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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.
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%...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...

Single Nucleotide Polymorphisms-SNPs

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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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Related Experiment Video

Updated: May 27, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Analyzing cancer samples with SNP arrays.

Peter Van Loo¹, Gro Nilsen, Silje H Nordgard

¹Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. Peter.VanLoo@med.kuleuven.be

Methods in Molecular Biology (Clifton, N.J.)

|December 2, 2011

Summary

Single nucleotide polymorphism (SNP) arrays reveal cancer genome changes. The ASCAT tool analyzes SNP array data, accounting for aneuploidy, nonaberrant cell admixture, and intratumor heterogeneity in cancer samples.

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Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

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Last Updated: May 27, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Single nucleotide polymorphism (SNP) arrays are crucial for identifying genomic alterations in cancer.
Analyzing SNP array data from tumors is challenging due to aneuploidy, nonaberrant cell contamination, and intratumor heterogeneity.

Purpose of the Study:

To describe how aneuploidy, nonaberrant cell admixture, and intratumor heterogeneity affect SNP array profiles.
To present an improved analysis method using the ASCAT (allele-specific copy number analysis of tumors) tool suite.
To demonstrate the application of ASCAT using breast carcinoma SNP array data.

Main Methods:

Data structure, plotting, interpretation, and segmentation of SNP array data.
Core ASCAT algorithm to determine nonaberrant cell fraction and tumor ploidy.
Calculation and interpretation of ASCAT profiles for copy number aberrations and neutral events.

Main Results:

ASCAT effectively accounts for confounding factors in SNP array analysis.
ASCAT profiles visualize copy number aberrations and copy-number-neutral events.
The study demonstrates detection of intratumor heterogeneity using ASCAT profiles.

Conclusions:

The ASCAT tool suite provides a robust method for analyzing SNP array data in cancer.
ASCAT facilitates a deeper understanding of genomic complexity in tumors.
The methodology is applicable to various cancer types, exemplified by breast carcinomas.