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Somatic genetic drift and multilevel selection in a clonal seagrass


All multicellular organisms are genetic mosaics owing to somatic mutations. The accumulation of somatic genetic variation in clonal species undergoing asexual (or clonal) reproduction may lead to phenotypic heterogeneity among autonomous modules (termed ramets). However, the abundance and dynamics of somatic genetic variation under clonal reproduction remain poorly understood. Here we show that branching events in a seagrass (Zostera marina) clone or genet lead to population bottlenecks of tissue that result in the evolution of genetically differentiated ramets in a process of somatic genetic drift. By studying inter-ramet somatic genetic variation, we uncovered thousands of single nucleotide polymorphisms that segregated among ramets. Ultra-deep resequencing of single ramets revealed that the strength of purifying selection on mosaic genetic variation was greater within than among ramets. Our study provides evidence for multiple levels of selection during the evolution of seagrass genets. Somatic genetic drift during clonal propagation leads to the emergence of genetically unique modules that constitute an elementary level of selection and individuality in long-lived clonal species.

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Fig. 1: Somatic genetic drift among ramets of a seagrass genet causes segregation of genetic variation.
Fig. 2: Inter-ramet genetic differentiation among the 24 seagrass ramets belonging to a single genet.
Fig. 3: Intra-ramet somatic genetic variation.
Fig. 4: Comparison of purifying selection regimes at the inter- and intra-ramet levels.
Fig. 5: Dynamics of intra-ramet somatic polymorphism on the basis of ultra-deep 1,370× resequencing.

Data availability

The DNA sequence data are available in the NCBI short read archive, BioProject no. PRJNA557092, SRA accession no. SRR9879327- SRR9879353. The data on genes and putative gene functions and on SNP verification are included in the supplementary tables. If not included in the supplementary material, the data visualized in Figs. 35 are deposited in PANGAEA (ref. 72).

Code availability

The custom-made computer code is available on Github at


  1. 1.

    Behjati, S. et al. Genome sequencing of normal cells reveals developmental lineages and mutational processes. Nature 513, 422–425 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Wang, L. et al. The architecture of intra-organism mutation rate variation in plants. PLoS Biol. 17, e3000191 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Frank, S. A. Somatic evolutionary genomics: mutations during development cause highly variable genetic mosaicism with risk of cancer and neurodegeneration. Proc. Natl Acad. Sci. USA 107, 1725–1730 (2010).

    CAS  PubMed  Google Scholar 

  4. 4.

    Pineda-Krch, M. & Lehtilä, K. Costs and benefits of genetic heterogeneity within organisms. J. Evol. Biol. 17, 1167–1177 (2004).

    CAS  PubMed  Google Scholar 

  5. 5.

    Honnay, O. & Bossuyt, B. Prolonged clonal growth: escape route or route to extinction? Oikos 108, 427–432 (2005).

    Google Scholar 

  6. 6.

    Buss, L. W. Evolution, development, and the units of selection. Proc. Natl Acad. Sci. USA 80, 1387–1391 (1983).

    CAS  PubMed  Google Scholar 

  7. 7.

    Jackson, J. B. C., Buss, L. W. & Cook, R. E. Population Biology and Evolution of Clonal Organisms (Yale Univ. Press, 1985).

  8. 8.

    Harper, J. L. Population Biology of Plants (Academic Press, 1977).

  9. 9.

    Gaul, H. Die verschiedenen bezugssysteme der mutationshäufigkeit bei pflanzen, angewendet auf dosis-effektkurven. Zeitschrift für Pflanzenzüchtung 38, 63–76 (1957).

    Google Scholar 

  10. 10.

    Larkin, P. J. & Scowcroft, W. R. Somaclonal variation—a novel source of variability from cell cultures for plant improvement. Theor. Appl. Genet. 60, 197–214 (1981).

    CAS  PubMed  Google Scholar 

  11. 11.

    Klekowski, E. J. & Kazarinovafukshansky, N. Shoot apical meristems and mutation—selective loss of disadvantageous cell genotypes. Am. J. Bot. 71, 28–34 (1984).

