers specific for mackerel. These primers successfully enabled PCR amplification of specific regions of genomic parvalbumin DNA from mackerel, but no amplification from 8 other fish samples, surimi, and 6 boiled fish pastes. The sensitivity of this method was sufficient to detect 5 ng of purified mackerel DNA mixed with 50 ng of surimi DNA. This rapid and specific method for the detection of allergenic mackerel would be beneficial in reducing food allergy caused by the ingestion of hidden allergen in processed food.

• Journal of Animal Science and Technology
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• v.55 no.1
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• pp.13-18
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• 2013

This study was conducted to estimate the Genomic Estimated Breeding Value (GEBV) using Genomic Best Linear Unbiased Prediction (GBLUP) method in Hanwoo (Korean native cattle) population. The result is expected to adapt genomic selection onto the national Hanwoo evaluation system. Carcass weight (CW), eye muscle area (EMA), backfat thickness (BT), and marbling score (MS) were investigated in 552 Hanwoo progeny-tested steers at Livestock Improvement Main Center. Animals were genotyped with Illumina BovineHD BeadChip (777K SNPs). For statistical analysis, Genetic Relationship Matrix (GRM) was formulated on the basis of genotypes and the accuracy of GEBV was estimated with 10-fold Cross-validation method. The accuracies estimated with cross-validation method were between 0.915~0.957. In 534 progeny-tested steers, the maximum difference of GEBV accuracy compared to conventional EBV for CW, EMA, BT, and MS traits were 9.56%, 5.78%, 5.78%, and 4.18% respectively. In 3,674 pedigree traced bulls, maximum increased difference of GEBV for CW, EMA, BT, and MS traits were increased as 13.54%, 6.50%, 6.50%, and 4.31% respectively. This showed that the implementation of genomic pre-selection for candidate calves to test on meat production traits could improve the genetic gain by increasing accuracy and reducing generation interval in Hanwoo genetic evaluation system to select proven bulls.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.9
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• pp.1236-1243
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• 2014

Genetics is important for breeding and selection of horses but there is a lack of well-established horse-related browsers or databases. In order to better understand horses, more variants and other integrated information are needed. Thus, we construct a horse genomic variants database including expression and other information. Horse Single Nucleotide Polymorphism and Expression Database (HSDB) (http://snugenome2.snu.ac.kr/HSDB) provides the number of unexplored genomic variants still remaining to be identified in the horse genome including rare variants by using population genome sequences of eighteen horses and RNA-seq of four horses. The identified single nucleotide polymorphisms (SNPs) were confirmed by comparing them with SNP chip data and variants of RNA-seq, which showed a concordance level of 99.02% and 96.6%, respectively. Moreover, the database provides the genomic variants with their corresponding transcriptional profiles from the same individuals to help understand the functional aspects of these variants. The database will contribute to genetic improvement and breeding strategies of Thoroughbreds.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.8
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• pp.1065-1070
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• 2006

The avian uncoupling protein (avUCP) is a member of the mitochondrial transporter superfamily that uncouples proton entry in the mitochondrial matrix from ATP synthesis. The sequencing analysis method was used to identify nucleotide polymorphisms within the avUCP gene in Korean native chicken (KNC). This study identified ten single nucleotide polymorphisms (SNPs) in the avUCP gene. We analyzed the SNPs of the avUCP gene to investigate whether polymorphism in the gene might be responsible for quantitative variations in economic traits in KNC. Three significant polymorphic sites for economic traits were avUCP C+282T (mean body weight, p<0.05), avUCP C+433T (daily percent lay, p<0.05), and avUCP T+1316C (daily percent lay, p<0.05). The frequency of each SNP was 0.125 (C+282T in avUCP gene exon 1 region), 0.150 (C+433T in avUCP gene intron 1 region), and 0.15 (T+1316C in avUCP gene exon 3 region), respectively. Among the identified SNPs, one pair of SNPs (genotype CC, C+282T and TT, avUCP C+433T) showed the highest daily percent lay (p<0.05) and mean body weight (p<0.05) and the frequency was 0.067. This study of the avUCP gene could be useful for genetic studies of this gene and selection on economic traits for KNC.

• Animal Bioscience
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• v.36 no.7
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• pp.1003-1009
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• 2023

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. Results: The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. Conclusion: The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

• Journal of Animal Science and Technology
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• v.52 no.5
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• pp.357-366
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• 2010

The emergence of next-generation sequencing technologies has lead to application of new computational and statistical methodologies that allow incorporating genetic information from entire genomes of many individuals composing the population. For example, using single-nucleotide polymorphisms (SNP) obtained from whole genome amplification platforms such as the Ilummina BovineSNP50 chip, many researchers are actively engaged in the genetic evaluation of cattle livestock using whole genome relationship analyses. In this study, we estimated the genomic relationship matrix (GRM) and compared it with one computed using a pedigree relationship matrix (PRM) using a population of Hanwoo. This project is a preliminary study that will eventually include future work on genomic selection and prediction. Data used in this study were obtained from 187 blood samples consisting of the progeny of 20 young bulls collected after parentage testing from the Hanwoo improvement center, National Agriculture Cooperative Federation as well as 103 blood samples from the progeny of 12 proven bulls collected from farms around the Kyong-buk area in South Korea. The data set was divided into two cases for analysis. In the first case missing genotypes were included. In the second case missing genotypes were excluded. The effect of missing genotypes on the accuracy of genomic relationship estimation was investigated. Estimation of relationships using genomic information was also carried out chromosome by chromosome for whole genomic SNP markers based on the regression method using allele frequencies across loci. The average correlation coefficient and standard deviation between relationships using pedigree information and chromosomal genomic information using data which was verified using a parentage test andeliminated missing genotypes was $0.81{\pm}0.04$ and their correlation coefficient when using whole genomic information was 0.98, which was higher. Variation in relationships between non-inbred half sibs was $0.22{\pm}0.17$ on chromosomal and $0.22{\pm}0.04$ on whole genomic SNP markers. The variations were larger and unusual values were observed when non-parentage test data were included. So, relationship matrix by genomic information can be useful for genetic evaluation of animal breeding.