• Title/Summary/Keyword: SNP variation

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VCS: Tool for Visualizing Copy Number Variation and Single Nucleotide Polymorphism

  • Kim, HyoYoung;Sung, Samsun;Cho, Seoae;Kim, Tae-Hun;Seo, Kangseok;Kim, Heebal
    • Asian-Australasian Journal of Animal Sciences
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    • v.27 no.12
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    • pp.1691-1694
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    • 2014
  • Copy number variation (CNV) or single nucleotide phlyorphism (SNP) is useful genetic resource to aid in understanding complex phenotypes or deseases susceptibility. Although thousands of CNVs and SNPs are currently avaliable in the public databases, they are somewhat difficult to use for analyses without visualization tools. We developed a web-based tool called the VCS (visualization of CNV or SNP) to visualize the CNV or SNP detected. The VCS tool can assist to easily interpret a biological meaning from the numerical value of CNV and SNP. The VCS provides six visualization tools: i) the enrichment of genome contents in CNV; ii) the physical distribution of CNV or SNP on chromosomes; iii) the distribution of log2 ratio of CNVs with criteria of interested; iv) the number of CNV or SNP per binning unit; v) the distribution of homozygosity of SNP genotype; and vi) cytomap of genes within CNV or SNP region.

High Level of Sequence-Variation in Sacbrood Virus (SBV) from Apis mellifera

  • Truong, A-Tai;Kim, Jung-Min;Lim, Su-Jin;Yoo, Mi-Sun;Cho, Yun Sang;Yoon, Byoung-Su
    • Journal of Apiculture
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    • v.32 no.4
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    • pp.281-293
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    • 2017
  • Sacbrood virus (SBV) is one of the main pathogenic RNA viruses of honeybee. SBV is found worldwide and many local strains have been reported, such as kSBV, cSBV, and wSBV. In this study, SBV-specific DNA fragments were cloned and sequenced by reverse-transcription PCR from 4 populations of A. mellifera, 4 sequences from 1 population belonged to the 2134D51 genotype (349 nucleotides, nt) and 12 sequences from 3 populations belonged to the 2100D0 genotype (400 nt) among the 16 determined sequences. A total of 87 points of mismatches were found by comparison with the most similar sequences in GenBank. Seventeen single-nucleotide polymorphisms (SNP) were detected, and 6 SNP-patterns in the 2100D0 genotype and 2 SNP-patterns in the 2134D51 genotype were identified based on SNP positions. In SNP-pattern 2, 10 SNPs were detected, but only 2 SNPs were found in SNP-pattern7. Meanwhile, one SNP-pattern was found from one RNA-sample, multi SNP-patterns were detected from other RNA-samples. Large numbers of SNP variants indicate that vast numbers of point-mutations on SBV have occurred since SBV invaded Korea and that SNP smay have been introduced individually over time. Thorough analysis of SNP variants will not only define the local infection-route, but also the relationships between SNP-pattern and SBV-pathogenic abilities.

Identification of functional SNPs in genes and their effects on plant phenotypes

  • Huq, Md. Amdadul;Akter, Shahina;Nou, Ill Sup;Kim, Hoy Taek;Jung, Yu Jin;Kang, Kwon Kyoo
    • Journal of Plant Biotechnology
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    • v.43 no.1
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    • pp.1-11
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    • 2016
  • Single nucleotide polymorphism (SNP) is an abundant form of genetic variation within individuals of species. DNA polymorphism can arise throughout the whole genome at different frequencies in different species. SNP may cause phenotypic diversity among individuals, such as individuals with different color of plants or fruits, fruit size, ripening, flowering time adaptation, quality of crops, grain yields, or tolerance to various abiotic and biotic factors. SNP may result in changes in amino acids in the exon of a gene (asynonymous). SNP can also be silent (present in coding region but synonymous). It may simply occur in the noncoding regions without having any effect. SNP may influence the promoter activity for gene expression and finally produce functional protein through transcription. Therefore, the identification of functional SNP in genes and analysis of their effects on phenotype may lead to better understanding of their impact on gene function for varietal improvement. In this mini-review, we focused on evidences revealing the role of functional SNPs in genes and their phenotypic effects for the purpose of crop improvements.

