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Copy number variants outperform SNPs to reveal genotype-temperature association in a marine species

Citation

Dorant, Yann et al. (2020), Copy number variants outperform SNPs to reveal genotype-temperature association in a marine species, Dryad, Dataset, https://doi.org/10.5061/dryad.vt4b8gtnv

Abstract

Copy number variants (CNVs) are a major component of genotypic and phenotypic variation in genomes. To date, our knowledge of genotypic variation and evolution has largely been acquired by means of single nucleotide polymorphism (SNPs) analyses. Until recently, the adaptive role of structural variants (SVs) and particularly that of CNVs has been overlooked in wild populations, partly due to their challenging identification. Here, we document the usefulness of Rapture, a derived reduced‐representation shotgun sequencing approach, to detect and investigate copy number variants (CNVs) alongside SNPs in American lobster (Homarus americanus) populations. We conducted a comparative study to examine the potential role of SNPs and CNVs in local adaptation by sequencing 1,141 lobsters from 21 sampling sites within the southern Gulf of St. Lawrence, which experiences the highest yearly thermal variance of the Canadian marine coastal waters. Our results demonstrated that CNVs account for higher genetic differentiation than SNP markers. Contrary to SNPs, for which no significant genetic–environment association was found, 48 CNV candidates were significantly associated with the annual variance of sea surface temperature, leading to the genetic clustering of sampling locations despite their geographic separation. Altogether, we provide a strong empirical case that CNVs putatively contribute to local adaptation in marine species and unveil stronger spatial signal of population structure than SNPs. Our study provides the means to study CNVs in nonmodel species and highlights the importance of considering structural variants alongside SNPs to enhance our understanding of ecological and evolutionary processes shaping adaptive population structure.

Methods

A total of 1,141 lobster samples were collected from 21 sites between May and July in 2016 in the southern area of the Gulf of St-Lawrence. DNA was extracted from half of the walking leg of each lobster using salt extraction (Aljanabi & Martinez, 1997) with an additional RNAse treatment following the manufacturer’s instructions. DNA quality was assessed using 1% agarose gel electrophoresis. Genomic DNA concentrations were normalized to 20ng/µl based on a fluorescence quantification method (AccuClear™ Ultra High Sensitivity dsDNA Quantitation Solution). Individual reduced-representation shotgun sequencing (i.e. RRS) libraries were prepared following the Rapture approach (Ali et al., 2016). Briefly, the Rapture approach is a form of RRS sequencing which combines double-digested libraries (i.e. GBS, ddRADseq) with a sequence capture step using DNA probes designed from known genomic sequences. Here, we used the same 9,818 targeted loci previously used for the American lobster and all the details about the wet protocol are described in Dorant et al. (2019). All Rapture libraries were sequenced on the Ion Torrent p1v3 chip at the Plateforme d’analyses génomiques of the Institute of Integrative and Systems Biology (IBIS, Université Laval, Québec, Canada http://www.ibis.ulaval.ca/en/home/). Two rounds of sequencing (i.e. two separated chips) were conducted for all Rapture libraries.

Usage Notes

Raw VCF consisted of 44374 unfiltered SNPs
batch_1.vcf

File containing estimated metrics for SNPs caracterization (i.e. singleton, duplicated, diverged, low confidence)
SNPs_caracterization_metrics.txt

Filtered VCF of 14534 SNPs identified as singletons
filtered_singleton_SNPs.vcf

Filtered VCF of 9659 SNPs identified as duplicated
filtered_duplicated_SNPs.vcf

Martix of normalized read depth for CNV loci
CNVs_normalized_read_depth.txt

Sea surface temperature data
Sea_surface_temperatures.txt

Script for SNPs classification (singleton, duplicated)
00_classify_snps_lobster_Rapture.R

Script for CNV data normalization
01-edgeR_normalization_CNVs_data.R

Funding

Natural Sciences and Engineering Research Council of Canada, Award: STPGP 462984‐14