• Korean Journal of Poultry Science
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• v.41 no.4
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• pp.305-311
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• 2014

Copy number variation (CNV) is a form of structural variation that shows various numbers of copies in segments of the DNA. It has been shown to account for phenotypic variations in human diseases and agricultural production traits. Currently, most of chicken breeds in the poultry industry are based on European-origin breeds that have been mostly provided from several international breeding companies. Therefore, National Institute of Animal Science, RDA has been trying to restore and improve Korean native chicken breeds (12 lines of 5 breeds) for about 20 years. Thanks to the recent advance of sequencing technologies, genome-wide CNV can be accessed in the higher resolution throughout the genome of species of interest. However, there is no systematic study available to dissect the CNV in the native chicken breed in Korea. Here, we report genome-wide copy number variations identified from a genome of Korean native chicken (Line L) by comparing between the chicken reference sequence assembly (Gallus gallus) and a de novo sequencing assembly of the Korean native chicken (Line L). Throughout all twenty eight chicken autosomes, we identified a total of 501 CNVs; defined as gain and loss of duplication and deletion respectively. Furthermore, we performed gene ontology (GO) analysis for the putative CNVs using DAVID, leading to 68 GO terms clustered independently. Of the clustered GO terms, genes related to transcription and gene regulation were mainly detected. This study provides useful genomic resource to investigate potential biological implications of CNVs with traits of interest in the Korean native chicken.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.19-21
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• 2012

모든 암 세포는 체세포 변이를 동반한다. 따라서 암 유전체 변이 분석에 의하여 암을 발생시키는 유전자 및 진단/치료법을 찾아낼 수 있다. 본 연구에서는 차세대 시퀀싱 데이터를 이용하여 암 특이적 단이 반복 변이(copy number variation, CNV) 유형을 밝히는 새로운 알고리즘을 제안한다. 제안하는 방식은 암 환자의 정상 세포와 암세포로부터 얻어진 정상 유전체와 암 유전체를 동시 분석하여 각각 CNV 후보 영역을 추출하며, 통계적 유의성 분석을 통하여 암 특이적 CNV 후보 영역을 선별하고, 다음 후처리 과정에서 참조 표준 서열(reference sequence)에 존재하는 오류 영역 보정 작업을 수행하여 정확한 암 특이적 CNV 영역을 추출해 낸다. 또한 다수의 대용량 유전체 데이터 동시 분석을 위하여 맵리듀스(MapReduce) 기법을 기반으로 하는 병렬 수행 알고리즘을 제안한다.

• Genomics & Informatics
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• v.6 no.3
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• pp.126-129
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• 2008

The robust identification and comprehensive profiling of copy number alterations (CNAs) is highly challenging. The amount of data obtained from high-throughput technologies such as array-based comparative genomic hybridization is often too large and it is required to develop a comprehensive and versatile tool for the detection and visualization of CNAs in a genome-wide scale. With this respective, we introduce a software framework, CGHscape that was originally developed to explore the CNAs for the study of copy number variation (CNV) or tumor biology. As a standalone program, CGHscape can be easily installed and run in Microsoft Windows platform. With a user-friendly interface, CGHscape provides a method for data smoothing to cope with the intrinsic noise of array data and CNA detection based on SW-ARRAY algorithm. The analysis results can be demonstrated as log2 plots for individual chromosomes or genomic distribution of identified CNAs. With extended applicability, CGHscape can be used for the initial screening and visualization of CNAs facilitating the cataloguing and characterizing chromosomal alterations of a cohort of samples.

• Journal of Animal Science and Technology
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• v.52 no.6
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• pp.491-498
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• 2010

Copy-number variation (CNV) in particular genomic segments owing to deletions or duplications can induce changes in cellular gene expression patterns and may increase susceptibility to diseases such as cancer. The aim of this study was to examine CNVs related to the incidence of epithelial ovarian cancer in chickens. Genomic DNA was extracted from blood cells and cancerous ovaries collected from four 120-week-old White Leghorn chickens and were used for array-based comparative genome hybridization (CGH) analysis. As a result, 25 amplified and 10 deleted CNV regions were detected in chicken ovarian cancer. Of these, 10 amplified and two deleted CNV regions contained genes associated with human ovarian cancer. Our study using a chicken model may provide a better understanding of human epithelial ovarian cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.3001-3006
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• 2016

