• Asian-Australasian Journal of Animal Sciences
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• v.15 no.1
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• pp.7-10
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• 2002

The breeding data relating to Hariana herd spread over 18 years (1979-96) were analysed to estimate genetic, phenotypic and environmental changes in characters of economic importance which might have taken place during the several years of selective breeding practiced in the herd. The average genetic changes in a given character were estimated by four methods. The phenotypic trends observed for different economic traits were not significant. On changing the method of estimation, magnitude and direction of genetic trends changed. Comparison of estimates of genetic trends by different methods showed that adjustments for biases due to non-random allotment of dams with respect to their age and merit suggested by Powell and Freeman (1974) were useful for increasing the precision of the estimates. Hence, this method was found to be the best method for estimation of genetic trends. The estimate of genetic trends by this method were 4.03${\pm}$6.21 days, 3.24${\pm}$5.33 kg, 0.15${\pm}$0.43 days, 0.09${\pm}$0.59 days, 0.01${\pm}$0.02 kg and 0.01${\pm}$0.01 kg for age at first calving, first lactation milk yield, first lactation length, first calving interval, first lactation milk yield per day lactation length and first lactation milk yield per day of calving interval, respectively.

• Journal of Software Engineering Society
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• v.27 no.1
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• pp.15-17
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• 2018

Fast response to livestock disease and raising the reproductive success rate contribute to the improvement of farm productivity and profit margins. Due to the decrease in farm workforce and aging population, efficient livestock healthcare and breeding management are needed. In this study, we developed a mobile application for livestock healthcare and breeding management based on the collected body temperature data by IoT sensors. In case of livestock health problem, users are notified immediately via a mobile application warning message. It also provides users with a livestock breeding date function, allowing them to respond without missing the breeding season.

• Plant Breeding and Biotechnology
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• v.6 no.4
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• pp.444-453
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• 2018

The rice bran oil contained in brown rice is composed of highly valued ingredient. Improving the content of unsaturated fatty acids in rice seed, such as oleic acid, linoleic acid, and ${\alpha}$-linolenic acid, would provide more benefit to human health. Fatty acid content is quantitative trait controlled by multiple genes. We have utilized high-density SNP data from highly advanced breeding populations to identify QTLs for fatty acid contents in brown rice. Here, we identified 51 major QTLs (M-QTLs) and 25 epistatic QTLs (EpQTLs) related to eleven fatty acid contents. Eight and four M-QTLs were pleiotropically associated with the content of different fatty acids in MT-RILs and DT-RILs, respectively. Total effect of M-QTLs for palmitic acid (16:0), oleic acid (18:1), and linoleic acid (18:2), could explain phenotypic variations of 36.7%, 63.7%, and 41% in MT-RILs, respectively. Alpha-linolenic acid which is important for a human's health could be explained phenotypic variation of 15.7% by six M-QTLs. These QTLs identified in this study can be used to improve nutritious content in rice breeding programs.

• Journal of Ginseng Research
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• v.44 no.4
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• pp.637-643
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• 2020

Background: Ginseng (Panax ginseng Meyer) is one of the world's most valuable medicinal plants with numerous pharmacological effects. Ginseng has been cultivated from wild mountain ginseng collections for a few hundred years. However, the genetic diversity of cultivated and wild ginseng populations is not fully understood. Methods: We developed 92 polymorphic microsatellite markers based on whole-genome sequence data. We selected five markers that represent clear allele diversity for each of their corresponding loci to elucidate genetic diversity. These markers were applied to 147 individual plants, including cultivars, breeding lines, and wild populations in Korea and neighboring countries. Results: Most of the 92 markers displayed multiple-band patterns, resulting from genome duplication, which causes confusion in interpretation of their target locus. The five high-resolution markers revealed 3 to 8 alleles from each single locus. The proportion of heterozygosity (H_e) ranged from 0.027 to 0.190, with an average of 0.132, which is notably lower than that of previous studies. Polymorphism information content of the markers ranged from 0.199 to 0.701, with an average of 0.454. There was no statistically significant difference in genetic diversity between cultivated and wild ginseng groups, and they showed intermingled positioning in the phylogenetic relationship. Conclusion: Ginseng has a relatively high level of genetic diversity, and cultivated and wild groups have similar levels of genetic diversity. Collectively, our data demonstrate that current breeding populations have abundant genetic diversity for breeding of elite ginseng cultivars.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.8
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• pp.1088-1096
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• 2008

