A comprehensive framework for detecting copy number variants from single nucleotide polymorphism data: ‘rCNV’, a versatile r package for paralogue and CNV detection

Abstract

Recent studies have highlighted the significant role of copy number variants (CNVs) in phenotypic diversity, environmental adaptation and species divergence across eukaryotes. The presence of CNVs also has the potential to introduce genotyping biases, which can pose challenges to accurate population and quantitative genetic analyses. However, detecting CNVs in genomes, particularly in non‐model organisms, presents a formidable challenge. To address this issue, we have developed a statistical framework and an accompanying r software package that leverage allelic‐read depth from single nucleotide polymorphism (SNP) data for accurate CNV detection. Our framework capitalises on two key principles. First, it exploits the distribution of allelic‐read depth ratios in heterozygotes for individual SNPs by comparing it against an expected distribution based on binomial sampling. Second, it identifies SNPs exhibiting an apparent excess of heterozygotes under Hardy–Weinberg equilibrium. By employing multiple statistical tests, our method not only enhances sensitivity to sampling effects but also effectively addresses reference biases, resulting in optimised SNP classification. Our framework is compatible with various NGS technologies (e.g. RADseq, Exome‐capture). This versatility enables CNV calling from genomes of diverse complexities. To streamline the analysis process, we have implemented our framework in the user‐friendly r package ‘rCNV’, which automates the entire workflow seamlessly. We trained our models using simulated data and validated their performance on four datasets derived from different sequencing technologies, including RADseq (Chinook salmon—Oncorhynchus tshawytscha), Rapture (American lobster—Homarus americanus), Exome‐capture (Norway spruce—Picea abies) and WGS (Malaria mosquito—Anopheles gambiae).

Pitfalls and recommendations for large-scale SNP genotyping in a non-model endangered species: the southern damselflies (Coenagrion mercuriale) as a case study

“…This could indicate that during capture, probes may have hybridised with other non-targeted but genetically close portions of the genome. This may indicate possible paralogues or copy number variant in the southern damselfly genome (Verdu et al ., 2016; Karunarathne et al ., 2023). Consequently, we discarded sequences resulting from these likely parasitic hybridisations and whose first 40 base pairs did not exactly correspond to probe sequences.…”

“…We hypothesised that extreme values in F IS were due to differences in depth of coverage: strong excess in heterozygotes could be correlated with excessively high depths, indicating possible duplication of the target genome region, whereas biases toward excess in homozygotes could be due to too low sequencing depth, making it impossible to detect alternative variants (Song et al ., 2016; Lou and Therkildsen, 2022; Karunarathne et al ., 2023). However, there was no clear relationship between loci sequencing depth and the levels of F IS (Figure 3), although loci with a high homozygote excess showed a slight tendency to exhibit low coverage.…”

Despite structural identity, ace-1 heterogenous duplication resistance alleles are quite diverse in Anopheles mosquitoes

“…While we cannot exclude the existence of D copy-number-alleles, our analyses suggest that this discrepancy may come from how the number of copies were determined in these two studies. Identifying S/R copy-number ratio only from the ratio of allelic coverage at a single diagnostic position (here the G119S point mutation, see Grau-Bové et al 2021 ) can lead to inaccurate copy-number estimations, especially with low depth of coverage (see simulations in Karunarathne et al 2023 ). In our study, using the average depth of coverage across the whole ace-1 gene to assess the number of copies and to deduce the genotype, proved to be more reliable (Supp.…”