• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.2
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• pp.109-127
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• 2014

Maize is one of the most important food and feed crops in the world including Southeast Asia. In spite of numberous efforts with conventional breeding, the maize productions remain low and the loss of yields by drought and downy mildew are still severe in Asia. Genetic improvement of maize has been performed with molecular marker and genetic engineering. Because maize is one of the most widely studied crop for its own genome and has tremendous diversity and variant, maize is considered as a forefront crop in development and estimation of molecular markers for agricultural useful trait in genetics and breeding. Using QTL (Quantitative Trait Loci) and MAS (Marker Assisted Breeding), molecular breeders are able to accelerate the development of drought tolerance or downy mildew resistance maize genotype. The present paper overviews QTL/MAS approaches towards improvement of maize production against drought and downy mildew. We also discuss here the trends and importance of molecular marker and mapping population in maize breeding.

• 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.

• Clinical and Experimental Reproductive Medicine
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• v.7 no.1_2
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• pp.27-34
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• 1980

The status of artificial insemination was surveyed by investigating 20796 heads of adult dairy cattle out of 30464 heads at 1649 dairy farms located Seoul and Gyung-Gi and Chung-Cheong province area, and the results are as follows: 1. There were 286 small farms feeding less than 5 heads occupying 17.3% of total farm number, and 1083 medium sized dairy farms feeding between $6{\sim}20$ heads occupying 65.64% and also 280 large dairy farms feeding more than 21 heads occupying 17.06% of total. 2. Population of cow aging from 2 to 6 years were 87.26% and among 20907 heads of dairy cattles which had calved previously 13196 heads had histories of one to four calving 90.40%. 3. The successful conception rate with first A. I. occupied only 53.43% and 92.4% after third A. I. service. The average A. I. frequency for successful conception per/head was 1.8 times. 4. The breeding status was pregnancy 61.72%, uncertain pregnancy 19.39%, and as physiological vacancy 12.48% and reproductive disorders 10.62%. 5. Among 1264 heads of reproductive disorders 856 heads were clue to ovarian diseases and occupied 68.98% of total disorders and uterus diseases 10.64% and repeat breeders occupied 17.64%.

• Plant Breeding and Biotechnology
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• v.6 no.4
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• pp.454-462
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• 2018

Yield and grade are the key factors that affect production value of peanut. The objective of this study was to identify QTLs for pod yield, hundred-seed weight, and total sound mature kernel (TSMK). A total of 90 recombinant inbred lines, derived from Tamrun OL07 and a breeding line Tx964117, were used as a mapping population and planted in Brownfield and Stephenville, Texas. A genetic map was developed using 1,211 SNP markers based on double digest restriction-site associated DNA sequencing (ddRAD-seq). A total of 10 QTLs were identified above the permutation threshold, three for yield, three for hundred-seed weight and four for TSMK, with LOD score values of 3.7 - 6.9 and phenotypic variance explained of 12.2% - 35.9%. Among those, there were several QTLs that were detected in more than one field experiment. The commonly detected QTLs in this study may be used as potential targets for future breeding program to incorporate yield and grade related traits through molecular breeding.

• Korean Journal of Agricultural Science
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• v.41 no.1
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• pp.41-46
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• 2014

The pedigree data of 86,639 heads from six major swine breeding farms were investigated to check levels of inbreeding and effective population size of breeding stocks. The average rate of inbreeding was 1.04%, 0.87%, 3.17%, 1.05%, 3.23% and 3.33% for farms A, B, C, D, E and F, respectively. The average inbreeding rate was highest for F farm and lowest for B farm. In farms D and E, there were quite large numbers of immigrant animals per generation compared to other farms. The effective population sizes calculated from the average rate of inbreeding were distributed between 83.0 and 814.8. Specific values were 282.3, 225.5, 83.0, 814.8, 302.9 and 175.7 for farms A, B, C, D, E and F, respectively. The results showed that there was no cause for concern over the current inbreeding level of major swine breeding farm populations and the inbreeding level was within an acceptable range. In addition, internal sharing rather than importing pig is necessary to strengthen seed sovereignty.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.5
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• pp.613-619
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• 2005

