• 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").

• Clinical and Experimental Reproductive Medicine
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• v.38 no.1
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• pp.6-9
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• 2011

Objective: To investigate a kallikrein-related peptidase 2 (KLK2) single nucleotide polymorphism (SNP) in relation to male infertility because of its role in semen processing. We investigated the genetic association of the KLK2+255G>A genotype with male infertility. Methods: We genotyped the SNP site located in intron 1 (+255G>A, rs2664155) of KLK2 from 218 men with male infertility (cases) and 220 fertile males (controls). Pyrosequencing analysis was performed for the genotyping. Results: The SNP of the KLK2 gene had a statistically significant association with male infertility (p<0.05). The odds ratio for the minor allele (+255A) in the pooled sample was 0.47 (95% confidence intervals, 0.26-0.85) for rs2664155. Conclusion: The relationship of KLK2 SNP to male infertility is statistically significant, especially within the non-azoospermia group. Further study is needed to understand the mechanisms associated with male infertility.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.7
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• pp.276-281
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• 2007

In this paper, we use Support Vector Machine to predict the susceptibility of chronic hepatitis from single nucleotide polymorphism data. Our data set consists of SNP data for 328 patients based on 28 SNPs and patients classes(chronic hepatitis, healthy). We use leave-one-out cross validation method for estimation of the accuracy. The experimental results show that SVM with SNP is capable of classifying the SNP data successfully for chronic hepatitis susceptibility with accuracy value of 67.1%. The accuracy of all SNPs with health related feature(sex, age) is improved more than 7%(accuracy 74.9%). This result shows that the accuracy of predicting susceptibility can be improved with health related features. With more SNPs and other health related features, SVM prediction of SNP data is a potential tool for chronic hepatitis susceptibility.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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• pp.224-226
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• 2003

In this paper, we succeeded SNP discrimination of DNA hybridization on microarray using new electrochemical system. Using the electrochemical method with a label-free DNA has Performed DNA chip microarray. This method is based on redox of an electrochemical ligand. We developed scanning system with high performance.

• Journal of Animal Science and Technology
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• v.64 no.5
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• pp.830-841
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• 2022

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

• Journal of Life Science
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• v.30 no.7
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• pp.614-624
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• 2020

The recent development of next-generation sequencing technology has enabled increased genomic analysis, but very few single nucleotide polymorphism (SNP) markers applicable to sweet persimmon (Diospyros kaki Thunb.) cultivars have been identified. In this study, SNP primers developed from five pollination-constant astringent (PCA) persimmons native to Korea were applied to discriminate between cultivars and verify their usability. The polymerase chain reactions of 19 SNP primers developed by Jung et al. were checked, with 11 primers finally selected. The other eight were very difficult to analyze in the agarose gel electrophoresis and QIAxcel Advanced System used in this experiment and were therefore excluded. The 11 SNP primers were applied through first and second verification to 76 cultivars and collection lines including 20 pollination-variant non-astringent (PVNA), 30 pollination-constant non-astringent (PCNA), 20 PCA, and six pollination-variant astringent (PVA). Of these, 38 were indistinguishable (eight PVNA, 18 PCNA, nine PCA, and three PVA). However, the results of applying the 11 SNP primers to new sweet persimmon cultivars, namely Gamnuri, Dannuri, Hongchoo, Jamisi, and Migamjosaeng, showed that they have the potential to be used as a unique marker for simultaneously determining between them.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.53-63
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• 2008

It is commonly believed that disease of human or economic traits of livestock are caused not by single gene acting alone, but by multiple genes interacting with one an-other. This issue is difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction (MDR) nonparametric statistical method, to improve the identification of single nucleotide polymorphism (SNP) associated with the Hanwoo(Korean cattle) carcass cold weight, is applied and compared with ANOVA results.

• 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.

• Journal of Genetic Medicine
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• v.12 no.2
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• pp.66-71
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• 2015

Down syndrome screening with cell-free DNA (cfDNA) in the maternal plasma has recently received much attention in the prenatal diagnostic field. Indeed, a large amount of evidence has already accumulated to show that screening tests with cfDNA are more sensitive and specific than conventional maternal serum and/or ultrasound screening. Globally, more than 1,000,000 of these noninvasive prenatal tests (NIPTs) have been performed to date. There are several different methods for NIPTs that are currently commercially available, including shotgun massively parallel sequencing, targeted massively parallel sequencing, and single nucleotide polymorphism (SNP)-based methods. All of these methods have their own advantages and disadvantages. In this review, I will focus specifically on the SNP-based NIPT.

• 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.