Data from milk performance testing were used to analyze genetic and environmental trends for purebred Tsigai, Improved Valachian and Lacaune sheep. 103,715 (Tsigai), 212,962 (Improved Valachian) and 2,196 (Lacaune) test-day records gathered by the State Breeding Institute of the Slovak Republic entered the analyses. The respective pedigree data comprised 23,724 (Tsigai), 51,401 (Improved Valachian) and 438 (Lacaune) records. The multiple-trait, mixed model methodology was used to predict the breeding values for daily milk yield, fat and protein content and to estimate the fixed and remaining random effects assumed to affect the above mentioned traits, separately for each breed. The breeding values for daily milk yield were adjusted for 150-day standardized lactation length by multiplying with the constant 150, as the breeding goal of the selection scheme in Slovakian sheep is to increase 150-day milk production and constant heritability throughout the whole lactation is assumed. The genetic trends were expressed as changes in averages of breeding values across birth years of animals. For Tsigai and Lacaune breeds, cumulative genetic changes over the analyzed period were 3.8 and 5.1 kg for 150-day milk, 0 and -0.16% for fat content and 0 and -0.12% for protein content. For Improved Valachian breed, either a low (1.6 kg for 150-day milk yield) or zero (fat and protein content) cumulative genetic change was found. The environmental trends were calculated as averages of solutions for flock-test day effect across years and months in which measurements were taken. A distinctive cyclical pattern which reflected short-time variation in milk production traits was found. Possible explanations for this phenomenon are given and discussed.

• Korean Journal of Agricultural Science
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• v.47 no.1
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• pp.119-130
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• 2020

With the current rapid growth and increase in the world's population, the demand for nutritious food and fibers and fuel will increase. Therefore, there is a serious need for the use of breeding programs with the full potential to produce high-yielding crops. However, existing breeding techniques are unable to meet the demand criteria even though genotyping techniques have significantly progressed with the discovery of molecular markers and next-generation sequencing tools, and conventional phenotyping techniques lag behind. Well-organized high-throughput plant phenotyping platforms have been established recently and developed in different parts of the world to address this problem. These platforms use several imaging techniques and technologies to acquire data for quantitative studies related to plant growth, yield, and adaptation to various types of abiotic or biotic stresses (drought, nutrient, disease, salinity, etc.). Phenotyping has become an impediment in genomics studies of plant breeding. In recent years, phenomics, an emerging domain that entails characterizing the full set of phenotypes in a given species, has appeared as a novel approach to enhance genomics data in breeding programs. Imaging techniques are of substantial importance in phenomics. In this study, the importance of current imaging technologies and their applications in plant phenotyping are reviewed, and their advantages and limitations in phenomics are highlighted.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.3
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• pp.334-340
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• 2019

Objective: The objective of this study was to investigate the genetic components of daily milk yield and to re-rank bulls in South Korea by estimated breeding value (EBV) under heat stress using the temperature-humidity index (THI). Methods: This study was conducted using 125,312 monthly test-day records, collected from January 2000 to February 2017 for 19,889 Holstein cows from 647 farms in South Korea. Milk production data were collected from two agencies, the Dairy Cattle Genetic Improvement Center and the Korea Animal Improvement Association, and meteorological data were obtained from 41 regional weather stations using the Automated Surface Observing System (ASOS) installed throughout South Korea. A random regression model using the THI was applied to estimate genetic parameters of heat tolerance based on the test-day records. The model included herd-year-season, calving age, and days-in-milk as fixed effects, as well as heat tolerance as an additive genetic effect, permanent environmental effect, and direct additive and permanent environmental effect. Results: Below the THI threshold (${\leq}72$; no heat stress), the variance in heat tolerance was zero. However, the heat tolerance variance began to increase as THI exceeded the threshold. The covariance between the genetic additive effect and the heat tolerance effect was -0.33. Heritability estimates of milk yield ranged from 0.111 to 0.176 (average: 0.128). Heritability decreased slightly as THI increased, and began to increase at a THI of 79. The predicted bull EBV ranking varied with THI. Conclusion: We conclude that genetic evaluation using the THI function could be useful for selecting bulls for heat tolerance in South Korea.

