Chromosomal inversions are ubiquitous in genomes and often coordinate complex phenotypes, such as the covariation of behavior and morphology in many birds, fishes, insects or mammals1,2,3,4,5,6,7,8,9,10,11. However, why and how inversions become associated with polymorphic traits remains obscure. Here we show that despite a strong selective advantage when they form, inversions accumulate recessive deleterious mutations that generate frequency-dependent selection and promote their maintenance at intermediate frequency. Combining genomics and in vivo fitness analyses in a model butterfly for wing-pattern polymorphism, Heliconius numata, we reveal that three ecologically advantageous inversions have built up a heavy mutational load from the sequential accumulation of deleterious mutations and transposable elements. Inversions associate with sharply reduced viability when homozygous, which prevents them from replacing ancestral chromosome arrangements. Our results suggest that other complex polymorphisms, rather than representing adaptations to competing ecological optima, could evolve because chromosomal rearrangements are intrinsically prone to carrying recessive harmful mutations.
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The raw sequence data were deposited in the NCBI SRA and accession numbers are indicated in Supplementary Table 2. The whole-genome VCF file is available upon request. Whole-genome assemblies were deposited in the NCBI under accession number PRJNA676017. All data underlying the fitness assays are available in Supplementary Table 4.
RepeatMasker results were parsed with scripts from https://github.com/4ureliek/Parsing-RepeatMasker-Outputs.git. Scripts used to compute the main analyses of this study are available at https://github.com/PaulYannJay/Mutation-load-analysis.
Sturtevant, A. H. A case of rearrangement of genes in drosophila. Proc. Natl Acad. Sci. USA 7, 235–237 (1921).
Dobzhansky, T. Genetics of the Evolutionary Process: Columbia Classics Edition (Columbia Univ. Press, 1972).
Lamichhaney, S. et al. Structural genomic changes underlie alternative reproductive strategies in the ruff (Philomachus pugnax). Nat. Genet. 48, 84–88 (2016).
Tuttle, E. M. et al. Divergence and functional degradation of a sex chromosome-like supergene. Curr. Biol. 26, 344–350 (2016).
Kunte, K. et al. doublesex is a mimicry supergene. Nature 507, 229–232 (2014).
Joron, M. et al. Chromosomal rearrangements maintain a polymorphic supergene controlling butterfly mimicry. Nature 477, 203–206 (2011).
Wang, J. et al. A Y-like social chromosome causes alternative colony organization in fire ants. Nature 493, 664–668 (2013).
Christie, M. R., McNickle, G. G., French, R. A. & Blouin, M. S. Life history variation is maintained by fitness trade-offs and negative frequency-dependent selection. Proc. Natl Acad. Sci. USA 115, 4441–4446 (2018).
Kess, T. et al. A migration-associated supergene reveals loss of biocomplexity in Atlantic cod. Sci. Adv. 5, eaav2461 (2019).
Stefansson, H. et al. A common inversion under selection in Europeans. Nat. Genet. 37, 129–137 (2005).
Abbott, J. K., Nordén, A. K. & Hansson, B. Sex chromosome evolution: historical insights and future perspectives. Proc. Biol. Sci. 284, 20162806 (2017).
Küpper, C. et al. A supergene determines highly divergent male reproductive morphs in the ruff. Nat. Genet. 48, 79–83 (2016).
Li, J. et al. Genetic architecture and evolution of the S locus supergene in Primula vulgaris. Nat. Plants 2, 16188 (2016).
Fisher, R. A. The Genetical Theory of Natural Selection (Clarendon Press, 1930); https://doi.org/10.5962/bhl.title.27468
Charlesworth, D. & Charlesworth, B. Theoretical genetics of batesian mimicry II. Evolution of supergenes. J. Theor. Biol. 55, 305–324 (1975).
Ford, E. B. Genetic Polymorphism (Faber & Faber, 1965).
Kopp, M. & Hermisson, J. The evolution of genetic architecture under frequency-dependent disruptive selection. Evolution 60, 1537–1550 (2006).
