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Polyandry and non-random fertilisation maintain long-term genetic diversity in an isolated island population of adders (Vipera berus)

Cite this dataset

Georges, Arthur; Madsen, Thomas (2024). Polyandry and non-random fertilisation maintain long-term genetic diversity in an isolated island population of adders (Vipera berus) [Dataset]. Dryad. https://doi.org/10.5061/dryad.73n5tb31c

Abstract

Conservation genetic theory suggests that small and isolated populations should be subjected to reduced genetic diversity i.e., heterozygosity and allelic diversity. Our 34 years study of an isolated island population of adders (Vipera berus) in southern Sweden challenges this notion. Despite a lack of gene flow and a yearly mean estimated reproductive adult population size of only 65 adult adders (range 12 to 171), the population has been able to maintain high levels of heterozygosity and allelic diversity similar to that observed in two mainland populations. Even a 14-year major “bottleneck” i.e., a reduction in adult adder numbers, encompassing at least four adder generations, did not result in any reduction in the island adders’ heterozygosity and allelic diversity. Female adders are polyandrous, and fertilisation is non-random, which our empirical data and modelling suggest underpinning the island adders’ ability to maintain a high level of heterozygosity. Our empirical results and subsequent modelling suggest that the positive genetic effects of polyandry in combination with non-random fertilisation, often overlooked in conservation genetic analyses, deserve greater consideration when predicting long-term survival of small and isolated populations.

README: Title: Polyandry and non-random fertilisation maintain long-term genetic diversity in an isolated island population of adders (Vipera berus)

Description of the Data and file structure

Blood samples were stored in 70% ethanol and DNA was isolated by phenolchloroform extraction.

Sequencing for SNP genotyping was performed by DArTseqTM (DArT Pty Ltd, Canberra), after complexity reduction using a combination of restriction enzymes. For fragment size selection and next generation sequencing details, see Kilian et al. (2012) and Georges et al. (2018). To achieve the most appropriate complexity reduction (the fraction of the genome represented, controlling average read depth and number of polymorphic loci), four combinations of restriction enzymes (Pstl enzyme combined with either Hpall, Sphl, Nspl or Msel) were evaluated and the restriction enzyme combination of Pstl (recognition sequence 5'-CTGCA|G-3') and Sphl (5'-GCATG|C-3') was selected.

Only fragments generated by the Psti-Sphl double digest were effectively amplified in 30 rounds of polymerase chain reaction (PCR). Amplifications consisted of an initial denaturation step of 94C for 1 min, followed by 30 cycles of PCR with the following temperature profile: denaturation at 94C for 20 s, annealing at 58C for 30 s and extension at 72C for 45 s, with an additional final extension at 72C for 7 min. After PCR, equimolar amounts of amplification products from each sample were pooled and applied to cBot (lllumina) bridge PCR for sequencing on the lllumina Hiseq 2500. The sequencing (single end) was run for 77 cycles.

Sequences generated from each lane were processed using proprietary DArT Pty Ltd analytical pipelines as described by Georges et al. (2018). In particular, one third of samples were processed twice from DNA, using independent adaptors, to allelic calls as technical replicates, and scoring consistency (repeatability) was used as the main selection criterion for high quality/low error rate markers. The resultant data set contained the SNP genotypes and various associated metadata of which CloneiD (unique identity of the sequence tag for a locus), repAvg (proportion of technical replicate assay pairs for which the marker score is identical), CallRate (proportion of individuals scored at a particular locus) and SnpPosition (position in the sequence tag at which the defined SNP variant base occurs) used in our analyses.

The SNP data and associated metadata were read into a genlight object (Jombart 2008) to facilitate processing with package dartR (Gruber et al. 2018). Only loci with 99% or greater repeatability (repAvg) were chosen for subsequent analysis. Further filtering was undertaken on the basis of call rate (95%). Finally, we filtered out secondary SNPs where they occurred in a single sequenced tag, retaining only one SNP at random. This additional filtering resulted in 7504 SNP markers for the 84 adders, and we regard the data remaining after filtering to be highly reliable.

The raw data as recieved from the commercial supplier (Diversity Arrays Technology) are held in compact binary form of a genlight object (R package Adegenet available from the
CRAN repository). This file has been compressed using adegenet: :saveRDS().

