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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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Related Experiment Video

Updated: Jun 17, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Tag SNP selection using particle swarm optimization.

Li-Yeh Chuang¹, Cheng-San Yang, Chang-Hsuan Ho

¹Dept. of Chemical Engineering, I-Shou University, Kaohsiung, Taiwan.

Biotechnology Progress

|December 30, 2009

Summary

This summary is machine-generated.

Selecting informative single nucleotide polymorphisms (SNPs) is crucial for genome-wide association studies. Binary particle swarm optimization (BPSO) effectively identifies tag SNPs with high accuracy, reducing costs and improving prediction for complex diseases.

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Single nucleotide polymorphisms (SNPs) are abundant genetic variations.
Genome-wide association studies (GWAS) identify variants linked to complex diseases.
Genotyping all SNPs is challenging; tag SNP selection is essential for efficiency.

Purpose of the Study:

To introduce a Binary Particle Swarm Optimization (BPSO) method for improved tag SNP selection.
To enhance the prediction accuracy of haplotype tag SNPs (htSNPs).
To evaluate BPSO's performance against existing methods like STAMPA and SVM/STSA.

Main Methods:

Developed a Binary Particle Swarm Optimization (BPSO) algorithm with local search capability.
Applied BPSO for tag SNP selection without relying on genomic block partitioning.
Compared BPSO with STAMPA and SVM/STSA using publicly available datasets.

Main Results:

BPSO consistently identified tag SNPs with higher prediction accuracy than STAMPA and SVM/STSA.
The BPSO method demonstrated improved accuracy across both smaller and larger datasets.
BPSO showed comparable time complexity to existing methods while offering superior accuracy.

Conclusions:

BPSO is an effective method for selecting accurate tag SNPs for GWAS.
The BPSO approach offers a scalable and accurate solution for reducing genotyping costs.
This method improves the reliability of identifying genetic variants associated with complex diseases.