• Asian-Australasian Journal of Animal Sciences
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• 제21권8호
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• pp.1109-1115
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• 2008

The reproduction component traits are important components of sow efficiency. The objective of this study was to evaluate the phenotypic and genetic parameters of four reproduction component traits (age at puberty (AP), preweaning number dead (PND), weaning to service interval (WSI), and intra-individual SD in litter size (IISDLS)) of sows in two Chinese indigenous pig breeds. Available reproductive records including 22,591 piglets born from 2,054 litters by 574 Jiangquhai sows and 464 Meishan sows were used in this investigation. A set of mixed models and restricted maximum likelihood methodology were used for the multiple trait analyses of these traits. The results showed that the estimates of heritabilities (${\pm}$standard error) for AP, PND, WSI and IISDLS were $0.40{\pm}0.05$, $0.06{\pm}0.03$, $0.20{\pm}0.02$ and 0.09{\pm}0.03 in Jiangquhai sows, and $0.35{\pm}0.06$, $0.05{\pm}0.03$, $0.18{\pm}0.03$ and $0.10{\pm}0.04$ in Meishan sows, respectively. There was moderate genetic correlation between AP and WSI, while there were low genetic correlations between the other pairwise traits. The genetic correlations were positive for most of the pairwise traits, except for the one between AP and IISDLS. The results indicated that all traits except for AP were difficult to make genetic improvement by traditional selection methods due to low heritabilities and the favorable improvement of AP might result in unfavorable changes of IISDLS due to the trend of genetic antagonism.

• Journal of Animal Science and Technology
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• 제50권5호
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• pp.601-612
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• 2008

본 연구는 한우의 체형과 체중이 일령에 따라 어떻게 변화하며, 선발형질인 도체형질과의 상관 또한 체중 및 체형의 변화에 따라 어떠한 형태로 변화하는지 알아보기 위하여 실시하였다. 분석에 이용한 형질은 체중, 체형 및 도체형질을 포함하여 모두 17가지 형질이며 거세우 161두의 자료를 이용하였다. 성장곡선 추정은 logistic 모형을 이용하였고, 추정한 모수를 토대로 변곡일령 및 변곡일령에서의 특성을 다시 계산하였다. 각 형질에 대한 성장곡선 모수를 분석한 결과 좌골폭은 조숙성, 흉위는 만숙성 형질인 것으로 나타났다. 등지방두께에 대한 흉심, 흉폭 및 요각폭의 순위상관계수는 6~24개월까지 꾸준히 증가하는 반면 다른 체형형질들은 18개월령 이후에 감소하는 것으로 나타났다. 본 연구는 표현형 자료에 대한 분석만이 이뤄졌으나, 한우 성장 단계에 따른 유전적 변화를 살펴보기 위해 유전모수 추정과 같은 추가적인 연구가 이뤄진다면 체형형질을 한우개량에 충분히 이용이 가능할 것으로 생각된다.

• Animal Bioscience
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• 제35권10호
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• pp.1499-1511
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• 2022

