• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.189-189
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• 2017

Salinity is one of the major abiotic stresses that severely affect crop production throughout the world; especially rice plant which is generally categorized as a typical glycophyte as it cannot grow in the presence of salinity. Phenotypic resistance of salinity is expressed as the ability to survive and grow in a salinity condition. Salinity resistance has, at least implicitly, been treated as a single trait. Physiological studies of rice suggest that a range of characteristics (such as low shoot sodium concentration, compartmentation of salt in older rather than younger leaves, high potassium concentration, high $K^+/Na^+$ ratio, high biomass and plant vigour) would increase the ability of the plant to cope with salinity. Criteria for evaluating and screening salinity tolerance in crop plants vary depending on the level and duration of salt stress and the plant developmental stage. Plant growth responses to salinity vary with plant life cycle; critical stages sensitive to salinity are germination, seedling establishment and flowering. We have established a standard protocol to evaluate large rice germplasms for overall performance based on specific physiological traits for salt tolerance at seedling stage. This protocol will help in identifying germplasms which can perform better in the presence of different salinity treatments based on single trait and also combination of different physiological traits. The salt tolerant germplasm can be taken forward into developing better varieties by conventional breeding and exploring genes for salt tolerance.

• Asian-Australasian Journal of Animal Sciences
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• 제21권11호
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• pp.1559-1571
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• 2008

A rule was developed to constrain the annual rate of inbreeding to a predefined value in a population with different lifetimes between sires and dams, and to maximize the selection response over generations. This rule considers that the animals in a population should be divided into sex-age classes based on the theory of gene flow, and restricts the increase of average inbreeding coefficient for new offspring by limiting the increase of the mean additive genetic relationship for parents selected. The optimization problem of this rule was formulated as a quadratic programming problem. Inputs for the rule were the BLUP estimated breeding values, the additive genetic relationship matrix of all animals, and the long-term contributions of sex-age classes. Outputs were optimal number and contributions of selected animals. In addition, this rule was combined with the optimization of emphasis given to QTL, and further increased the genetic gain over the planning horizon. Stochastic simulations of closed nucleus schemes for pigs were used to investigate the potential advantages obtained from this rule by combining the standard QTL selection, optimal QTL selection and conventional BLUP selection. Results showed that the predefined rates of inbreeding were actually achieved by this rule in three selection strategies. The rule obtained up to 9.23% extra genetic gain over truncation selection at the same rates of inbreeding. The combination of the extended rule and the optimization of emphasis given to QTL allowed substantial increases in selection response at a fixed annual rate of inbreeding, and solved substantially the conflict between short-term and long-term selection response in QTL-assisted selection schemes.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2005년도 추계학술발표회 요지
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• pp.266-267
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• 2005

• 한국작물학회:학술대회논문집
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• 한국작물학회 2000년도 춘계 학술대회지
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• pp.378-379
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• 2000

• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2020년도 춘계학술대회
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• pp.108-108
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• 2020

• Asian-Australasian Journal of Animal Sciences
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• 제15권7호
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• pp.932-937
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• 2002

A partial genome scan using porcine microsatellites was carried out to detect quantitative trait loci (QTL) for backfat thickness (BFT) in a pig reference population. This population carried QTL on chromosomes 1, 13 and 18. The QTL on chromosome 1 was located between marker loci S0113 and SW1301. The QTL corresponded to very low density lipoprotein receptor gene (VLDLR) in location and in biological effects, suggesting that VLDLR might be a candidate gene. The QTL found on chromosome 13 was found between marker loci SWR1941 and SW864, but significance for the marker-trait association was inconsistent by using data with different generations. The QTL on chromosome 18 was discovered between markers S0062 and S0117, and it was in proximity of the regions where IGFBP3 and GHRHR were located. The porcine obese gene might be also a candidate gene for the QTL on chromosome 18. In order to understand genetic architecture of BFT better, fine mapping and positional comparative candidate gene analyses are necessary.

• Asian-Australasian Journal of Animal Sciences
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• 제12권2호
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• pp.165-168
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• 1999

