On the causes of geographically heterogeneous parallel evolution in sticklebacks

Abstract

The three-spined stickleback (Gasterosteus aculeatus) is an important model system for the study of parallel evolution in the wild, having repeatedly colonized and adapted to freshwater from the sea throughout the northern hemisphere. Previous studies identified numerous genomic regions showing consistent genetic differentiation between freshwater and marine ecotypes but these had typically limited geographic sampling and mostly focused on the Eastern Pacific region. We analysed population genomic data from global samples of the three-spined stickleback marine and freshwater ecotypes to detect loci involved in parallel evolution at different geographic scales. Most signatures of parallel evolution were unique to the Eastern Pacific and trans-oceanic marine–freshwater differentiation was restricted to a limited number of shared genomic regions, including three chromosomal inversions. On the basis of simulations and empirical data, we demonstrate that this could result from the stochastic loss of freshwater-adapted alleles during the invasion of the Atlantic basin and selection against freshwater-adapted variants in the sea, both of which can reduce standing genetic variation available for freshwater adaptation outside the Eastern Pacific region. Moreover, the elevated linkage disequilibrium associated with marine–freshwater differentiation in the Eastern Pacific is consistent with secondary contact between marine and freshwater populations that evolved in isolation from each other during past glacial periods. Thus, contrary to what earlier studies from the Eastern Pacific region have led us to believe, parallel marine–freshwater differentiation in sticklebacks is far less prevalent and pronounced in all other parts of the species global distribution range.

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Fig. 1: Linkage disequilibrium network analysis.
Fig. 2: Genetic parallelism identified by the unsupervised and supervised methods.
Fig. 3: Ecological genetics in simulated data.
Fig. 4: Genomic differentiation in simulated data.

Data availability

The RAD-seq data have been uploaded to the GenBank under accession numbers SAMN14078677 to SAMN14078738 (https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA605695). Previously published sequencing data are retrieved from studies specified in Supplementary Table 1.

Code availability

The scripts used for analysing empirical data (genotype likelihood estimation, filtering, LDna) and simulated data are available in DRYAD repository: https://doi.org/10.5061/dryad.b2rbnzsb1.

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Acknowledgements

We are grateful to the following people who helped in obtaining the samples used in this study: J. DeFaveri, A. Adill, W. Aguirre, T. Bakker, A. Bell, M. Bell, B. Borg, F. Franzén, A. Goto, A. Hendry, G. Herczeg, F. von Hippel, A. Hirvonen, J. Hämäläinen, M. Kaukoranta, A. Kijewska, D. Kingsley, Y. Kosaka, L. Kvarnemo, D. Lajus, T. Leinonen, A. Levsen, S. McCairns, A. Millet, J. Morozinska, C. Munk, H. Mäkinen, A. Nolte, K. Østbye, W. Pekkola, J. Pokela, M. Ravinet, K. Räsänen, D. Schluter, M. Seymor, T. Shikano, P. Sjöstrand, G. Staines, B. Stelbrink, I. Syvänperä, A. Vasemägi, M. Webster, J. Willacker, H. Winkler and L. Zaveik. Our research was supported by Academy of Finland grant nos. 250435, 263722, 265211 and 1307943 to J.M. and grant no. 316294 to P.M., the Finnish Cultural Foundation grant no. 00190489 to P.K. and the Chinese Scholarship Council grant no. 201606270188 to B.F. We thank J. DeFaveri for feedback and linguistic corrections.

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P.K. and J.M. conceived the concept of the study, with contributions from P.M. and B.F. B.F. and P.K. carried out analyses with significant contributions from P.M. P.K. and B.F. led the writing, with significant contributions from P.M. and J.M. X.F. contributed to LiftOver analysis. B.F. visualized the data. All authors accepted the final version of this manuscript.

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Correspondence to Bohao Fang or Petri Kemppainen.

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Extended data

Extended Data Fig. 1 Visualization of all LD-clusters identified by LDna.

In each panel, I) the top and II) middle plots represent the marine–freshwater differentiation (FST) of the clustered loci of the individuals in the Atlantic and Eastern Pacific, respectively. III) The bottom left plot shows population differentiation based on loci in each LD-cluster (principal component analysis; PCA). Only one chromosome is presented on the x axis when the clustered loci were located on a single chromosome. IV) The bottom right plot depicts the number of in-group samples (as positive value) and the remaining samples (as negative value). Global samples from various regions are shown in different colours; freshwater ecotypes are indicated by light-colour and marine ecotype by dark-colour. The same colour scheme was used in the PCA. The p-values were obtained from permutation tests of cluster separation (Supplementary Information 1).

