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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%...

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Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

CNVmap: A Method and Software To Detect and Map Copy Number Variants from Segregation Data.

Matthieu Falque¹, Kamel Jebreen^2,3, Etienne Paux⁴

¹Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France matthieu.falque@inrae.fr.

|December 29, 2019

Summary

This study introduces a new method to detect and genetically map copy-number variants (CNVs) using existing genetic mapping data. This approach leverages segregation data to identify structural variations, adding value to previous research.

Keywords:

Copy number variation (CNV)allele frequency profiles non-Mendelian markers segregating populations

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Area of Science:

Genetics
Bioinformatics
Genomics

Background:

Single nucleotide polymorphisms (SNPs) are commonly used for genetic analysis, but structural variations like copy-number variants (CNVs) are crucial for understanding genetic diversity and trait variation.
Existing methods for CNV detection often overlook valuable genotyping data from segregating populations used in quantitative trait loci (QTL) mapping.

Purpose of the Study:

To develop an original method for simultaneously detecting and genetically mapping copy-number variants (CNVs) using established mapping panels.
To exploit heterozygous states of duplicated loci in genome-wide allelic profiles for CNV identification.

Main Methods:

Developed a novel computational method and software to detect and map CNVs based solely on segregation data from mapping panels.
Exploited peaks in genome-wide allelic profiles to identify specific signatures of CNVs.
Validated the approach on simulated and experimental biparental mapping panels in maize and wheat populations.

Main Results:

The method successfully detects and maps up to 33 predefined types of CNVs.
Identified CNV events corresponding to single extra copies in parental lines, with allelic values from either parent.
Observed cases with multiple additional copies, particularly in wheat, often located on homeologues.

Conclusions:

This novel method provides a cost-effective way to extract valuable CNV information from existing genetic mapping datasets.
The approach enhances the utility of data generated over the past decade from genetic mapping panels.
It offers a powerful tool for understanding the contribution of structural variations to genetic diversity and trait variation.