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CNVmap: a method and software to detect and map copy number variants from segregation data

Abstract : 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.
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Déposant : Christine Molé <>
Soumis le : mardi 12 mai 2020 - 09:29:11
Dernière modification le : vendredi 29 mai 2020 - 09:50:03




Matthieu Falque, Kamel Jebreen, Etienne Paux, Carsten Knaak, Sofiane Mezmouk, et al.. CNVmap: a method and software to detect and map copy number variants from segregation data. Genetics, Genetics Society of America, 2020, 214 (3), pp.561-576. ⟨10.1534/genetics.119.302881⟩. ⟨hal-02570414⟩



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