    Google Scholar 

  12. 12.

    Sutherland, W. J. & Watkinson, A. R. Somatic mutation: do plants evolve differently? Nature 320, 305 (1986).

    Google Scholar 

  13. 13.

    Fagerström, T., Briscoe, D. A. & Sunnucks, P. Evolution of mitotic cell-lineages in multicellular organisms. Trends Ecol. Evol. 13, 117–120 (1998).

    PubMed  Google Scholar 

  14. 14.

    Lynch, M. Evolution of the mutation rate. Trends Genet. 26, 345–352 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Gill, D. E., Chao, L., Perkins, S. L. & Wolf, J. B. Genetic mosaicism in plants and clonal animals. Annu. Rev. Ecol. Syst. 26, 423–444 (1995).

    Google Scholar 

  16. 16.

    Antolin, M. F. & Strobeck, C. The population genetics of somatic mutations. Am. Nat. 126, 52–62 (1985).

    Google Scholar 

  17. 17.

    Breese, E. L., Hayward, M. D. & Thomas, A. C. Somatic selection in perennial ryegrass. Heredity 20, 367–379 (1965).

    Google Scholar 

  18. 18.

    Santelices, B., Gallegos Sánchez, C. & González, A. V. Intraorganismal genetic heterogeneity as a source of genetic variation in modular macroalgae. J. Phycol. 54, 767–771 (2018).

    PubMed  Google Scholar 

  19. 19.

    Schoen, D. J. & Schultz, S. T. Somatic mutation and evolution in plants. Annu. Rev. Ecol. Evol. Syst. 50, 49–73 (2019).

    Google Scholar 

  20. 20.

    Simberloff, D. & Leppanen, C. Plant somatic mutations in nature conferring insect and herbicide resistance. Pest Manage. Sci. 75, 14–17 (2019).

    CAS  Google Scholar 

  21. 21.

    Reusch, T. B. H. & Boström, C. Widespread genetic mosaicism in the marine angiosperm Zostera marina is correlated with clonal reproduction. Evol. Ecol. 25, 899–913 (2011).

    Google Scholar 

  22. 22.

    Arnaud-Haond, S. et al. Implications of extreme life span in clonal organisms: millenary clones in meadows of the threatened seagrass Posidonia oceanica. PLoS ONE 7, e30454 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Bricker, E., Calladine, A., Virnstein, R. & Waycott, M. Mega clonality in an aquatic plant—a potential survival strategy in a changing environment. Front Plant Sci. 9, 435 (2018).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Olsen, J. L. et al. The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea. Nature 530, 331–335 (2016).

    CAS  PubMed  Google Scholar 

  25. 25.

    Sintes, T., Marbà, N. & Duarte, C. M. Modeling nonlinear seagrass clonal growth: assessing the efficiency of space occupation across the seagrass flora. Estuaries Coast. 29, 72–80 (2006).

    Google Scholar 

  26. 26.

    Sung, W. et al. Evolution of the insertion–deletion mutation rate across the tree of life. G3 6, 2583–2591 (2016).

    CAS  PubMed  Google Scholar 

  27. 27.

    Poethig, S. Genetic mosaics and cell lineage analysis in plants. Trends Genet. 5, 273–277 (1989).

    CAS  PubMed  Google Scholar 

  28. 28.

    Pineda-Krch, M. & Lehtilä, K. Cell lineage dynamics in stratified shoot apical meristems. J. Theor. Biol. 219, 495–505 (2002).

    PubMed  Google Scholar 

  29. 29.

    Klekowski, E. J. Plant clonality, mutation, diplontic selection and mutational meltdown. Biol. J. Linn. Soc. 79, 61–67 (2003).

    Google Scholar 

  30. 30.

    Burian, A., Barbier de Reuille, P. & Kuhlemeier, C. Patterns of stem cell divisions contribute to plant longevity. Curr. Biol. 26, 1385–1394 (2016).

    CAS  PubMed  Google Scholar 

  31. 31.

    Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Identification of neutral tumor evolution across cancer types. Nat. Genet. 48, 238–244 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Williams, M. J. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat. Genet. 50, 895–903 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Schultz, S. T. & Scofield, D. G. Mutation accumulation in real branches: fitness assays for genomic deleterious mutation rate and effect in large‐statured plants. Am. Nat. 174, 163–175 (2009).

    PubMed  Google Scholar 

  35. 35.

    Willis, J. H. Inbreeding load, average dominance and the mutation rate for mildly deleterious alleles in Mimulus guttatus. Genetics 153, 1885–1898 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Otto, S. P. & Orive, M. E. Evolutionary consequences of mutation and selection within an individual. Genetics 141, 1173–1187 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Orive, M. E. Somatic mutations in organisms with complex life histories. Theor. Popul. Biol. 59, 235–249 (2001).

    CAS  PubMed  Google Scholar 

  38. 38.

    Otto, S. P. & Hastings, I. M. Mutation and selection within the individual. Genetica 102, 507–524 (1998).

    PubMed  Google Scholar 

  39. 39.

    Tarabichi, M. et al. Neutral tumor evolution? Nat. Genet. 50, 1630–1633 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Frank, M. H. & Chitwood, D. H. Plant chimeras: the good, the bad, and the ‘Bizzaria’. Dev. Biol. 419, 41–53 (2016).

    CAS  PubMed  Google Scholar 

  41. 41.

    Smith, M. L., Bruhn, J. N. & Anderson, J. B. The fungus Armillaria bulbosa is among the largest and oldest living organisms. Nature 356, 428–431 (1992).

    Google Scholar 

  42. 42.

    Schmid-Siegert, E. et al. Low number of fixed somatic mutations in a long-lived oak tree. Nat. Plants 3, 926–929 (2017).

    PubMed  Google Scholar 

  43. 43.

    Plomion, C. et al. Oak genome reveals facets of long lifespan. Nat. Plants 4, 440–452 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    de Witte, L. C. & Stöcklin, J. Longevity of clonal plants: why it matters and how to measure it. Ann. Bot. 106, 859–870 (2010).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Ally, D., Ritland, K. & Otto, S. P. Aging in a long-lived clonal tree. PLoS Biol. 8, e1000454 (2010).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Buss, L. W. The Evolution of Individuality (Princeton Univ. Press, 1987).

  47. 47.

    Santelices, B. How many kinds of individuals are there? Trends Ecol. Evol. 14, 152–155 (1999).

    CAS  PubMed  Google Scholar 

  48. 48.

    Van Oppen, M. J. H., Souter, P., Howells, E. J., Heyward, A. & Berkelmans, R. Novel genetic diversity through somatic mutations: fuel for adaptation of reef corals? Diversity 3, 405–423 (2011).

    Google Scholar 

  49. 49.

    Gustafsson, C. & Boström, C. Algal mats reduce eelgrass (Zostera marina L.) growth in mixed and monospecific meadows. J. Exp. Mar. Biol. Ecol. 461, 85–92 (2014).

    Google Scholar 

  50. 50.

    Reusch, T. B. H., Chapman, A. R. O. & Gröger, J. P. Blue mussels (Mytilus edulis) do not interfere with eelgrass (Zostera marina) but fertilize shoot growth through biodeposition. Mar. Ecol. Prog. Ser. 108, 265–282 (1994).

    Google Scholar 

  51. 51.

    Gustafsson, B. G. & Westman, P. On the causes for salinity variations in the Baltic Sea during the last 8500 years. Paleoceanography 17, 12-11–12-14 (2002).

    Google Scholar 

  52. 52.

    Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data (Babraham Institute, 2010);

  53. 53.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Van der Auwera, G. A. et al. From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.11–11.10.33 (2013).

    Google Scholar 

  57. 57.

    Ginestet, C. ggplot2: elegant graphics for data analysis. J. R. Stat. Soc. A 174, 245–246 (2011).

    Google Scholar 

  58. 58.

    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Noorbakhsh, J. & Chuang, J. H. Uncertainties in tumor allele frequencies limit power to infer evolutionary pressures. Nat. Genet. 49, 1288–1289 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Python Language Reference v.3 (Python Software Foundation);

  61. 61.