UNDERSTANDING OF SINGLE NUCLEOTIDE POLYMORPHISM OF HUMAN GENOME (인간 게놈의 단일염기변형 (Single Nucleotide Polymorphism; SNP)에 대한 이해)

  • Oh, Jung-Hwan;Yoon, Byung-Wook
    • Journal of the Korean Association of Oral and Maxillofacial Surgeons
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    • v.34 no.4
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    • pp.450-455
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    • 2008
  • A Single Nucleotide Polymorphism (SNP) is a small genetic change or variation that can occur within a DNA sequence. It's the difference of one base at specific base pair position. SNP variation occurs when a single nucleotide, such as an A, replaces one of the other three nucleotide letters-C, G, or T. On average, SNP occur in the human population more than 1 percent of the time. They occur once in every 300 nucleotides on average, which means there are roughly 10 million SNPs in the human genome. Because SNPs occur frequently throughout the genome and tend to be relatively stable genetically, they serve as excellent biological markers. They can help scientists locate genes that are associated with disease such as heart disease, cancer, diabetes. They can also be used to track the inheritance of disease genes within families. SNPs may also be associated with absorbance and clearance of therapeutic agents. In the future, the most appropriate drug for an individual could be determined in advance of treatment by analyzing a patient's SNP profile. This pharmacogenetic strategy heralds an era in which the choice of drugs for a particular patient will be based on evidence rather than trial and error (so called "personalized medicine").

Comparison of SNP Variation and Distribution in Indigenous Ethiopian and Korean Cattle (Hanwoo) Populations

  • Edea, Zewdu;Dadi, Hailu;Kim, Sang-Wook;Dessie, Tadelle;Kim, Kwan-Suk
    • Genomics & Informatics
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    • v.10 no.3
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    • pp.200-205
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    • 2012
  • Although a large number of single nucleotide polymorphisms (SNPs) have been identified from the bovine genome-sequencing project, few of these have been validated at large in Bos indicus breeds. We have genotyped 192 animals, representing 5 cattle populations of Ethiopia, with the Illumina Bovine 8K SNP BeadChip. These include 1 Sanga (Danakil), 3 zebu (Borana, Arsi and Ambo), and 1 zebu ${\times}$ Sanga intermediate (Horro) breeds. The Hanwoo (Bos taurus) was included for comparison purposes. Analysis of 7,045 SNP markers revealed that the mean minor allele frequency (MAF) was 0.23, 0.22, 0.21, 0.21, 0.23, and 0.29 for Ambo, Arsi, Borana, Danakil, Horro, and Hanwoo, respectively. Significant differences of MAF were observed between the indigenous Ethiopian cattle populations and Hanwoo breed (p < 0.001). Across the Ethiopian cattle populations, a common variant MAF (${\geq}0.10$ and ${\leq}0.5$) accounted for an overall estimated 73.79% of the 7,045 SNPs. The Hanwoo displayed a higher proportion of common variant SNPs (90%). Investigation within Ethiopian cattle populations showed that on average, 16.64% of the markers were monomorphic, but in the Hanwoo breed, only 6% of the markers were monomorphic. Across the sampled Ethiopian cattle populations, the mean observed and expected heterozygosities were 0.314 and 0.313, respectively. The level of SNP variation identified in this particular study highlights that these markers can be potentially used for genetic studies in African cattle breeds.

MarSel : The LD-based Marker Selection System for the Large-scale Datasets (MarSel : Large-scale Dataset에 대한 LD기반의 Marker 선택 시스템)

  • 김상준;여상수;김성권
    • Proceedings of the Korean Information Science Society Conference
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    • 2004.10b
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    • pp.253-255
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    • 2004
  • 인간(human)에게 나타나는 다양성(variation)은 인체의 유전체(genome) 안에서 발생된 SNP(Single Nucleotide Polymorphism)에 의해 나타난다고 알려져 있다. 유전체내의 SNP과 다양성에 대한 연관 연구(Associate study)를 할 때에 약 30여 억 개로 추정되는 염기서열(DNA sequence)물 모두 분석한다면 많은 비용과 시간을 필요로 할 것이다. 이런 비용과 시간을 줄이기 위친 적은 수의 대표 SNP(=tagSNP)을 찾는 연구가 현재 진행 중이다. 우리는 LD계수|D;|을 block 분할에 이용하여 생물학적인 의미를 부여한 후, 전산적인 최적해를 찾는 접근을 이용했다. 또한, 기존 연구에서는 large-scale data에 대한 처리가 불가능해서 chromosome의 일부분의 데이터에 대해서안 분석이 시도되었다. 더욱 광범위한 분석을 위해서 chromosome 단위의 처리가 필요하다. 우리는 chromosome단위의 SNP data를 한 번에 처리가 가능한 시스템인 MarSel를 구현하였다

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Linkage Disequilibrium (LD) Mapping and Tagging SNP Selection of C-Fos Induced Growth Factor (Figf) Gene in Korean Population