Extended Data Fig. 2 Ability of LDna to recover marine–freshwater differentiated regions from Jones et al.11.

Jones et al.11 identified 812 regions showing parallel marine–freshwater differentiation in the Eastern Pacific (“i-regions”) and 81 regions showing global parallelism (“m-f regions”). (a) The proportions of m-f and i-regions that were correctly recovered by LDna (red; at least one SNP from 29 LD-clusters mapped to these regions), the proportion or regions for which we had data but LDna analyses failed to recover (cyan), and regions for which we had no genetic data (blue). (b) Number of high LD-SNPs (produced by the first LDna-filtering step) and raw SNPs (bottom row) in regions that were and were not recovered by LDna and (c) size of the regions that were and were not recovered by LDna (on log10 scale). (d) FST from raw SNPs located within regions that were and were not recovered by LDna. Overall, m-f regions and i- regions that were not recovered by LDna were generally smaller, contained fewer SNPs (that is had lower sequencing coverage) and exhibited lower FST than the regions correctly recovered by LDna.

Extended Data Fig. 3 Genome-wide marine–freshwater differentiation (FST) in the Atlantic, Eastern Pacific and Western Pacific Oceans.

(ac) SNP-based FST of the individuals in the Atlantic (ATL), Eastern Pacific (EP) and Western Pacific (WP), respectively. Ecotype pairs follow the main analyses (Extended Data Table 2). (d) Window-based FST (win-size=100 kb) between EP freshwater samples (n = 13) and EP marine samples (n = 4). (e) Window-based FST between EP freshwater samples (n = 13) and all Pacific marine samples (n = 13). (d, e) are significantly correlated (r = 0.904, p < 0.0001). (f, g) SNP-based EP genetic parallelism (LD-clusters 2, 21, 29) for the same ecotype comparison as (d, e), respectively. Loci from LD-clusters involved in genetic parallelism are colour-coded for all panels (refer to main Fig. 2).

Extended Data Fig. 4 PCA plot of LDna clusters with population identification.

See Supplementary Table 1 for population identifiers.

Extended Data Fig. 5 Population diversity and Isolation-By-Distance (IBD) in marine three-spined stickleback populations.

(a) Boxplots of individual heterozygosity (proportion heterozygous positions per individual) of marine individuals in different geographical regions (EP = Eastern Pacific, WP = Western Pacific and ATL = Atlantic; GLM, F2,64 = 43.05, P < 0.001). (b) Boxplots of individual heterozygosity of LD-cluster 2 in different geographical regions (GLM, F2,64 = 91.9, P < 0.001). (c) IBD between marine populations. Note that the different scales of empirical and simulated heterozygosity in (a, b) are not relevant. This is because in the simulations of all allele frequencies started from 0.5 and while a burn in of 10k generations was appropriate for loci linked to QTL, neutral loci would have required four times more generations to reach equilibrium (see Supplementary Information 3). However, the trends in terms of loss of heterozygosity away from the ancestral Eastern Pacific marine populations is still informative and consistent with the empirical data.

Extended Data Fig. 6 Mercator projection of global three-spined stickleback populations used in the study.

166 three-spined stickleback individuals from 63 localities were used, including 119 freshwater individuals and 47 marine individuals. For a complete list of samples, see Supplementary Table 1.

Extended Data Fig. 7 Summary of all LD-clusters.

Shaded rows (LD-clusters) contribute to genetic parallelism of regional or trans-oceanic freshwater populations.

Extended Data Fig. 8 Sampling schemes for FST analyses.

The table specifies sampling schemes used for FST analyses and figures.

Supplementary information

Supplementary Information

Supplementary notes 1–5, references, Figs. 1–3 and Table 1.

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Fang, B., Kemppainen, P., Momigliano, P. et al. On the causes of geographically heterogeneous parallel evolution in sticklebacks. Nat Ecol Evol 4, 1105–1115 (2020). https://doi.org/10.1038/s41559-020-1222-6

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