To determine the effects of genetic and environmental influences on cell mediated immune (CMI) responses in broiler rabbits, contact sensitivity to 2,4-Dinitrochlorobenzene (DNCB) was assessed in three temperate broiler breeds of rabbits, namely Soviet Chinchilla, New Zealand White and Grey Giant. The feasibility of using the contact sensitivity to DNCB as a marker trait in selection for disease resistance was examined. There were highly significant differences between breeds (p<0.01) in initial skin thickness and contact sensitivities to DNCB at 24, 48 and 72 hours. Initial skin thickness was greatest in the Soviet Chinchilla breed (mean 2.2484 mm), and was significantly greater (p<0.01) in males (2.4963 mm) than in females (1.7846 mm) (p<0.01). Highest contact sensitivity to DNCB was in the New Zealand White breed with mean increase in skin thickness of 1.1884, 0.9072 and 0.5879 mm at 24, 48 and 72 hours post challenge respectively. Delayed type hypersensitivity (DTH) reaction to DNCB at 24 hours post challenge had a highly significant association (p<0.01) with the incidence of body mange in rabbits. The results indicated a lowered contact sensitivity to DNCB at 24 hours post challenge was associated significantly (p<0.01) with an increase in incidence and severity of body mange, suggesting its potential value as a marker. The correlation s among contact sensitivities at 24, 48 and 72 hours were positive and highly significant (p<0.01); correlations between initial skin thickness and contact sensitivities were negative and highly significant (p<0.01). Another notable significant correlation was between body weight and delayed type hypersensitivity at 24 hours indicating that an enhanced CMI might be associated with better growth rate and general wellbeing.

• 농업과학연구
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• 제45권4호
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• pp.707-713
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• 2018

This study was done to search for positional candidate genes associated with the back fat thickness trait using a Genome-Wide Association Study (GWAS) in purebred Yorkshires (N = 1755). Genotype and phenotype analyses were done for 1,642 samples. As a result of the associations with back fat thickness using the Gemma program (ver. 0.93), when the genome-wide suggestive threshold was determined using the Bonferroni method ($p=1.61{\times}10^{-5}$), the single nucleotide polymorphism (SNP) markers with suggestive significance were identified in 1 SNP marker on chromosome 2 (MARC0053928; $p=3.65{\times}10^{-6}$), 2 SNP markers on chromosome 14 (ALGA0083078; $p=7.85{\times}10^{-6}$, INRA0048453; $p=1.27{\times}10^{-5}$), and 1 SNP marker on chromosome 18 (ALGA0120564; $p=1.44{\times}10^{-5}$). We could select positional candidate genes (KCNQ1, DOCK1, LOC106506151, and LOC110257583), located close to the SNP markers. Among these, we identified a potassium voltage-gated channel subfamily Q member gene (KCNQ1) and the dedicator of cytokinesis 1 (DOCK1) gene associated with obesity and Type-2 diabetes. The SNPs and haplotypes of the KCNQ1 and DOCK1 genes can contribute to understanding the genetic structure of back fat thickness. Additionally, it may provide basic data regarding marker assisted selection for a meat quality trait in pigs.

• 농업과학연구
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• 제45권1호
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• pp.50-57
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• 2018

Meat comes from the skeletal muscles of farm animals, such as pigs, chickens, and cows. Skeletal muscles are composed of many muscle fibers. Muscle fibers are categorized into three types, fiber type I, IIA, and IIB, based on their contractile speed and metabolic properties. Different muscle fiber types have different biochemical, physiological, and biophysical characteristics. Especially, the characteristics of muscle fiber type I and IIB are opposite to each other. Muscle fiber type I has a relatively strong oxidative metabolic trait and a higher content of lipids. In contrast to fiber type I, muscle fiber type IIB has a strong glycolytic metabolic trait and a relatively lower content of lipids and a higher content of glycogen. Muscle fiber type IIA has intermediate properties between fiber type I and IIB. Thus, muscles with different fiber type compositions exhibit different ante- and post-mortem muscle characteristics. In particular, the different metabolic traits of muscles due to the different compositions of the fiber types strongly affect the biochemical and physiological processes during the conversion of muscle to meat and subsequently influence the quality of the meat. Therefore, understating muscle metabolism and muscle fiber characteristics is very important when discussing the traits of meat quality. This review is an overview on basic muscle metabolism, muscle fiber characteristics, and their influence on meat quality and finally provides a comprehensive understanding about the fundamental traits of muscles and meat quality.

• Asian-Australasian Journal of Animal Sciences
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• 제17권7호
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• pp.914-918
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• 2004

The random regression model methodology was applied into the estimation of genetic parameters for body weights in Chinese Simmental cattle to replace the traditional multiple trait models. The variance components were estimated using Gibbs sampling procedure on Bayesion theory. The data were extracted for Chinese Simmental cattle born during 1980 to 2000 from 6 national breeding farms, where records from 3 months to 36 months were only used in this study. A 3 orders Legendre polynomial was defined as the submodel to describe the general law of that body weight changing with months of age in population. The heritabilities of body weights from 3 months to 36 months varied between 0.31 and 0.48, where the heritabilities from 3 months to 12 months slightly decreased with months of age but ones from 13 months to 36 months increased with months of age. Specially, the heritabilities at eighteenth and twenty-fourth month of age were 0.33 and 0.36, respectively, which were slightly greater than 0.30 and 0.31 from multiple trait models. In addition, the genetic and phenotypic correlations between body weights at different month ages were also obtained using regression model.