Objective: This study was aimed to estimate the genetic parameters, including genetic and phenotypic correlations, of milk yield, lactation curve traits and milk composition of Thai dairy cattle from three government research farms. Methods: The data of 25,789 test-day milk yield and milk composition records of 1,468 cattle from lactation 1 to 3 of Holstein Friesian (HF) and crossbred HF dairy cattle calved between 1990 and 2015 from three government research farms in Thailand were analysed. 305-day milk yield was estimated by the Wood model and a test interval method. The Wood model was used for estimating cumulative 305-day milk yield, peak milk yield, days to peak milk yield and persistency. Genetic parameters were estimated using linear mixed models with herd, breed group, year and season of calving as fixed effects, and animals linked to a pedigree as random effects, together with a residual error. Univariate models were used to estimate variance components, heritability, estimated breeding values (EBVs) and repeatability of each trait, while pairwise bivariate models were used to estimate covariance components and correlations between traits in the same lactation and in the same trait across lactations. Results: The heritability of 305-day milk yield, peak milk yield and protein percentage have moderate to high estimates ranging from 0.19 to 0.45 while days to peak milk yield, persistency and fat percentage have low heritability ranging from 0.08 to 0.14 in lactation 1 cows. Further, heritability of most traits considered was higher in lactation 1 compared with lactations 2 and 3. For cows in lactation 1, high genetic correlations were found between 305-day milk yield and peak milk yield (0.86±0.07) and days to peak milk yield and persistency (0.99±0.02) while estimates of genetic correlations between the remaining traits were imprecise due to the high standard errors. The genetic correlations within the traits across lactation were high. There was no consistent trend of EBVs for most traits in the first lactation over the study period. Conclusion: Both the Wood model and test interval method can be used for milk yield estimates in these herds. However, the Wood model has advantages over the test interval method as it can be fitted using fewer test-day records and the estimated model parameters can be used to derive estimates of other lactation curve parameters. Milk yield, peak milk yield and protein percentage can be improved by a selection and mating program while days to peak milk yield, persistency and fat percentage can be improved by including into a selection index.

• 한국가금학회지
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• 제47권4호
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• pp.267-274
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• 2020

본 연구에서는 국립축산과학원 가금연구소에서 보유하고 있는 흑색 재래종토종닭 L계통, 한국화이트레그혼 F, K계통의 150일령 및 270일령 체중, 시산일령, 시산 및 270일령 난중, 270일령 산란수 총 6가지 경제형질에 대한 유전모수를추정하였다. 기초통계량에서 150일령 및 270일령 체중은 L계통이 각각 1,714 g, 2,032 g으로 F, K계통에 비해 무겁게 나타났으며, 시산일령은 L, F, K계통에서 153, 140, 143일로 나타났으며, L계통의 시산난중 및 270일령 난중은 각각 37 g, 54 g으로 F, K계통에 비해 가벼웠으며, 270일령까지 산란수는 76개로 F, K계통의 산란수인 100, 99개에 비해 적은 경향을 나타냈다. 재래종토종닭과 화이트레그혼의 품종 간 경제형질 능력의 차이를 보였으며, 화이트레그혼 품종 내 F, K계통 간 경제형질 능력의 유의적인 차이는 없는 것으로 나타났다. L, F, K계통에서 150일령 체중의 유전력은 0.48~0.52로 고도의 유전력으로 나타났으며, 270일령 체중은 0.56~0.57로 고도의 유전력, 270일령 난중은 0.55~0.59로 고도의 유전력, 시산일령은 0.31~0.45로 중도에서 고도의 유전력, 270일령 산란수는 0.20~0.22로 중도의 유전력, 마지막으로 시산난중은 0.15~0.16으로 저도의 유전력을 보였다. 시산일령과 270일령까지 산란수의 유전 및 표현형 상관계수는 각각 -0.73~-0.63, -0.48~-0.42로 부의 상관관계가 나타났다. 시산일령과 체중의 유전 상관은 0.05~0.17로 정의 상관관계를 보였으나, 표현형 상관은 -0.04~0.10으로 대체적으로 부의 상관관계를 나타냈다. 또한, 270일령까지 산란수와 150일령, 270일령 체중 및 270일령 난중의 유전상관은 각각 -0.16~0.01, -0.14~-0.03, -0.45~-0.24로 나타났고, 표현형 상관은 각각 -0.08~0.07, -0.13~0.04, -0.15~-0.11로 나타나 전반적으로 부의 상관을 보였다. 270일령 난중과 150, 270일령 체중은 0.36~0.49의 유전 상관과 0.29~0.37의 표현형 상관을 나타냈으며, 모두 정의 상관을 나타냈다. 270일령 난중과 시산난중은 0.50~0.73의 유전 상관과 0.21~0.32의 표현형 상관을 나타냈으며, 모둔 정의 상관을 나타냈고, 유전 상관이 표현형 상관보다 높게 나타났다. 시산난중과 체중의 상관 관계는 0.22~0.39로 정의 유전 상관과 0.07~0.16의 정의 표현형 상관을 보였으며, 시산난중과 시산일령은 0.48~0.67로 높은 정의 유전 상관과 0.27~0.39 정의 표현형 상관을 보였다. 그리고 150일과 270일 체중 간 유전 상관 0.90~0.91, 표현형 상관 0.68~0.80으로 상당히 높은 상관 관계가 있는 것으로 나타났다.

