DOI: 10.5061/dryad.msbcc2fx7
title: Copy Number variants in Mallotus villosus

description: Taking advantage of recent developments allowing CNV analysis from RAD-seq
 data (10.1111/mec.15565), we investigated how variation in fitness-related
 traits, local environmental conditions and demographic history are
 associated with CNVs, and how subsequent copy number variation drives
 population genetic structure in a marine fish, the capelin (Mallotus
 villosus). We collected 1536 DNA samples from 35 sampling sites in the
 north Atlantic Ocean and identified 6620 putative CNVs. Raw sequencing
 data for GBS libraries have been published in a previous study (Cayuela et
 al. 2020,  10.1111/mec.15499) and are available under accession
 no. PRJNA631144. Here, we provide the files of normalized read depth for
 the 6620 putative CNVs and environmental data used in our CNV analyses.
description: <strong>Sampling
 area, phenotypic analyses</strong> We sampled 1536 capelins from 35
 spawning sites in the Northwest Atlantic, in Canadian and Greenland waters
 (<b>Fig.1 </b>and <b>Supplementary material
 S1</b>, <b>Table S1</b>). Three sites were sampled in
 the ARC lineage, four sites in GRE lineage, and 28 sites in the NWA
 lineage. Within the NWA lineage, sampling sites included 18 beach spawning
 sites, six demersal spawning sites, and four offshore sites. In the whole
 dataset, the median sample size was 46.5 (range: 19 to 50) individuals per
 site. The fish were collected and immediately frozen, and a piece of fin
 was preserved in RNAlater. The gonadosomatic index was measured at the
 laboratory on 843 individuals (302 females and 541 males) from the 18
 beach spawning sites of the NWA lineage. Because male gonads were very
 small resulting in large measurement error of the gonadosomatic index
 (data not shown), we focused our analyses on females only. <strong>DNA sequencing,
 genotyping, and discovery of putative CNVs</strong> Both DNA extractions and library
 preparations were performed following protocols fully described in Cayuela
 et al. (2020). Libraries were size-selected using a BluePippin prep (Sage
 Science), amplified by PCR and sequenced on the Ion Proton P1v2 chip
 (single-end sequencing). Eighty-two individuals were sequenced per
 chip.Barcodes were removed using cutadapt (Martin 2011) and trimmed to 80
 bp, allowing for an error rate of 0.2. They were then demultiplexed using
 the ‘process_radtags’ module of Stacks v1.48 (Catchen et al. 2013) and
 aligned to the capelin draft genome (Cayuela et al. 2020) assembly using
 bwa-mem (Li 2013) with default parameters. Next, aligned reads were processed with
 Stacks v.1.48 for SNP calling and genotyping. The ‘pstacks’ module was
 used with a minimum depth of 3 and up to 3 mismatches were allowed in the
 catalog creation. We then ran the ‘populations’ module to produce a vcf
 file that was further filtered using python
 (https://github.com/enormandeau/stacks_workflow) and bash scripts. SNPs
 were kept if they displayed a read depth greater than 4 and less than 70
 (Dorant et al. 2020). Then, we kept SNPs present in at least 70 % of
 individuals in each sampling location. We identified putative CNVs using
 the <i>HDplot</i> approach proposed by McKinney et al. (2017).
 The duplicated loci detected by this method can be variant or invariant in
 copy number among the 35 spawning sites of our study area. A duplicated
 locus is considered as a CNV only if a correlation between the normalized
 read depth of a marker and a phenotypic trait or an environmental variable
 was detected. For this reason, we systematically use the term of “putative
 CNVs” when we refer to the markers identified using the
 <i>HDplot</i> method. We used a refined version of this
 approach (Dorant et al. 2020) that discriminates “singleton” SNPs (i.e.,
 non-duplicated) from “duplicated” SNPs (i.e., combining duplicated SNPs
 and SNPs with a high coverage) using 5 parameters: (1) proportion of
 heterozygotes (PropHet), (2) Fis, (3) median of allele ratio for
 heterozygotes (MedRatio), (4) median of SNP read depth for heterozygotes
 (MedDepthHet) and (5) median of SNP read depth for homozygotes
 (MedDepthHom). The parameters were calculated using a custom python script
 (available at https://github.com/enormandeau/stacks_workflow) parsing the
 filtered VCF file. The five parameters were plotted pairwise to visualize
 their distribution across all loci (see <b>Supplementary
 material</b>, <b>Figures S1</b> and
 <b>S2</b>). Based on the graphical demonstration by McKinney
 et al (2017) and the approach proposed by Dorant et al. (2020), we
 considered different combinations of parameters and graphically fixed the
 cut-off of the four categories of SNPs (singleton SNPs, duplicated SNPs,
 high coverage SNPs, and low confidence). We then kept one single marker
 per locus (the one with the highest minor allele frequency) and extracted
 the read depth of duplicated loci to construct the dataset of putative
 CNVs using vcftools. For more simplicity, the term “putative CNV” is used
 to define all loci classified as “duplicated” and “high coverage”.
 Following the procedure described in Dorant et al. (2020), read counts of
 putative CNV loci were normalized to account for differences in sequencing
 effort across all samples. Normalization was performed using the Trimmed
 mean of M-values method originally described for RNA-seq count
 normalization and implemented in the R package edgeR (Robinson &
 Oshlack 2010). The correction accounts for the fact that for an individual
 with a higher copy number at a given locus, that locus will contribute
 proportionally more to the sequencing library than it will for an
 individual with lower copy number at that locus. This procedure was
 applied to the whole dataset (i.e., the 1538 capelins from 35 spawning
 sites) to detect the putative CNVs present throughout the study
 area.
description: Three files are available: The normalized read
 depth matrix : gdepth_normalized_CNVs.txt The file with
 environmental (temperature, cholorophyll concentration, and salinity for
 the beach spawning sites within the NWA lineage:
 environment_data.txt The file with GSI data for both
 sexes: GSI_data.txt