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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Dynamic programming for single nucleotide polymorphism ID identification in systematic association studies.

Cheng-Hong Yang1, Li-Yeh Chuang, Yu-Huei Cheng

  • 1Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.

The Kaohsiung Journal of Medical Sciences
|June 9, 2009
PubMed
Summary
This summary is machine-generated.

A new tool identifies single nucleotide polymorphism (SNP) IDs from sequence data, crucial for personalized medicine. This system accurately maps sequence positions to official SNP IDs, improving genetic association studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are vital for personalized medicine and genetic association studies.
  • Current methods often lack specific SNP IDs (e.g., NCBI dbSNP), hindering data integration and reproducibility.
  • A user-friendly tool for accurate SNP ID identification from sequence data is needed.

Purpose of the Study:

  • To develop a novel web-based system for accurate SNP ID identification from sequence data.
  • To provide a user-friendly interface for researchers to map sequence positions to official SNP IDs.
  • To improve upon existing tools like NCBI SNP-BLAST by ensuring correct targeted and flanking SNP identification.

Main Methods:

  • Utilized a dynamic programming algorithm to align input sequences with the SNP FASTA database.
  • Developed a web-based tool integrating the NCBI dbSNP database.
  • Implemented support for various sequence formats (ACGT, dNTP, IUPAC) and bidirectional sequence matching.

Main Results:

  • The system accurately identifies targeted SNP IDs (SNP hits) and nearby flanking SNPs.
  • Results are presented with chromosomal order and contig positions.
  • The tool consistently provides correct SNP IDs, overcoming limitations of SNP-BLAST.

Conclusions:

  • This novel system offers a reliable method for identifying SNP IDs from literature and keyed-in sequences.
  • It enhances systematic association studies by providing accurate SNP annotations.
  • The freely available web tool (http://bio.kuas.edu.tw/SNPosition/) supports personalized medicine research.