• Clinical and Experimental Reproductive Medicine
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• v.21 no.3
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• pp.281-284
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• 1994

Sex determination in genomic DNA from human blood leucocytes was performed by amplification of human Y chromosome-specific DNA sequences using PCR technique. A clear DNA fragment(154 nucleotides long) was appeared only in the male genomic DNA, but no specific band was observed in the case of female genomic DNA and negative control. To know the sensitivity of this method, the amplification reaction was performed in genomic DNA diluted to 2pg equivalent to the amonut present in the single human cell, and clear band also observed. The PCR amplification was so succesfully performed in the single leucocyte separated from human blood using micromanipulator that this techniqe is assumed to be applied to single blstomere before embryo transfer.

• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.108-113
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• 2007

When conducting large-scale cohort studies, numerous statistical issues arise from the range of study design, data collection, data analysis and interpretation. In genomic cohort studies, these statistical problems become more complicated, which need to be carefully dealt with. Rapid technical advances in genomic studies produce enormous amount of data to be analyzed and traditional statistical methods are no longer sufficient to handle these data. In this paper, we reviewed several important statistical issues that occur frequently in large-scale genomic cohort studies, including measurement error and its relevant correction methods, cost-efficient design strategy for main cohort and validation studies, inflated Type I error, gene-gene and gene-environment interaction and time-varying hazard ratios. It is very important to employ appropriate statistical methods in order to make the best use of valuable cohort data and produce valid and reliable study results.

• Korean Journal of Breeding Science
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• v.43 no.2
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• pp.103-114
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• 2011

In recent years, genomic resources and information have accumulated at an ever increasing pace, in many plant species, through whole genome sequencing, large scale analysis of transcriptomes, DNA markers and functional studies of individual genes. Well-characterized species within key plant taxa, co-called "model systems", have played a pivotal role in nucleating the accumulation of genomic information and databases, thereby providing the basis for comparative genomic studies. In addition, recent advances to "Next Generation" sequencing technologies have propelled a new wave of genomics, enabling rapid, low cost analysis of numerous genomes, and the accumulation of genetic diversity data for large numbers of accessions within individual species. The resulting wealth of genomic information provides an opportunity to discern evolutionary processes that have impacted genome structure and the function of genes, using the tools of comparative analysis. Comparative genomics provides a platform to translate information from model species to crops, and to relate knowledge of genome function among crop species. Ultimately, the resulting knowledge will accelerate the development of more efficient breeding strategies through the identification of trait-associated orthologous genes and next generation functional gene-based markers.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.119-126
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• 2024

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

• Genomics & Informatics
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• v.2 no.3
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• pp.113-120
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• 2004

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2002.10a
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• pp.81-81
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• 2002

• Proceedings of the PSK Conference
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• 2004.10a
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• pp.105-105
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• 2004

• BMB Reports
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• v.57 no.1
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• pp.1-1
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• 2024

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.11
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• pp.1546-1550
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• 2006

The purpose of this study was to assess the genetic variation and establish the relationship amongst breeds and strains using 15 chicken specific microsatellite markers. A total of 285 unrelated DNA samples from four Korean native chicken strains (Black strain of Korean native chicken; KL, Red Brown strain of Korean native chicken; KR, Ogol strain of Korean native chicken; KS and Yellow Brown strain of Korean native chicken; KY) and three introduced chicken breeds (F strain of White Leghorn; LF, K strain of White Leghorn; LK, Rhode Island Red; RC and Cornish; CN) were genotyped to estimate within and between breed genetic diversity indices. All the loci analyzed in 15 microsatellite markers showed a polymorphic pattern and the number of alleles ranged from 5 to 14. The polymorphism information content (PIC) of UMA1019 was the highest (0.872) and that of ADL0234 was the lowest (0.562). The expected total heterozygosity (He) within breed and mean number of observed alleles ranged from 0.540 (LF) to 0.689 (KY), and from 3.47 (LK) to 6.07 (KR), respectively. The genetic variation of KR and KY were the highest and the lowest within Korean native strains, respectively. The genetic distance results showed that Korean native chicken strains were separated with the three introduced chicken breeds clustered into another group. The lowest distance (0.149) was observed between the KR and KL breeds and the highest distance (0.855) between the KR and LK breeds. The microsatellite polymorphism data were shown to be useful for assessing the genetic relationship between Korean native strains and other foreign breeds.

• Journal of Embryo Transfer
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• v.22 no.3
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• pp.167-172
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• 2007

Pituitary-specific transcription factor (PIT1) 유전자는 동물의 성장을 조절하고 근육 형성에 관여하는 유전자로서 최근에는 단일염기다형성 변이가 한우를 비롯한 동물에서 관찰되었으며, 한우의 경제 형질과 연관성이 보고되었다. 본 연구는 PIT1 유전자의 단일염기다형성 변이가 한우에서 성장 인자에 미치는 영향과 경제 형질에 대한 유전자형간 육종가와의 상관성에 대해 알아보고자 하였다. 도체 성적을 보유하고 있는 한우 후보종모우 집단 268두를 대상으로 PIT1 유전자 A1256G 다형성을 조사하여 유전자형의 빈도를 분석하였고 각각의 유전형에 따른 기본적인 검정 성적을 바탕으로 경제 형질과의 연관성을 비교 분석하였다. 268두의 한우에서 PIT1 유전자의 A1256G 유전자형 빈도는 MseI 제한 효소를 사용했을 때 A 유전자 빈도(0.37)보다 G 유전자 빈도(0.62)가 높게 나타났다. 통계적 분석을 통하여 각 유전자형에 대한 경제 형질과의 관련성을 분석한 결과, 각 유전자형 간에 12개월령 체중 (body weight 12, BW12)에서 유의한 차이를 보였고, 등지방 두께 육종가 (Backfat thickness-estimated breeding value, BF-EBV)와도 유의한 차이가 있었지만 (p<0.05), marbling score (MS), carcass weight (CW), M. longissimus dorsi area (LDA) 등 다른 경제 형질과는 통계학적으로 유의한 차이가 없었다. PIT1 유전자의 A1256G 다형성은 한우의 성장과 도체체중에 관여하는 인자로 작용하는 것으로 보여진다.