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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...

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%...

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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Related Experiment Video

Updated: May 27, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

A multi-sample based method for identifying common CNVs in normal human genomic structure using high-resolution aCGH

Chihyun Park¹, Jaegyoon Ahn, Youngmi Yoon

¹Department of Computer Science, Yonsei University, Seoul, South Korea.

|November 11, 2011

Summary

This summary is machine-generated.

A new algorithm, MGVD, accurately detects copy number variations (CNVs) in high-resolution genomic data from multiple samples. This method offers improved sensitivity and speed for identifying genomic aberrations.

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Detection of Copy Number Alterations Using Single Cell Sequencing

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Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Identifying copy number variations (CNVs) in normal human genomic data is challenging due to noise and complex signal intensities.
Existing CNV detection algorithms struggle with high-resolution data and are not optimized for analyzing normal human samples, often requiring multi-sample approaches.
Current methods are typically designed for single-sample analysis, limiting their effectiveness for comprehensive genomic variation detection.

Purpose of the Study:

To develop a specialized algorithm for detecting common CNVs from high-resolution, multi-sample array comparative genomic hybridization (aCGH) data.
To address limitations of existing methods in handling noise and analyzing normal human genomes.
To introduce a more precise measure for genomic variant location, termed CNV zones (CNVZs).

Main Methods:

Developed a multi-sample-based genomic variations detector (MGVD) utilizing segmentation for common breakpoint identification across samples.
Employed a k-means-based clustering strategy to analyze multiple samples with varying genomic intensities simultaneously.
Introduced CNV zones (CNVZs) as a refined metric for pinpointing genomic variant locations, surpassing traditional CNV regions (CNVRs).

Main Results:

MGVD demonstrated high sensitivity and a low false discovery rate on simulated datasets.
The algorithm outperformed most existing methods in analyzing real, high-resolution HapMap datasets.
MGVD exhibited the fastest runtime among evaluated algorithms for actual high-resolution aCGH data analysis.

Conclusions:

MGVD is an effective specialized algorithm for detecting common CNVs in high-resolution, multi-sample aCGH data.
The identified CNVZs can be valuable in association studies for linking phenotypes to genomic aberrations.
The C++ based MGVD algorithm is freely available for Linux and MS Windows, facilitating broader research application.