Indigenous Yakutian cattle' adaptation to the hardest subarctic conditions makes them a valuable genetic resource for cattle breeding in the Siberian area. Since early last century, crossbreeding between native Yakutian cattle and imported Simmental and Kholmogory breeds has been widely adopted. In this study, variations at 22 polymorphic microsatellite loci in 5 populations of Yakutian, Kholmogory, Simmental, Yakutian-Kholmogory and Yakutian-Simmental cattle were analysed to estimate the genetic contribution of Yakutian cattle to the two hybrid populations. Three statistical approaches were used: the weighted least-squares (WLS) method which considers all allele frequencies; a recently developed implementation of a Markov chain Monte Carlo (MCMC) method called likelihood-based estimation of admixture (LEA); and a model-based Bayesian admixture analysis method (STRUCTURE). At population-level admixture analyses, the estimate based on the LEA was consistent with that obtained by the WLS method. Both methods showed that the genetic contribution of the indigenous Yakutian cattle in Yakutian-Kholmogory was small (9.6% by the LEA and 14.2% by the WLS method). In the Yakutian-Simmental population, the genetic contribution of the indigenous Yakutian cattle was considerably higher (62.8% by the LEA and 56.9% by the WLS method). Individual-level admixture analyses using STRUCTURE proved to be more informative than the multidimensional scaling analysis (MDSA) based on individual-based genetic distances. Of the 9 Yakutian-Simmental animals studied, 8 showed admixed origin, whereas of the 14 studied Yakutian-Kholmogory animals only 2 showed Yakutian ancestry (>5%). The mean posterior distributions of individual admixture coefficient (q) varied greatly among the samples in both hybrid populations. This study revealed a minor existing contribution of the Yakutian cattle in the Yakutian-Kholmogory hybrid population, but in the Yakutian-Simmental hybrid population, a major genetic contribution of the Yakutian cattle was seen. The results reflect the different crossbreeding patterns used in the development of the two hybrid populations. Additionally, molecular evidence for differences among individual admixture proportions was seen in both hybrid populations, resulting from the stochastic process in crossing over generations.

• Journal of Animal Science and Technology
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• v.53 no.4
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• pp.297-302
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• 2011

This study was carried out to estimate average inbreeding coefficients, relatedness and effective population size of breeding bulls and to suggest optimal alternatives on problems of current Hanwoo improvement system. Data on proven and young bulls were obtained from 1,128 heads of Livestock Improvement Main Center from 1983 to 2008. Pedigree information on proven and young bulls was obtained from 3,760 heads of Korea Animal Improvement Association. Average inbreeding coefficients and average relatedness of proven and young bulls were estimated at the range of 0.04-0.07%, 0.10-6.82%, respectively. Effective population size was estimated for 220 heads from the average rate of inbreeding of last 26 years. Average inbreeding coefficient is rising rapidly for the last two years as well as average relatedness. Effective population size was estimated for 47 heads for the last five years. These results suggest that selection criteria of proven bulls should include not only genetic evaluation of carcass performance from progeny-test, but also inbreeding and relationship coefficients, in order to maintain genetic variability of Hanwoo. In addition, effective population size should be increased by increasing the number of proven bulls.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.5
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• pp.597-602
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• 2001

