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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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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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Comparing Copy Number Variations and SNPs

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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 13, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Inferring combined CNV/SNP haplotypes from genotype data.

Shu-Yi Su¹, Julian E Asher, Marjo-Riita Jarvelin

¹Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London W2 1PG, UK.

Bioinformatics (Oxford, England)

|April 22, 2010

Summary

This study introduces polyHap v2.0, a novel method for inferring copy number variation (CNV) genotypes and haplotypic phase. The software accurately estimates missing genotypes and allelic configurations, advancing genetic variation research.

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Published on: June 21, 2018

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Copy number variations (CNVs) are a significant source of human genetic diversity and are linked to complex diseases.
Inferring CNV haplotypic phase is crucial for evolutionary and disease susceptibility studies, but existing methods are limited in scope.
Current research lacks genome-wide applicable methods for determining CNV haplotypic phase.

Purpose of the Study:

To develop and present a novel computational method for inferring missing CNV genotypes, predicting CNV allelic configuration, and determining CNV haplotypic phase.
To implement this method in a user-friendly software package, polyHap v2.0, for genome-wide application.
To provide a robust tool for analyzing the evolutionary history and disease impact of CNVs.

Main Methods:

A hidden Markov model (HMM) approach is utilized to model the joint haplotype structure between CNVs and SNPs.
Haplotype phase for both CNVs and SNPs is inferred simultaneously.
A sampling algorithm is employed to generate confidence measures for each estimate.

Main Results:

polyHap v2.0 demonstrated accurate estimation of missing CNV genotypes and allelic configurations on simulated datasets.
The method successfully inferred CNV haplotypic phase on phase-known CNV-SNP genotype datasets.
Validation on a real-world dataset (Chromosome 2 deletion region) confirmed the method's accuracy in practical applications.

Conclusions:

polyHap v2.0 provides an accurate and scalable solution for inferring CNV genotypes and haplotypic phase.
The software enables simultaneous inference of CNV and SNP haplotypic phase, advancing genetic variation analysis.
This method has significant implications for understanding the role of CNVs in evolution and complex diseases.