• BMB Reports
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• v.40 no.4
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• pp.525-531
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• 2007

As a $\beta_2$-adrenergic agonist, clenbuterol decreases body fat, but the molecular mechanism underlying this process is unclear. In the present study, we treated 293T and L-02 cells with clenbuterol and found that clenbuterol downregulates SREBP-1c expression and upregulates CREB1 expression. Considering SREBP-1c has the function of regulating the transcription of several lipogenic enzymes, we considered that the downregulation of SREBP-1c is responsible for body fat reduction by clenbuterol. Many previous studies have found that clenbuterol markedly increases intracellular cAMP levels, therefore, we also investigated whether CREB1 is involved in this process. The data from our experiments indicate that CREB1 overexpression inhibits SREBP-1c transcription, and that this action is antagonized by CREB2, a competitive inhibitor of CREB1. Furthermore, since PPARs are able to repress SREBP-1c transcription, we investigated whether clenbuterol and CREB1 function via a pathway involving PPAR activation. However, our results showed that clenbuterol or CREB1 overexpression suppressed PPARs transcription in 293T and L-02 cells, which suggested that they impair SREBP-1c expression in other ways.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.14-14
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• 2017

The discipline of plant breeding is experiencing a renaissance impacting crop improvement as a result of new technologies, however fundamental questions remain for predicting the phenotype and how the environment and genetics shape it. Inexpensive DNA sequencing, genotyping, new statistical methods, high throughput phenotyping and gene-editing are revolutionizing breeding methods and strategies for improving both quantitative and qualitative traits. Genomic selection (GS) models use genome-wide markers to predict performance for both phenotyped and non-phenotyped individuals. Aerial and ground imaging systems generate data on correlated traits such as canopy temperature and normalized difference vegetative index that can be combined with genotypes in multivariate models to further increase prediction accuracy and reduce the cost of advanced trials with limited replication in time and space. Design of a GS training population is crucial to the accuracy of prediction models and can be affected by many factors including population structure and composition. Prediction models can incorporate performance over multiple environments and assess GxE effects to identify a highly predictive subset of environments. We have developed a methodology for analyzing unbalanced datasets using genome-wide marker effects to group environments and identify outlier environments. Environmental covariates can be identified using a crop model and used in a GS model to predict GxE in unobserved environments and to predict performance in climate change scenarios. These new tools and knowledge challenge the plant breeder to ask the right questions and choose the tools that are appropriate for their crop and target traits. Contemporary plant breeding requires teams of people with expertise in genetics, phenotyping and statistics to improve efficiency and increase prediction accuracy in terms of genotypes, experimental design and environment sampling.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.3
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• pp.303-308
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• 2013

Subfertility problems are encountered frequently in the cattle and buffalo bulls commercially maintained for semen production in dairy farms and under field conditions for natural insemination. Reports are scarce on the incidence of subfertility in breeding bulls, especially in India. The objective of the present study was to assess the incidence of the male reproductive anomalies leading to disposal of bovine bulls at GADVASU dairy farm, Ludhiana, Punjab (India). Data on frequency of various subfertility and disposal pattern of bulls maintained at the dairy farm, GADVASU, were collected for 12 yrs (1999 to 2010) and compiled from different record registers. Percentage of bulls that produced freezable semen (out of reserved ones) was less in cattle (25.641%) as compared to that of buffalo (30.4%). Various subfertility traits like poor libido and unacceptable seminal profile were found to be the significant reasons (p<0.01) for culling of the breeding bulls. Inadequate sex drive and poor semen quality were the main contributing factors for bull disposal in cattle whereas poor semen freezability was most frequently observed in buffalo bulls. All the male reproductive traits were significantly different (p<0.05) for the periods of birth, except for semen volume, initial motility (IM), age at last semen collection (ALSC) and age at disposal. The ages at first and last semen collection as well as freezing (i.e. AFSC, ALSC and AFSF, ALSF, respectively) and age at disposal (AD) were higher in buffalo. The spermatological parameters and semen production period (SPP) were higher in cattle. The age at first semen donation and breeding period could be reduced by introducing the bulls to training at an early age. The results revealed an increasing trend in individual motility (IM) while semen volume, AFSC, AFSF, AD, FSPP, SPP, ALSC and ALSF showed a decreasing, however, not a definite trend, over the periods. The semen donation traits like, AFSF, of the cattle and buffalo bulls could be predicted from the AFSC, using prediction equation derived in the present study.