    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).

    Google Scholar 

  63. 63.

    R Core Team R: a language and environment for statistical computing v.3.6.1 (R Foundation for Statistical Computing, 2019);

  64. 64.

    Kamvar, Z. N., Brooks, J. C. & Grünwald, N. J. Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6, 208 (2015).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Abyzov, A., Urban, A. E., Snyder, M. & Gerstein, M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 21, 974–984 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Robinson, J. T., Thorvaldsdóttir, H., Wenger, A. M., Zehir, A. & Mesirov, J. P. Variant review with the integrative genomics viewer. Cancer Res. 77, e31–e34 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Bataillon, T. et al. Inference of purifying and positive selection in three subspecies of chimpanzees (Pan troglodytes) from exome sequencing. Genome Biol. Evol. 7, 1122–1132 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Alexa, A. & Rahnenfuhrer, J. topGO: Enrichment Analysis for Gene Ontology v.2.36.0.a (R package, 2019).

  71. 71.

    Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Yu, L. et al. Data from: Genomic data of marine flowering plant Zostera marina (PANGAEA, 2020);

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This study was supported by a four-year PhD scholarship from the China Scholarship Council to L.Y. and by a fellowship from the Åbo Akademi University Foundation to C.B. We thank J. L. Olsen and B. Werner for helpful comments on earlier versions of the manuscript and in particular I. Baums for discussing methods for sequencing-independent SNP verification via restriction enzyme digestion. We thank the Archipelago Centre Korpoström (Finland) for excellent working facilities, K. Gagnon for field assistance and S. Landis for creating some of the illustrations. Sampling permit no. MH 5448/2015 was granted through Parks and Wildlife Finland (Metsähallitus).

Author information




T.B.H.R. and L.Y. designed the study. C.B. provided access to the biological samples and conducted the sampling. T.B. and T.D. assisted with the bioinformatic analyses. S.F. prepared the libraries and performed the resequencing. L.Y., T.D. and T.B.H.R. analysed, interpreted and discussed the results. T.B.H.R. and L.Y. wrote the paper, with input from all authors.

Corresponding author

Correspondence to Thorsten B. H. Reusch.

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Extended data

Extended Data Fig. 1 Somatic genetic drift in clonal organisms and possible categories of single nucleotide polymorphisms (SNPs).

Multicellular organisms originate from a single zygote and achieve growth and development via mitosis. The diagram indicates possible genotypes at a locus within the ramet (=box). Heterozygous polymorphisms present in the zygote (blue) are inherited in all offspring cells, and result in consistent heterozygous calls across all ramets. This class of SNPs needs to be excluded before making inferences on somatically derived polymorphisms (cf. Extended Data Fig. 2, ‘Remove 31,777 SNPs that are identically heterozygous among all 24 ramets’). During growth, cells acquire somatic mutations (red) owing to mitotic errors that initially emerge as genetic mosaics, that is the somatic polymorphisms are present in only a subset of cells. When clonal organisms form another iterative unit (=ramet), a subpopulation of cells in the parent ramet are progenitors for the asexual offspring. The genetic bottleneck accompanying this random sampling process determines the segregation of somatic polymorphisms into the new ramets through somatic genetic drift. This process may convert intra-ramet SNP into different fixed ramet genotypes (cf. Fig. 5d). In case 1, somatic genetic drift restores the wild-type genotype present in the zygote. In cases 2, somatic genetic drift separates the intra-ramet SNP at that locus into a fixed state. In case 3, a novel mutation emerges as an intra-ramet SNP, which would require additional rounds of somatic genetic drift to either being fixed or lost.

Extended Data Fig. 2 Workflow for inter- and intra-ramet SNP calling.