• Asian-Australasian Journal of Animal Sciences
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• 제17권10호
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• pp.1350-1353
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• 2004

To further investigate the regions on porcine chromosome 7 that are responsible for economically important traits, phenotypic data from a total of 287 F2 individuals were collected and analyzed from 1998 to 2000. All animals were genotyped for eight microsatellite loci spanning the length of chromosome 7. QTL analysis was performed using interval mapping under the line-cross model. A permutation test was used to establish significance levels associated with QTL effects. Observed QTL effects were (chromosomewide significance, position of maximum significance in centimorgans): Birth weight (<0.01, 3); Carcass length (<0.05, 80); Longissimus muscle area (<0.01, 69); Skin percentage (<0.01, 69); Bone percentage (<0.01, 74); Fat depths at shoulder (<0.05, 54);Mean fat depth (<0.05, 81); Moisture in m. Longissimus Dorsi (<0.05, 88). Additional evidence was also found which suggested QTL for dressing percentage and fat depths at buttock. This study offers confirmation of several QTL affecting growth and carcass traits on SSC7 and provides an important step in the search for the actual major genes involved in the traits of economic interest.

• Asian-Australasian Journal of Animal Sciences
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• 제25권11호
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• pp.1507-1510
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• 2012

Quantitative trait loci (QTL) associated with fat deposition traits in pigs are important gene positions in a chromosome that influence meat quality of pork. For QTL study, a three generation resource population was constructed from a cross between Korean native boars and Landrace sows. A total of 240 F2 animals from intercross of F1 were produced. 80 microsatellite markers covering chromosomes 1 to 10 were selected to genotype the resource population. Intervals between adjacent markers were approximately 19 cM. Linkage analysis was performed using CRIMAP software version 2.4 with a FIXED option to obtain the map distances. For QTL analysis, the public web-based software, QTL express (http://www.qtl.cap.ed.ac.uk) was used. Two significant and two suggestive QTL were identified on SSC 6, 7, and 8 as affecting body fat and IMF traits. For QTL affecting IMF, the most significant association was detected between marker sw71 and sw1881 on SSC 6, and a suggestive QTL was identified between sw268 and sw205 on SSC8. These QTL accounted for 26.58% and 12.31% of the phenotypic variance, respectively. A significant QTL affecting IMF was detected at position 105 cM between markers sw71 and sw1881 on SSC 6.

• 한국육종학회지
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• 제43권5호
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• pp.360-367
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• 2011

Beta-carotene producing transformants were produced in the background of 'Nagdongbyeo', a Japonica rice cultivar. Introgression of the carotenoid locus in the transformant, PAC4-2 into the elite cultivar 'Ilpumbyeo' was started. To initiate a backcrossing program, we surveyed 220 SSR markers and found that 38% of them were polymorphic between 'Ilpumbyeo' as a recurrent parent and the PAC4-2 as a recipient parent. The selection strategy comprising foreground and background selection was employed. First, foreground selection was practiced in $BC_1$, $BC_2$, and $BC_3$ generations using the transgene specific PCR-based marker in addition to visual scoring of the seed color. Marker-based background selection combined with phenotypic selection was employed from $BC_3F_2$ to $BC_3F_4$ generations. Blast search indicated that the transgene PAC4-2 was located between SSR markers, RM6 and RM482. 240 $BC_3F_3$ and 63 $BC_3F_4$ lines were evaluated for four agronomic traits including days to heading. Most of the lines were similar to Ilpumbyeo in agronomic traits evaluated. The percentage of PAC4-2 genome ranged from 4% to 21% with a mean of 12.5%, which was higher than the expected for an unselected $BC_3$ backcross population. This could be explained by the fact that two genes for beta-carotene and the stripe virus resistance were targeted in this study. We selected 10 representative $BC_3F_5$ lines from 63 $BC_3F_4$ lines based on agronomic traits and carotenoids content. The selection strategy would be appropriate for the introgression of beta-carotene gene in a breeding program.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.303-303
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• 2022

