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Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

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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Related Experiment Video

Updated: Jun 18, 2026

The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform

The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform

Published on: September 27, 2016

SKM-SNP: SNP markers detection method.

Yang Liu¹, Mark Li, Yiu M Cheung

¹Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.

Journal of Biomedical Informatics

|November 21, 2009

Summary

This summary is machine-generated.

SKM-SNP identifies relevant single nucleotide polymorphism (SNP) markers for disease association studies. This program uses clustering to find SNPs that distinguish patient and normal samples, aiding in genetic research.

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Identifying genetic markers associated with diseases like Schizophrenia and Parkinson's is crucial for understanding disease mechanisms.
Genome-wide association studies (GWAS) generate vast amounts of single nucleotide polymorphism (SNP) data, requiring efficient analysis tools.

Purpose of the Study:

To introduce SKM-SNP, a novel program for detecting relevant SNPs for disease association.
To develop a method for identifying subsets of SNPs that effectively categorize disease and normal sample groups.

Main Methods:

Utilized a subspace categorical clustering algorithm to assign weights to SNPs within patient and normal sample groups.
Employed these weights to identify informative SNP subsets capable of distinguishing between disease states.
Validated the SKM-SNP program on genome-wide SNP datasets for Schizophrenia and Parkinson Disease.

Main Results:

The SKM-SNP program successfully identified relevant SNPs that effectively categorize disease samples.
Demonstrated the program's capability in analyzing complex genetic data from neurological disorders.
The method showed potential in pinpointing genetic variations linked to specific diseases.

Conclusions:

SKM-SNP provides an effective computational approach for identifying disease-associated SNPs.
The program aids in understanding the genetic architecture of complex diseases.
SKM-SNP is a valuable tool for genetic association studies and biomarker discovery.