This study was conducted to examine the relationships between the methods used to determine the number of transferable embryos collected per flush and the estimated cumulative genetic improvements in the Japanese Holstein MOET breeding population. Cumulative genetic improvements were predicted by Monte Carlo simulation using three different determination methods (MODEL 1, MODEL 2, and MODEL 3), for calculating the number of embryos collected per flush. Moreover EBVs were estimated including or ignoring coefficients of inbreeding in MME. Inbreeding coefficients were also predicted. The number of transferable embryos was determined using normal, gamma, and Poisson distributions in MODEL 1, gamma and Poisson distributions in MODEL 2, and only the Poisson distribution in MODEL 3. The fitness of MODEL 2 in relation to field data from Hokkaido Japan was the best, and the results for MODEL3 indicated that this model is unsuitable for determining the number of transferable embryos. The largest cumulative genetic improvement (3.11) in the 10th generation was predicted by MODEL 3 and the smallest (2.83) by MODEL 2. Mean coefficients of correlation between the true and estimated breeding values were 0.738, 0.729, and 0.773 in MODELS 1, 2, and 3, respectively. It is suggested that the smallest genetic improvement in MODEL 2 resulted from the smallest correlation coefficient between the true and estimated breeding values. The differences in milk, fat, and protein yields between MODELS 2 and 3 were 182.0, 7.0, and 5.6 kg, respectively, in real units when each trait was independently selected. The inbreeding coefficient was the highest (0.374) in MODEL 2 and the lowest (0.357) in MODEL 3. The effects of different methods for determining the number of transferable embryos per flush on genetic improvements and inbreeding coefficients of the simulated populations were remarkable. The effects of including coefficients of inbreeding in MME, however, were unclear.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.9
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• pp.1217-1224
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• 2006

The registration data of 15 populations from nine major swine breeding farms were investigated to check levels of inbreeding and the current status of pedigree structures of breeding stocks. The average rate of inbreeding per generation was 0.208%, 0.209%, 0.098%, 0.307% and 0.071% for farms D, S, K, H, and Y in Duroc, 0.071%, 0.188%, 0.685%, 0.336%, and 0.449% for farms S, H, C, J, and W in Landrace, and 0.243%, 0.123%, 0.103%, 0.165%, and 0.286% for farms D, S, G, H, and J in Yorkshire, respectively. The average inbreeding rate was highest for Landrace, intermediate for Yorkshire, and lowest for Duroc farms. In Landrace and Yorkshire populations there were few immigrant animals per generation. In Duroc, however, there were quite large numbers of immigrant animals per generation compared to other breeds. The effective population sizes calculated from the average rate of inbreeding were distributed between 73.0 and 708.7. Specific values were 204.8, 239.7, 508.8, 163.0 and 708.2 for farms D, S, K, H, and Y in Duroc, 708.7, 266.5, 73.0, 148.9, and 111.3 for farms S, H, C, J, and W in Landrace, and 205.5, 406.0, 486.9, 302.6 and 175.0 for farms D, S, G, H, and J in Yorkshire, respectively. The values were acceptable for natural selection for fitness and inbreeding depression. The results showed that there was no cause for concern over the current inbreeding level of major swine breeding farm populations and the inbreeding level was within an acceptable range.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.5
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• pp.628-635
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• 2018

Objective: To determine the effects of genomic breeding values (GBV) and single nucleotide polymorphisms (SNP) on the total number of piglets born (TNB) in 3 pig breeds (Berkshire, Landrace, and Yorkshire). Methods: After collecting genomic information (Porcine SNP BeadChip) and phenotypic TNB records for each breed, the effects of GBV and SNP were estimated by using single step best linear unbiased prediction (ssBLUP) method. Results: The heritability estimates for TNB in Berkshire, Landrace, and Yorkshire breeds were 0.078, 0.107, and 0.121, respectively. The breeding value estimates for TNB in Berkshire, Landrace, and Yorkshire breeds were in the range of -1.34 to 1.47 heads, -1.79 to 1.87 heads, and -2.60 to 2.94 heads, respectively. Of sows having records for TNB, the reliability of breeding value for individuals with SNP information was higher than that for individuals without SNP information. Distributions of the SNP effects on TNB did not follow gamma distribution. Most SNP effects were near zero. Only a few SNPs had large effects. The numbers of SNPs with absolute value of more than 4 standard deviations in Berkshire, Landrace, and Yorkshire breeds were 11, 8, and 19, respectively. There was no SNP with absolute value of more than 5 standard deviations in Berkshire or Landrace. However, in Yorkshire, four SNPs (ASGA 0089457, ASGA0103374, ALGA0111816, and ALGA0098882) had absolute values of more than 5 standard deviations. Conclusion: There was no common SNP with large effect among breeds. This might be due to the large genetic composition differences and the small size of reference population. For the precise evaluation of genetic performance of individuals using a genomic selection method, it may be necessary to establish the appropriate size of reference population.