• 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.

• Clinical and Experimental Reproductive Medicine
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• v.51 no.2
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• pp.91-101
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• 2024

Infertility is a complex disease characterized by extreme genetic heterogeneity, compounded by various environmental factors. While there are exceptions, individual genetic and genomic variations related to infertility are typically rare, often family-specific, and may serve as susceptibility factors rather than direct causes of the disease. Consequently, identifying the cause of infertility and developing prevention and treatment strategies based on these factors remain challenging tasks, even in the modern genomic era. In this review, we first examine the genetic and genomic variations associated with infertility, and subsequently summarize the concepts and methods of preimplantation genetic testing in light of advances in genome analysis technology.

• 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.