Chouteau, M., Llaurens, V., Piron-Prunier, F. & Joron, M. Polymorphism at a mimicry supergene maintained by opposing frequency-dependent selection pressures. Proc. Natl Acad. Sci. USA 20, 1702482 (2017).
Sinervo, B. & Lively, C. M. The rock–paper–scissors game and the evolution of alternative male strategies. Nature 380, 240–243 (1996).
Giner-Delgado, C. et al. Evolutionary and functional impact of common polymorphic inversions in the human genome. Nat. Commun. 10, 4222 (2019).
Mallet, J. & Barton, N. H. Strong natural selection in a warning-color hybrid zone. Evolution 43, 421–431 (1989).
Chouteau, M., Arias, M. & Joron, M. Warning signals are under positive frequency-dependent selection in nature. Proc. Natl Acad. Sci. USA 113, 2164–2169 (2016).
Gordon, I. J. Polymorphism of the tropical butterfly, Danaus chrysippus L., in Africa. Heredity 53, 583–593 (1984).
Majerus, M. E. N. Ladybirds (Collins New Naturalist, 1994).
Nadeau, N. J. et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature 534, 106–110 (2016).
Jay, P. et al. Supergene evolution triggered by the introgression of a chromosomal inversion. Curr. Biol. 28, 1839–1845.e3 (2018).
Kirkpatrick, M. How and why chromosome inversions evolve. PLoS Biol. 8, e1000501 (2010).
Berdan, E. L., Blanckaert, A., Butlin, R. K. & Bank, C. Deleterious mutation accumulation and the long-term fate of chromosomal inversions. Preprint at bioRxiv https://doi.org/10.1101/606012 (2020).
Faria, R., Johannesson, K., Butlin, R. K. & Westram, A. M. Evolving inversions. Trends Ecol. Evol. 34, 239–248 (2019).
Stoletzki, N. & Eyre-Walker, A. Estimation of the neutrality index. Mol. Biol. Evol. 28, 63–70 (2011).
Kirkpatrick, M. & Barton, N. Chromosome inversions, local adaptation and speciation. Genetics 173, 419–434 (2006).
Llaurens, V., Whibley, A. & Joron, M. Genetic architecture and balancing selection: the life and death of differentiated variants. Mol. Ecol. 26, 2430–2448 (2017).
Maisonneuve, L., Chouteau, M., Joron, M. & Llaurens, V. Evolution and genetic architecture of disassortative mating at a locus under heterozygote advantage. Evolution https://doi.org/10.1111/evo.14129 (2020).
Wang, J. et al. Sequencing papaya X and Yh chromosomes reveals molecular basis of incipient sex chromosome evolution. Proc. Natl Acad. Sci. USA 109, 13710–13715 (2012).
Stolle, E. et al. Degenerative expansion of a young supergene. Mol. Biol. Evol. 36, 553–561 (2019).
Lindtke, D. et al. Long-term balancing selection on chromosomal variants associated with crypsis in a stick insect. Mol. Ecol. 26, 6189–6205 (2017).
Knief, U. et al. A sex-chromosome inversion causes strong overdominance for sperm traits that affect siring success. Nat. Ecol. Evol. 1, 1177–1184 (2017).
Mérot, C., Llaurens, V., Normandeau, E., Bernatchez, L. & Wellenreuther, M. Balancing selection via life-history trade-offs maintains an inversion polymorphism in a seaweed fly. Nat. Commun. 11, 670 (2020).
Helleu, Q. et al. Rapid evolution of a Y-chromosome heterochromatin protein underlies sex chromosome meiotic drive. Proc. Natl Acad. Sci. USA 113, 4110–4115 (2016).
Castermans, D. et al. Identification and characterization of the TRIP8 and REEP3 genes on chromosome 10q21.3 as novel candidate genes for autism. Eur. J. Hum. Genet. 15, 422–431 (2007).
Mérot, C., Oomen, R. A., Tigano, A. & Wellenreuther, M. A roadmap for understanding the evolutionary significance of structural genomic variation. Trends Ecol. Evol. 35, 561–572 (2020).
