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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%...

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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Updated: May 22, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Copy number variation detection and genotyping from exome sequence data.

Niklas Krumm¹, Peter H Sudmant, Arthur Ko

¹Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.

Genome Research

|May 16, 2012

Summary

This summary is machine-generated.

This study introduces a new method for detecting copy number variations (CNVs) from exome sequencing data, improving accuracy for genetic studies. The CoNIFER pipeline reliably identifies rare CNVs and genotypes copy number polymorphisms.

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Area of Science:

Genomics
Bioinformatics
Human Genetics

Background:

Exome sequencing is effective for single-nucleotide variants but limited for genic copy number variation (CNV) detection due to sparse and nonuniform capture.
Accurate identification of genic CNVs is crucial for understanding genetic diseases.
Existing methods struggle with the nuances of exome data for CNV analysis.

Purpose of the Study:

To develop a novel computational method for sensitive and specific discovery of rare genic CNVs from exome sequencing data.
To enable reliable genotyping of copy number polymorphic (CNP) loci using exome data.
To provide a user-friendly pipeline (CoNIFER) for broad application in human genetic studies.

Main Methods:

Development of a novel method utilizing singular value decomposition (SVD) normalization for exome data analysis.
Validation using 122 trios (366 exomes) to estimate algorithm precision for de novo and inherited rare CNVs.
Comparison of exome-based CNP genotyping with whole-genome data and assessment of absolute copy number estimation accuracy.

Main Results:

The method achieves 94% overall precision in reliably predicting rare CNVs involving three or more consecutive exons.
Exome-based CNP genotyping shows strong correlation with whole-genome data (median r(2) = 0.91), particularly for loci with <8 copies.
Accurate estimation of absolute copy number for multi-allelic genes (78% call level) was achieved.

Conclusions:

The CoNIFER pipeline offers a reliable approach for discovering disruptive genic CNVs missed by standard methods.
This exome-based approach enhances the characterization of copy number variation in human genetic studies.
The method has broad applicability for identifying genetic variants associated with disease.