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A new framework for detecting copy number variants from single nucleotide polymorphism data: ‘rCNV’, a versatile R package for paralogs and CNVs detection

Recent studies show that copy number variants (CNVs), due to their ubiquitous presence in eukaryotes, contribute to phenotypic variation, environmental adaptation, and fuel species divergence at a previously unknown rate. However, the detection of CNVs in genomes, especially in non-model organisms is challenging due to the need for costly genomic resources and complex computational infrastructure. Therefore, to provide researchers with a low-cost and easily accessible resource, we developed a robust statistical framework and an R software package to detect CNVs from SNPs. The methods implemented in the framework use read-depth variation from high throughput sequence data, and relies on the proportion of heterozygotes in populations and their allelic ratios. The framework consists of two main steps: 1) flagging SNP deviants using allelic ratio deviations from the expectation and excess of heterozygotes under Hardy-Weinberg Equilibrium; followed by 2) filtering putative CNVs from deviant SNPs using data-specific statistical approaches. Our framework is well-catered for most GBS technologies (e.g., RADseq, Exome-capture, WGS). As such, it allows calling CNVs from genomes of varying complexity. The framework is implemented in the R package “rCNV” which effortlessly automates the analysis. We tested our models on four datasets obtained from different sequencing technologies (i.e., RADseq: Chinook salmon – Oncorhynchus tshawytscha, American lobster – Homarus americanus, Exome-capture: Norway Spruce – Picea abies, and WGS: Malaria mosquito - Anopheles gambiae). Our models detected CNVs with substantial accuracy and were significantly efficient with statistically low confident data, where average read-depth and number of samples are limited.