Mayjonade, B. et al. Extraction of high-molecular-weight genomic DNA for long-read sequencing of single molecules. Biotechniques 61, 203–205 (2016).
Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Kiełbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).
Kolmogorov, M. et al. Chromosome assembly of large and complex genomes using multiple references. Genome Res. 28, 1720–1732 (2018).
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
Cantarel, B. L. et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188–196 (2008).
Saenko, S. V. et al. Unravelling the genes forming the wing pattern supergene in the polymorphic butterfly Heliconius numata. Evodevo 10, 16 (2019).
Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker Open-4.0 http://www.repeatmasker.org (2013–2015).
Campbell, M. S. et al. MAKER-P: a tool kit for the rapid creation, management, and quality control of plant genome annotations. Plant Physiol. 164, 513–524 (2014).
Lavoie, C. A., Platt, R. N., Novick, P. A., Counterman, B. A. & Ray, D. A. Transposable element evolution in Heliconius suggests genome diversity within Lepidoptera. Mob. DNA 4, 21 (2013).
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
Martin, S. H. et al. Natural selection and genetic diversity in the butterfly Heliconius melpomene. Genetics 203, 525–541 (2016).
Davey, J. W. et al. Major improvements to the Heliconius melpomene genome assembly used to confirm 10 chromosome fusion events in 6 million years of butterfly evolution. G3 (Bethesda) 6, 695–708 (2016).
Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939 (2011).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).
Lartillot, N. & Philippe, H. A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol. Biol. Evol. 21, 1095–1109 (2004).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Araujo, F. A., Barh, D., Silva, A., Guimarães, L. & Ramos, R. T. J. GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data. Sci. Rep. 8, 1794 (2018).
Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).
Le Poul, Y. et al. Evolution of dominance mechanisms at a butterfly mimicry supergene. Nat. Commun. 5, 5644 (2014).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).
Wickham., H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
We thank E. d’Alençon and M.-P. Dubois for their help in the laboratory; T. Aubier for DNA extraction; M. McClure, M. Tuatama and R. Mori-Pezo for their help during field work; P. David, P. Nosil and R. Villoutreix for their careful and critical reading of the manuscript; and K. Lhose, D. Laetsch, B. Nabholz, P.A. Gagnaire, M. Gautier, C. Lemaitre, F. Legeai, M. Elias and A.-S. Fiston-Lavier for insightful discussions. We thank the Peruvian government for providing the necessary research permits (236-2012-AG-DGFFS-DGEFFS, 201-2013-MINAGRI-DGFFS/DGEFFS and 002-2015-SERFOR-DGGSPFFS). This research was supported by Agence Nationale de la Recherche (ANR) grants no. ANR-12-JSV7-0005 and no. ANR-18-CE02-0019-01 and European Research Council grant no. ERC-StG-243179 to M.J., and by fellowships from the Natural Sciences and Engineering Research Council of Canada, a Marie Sklodowska-Curie fellowship (FITINV, N 655857) and an ‘Investissement d’Avenir’ grant managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01) to M.C. V.L. was supported by the ANR grant DOMEVOL (no. ANR-JCJC-SVSE7-2013), and H.B. by the Emergence program from Paris City Council. This project benefited from the Montpellier Bioinformatics Biodiversity platform supported by the LabEx CeMEB, ANR ‘Investissements d’Avenir’ program ANR-10-LABX-04-01. MGX (H.P.) acknowledges financial support from the France Génomique National infrastructure, funded as part of ANR ‘Investissement d’Avenir’ program ANR-10-INBS-09.
The authors declare no competing interests.
Peer review information Nature Genetics thanks Megan Dennis, Laurent Keller, Clemens Küpper and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Alignment of genome assemblies focused on the region of the supergene on chromosome 15.
a, Alignment of genome assemblies of H. numata silvana (Hn0, genome 38) and the H. melpomene Hmel2.5 genome focused on the region of the supergene on chromosome 15. No major chromosomal rearrangement is observed between Hn0 and Heliconius melpomene on chromosome 15. b, Alignment of the supergene region of genome 38 (H. n. silvana) against other H. numata genome assemblies.