All 24 ramets were sequenced at an average coverage of 81x on an Illumina Hiseq 4000 platform (left, for details see Supplementary Table 3), while a subset of three ramets (R08, R10, and R12) were further sequenced at an average coverage of 1370x on Novaseq 6000 platform (right, for details see Supplementary Table 8). The filtered 81x dataset was used to (i) estimate the number of heterozygous loci in the initial zygote in a conservative way with respect to not underestimate the true nucleotide diversity, (ii) call fixed SNPs among ramets, and to (iii) identify SNPs that were genetically different between the reference genome and the initial zygote. Here, ‘informative’ SNPs with respect to a somatic generation of polymorphism refer to all SNPs but those homozygously different to the reference. Informative SNPs were used to verify the clonality of the 24 samples, and to make inferences on somatic mutations after subtracting the identical heterozygous loci shared among R01-24. The 1370x dataset was used to call intra-ramet somatic polymorphisms (composed of both, fixed heterozygous genotypes and mosaic ones with f < 0.5), which allowed us to check the proportion of mosaic intra-ramet SNPs vs. fixed heterozygous genotypes. In the 81x data set, custom filtering steps (hard filtering) using the GATK pipeline with conservative parameters intended to minimize the error of detecting false positive SNP calls. During subsequent custom filtering using the Vcftools, identical homozygous SNPs with respect to the reference were removed. In the ultra-deep sequenced data set, each of the three SNP calling approaches were technically independent and thus verified. Note that for the intra-ramet level, the analysis focused on SNPs that were co-occurring in two or three ramets.

Extended Data Fig. 3 Neighbor-joining tree using insertion/deletion (indel) based genetic differences among 24 seagrass Zostera marina ramets.

The 1,654 inter-ramet indel polymorphisms among the 24 ramets were used to quantify the pairwise genetic differences as number of different alleles. The genetic distance matrix was used to construct a neighbor-joining tree, which displays a similar topology to that revealed by inter-ramet SNPs (Fig. 2d). Scale bar = 50 differences.

Extended Data Fig. 4 Microsatellite polymorphisms among 24 seagrass Zostera marina ramets.

We genotyped all ramets for 8 microsatellite loci (Genbank accession numbers: AJ009904.1, AJ009901.1, AJ249304.1, AJ249306.1, AJ009898.1, AJ009900.1, AJ249307.1, and AJ249305.1) and detected a total of 7 somatic polymorphisms as microsatellite alleles not present in the predominant genotype. Their presence/absence among the 24 ramets is consistent with the NJ tree based on 7,054 SNPs (Fig. 2d) and 1,652 indels (Extended Data Fig. 3). Two polymorphisms were present in more than one ramet and their appearance within one branch is indicated by the microsatellite locus designation. Small insets show two identified three-allelic, mosaic genotypes with novel microsatellite alleles that were only present in one ramet each.

Extended Data Fig. 5 Dirichlet clustering of variant read frequency of SNPs shared by 3 ramets based on 1370x resequencing.

The 2,246 SNPs shared among all three ramets were filtered by a minimum coverage of 500x after which 1,768 SNPs were retained. These were divided into clusters using a Dirichlet process. All pairwise VRF among ramets R08, R10 and R12 of the resulting six Dirichlet clusters (top number) are depicted. Each panel was divided into grids, a color scale depicts the density of points in each grid point representing SNP counts. Lines indicate expected allele frequencies for fixed heterozygote SNPs at x = 0.5 and y = 0.5, respectively, while the line x = y depicts equal allele frequencies among ramets. a, VRF in R08 vs. R10. b, VRF in R12 vs. R10. c, VRF in R08 vs. R12.

Extended Data Fig. 6 Zostera marina as a clonal plant.

Ramets are physiologically independent and genetically unique ‘individuals’. Somatic mutations emerge as intra-ramet SNPs. Along with growth and branching of the genet, somatic genetic drift separates the intra-ramet SNP into different fixed genotypes among ramets (case 1a or 1b), while in others, they may persist as mosaic, intra-ramet polymorphism (case 2). Note that ramets quickly become physiologically independent, as the rhizome connection among them will rot away within one or two years (case 3).

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and Tables 1–14.

Reporting Summary

Supplementary Data 1

Detailed information for 432 inter-ramet non-synonymous SNPs.

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Yu, L., Boström, C., Franzenburg, S. et al. Somatic genetic drift and multilevel selection in a clonal seagrass. Nat Ecol Evol 4, 952–962 (2020).

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