• Asian-Australasian Journal of Animal Sciences
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• 제19권12호
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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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• 제37권4호
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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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• 제62권3호
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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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• 제29권1호
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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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• 제16권1호
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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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• 제55권1호
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• pp.13-18
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• 2013

본 연구는 농협 한우개량사업소 후대검정우 552두의 도체중, 배최장근단면적, 등지방두께 및 근내지방도를 측정한 후 고밀도 SNP 패널(777K)을 사용하여 유전체 혈연 행렬(Genetic Relationship Matrix, GRM)을 추정하고 GBLUP (Genomic Best Linear Unbiased Prediction) 방법으로 GEBV (Genomic Estimated Breeding Value)를 구하여 교차 검증(Cross-validation) 방법으로 그 정확도를 추정함으로써 유전체 선발 기법을 한우 유전평가 체계에 적용하기 위한 기초자료로 이용하고자 수행하였다. 교차 검증 방법으로 각 형질별로 추정된 유전체 육종가의 정확도는 0.915~0.957로 상당히 높게 추정되었다. 대립유전자의 빈도로 계산된 유전체 혈연 행렬을 이용하여 GBLUP 방법으로 추정된 육종가 정확도의 최대 차이는 후대검정우 534두에 대하여 도체중, 배최장근단면적, 등지방 두께 및 근내지방도 순으로 각각 9.56%, 5.78%, 5.78% 및 4.18% 정도의 수준으로 상승했고, 혈통 기록상의 모든 개체 3,674두에 대해서는 형질 별로 최대 13.54%, 6.50%, 6.50% 및 4.31% 정도의 수준으로 증가한 결과가 추정되었다. 이는 한우 보증씨수소의 선발 시스템에서 아직 표현형 자료를 생산할 수 없는 당대검정 후보축 대한 집단을 조성할 때 유전체 정보를 이용한 사전 선발을 활용하면 기존의 상대적으로 낮았던 육종가의 정확도의 상승 효과와 세대 간격의 단축으로 인하여 유전적 개량량을 증대시킬 수 있을 것으로 기대된다. 본 연구에서 genomic breeding value 추정을 위하여 조성된 집단의 경우는 후대 검정우 집단으로서 개체들 간의 혈연관계가 높으며, 이미 전통적인 BLUP 방법으로도 상당히 높은 정확도를 가진 집단을 이용하였다. 그러나, 현재 한우 집단에 대한 유전체 자료 구축 시 이용할 수 있는 정확한 자료는 후대검정우 집단 외에는 참조 집단을 조성할 수 있는 대안이 없으므로, 지속적인 유전체 검정을 위해서는 다양한 유전적 조성이 구축된 참조 집단을 구축해야 할 것으로 사료된다. 또한 유전체 검정을 통한 정확도 상승효과를 기대하기 위해서 지속적으로 참조 집단의 크기를 늘릴 필요성이 있다.

• Asian-Australasian Journal of Animal Sciences
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• 제27권9호
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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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• 제19권8호
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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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• 제36권7호
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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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• 제52권5호
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• pp.357-366
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• 2010

한우의 유전체 전장의 정보를 Illumina BeadArray$^{TM}$ Bovine SNP50 assay를 이용하여 단일염기다형 현상을 조사한 결과, 유전적 다양성을 보이는 좌위가 약 32,567 좌위 이상에서 다양성을 보이고 있었으며 약 5,554 좌위에서 다양성이 조사되지 않았다. 이는 조사된 자료의 가계집단의 수가 크게 제한되었기 때문에 기인될 수 있으며 또 다른 원인으로는 한우 종축집단의 크기가 작을 수 있다는 현상을 반증한다고 사료된다. 유전분석의 기초가 되는 혈통기록에 의한 개체간 혈연관계를 유전체 정보에 의한 혈연관계와 비교하여 본 결과, 유전체 정보에 의한 혈연관계의 크기가 혈통기록에 의한 혈연관계보다 좀 더 정확하게 추정될 수 있다는 장점이 있으며 혈통기록상의 오류로 그릇된 혈연관계의 크기를 유전체 정보를 통하여 보완할 수 있다는 장점이 있다. 이러한 장점을 활용하면 유전체정보를 이용한 유전능력 평가의 정확성을 크게 향상시킬 수 있을 것으로 사료되었다.