Genome-wide signatures of convergent evolution in echolocating mammals

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Evolution is typically thought to proceed through divergence of genes, proteins and ultimately phenotypes1, 2, 3. However, similar traits might also evolve convergently in unrelated taxa owing to similar selection pressures4, 5. Adaptive phenotypic convergence is widespread in nature, and recent results from several genes have suggested that this phenomenon is powerful enough to also drive recurrent evolution at the sequence level6, 7, 8, 9. Where homoplasious substitutions do occur these have long been considered the result of neutral processes. However, recent studies have demonstrated that adaptive convergent sequence evolution can be detected in vertebrates using statistical methods that model parallel evolution9, 10, although the extent to which sequence convergence between genera occurs across genomes is unknown. Here we analyse genomic sequence data in mammals that have independently evolved echolocation and show that convergence is not a rare process restricted to several loci but is instead widespread, continuously distributed and commonly driven by natural selection acting on a small number of sites per locus. Systematic analyses of convergent sequence evolution in 805,053 amino acids within 2,326 orthologous coding gene sequences compared across 22 mammals (including four newly sequenced bat genomes) revealed signatures consistent with convergence in nearly 200 loci. Strong and significant support for convergence among bats and the bottlenose dolphin was seen in numerous genes linked to hearing or deafness, consistent with an involvement in echolocation. Unexpectedly, we also found convergence in many genes linked to vision: the convergent signal of many sensory genes was robustly correlated with the strength of natural selection. This first attempt to detect genome-wide convergent sequence evolution across divergent taxa reveals the phenomenon to be much more pervasive than previously recognized.

At a glance


  1. Convergence hypotheses and genomic distribution of support.
    Figure 1: Convergence hypotheses and genomic distribution of support.

    a, For each locus, the goodness-of-fit of three separate phylogenetic hypotheses was considered: (left) H0, the accepted species phylogeny based on recent findings (for example, refs 14, 23, 24, 25); (top-right panel) H1, or ‘bat–bat convergence’, in which echolocating bat lineages (shown in brown) are forced to form a monophyletic group to the exclusion of non-echolocating Old World fruit bats (shown in orange); and (bottom-right panel) H2, or ‘bat–dolphin convergence’, in which the echolocating bat lineages and the dolphin (blue) form a monophyletic group to the exclusion of all non-echolocating mammals. See Methods for details of model fitting and topologies. b, The distribution of convergence signal across 2,326 loci in 14–22 representative mammalian taxa, as measured by locus-wise mean site-specific likelihood support for the species topology (H0) over (left) the ‘bat–bat’ hypothesis uniting echolocating bats (that is, ΔSSLS (H1)) and (right) bat–dolphin hypothesis (that is, ΔSSLS (H2)). Representative hearing and vision loci are shown in green and blue, respectively; for each locus significance levels based on simulation denote whether it had significant counts of convergent sites after correcting for expected counts in random (control) phylogenies (*), and additionally whether strength of positive selection (dN/dS) and convergence (ΔSSLS) at sites under selection in echolocators were correlated (**); see Supplementary Table 4 and Methods.

  2. Relationship between strength of convergence signal and adaptive selection.
    Figure 2: Relationship between strength of convergence signal and adaptive selection.

    a, b, For hypotheses H1(a) and H2(b) (n = 2,030 and 1,876 loci, respectively), the 95% confidence intervals of the coefficient (slope) for locus-wise regressions between site-wise support for convergence and site-wise ω for sites under diversifying selection are plotted. In each plot, loci showing a negative relationship, as characterized by a slope significantly below zero, are consistent with an evolutionary trajectory of adaptive convergence (purple line, with filled circle indicating upper 95% limit) and loci showing a positive relationship, with a slope of greater than zero, are consistent with an evolutionary trajectory of adaptive divergence (orange line, with filled circle indicating lower 95% limit). Insets show two examples of adaptive convergence and divergence under each hypothesis. Full details of ω estimation and regression fitting are given in the Methods.