Background: The cancer progression of oral leukoplakia is an important watchpoint in the follow-up observation of the patients. However, potential malignancies of oral leukoplakia cannot be estimated by histopathologic assessment alone. We evaluated genetic abnormalities at the level of copy number variation (CNV) to investigate the risk for developing cancer in oral leukoplakias. Materials and Methods: The current study used 27 oral leukoplakias with histological evidence of dysplasia. The first group (progressing dysplasia) consisted of 7 oral lesions from patients with later progression to cancer at the same site. The other group (non-progressing dysplasia) consisted of 20 lesions from patients with no occurrence of oral cancer and longitudinal follow up (>7 years). We extracted DNA from Formalin-Fixed Paraffin-Embedded (FFPE) samples and examined chromosomal loci and frequencies of CNVs using Taqman copy number assays. Results: CNV frequently occurred at 3p, 9p, and 13q loci in progressing dysplasia. Our results also indicate that CNV at multiple loci-in contrast to single locus occurrences-is characteristic of progressing dysplasia. Conclusions: This study suggests that genetic abnormalities of the true precancer demonstrate the progression risk which cannot be delineated by current histopathologic diagnosis.

• Genomics & Informatics
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• v.10 no.3
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• pp.194-199
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• 2012

In addition to single-nucleotide polymorphisms (SNP), copy number variation (CNV) is a major component of human genetic diversity. Among many whole-genome analysis platforms, SNP arrays have been commonly used for genomewide CNV discovery. Recently, a number of CNV defining algorithms from SNP genotyping data have been developed; however, due to the fundamental limitation of SNP genotyping data for the measurement of signal intensity, there are still concerns regarding the possibility of false discovery or low sensitivity for detecting CNVs. In this study, we aimed to verify the effect of combining multiple CNV calling algorithms and set up the most reliable pipeline for CNV calling with Affymetrix Genomewide SNP 5.0 data. For this purpose, we selected the 3 most commonly used algorithms for CNV segmentation from SNP genotyping data, PennCNV, QuantiSNP; and BirdSuite. After defining the CNV loci using the 3 different algorithms, we assessed how many of them overlapped with each other, and we also validated the CNVs by genomic quantitative PCR. Through this analysis, we proposed that for reliable CNV-based genomewide association study using SNP array data, CNV calls must be performed with at least 3 different algorithms and that the CNVs consistently called from more than 2 algorithms must be used for association analysis, because they are more reliable than the CNVs called from a single algorithm. Our result will be helpful to set up the CNV analysis protocols for Affymetrix Genomewide SNP 5.0 genotyping data.

• Genomics & Informatics
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• v.6 no.4
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• pp.231-234
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• 2008

Precise and reliable identification of CNV is still important to fully understand the effect of CNV on genetic diversity and background of complex diseases. SNP marker has been used frequently to detect CNVs, but the analysis of SNP chip data for identifying CNV has not been well established. We compared various normalization methods for CNV analysis and suggest optimal normalization procedure for reliable CNV call. Four normal Koreans and NA10851 HapMap male samples were genotyped using Affymetrix Genome-Wide Human SNP array 5.0. We evaluated the effect of median and quantile normalization to find the optimal normalization for CNV detection based on SNP array data. We also explored the effect of Robust Multichip Average (RMA) background correction for each normalization process. In total, the following 4 combinations of normalization were tried: 1) Median normalization without RMA background correction, 2) Quantile normalization without RMA background correction, 3) Median normalization with RMA background correction, and 4) Quantile normalization with RMA background correction. CNV was called using SW-ARRAY algorithm. We applied 4 different combinations of normalization and compared the effect using intensity ratio profile, box plot, and MA plot. When we applied median and quantile normalizations without RMA background correction, both methods showed similar normalization effect and the final CNV calls were also similar in terms of number and size. In both median and quantile normalizations, RMA backgroundcorrection resulted in widening the range of intensity ratio distribution, which may suggest that RMA background correction may help to detect more CNVs compared to no correction.

