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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

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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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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Related Experiment Video

Updated: Jul 18, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data.

Benilton Carvalho¹, Henrik Bengtsson, Terence P Speed

¹Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.

Biostatistics (Oxford, England)

|December 26, 2006

Summary

This study introduces a new preprocessing method for Affymetrix single-nucleotide polymorphism (SNP) chips. The improved methodology enhances the accuracy of genotype calls for DNA sequence variant identification, aiding disease-associated genetic research.

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

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Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Microarray technologies require critical preprocessing steps to convert raw intensity data into usable measurements.
Gene expression analysis is a popular microarray application, with statistical methods improving accuracy over ad hoc procedures.
Emerging microarray applications include DNA sequence variant identification for disease-associated genes.

Purpose of the Study:

To describe a novel preprocessing methodology for DNA sequence variant identification using microarray technology.
To detail a specific methodology for preprocessing Affymetrix single-nucleotide polymorphism (SNP) chips and generating genotype calls.
To demonstrate improvements over existing approaches using real-world data.

Main Methods:

Development of a new preprocessing pipeline tailored for SNP chip data.
Application of the methodology to Affymetrix SNP chips for genotype calling.
Validation using data from three large-scale studies with numerous independent calls.

Main Results:

The proposed preprocessing procedure significantly improves the accuracy and precision of genotype calls.
Demonstrated enhancement of existing methods for DNA sequence variant identification.
Successful implementation and availability of the methods in the R/Bioconductor package 'oligo'.

Conclusions:

The developed preprocessing methodology offers a statistically robust approach for SNP chip analysis.
This advancement facilitates more accurate identification of DNA sequence variants associated with diseases.
The 'oligo' package provides accessible tools for researchers to implement these improved methods.