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Referenced accessions

Sequence Read Archive


  1. Soskine, M. & Tawfik, D. S. Mutational effects and the evolution of new protein functions. Nature Rev. Genet. 11, 572582 (2010)
  2. Clark, A. G. et al. Evolution of genes and genomes on the Drosophila phylogeny. Nature 450, 203218 (2007)
  3. Hughes, J. F. et al. Chimpanzee and human Y chromosomes are remarkably divergent in structure and gene content. Nature 463, 536539 (2010)
  4. Hoy, R. R. Evolution. Convergent evolution of hearing. Science 338, 894895 (2012)
  5. Grant, P. R., Grant, B. R., Markert, J. A., Keller, L. F. & Petren, K. Convergent evolution of Darwin’s finches caused by introgressive hybridization and selection. Evolution 58, 15881599 (2004)
  6. Zhang, J. Z. & Kumar, S. Detection of convergent and parallel evolution at the amino acid sequence level. Mol. Biol. Evol. 14, 527536 (1997)
  7. Kriener, K., O’hUigin, C., Tichy, H. & Klein, J. Convergent evolution of major histocompatibility complex molecules in humans and New World monkeys. Immunogenetics 51, 169178 (2000)
  8. Li, G. et al. The hearing gene Prestin reunites echolocating bats. Proc. Natl Acad. Sci. USA 105, 1395913964 (2008)
  9. Castoe, T. A. et al. Evidence for an ancient adaptive episode of convergent molecular evolution. Proc. Natl Acad. Sci. USA 106, 89868991 (2009)
  10. Liu, Y., Rossiter, S. J., Han, X., Cotton, J. A. & Zhang, S. Cetaceans on a molecular fast track to ultrasonic hearing. Curr. Biol. 20, 18341839 (2010)
  11. Vater, M. & Kössl, M. in Echolocation in Bats and Dolphins (eds Thomas, J. T., Moss, C. F. & Vater, M.) 8998 (Univ. Chicago Press, 2004)
  12. Au, W. W. L. & Simmons, J. A. Echolocation in dolphins and bats. Phys.Today 60, 4045 (2007)
  13. Teeling, E. C. et al. Microbat paraphyly and the convergent evolution of a key innovation in Old World rhinolophoid microbats. Proc. Natl Acad. Sci. USA 99, 14311436 (2002)
  14. Teeling, E. C. et al. Molecular evidence regarding the origin of echolocation and flight in bats. Nature 403, 188192 (2000)
  15. Jones, G. & Holderied, M. W. Bat echolocation calls: adaptation and convergent evolution. Proc. R. Soc. B 274, 905912 (2007)
  16. Davies, K. T. J., Cotton, J. A., Kirwan, J. D., Teeling, E. C. & Rossiter, S. J. Parallel signatures of sequence evolution among hearing genes in echolocating mammals: an emerging model of genetic convergence. Heredity 108, 480489 (2012)
  17. Liu, Y. et al. The voltage-gated potassium channel subfamily KQT member 4 (KCNQ4) displays parallel evolution in echolocating bats. Mol. Biol. Evol. 29, 14411450 (2012)
  18. Shen, Y.-Y., Liang, L., Li, G.-S., Murphy, R. W. & Zhang, Y.-P. Parallel evolution of auditory genes for echolocation in bats and toothed whales. PLoS Genet. 8, e1002788 (2012)
  19. Liu, Y. et al. Convergent sequence evolution between echolocating bats and dolphins. Curr.Biol. 20, R53R54 (2010)
  20. Zhang, G. et al. Comparative analysis of bat genomes provides insight into the evolution of flight and immunity. Science 339, 456460 (2013)
  21. Sun, Y.-B. et al. Genome-wide scans for candidate genes involved in the aquatic adaptation of dolphins. Genome Biol. Evol. 5, 130139 (2013)
  22. Jones, G. & Teeling, E. C. The evolution of echolocation in bats. Trends Ecol. Evol. 21, 149156 (2006)
  23. Murphy, W. J., Pringle, T. H., Crider, T. A., Springer, M. S. & Miller, W. Using genomic data to unravel the root of the placental mammal phylogeny. Genome Res. 17, 413421 (2007)
  24. Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476482 (2011)
  25. Zhou, X. et al. Phylogenomic analysis resolves the interordinal relationships and rapid diversification of the laurasiatherian mammals. Syst. Biol. 61, 150164 (2012)
  26. Zhao, H. B. et al. The evolution of color vision in nocturnal mammals. Proc. Natl Acad. Sci. USA 106, 89808985 (2009)
  27. Zhao, H. B. et al. Rhodopsin molecular evolution in mammals inhabiting low light environments. PLoS ONE 4, e8326 (2009)
  28. Fasick, J. I. & Robinson, P. R. Spectral-tuning mechanisms of marine mammal rhodopsins and correlations with foraging depth. Vis. Neurosci. 17, 781788 (2000)
  29. Terrinoni, A. et al. Role of p63 and the Notch pathway in cochlea development and sensorineural deafness. Proc. Natl. Acad. Sci. USA 110, 73007305 (2013)
  30. Ryan, A. F. The cell cycle and the development and regeneration of hair cells. Curr. Top. Dev. Biol. 57, 449466 (2003)
  31. Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 19691973 (2012)
  32. Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 19661967 (2009)
  33. She, R. et al. genBlastG: using BLAST searches to build homologous gene models. Bioinformatics 27, 21412143 (2011)
  34. Kim, E. B. et al. Genome sequencing reveals insights into physiology and longevity of the naked mole rat. Nature 479, 223227 (2011)
  35. Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 10611067 (2007)
  36. Parra, G., Bradnam, K., Ning, Z., Keane, T. & Korf, I. Assessing the gene space in draft genomes. Nucleic Acids Res. 37, 289297 (2009)
  37. Bininda-Emonds, O. R. transAlign: using amino acids to facilitate the multiple alignment of protein-coding DNA sequences. BMC Bioinform. 6, 156 (2005)
  38. Katoh, K., Kuma, K., Toh, H. & Miyata, T. MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 33, 511518 (2005)
  39. Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540552 (2000)
  40. Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 4457 (2009)
  41. Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 113 (2004)
  42. Stamatakis, A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 26882690 (2006)
  43. Stamatakis, A. in Proceedings of 20th IEEE/ACM International Parallel and Distributed Processing Symposium (IPDPS2006) (High Performance Computational Biology Workshop, 2006)
  44. Hancock, A. M. et al. Human adaptations to diet, subsistence, and ecoregion are due to subtle shifts in allele frequency. Proc. Natl Acad. Sci. USA 107, 89248930 (2010)
  45. Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 15861591 (2007)
  46. Junier, T. & Zdobnov, E. M. The Newick utilities: high-throughput phylogenetic tree processing in the UNIX shell. Bioinformatics 26, 16691670 (2010)
  47. Ranwez, V. et al. OrthoMaM: a database of orthologous genomic markers for placental mammal phylogenetics. BMC Evol.Biol. 7, 241 (2007)
  48. Lartillot, N., Lepage, T. & Blanquart, S. PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating. Bioinformatics 25, 22862288 (2009)
  49. Yang, Z., Nielsen, R., Goldman, N. & Pedersen, A. M. Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155, 431449 (2000)
  50. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J. R. Stat. Soc. B. 57, 289300 (1995)
  51. Bielawski, J. P. & Yang, Z. H. A maximum likelihood method for detecting functional divergence at individual codon sites, with application to gene family evolution. J. Mol. Evol. 59, 121132 (2004)
  52. Wong, W. S., Yang, Z., Goldman, N. & Nielsen, R. Accuracy and power of statistical methods for detecting adaptive evolution in protein coding sequences and for identifying positively selected sites. Genetics 168, 10411051 (2004)
  53. Fitch, W. M. & Margoliash, E. Construction of phylogenetic trees. Science 155, 279284 (1967)
  54. Schneider, A. et al. Estimates of positive Darwinian selection are inflated by errors in sequencing, annotation, and alignment. Genome Biol. Evol. 1, 114118 (2009)
  55. The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genet. 25, 2529 (2000)
  56. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Res. 40, D109D114 (2012)
  57. Prasad, T. S. K. et al. Human protein reference database - 2009 update. Nucleic Acids Res. 37, D767D772 (2008)
  58. Lewis, B. P., Burge, C. B. & Bartel, D. P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 1520 (2005)
  59. Becker, K. G., Barnes, K. C., Bright, T. J. & Wang, S. A. The Genetic Association Database. Nature Genet. 36, 431432 (2004)