  • Kim, Sook;Yoo, Yeon-Kyung;Jang, Hye-Yoon;Shin, Eun-Soon;Cho, Eun-Young;Kim, Eu-Gene;NamKung, Jung-Hyun;Yang, Jun-Mo;Lee, Jong-Eun
    • Molecular & Cellular Toxicology
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    • v.2 no.1
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    • pp.7-10
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    • 2006
  • We performed comprehensive SNP validation and linkage disequilibrium (LD) analysis of the c-fos induced growth factor (Figf) gene in Korean population. Out of 32 SNPs, only 9 SNPs were polymorphic in Korean population. Validated SNPs formed a single extended haplotype block with strong LD through the entire length of the gene. Tagging SNP analysis picked only 2 SNPs to represent most of the genetic variation information of the Figf gene. Our results demonstrate the utility of LD block and tagging SNP analysis for an efficient way of performing a candidate gene based association study.

Prediction of genomic breeding values of carcass traits using whole genome SNP data in Hanwoo (Korean cattle) (한우에 있어서 유전체 육종가 추정)

  • Lee, Seung Hwan;Kim, Heong Cheul;Lim, Dajeong;Dang, Chang Gwan;Cho, Yong Min;Kim, Si Dong;Lee, Hak Kyo;Lee, Jun Heon;Yang, Boh Suk;Oh, Sung Jong;Hong, Seong Koo;Chang, Won Kyung
    • Korean Journal of Agricultural Science
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    • v.39 no.3
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    • pp.357-364
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    • 2012
  • Genomic breeding value (GEBV) has recently become available in the beef cattle industry. Genomic selection methods are exceptionally valuable for selecting traits, such as marbling, that are difficult to measure until later in life. One method to utilize information from sparse marker panels is the Bayesian model selection method with RJMCMC. The accuracy of prediction varies between a multiple SNP model with RJMCMC (0.47 to 0.73) and a least squares method (0.11 to 0.41) when using SNP information, while the accuracy of prediction increases in the multiple SNP (0.56 to 0.90) and least square methods (0.21 to 0.63) when including a polygenic effect. In the multiple SNP model with RJMCMC model selection method, the accuracy ($r^2$) of GEBV for marbling predicted based only on SNP effects was 0.47, while the $r^2$ of GEBV predicted by SNP plus polygenic effect was 0.56. The accuracies of GEBV predicted using only SNP information were 0.62, 0.68 and 0.73 for CWT, EMA and BF, respectively. However, when polygenic effects were included, the accuracies of GEBV were increased to 0.89, 0.90 and 0.89 for CWT, EMA and BF, respectively. Our data demonstrate that SNP information alone is missing genetic variation information that contributes to phenotypes for carcass traits, and that polygenic effects compensate genetic variation that whole genome SNP data do not explain. Overall, the multiple SNP model with the RJMCMC model selection method provides a better prediction of GEBV than does the least squares method (single marker regression).

Association Analysis between Insulin-like Growth Factor Binding Protein 3 (IGFBP3) Polymorphisms and Carcass Traits in Cattle

  • Cheong, Hyun Sub;Yoon, Du-Hak;Kim, Lyoung Hyo;Park, Byung Lae;Lee, Hye Won;Namgoong, Sohg;Kim, Eun Mi;Chung, Eui Ryong;Cheong, Il-Cheong;Shin, Hyoung Doo
    • Asian-Australasian Journal of Animal Sciences
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    • v.21 no.3
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    • pp.309-313
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    • 2008
  • The insulin-like growth factor binding protein 3 (IGFBP3) has been investigated as a candidate gene for growth promoting effects in beef cattle and a modulator of IGF bioactivity. Previously, we have reported twenty two sequence variants discovered in Korean native cattle (Hanwoo). In this study, we examined the association between gene-specific polymorphisms of IGFBP3 and cold carcass weight (CW) and marbling score (MS) among Korean native cattle. Among twenty two polymorphisms, four common polymorphic sites (-854G>C, -100G>A, +421G>T and +3863C>A) were genotyped in our beef cattle (n = 437). Statistical analysis revealed that one common polymorphism in the promoter region (-854G>C) showed putative associations with MS (p = 0.03). IGFBP3 variation/haplotype information analyzed in this study will provide valuable information into strategies for the production of a commercial line of beef cattle.

Comparison of Normalization Methods for Defining Copy Number Variation Using Whole-genome SNP Genotyping Data

  • Kim, Ji-Hong;Yim, Seon-Hee;Jeong, Yong-Bok;Jung, Seong-Hyun;Xu, Hai-Dong;Shin, Seung-Hun;Chung, Yeun-Jun
    • 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.