Extended Data Fig. 2 PCA computed along the genome for H. numata specimens.
a, PCA computed on SNPs on the chromosome 15 but not within the supergene region. Each dot represents the position of a specimen on the PCA two first axis. The PCA reflect the geographic structure of the dataset. b, PCA computed on SNPs on P1 segment. The first axis of the PCA reflects individual genotypes for the inversion: homozygote for the ancestral gene order (P0/P0), homozygote for the inversion (P1/P1), or heterozygote (P0/P1). The second axis of the PCA reflects the geographic structure of the dataset (samples from French Guiana are highlighted with a black dotted edge). c, PCA computed on SNPs on P2+P3 segment. The first axis of the PCA reflects individual genotypes for the two inversions: homozygote for the ancestral gene order (P0/P0), homozygote for the two inversions (P23/P23), or heterozygote (P0/P23); the second axis of the PCA reflects the geographic structure of the dataset. Proportion of variance explained by axes are indicated in brackets. d, PCA computed on SNPs from the P1+P2+P3 segments. The two axes reflect individual genotypes at the supergene alongside the geographic structure of the dataset. Genotypes at the supergene (Supplementary Table 2) are inferred based on these analyses. To handle overplotting and allow a better visualization, dots were randomly moved from their original position using the geom_jitter fonction from ggplot2 R package, allowing 0.02 amount of height and width variation.
Extended Data Fig. 3 Differential gene expression across the chromosome 15.
Expression difference in early pupal (24h) wing discs between Hn0 and Hn123. Expression differences between groups was assessed with Genewise Negative Binomial Generalized Linear Models with Quasi-likelihood tests (EdgeR packageREF) and Benjamini & Hochberg (‘BH’/’fdr’) multiple test corrections. The -log10 of the false discovery rate (FDR) is plotted along the chromosome 15, with each dot representing a different transcript. Many genes within the inversion segments are differentially expressed between Hn0 and Hn123 and overall, the supergene is significantly enriched (p = 0.01, one-sided block Jackknife resampling test with 100 blocks of 400 transcripts) in differentially expressed genes. No gene ontology terms were over-represented among differently expressed genes. REF : Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Extended Data Fig. 4 Phylogenies of the silvaniform clade with H. cydno and H. melpomene as outgroups, using the genomic segments orthologous to P1, P2 and P3 in H. numata.
Phylogenies of the silvaniform clade with H. cydno and H. melpomene as outgroups, using the genomic segments orthologous to P1, P2 and P3 in H. numata. Phylogenies computed with RAxMLREF1 using the GTRCAT model and only individuals homozygous for the inversions or the standard arrangement. a, Phylogeny of segments orthologous to P2 and P3. This shows the unique origin of the P2 and P3 inversions within H. numata. b, Phylogeny of segments orthologous to P1. This show the introgression of P1 from H. pardalinus into H. numata. Incongruent position of H. elevatus, H. hecale and H. ethilla result from incomplete lineage sorting at the clade level around the gene cortex and to gene flow among species of the clade (especially an introgression between H. elevatus and H. melpomene)REF2. REF1 : A. Stamatakis, RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 30, 1312–1313 (2014). REF2: Nadeau, N. J. et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature 534, 106–110 (2016).
Extended Data Fig. 5 Analysis of divergence times between Hn123 and Hn0 and Fst analysis along the chromosome 15.