The metadata associated with each individual (the population to which it belongs) is held in comma delimited file ind_metadata.csv.
The filtered data (see methods above) are also held in an adegenet genlight object, compressed using adegenet::saveRDS().
To access the data, use

library(adegenet)
gl. raw <- readRDS(file="adder _raw.RDSdata")
gl. filtered <- read RDS(file="adder_filtered_run1.RDSdata")

and then work with the genlight object as outlined by Jombart, 2008 or Mijangos et al. 2022.

  • Jombart T (2008) adegenet, a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403-1405.
  • Mijangos, J., Gruber, B., Berry, 0., Pacioni, C. and Georges, A. 2022. dartR v2: an accessible genetic analysis platform for conservation, ecology, and agriculture Methods in Ecology and Evolution 13:2150-2158

Sharing/access Information

None

Methods

Blood samples were stored in 70% ethanol and DNA was isolated by phenol–chloroform extraction.

Sequencing for SNP genotyping was performed by DArTseqTM (DArT Pty Ltd, Canberra), after complexity reduction using a combination of restriction enzymes. For fragment size selection and next generation sequencing details, see Kilian et al. (2012) and Georges et al. (2018). To achieve the most appropriate complexity reduction (the fraction of the genome represented, controlling average read depth and number of polymorphic loci), four combinations of restriction enzymes (Pstl enzyme combined with either Hpall, Sphl, Nspl or Msel) were evaluated and the restriction enzyme combination of Pstl (recognition sequence 5'-CTGCA|G-3') and Sphl (5'-GCATG|C-3') was selected.

Only fragments generated by the Psti-Sphl double digest were effectively amplified in 30 rounds of polymerase chain reaction (PCR). Amplifications consisted of an initial denaturation step of 94°C for 1 min, followed by 30 cycles of PCR with the following temperature profile: denaturation at 94°C for 20 s, annealing at 58°C for 30 s and extension at 72°C for 45 s, with an additional final extension at 72°C for 7 min. After PCR, equimolar amounts of amplification products from each sample were pooled and applied to cBot (lllumina) bridge PCR for sequencing on the lllumina Hiseq 2500. The sequencing (single end) was run for 77 cycles.

Sequences generated from each lane were processed using proprietary DArT Pty Ltd analytical pipelines as described by Georges et al. (2018). In particular, one third of samples were processed twice from DNA, using independent adaptors, to allelic calls as technical replicates, and scoring consistency (repeatability) was used as the main selection criterion for high quality/low error rate markers. The resultant data set contained the SNP genotypes and various associated metadata of which CloneiD (unique identity of the sequence tag for a locus), repAvg (proportion of technical replicate assay pairs for which the marker score is identical), CallRate (proportion of individuals scored at a particular locus) and SnpPosition (position in the sequence tag at which the defined SNP variant base occurs) used in our analyses.

The SNP data and associated metadata were read into a genlight object (Jombart 2008) to facilitate processing with package dartR (Mijangos et al., 2022). Only loci with 99% or greater repeatability (repAvg) were chosen for subsequent analysis. Further filtering was undertaken on the basis of call rate (95%). Finally, we filtered out secondary SNPs where they occurred in a single sequenced tag, retaining only one SNP at random. This additional filtering resulted in 7504 SNP markers for the 84 adders, and we regard the data remaining after filtering to be highly reliable.

  • Georges A, Gruber B, Pauly GB, White DF, Adams M, Young MJ, Kilian A, Zhang X, Shaffer HB, Unmack PJ (2018) Genomewide SNP markers breathe new life into phylogeography and species delimitation for the problematic short-necked turtles (Chelidae, Emydura) of eastern Australia. Mol Ecol 27:5195–5213.
  • Jombart T (2008) adegenet, a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403-1405.
  • Kilian A, Wenzl P, Huttner E, Carling J, Xia L, Blois H, Caig V, Heller-Uszynska K, Jaccoud D, Hopper C, Aschenbrenner-Kilian M, Evers M, Peng P, Cayla C, Hok P, Uszynski G (2012) Diversity arrays technology, a generic genome profiling technology on open platforms. Methods Mol Biol 888:67-89.
  • Mijangos, J., Gruber, B., Berry, O., Pacioni, C. and Georges, A. 2022. dartR v2: an accessible genetic analysis platform for conservation, ecology, and agriculture Methods in Ecology and Evolution 13:2150–2158.

Funding

Australian Research Council