• Journal of Korean Neurosurgical Society
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• v.50 no.6
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• pp.486-491
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• 2011

Objective : Structural genetic variation, including copy-number variation (CNV), constitutes a substantial fraction of total genetic variability, and the importance of structural variants in modulating susceptibility is increasingly being recognized. CNV can change biological function and contribute to pathophysiological conditions of human disease. Its relationship with common, complex human disease in particular is not fully understood. Here, we searched the human genome to identify copy number variants that predispose to moya-moya type cerebrovascular disease. Methods : We retrospectively analyzed patients who had unilateral or bilateral steno-occlusive lesions at the cerebral artery from March, 2007, to September, 2009. For the 20 subjects, including patients with moyamoya type pathologies and three normal healthy controls, we divided the subjects into 4 groups : typical moyamoya (n=6), unilateral moyamoya (n=9), progression unilateral to typical moyamoya (n=2) and non-moyamoya (n=3). Fragmented DNA was hybridized on Human610Quad v1.0 DNA analysis BeadChips (Illumina). Data analysis was performed with GenomeStudio v2009.1, Genotyping 1.1.9, cnvPartition_v2.3.4 software. Overall call rates were more than 99.8%. Results : In total, 1258 CNVs were identified across the whole genome. The average number of CNV was 45.55 per subject (CNV region was 45.4). The gain/loss of CNV was 52/249, having 4.7 fold higher frequencies in loss calls. The total CNV size was 904,657,868, and average size was 993,038. The largest portion of CNVs (613 calls) were 1M-10M in length. Interestingly, significant association between unilateral moyamoya disease (MMD) and progression of unilateral to typical moyamoya was observed. Conclusion : Significant association between unilateral MMD and progression of unilateral to typical moyamoya was observed. The finding was confirmed again with clustering analysis. These data demonstrate that certain CNV associate with moyamoya-type cerebrovascular disease.

• Genomics & Informatics
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• v.8 no.3
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• pp.138-141
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• 2010

Together with single nucleotide polymorphism (SNP), copy number variations (CNV) are recognized to be the major component of human genetic diversity and used as a genetic marker in many disease association studies. Affymetrix Genome-wide SNP 5.0 is one of the commonly used SNP array platforms for SNP-GWAS as well as CNV analysis. However, there has been no report that validated the accuracy and reproducibility of CNVs identified by Affymetrix SNP array 5.0. In this study, we compared the characteristics of CNVs from the same set of genomic DNAs detected by three different array platforms; Affymetrix SNP array 5.0, Agilent 2X244K CNV array and NimbleGen 2.1M CNV array. In our analysis, Affymetrix SNP array 5.0 seems to detect CNVs in a reliable manner, which can be applied for association studies. However, for the purpose of defining CNVs in detail, Affymetrix Genome-wide SNP 5.0 might be relatively less ideal than NimbleGen 2.1M CNV array and Agilent 2X244K CNV array, which outperform Affymetrix array for defining the small-sized single copy variants. This result will help researchers to select a suitable array platform for CNV analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.356-359
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• 2008

최근 생물정보학 분야에서 인간 유전체에 존재하는 CNV(copy number variation)에 관한 연구가 주목 받고 있다. CNV 영역은 1kbp-3Mbp 사리의 서열이 반복되거나 결실되는 변이 영역으로 정의된다. 우리는 선행연구에서 기가 시퀀싱(giga sequencing)의 결과 산출되는 DNA 서열조각인 리드(read)를 레퍼런스 시퀀스에 서열 정렬하여 CNV 영역을 찾아내는 새로운 CNV 검색 방식을 제안하였다. 후속 연구로서 본 논문에서는 DNA 서열에 존재하는 repeat 영역 문제를 해결하기 위한 새로운 방안을 제안하고, 리드의 출현 빈도 정보를 분석하여 CNV 영역을 찾아내는 CNV 영역 검색 알고리즘을 보인다. 제안된 알고리즘 Gaussian 분포를 갖는 출현 빈도 정보로부터 통계적 유의성을 갖는 영역을 추출하여 CNV 영역후보로 하고, 다음 경제 과정을 거쳐 최종의 CNV 영역을 추출한다. 성능 평가를 위하여 프로토타임 시스템을 개발하였으며, 시뮬레이션 실험을 수행하였다. 실험 결과에 의하여 제안된 방식은 반복되거나 결실되는 형태의 CNV 영역을 효율적으로 검출하며, 또한 다양한 크기의 CNV 영역을 효율적으로 검출할 수 있음을 입증한다.