a, Divergence time estimates computed with Phylobayes61 on 5kb sliding windows. Bold red and blue lines represent the LOESS smoothing (span = 0.05) of the raw data (thin lines) and give the upper and lower bound of the times inversions P2 and P3 occurred. This supports the formation of P supergene by the stepwise accretion of P1, P2 and P3. b, Fst analysis between the three main supergene alleles : without inversion (Hn0), with P1 inversion (Hn1) and with all three inversion P1, P2 and P3 (Hn123). A ‘suspension bridge’ pattern of differentiation can be observed at P2-P3 by comparing Hn123 to Hn0 and Hn1 haplotypes, suggesting the rare occurrence of recombination around the center of the inversion, as predicted by Ref. 27. A peak of differentiation can be seen between Hn1 and Hn123 around the gene cortex, which controls melanic variations of the wing pattern in Heliconius butterflies25. This peak was unexpected since these two classes of haplotypes have the same genomic orientation (P1 inversion) in this region. Moreover, this region also show the highest differential gene expression when comparing Hn1 to Hn123 (Extended data Fig. 3). Analyses of assemblies as well as of read coverage (data not shown) do not support the presence of major rearrangements between Hn1 and Hn123 at this position, suggesting that this peak of differentiation on cortex is caused by selection on wing pattern divergence rather than recombination suppression via structural variation.
Extended Data Fig. 6 Proportion of TE classes and timing of TE insertion in distinct genomic regions.
a, Proportion of TE classes in whole genome, inversions, and insertions in inversions. b, Time of activity for the distinct classes of transposable elements found in inversions P1, P2, or P3 separately.
Extended Data Fig. 7 Proportion of transposable elements in the whole genome and in distinct genomic regions in H. numata and in H. erato and H. melpomene.
a, Proportion of transposable elements in the whole genome and for the region of the 3 inversions for inverted (nP1 = 7, nP2 = 6, nP3 = 6) and non-inverted (ancestral, nP1 = 3, nP2 = 4, nP3 = 4) segments in H. numata. Boxplot elements: central line: median, box limits: 25th and 75th percentiles, whiskers: 1.5x interquartile range. b, Proportion of transposable elements in the whole genome and in 4 regions associated with wing-pattern variation in H. erato and H. melpomene. Only one genome for each species was used : H. melpomene melpomene reference genome V2.5 and H. erato demophon reference genome V1 (http://ensembl.lepbase.org). Genomic positions of the wing pattern associated regions were defined according to REF1-4. REF1: Nadeau, N. J. et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature 534, 106–110 (2016). REF2 : S. M. Van Belleghem, P. Rastas, A. Papanicolaou, S. H. Martin, C. F. Arias, M. A. Supple, J. J. Hanly, J. Mallet, J. J. Lewis, H. M. Hines, M. Ruiz, C. Salazar, M. Linares, G. R. P. Moreira, C. D. Jiggins, B. A. Counterman, W. O. McMillan, R. Papa, Complex modular architecture around a simple toolkit of wing pattern genes. Nature Ecology & Evolution.1, 1–12 (2017). REF3 : J. J. Lewis, R. C. Geltman, P. C. Pollak, K. E. Rondem, S. M. V. Belleghem, M. J. Hubisz, P. R. Munn, L. Zhang, C. Benson, A. Mazo-Vargas, C. G. Danko, B. A. Counterman, R. Papa, R. D. Reed, Parallel evolution of ancient, pleiotropic enhancers underlies butterfly wing pattern mimicry. PNAS. 116, 24174–24183 (2019). REF4 : R. W. R. Wallbank, S. W. Baxter, C. Pardo-Diaz, J. J. Hanly, S. H. Martin, J. Mallet, K. K. Dasmahapatra, C. Salazar, M. Joron, N. Nadeau, W. O. McMillan, C. D. Jiggins, Evolutionary Novelty in a Butterfly Wing Pattern through Enhancer Shuffling. PLOS Biology. 14, e1002353 (2016).
Extended Data Fig. 8 Mutation accumulation analysis in H. pardalinus.
Density curve representing the whole genome distribution computed in 500kb windows across 12 H. pardalinus specimens. Shades of blue are used to display 0.95 and 0.975 quantiles. P1 shows an excess of non-synonymous polymorphisms and substitutions compared to whole genome.
Supplementary Figs. 1 and 2
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Jay, P., Chouteau, M., Whibley, A. et al. Mutation load at a mimicry supergene sheds new light on the evolution of inversion polymorphisms. Nat Genet 53, 288–293 (2021). https://doi.org/10.1038/s41588-020-00771-1
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