CNVmap: A Method and Software To Detect and Map Copy Number Variants from Segregation Data
Publication Type
Original research

Single nucleotide polymorphisms (SNPs) are used widely for detecting quantitative trait loci, or for searching for causal
variants of diseases. Nevertheless, structural variations such as copy-number variants (CNVs) represent a large part of natural genetic
diversity, and contribute significantly to trait variation. Numerous methods and softwares based on different technologies (amplicons,
CGH, tiling, or SNP arrays, or sequencing) have already been developed to detect CNVs, but they bypass a wealth of information such
as genotyping data from segregating populations, produced, e.g., for QTL mapping. Here, we propose an original method to both
detect and genetically map CNVs using mapping panels. Specifically, we exploit the apparent heterozygous state of duplicated loci:
peaks in appropriately defined genome-wide allelic profiles provide highly specific signatures that identify the nature and position of
the CNVs. Our original method and software can detect and map automatically up to 33 different predefined types of CNVs based on
segregation data only. We validate this approach on simulated and experimental biparental mapping panels in two maize populations
and one wheat population. Most of the events found correspond to having just one extra copy in one of the parental lines, but the
corresponding allelic value can be that of either parent. We also find cases with two or more additional copies, especially in wheat,
where these copies locate to homeologues. More generally, our computational tool can be used to give additional value, at no cost, to
many datasets produced over the past decade from genetic mapping panels.

Publisher Country
United States of America
Publication Type
Both (Printed and Online)