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Author information

  1. These authors contributed equally to this work.

    • Joe Parker &
    • Georgia Tsagkogeorga


  1. School of Biological and Chemical Sciences, Queen Mary, University of London, London E1 4NS, UK

    • Joe Parker,
    • Georgia Tsagkogeorga,
    • James A. Cotton &
    • Stephen J. Rossiter
  2. BGI-Europe, Ole Maaløes Vej 3, DK-2200 Copenhagen N, Denmark

    • Yuan Liu
  3. Center for Translational Genomics and Bioinformatics, San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milano, Italy

    • Paolo Provero &
    • Elia Stupka
  4. Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, I-10126 Torino, Italy

    • Paolo Provero
  5. Present address: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

    • James A. Cotton


S.J.R. conceived the study and secured funding together with J.A.C. and E.S. J.P. conducted all phylogenetic, convergence and selection analyses with input from S.J.R., G.T. and J.A.C. Processing and analyses of sequence data was undertaken by G.T., with input from E.S., who also conducted gene ontology analyses with P.P. Raw sequence data was generated under direction of Y.L. and S.J.R. The paper was written and figures prepared by J.P. and S.J.R. with input from G.T., J.A.C. and E.S.

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The authors declare no competing financial interests.

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Short-read data have been deposited into the Short Read Archive under accession numbers SRR924356, SRR924359, SRR924361 and SRR924427.

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    This file contains Supplementary Tables 1-13, Supplementary Figures 1- 9, Supplementary Methods and Supplementary References.

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