The purpose of this study was to evaluate agronomic characteristics and identify the useful traits to utilize the wheat genetic resources for breeding programs by understanding the phenotypic variation among germplasm through multivariate analysis. In this study, a total of 394 wheat accessions were characterized for 15 agronomic traits using the National Agrobiodiversity Center (NAC) descriptor list, of which 31 accessions from 6 species and 363 unidentified accession (Triticum spp.) available at the NAC, Rural Development Administration (RDA), Korea. Growth characteristics such as leaf width, culm length, spike length, spikelet length, solid stemmed, days to heading, days to maturity, grain-filing period, and also seed characteristics such as width, height, area, perimeter, circle, solidity, and germination percent were studied. Among the 15 agronomic characteristics, the germination percent showed the smallest variation between resources (CV = 0.4%), and the spikelet length (CV = 66.5%) showed the highest variation. A strong positive correlation was found between seed traits such as seed height and seed area (r = 0.90), seed height and seed perimeter (r = 0.87) and seed length and width (r = 0.80). Principal component analysis (PCA) was conducted and the first five principal components comprised 76.7% of the total variance. Among the first five PCs, PCI accounted for 28.5% and PC2 for 20.0%. Wheat resources (394) were classified into four clusters based on cluster analysis, consisting of 215 resources(I), 117 resources(II), 48 resources(III), and 14 resources(IV). Among the clusters, the resources belonging to Cluster III showed the lowest seed width, height, area, and perimeter characteristics compared to other clusters. The wheat resources belonging to cluster IV had small seed width and low germination percent, but took longer to form heads and mature than resources in other clusters. These results will serve as the basis for further genetic diversity studies, and important agronomic characteristics will be used for improving wheat, including developing high-yielding and resistant varieties to biotic and abiotic stresses via breeding programs.

• 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.

• Animal Bioscience
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• 제37권3호
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• pp.428-436
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• 2024

Objective: This study compared five distinct sets of biological pathways and associated genes related to semen volume (VOL), number of sperm (NS), and sperm motility (MOT) in the Thai multibreed dairy population. Methods: The phenotypic data included 13,533 VOL records, 12,773 NS records, and 12,660 MOT records from 131 bulls. The genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNPs) from 72 animals. The SNP additive genetic variances for VOL, NS, and MOT were estimated for SNP windows of one SNP (SW1), ten SNP (SW10), 30 SNP (SW30), 50 SNP (SW50), and 100 SNP (SW100) using a single-step genomic best linear unbiased prediction approach. The fixed effects in the model were contemporary group, ejaculate order, bull age, ambient temperature, and heterosis. The random effects accounted for animal additive genetic effects, permanent environment effects, and residual. The SNPs explaining at least 0.001% of the additive genetic variance in SW1, 0.01% in SW10, 0.03% in SW30, 0.05% in SW50, and 0.1% in SW100 were selected for gene identification through the NCBI database. The pathway analysis utilized genes associated with the identified SNP windows. Results: Comparison of overlapping and non-overlapping SNP windows revealed notable differences among the identified pathways and genes associated with the studied traits. Overlapping windows consistently yielded a larger number of shared biological pathways and genes than non-overlapping windows. In particular, overlapping SW30 and SW50 identified the largest number of shared pathways and genes in the Thai multibreed dairy population. Conclusion: This study yielded valuable insights into the genetic architecture of VOL, NS, and MOT. It also highlighted the importance of assessing overlapping and non-overlapping SNP windows of various sizes for their effectiveness to identify shared pathways and genes influencing multiple traits.