Skip to main content

Evidence that DNA repair genes, a family of tumor suppressor genes, are associated with evolution rate and size of genomes

  • The Correction to this article has been published in Human Genomics 2019 13:29
  • The Letter to the Editor to this article has been published in Human Genomics 2020 14:12


Adaptive radiation and evolutionary stasis are characterized by very different evolution rates. The main aim of this study was to investigate if any genes have a special role to a high or low evolution rate. The availability of animal genomes permitted comparison of gene content of genomes of 24 vertebrate species that evolved through adaptive radiation (representing high evolutionary rate) and of 20 vertebrate species that are considered as living fossils (representing a slow evolutionary rate or evolutionary stasis). Mammals, birds, reptiles, and bony fishes were included in the analysis. Pathway analysis was performed for genes found to be specific in adaptive radiation or evolutionary stasis respectively. Pathway analysis revealed that DNA repair and cellular response to DNA damage are important (false discovery rate = 8.35 × 10−5; 7.15 × 10−6, respectively) for species evolved through adaptive radiation. This was confirmed by further genetic in silico analysis (p = 5.30 × 10−3). Nucleotide excision repair and base excision repair were the most significant pathways. Additionally, the number of DNA repair genes was found to be linearly related to the genome size and the protein number (proteome) of the 44 animals analyzed (p < 1.00 × 10−4), this being compatible with Drake’s rule. This is the first study where radiated and living fossil species have been genetically compared. Evidence has been found that cancer-related genes have a special role in radiated species. Linear association of the number of DNA repair genes with the species genome size has also been revealed. These comparative genetics results can support the idea of punctuated equilibrium evolution.


Adaptive radiation is a well-known phenomenon in evolutionary biology, where a taxon is split in multiple species which become adapted in a variety of environments in short evolutionary time. Although this phenomenon is mostly known in islands like the great examples of Darwin finches [1] and the Hawaiian drosophilas, other major adaptive radiations have occurred in other animals like cichlids, bats, and cetaceans [2,3,4,5]. It is very likely that common evolutionary and molecular processes have been followed in all taxa that have experienced adaptive radiation [6, 7]. No such common molecular pathways have been identified so far.

We could consider living fossil species and adaptive radiation as two very different evolutionary strategies: slow evolutionary rate versus rapid evolutionary rate respectively. Living fossils are characterized by morphological stasis, low taxonomic diversity, and certain rareness. Quantitative criteria have been published recently [8, 9]. The apparent absence of diversification and their morphological stability suggest highly effective adaptations that reduce the need for phenotypic change, regardless of environmental or genetic changes [8, 10]. Living fossils are frequently referred to as an example of evolutionary success and evolutionary stasis [11, 12]. Evolutionary stasis is a common finding in the fossil record [13]. The punctuated equilibrium theory of evolution is based on these fossil observations [14, 15]. Characteristic examples of taxa that are considered by most biologists as living fossils are the crocodilians, coelacanths, and ornithorhynchus. Like in the case of adaptive radiation, our knowledge is insufficient for any special genes that are under selection in living fossil species.

This study was mainly aiming at the identification of any common molecular pathways that contributed to a special evolutionary process in animals. We are mostly interested on genes that are related with disease, since evolutionary studies may contribute to a better understanding of the function of those genes. We supposed that living fossil species (LF) and radiated species (R, those that have been evolved through adaptive radiation) represent two animal categories with a very different rate and form of evolution. We took advantage of the plentiful animal genomes that have been sequenced since presently, and we performed an analytical comparative genetics study. Strict inclusion and statistical criteria were applied (see the “Methods” section). In total, 20 LF and 24 R vertebrate genomes (bony fishes, reptiles, birds, mammals) have been analyzed. Interestingly, only one major genetic difference was revealed related to DNA repair genes, one of the most important categories of tumor suppressor genes.


Species included in this study—genome data

The literature was carefully searched for all animal species that can be characterized as living fossils (LF) (slow evolutionary rate) or radiated (R) (they have experienced adaptive radiation). Additional inclusion criteria are as follows: species with a completed genome project, species with available annotation and gene symbol data (for reliable interspecies comparison). Annotation of genomes has been performed by the submitters under the same NCBI standards. We included animal classes with representative species in both living fossil species and radiated species for a reliable comparison. Genome and gene data used for this work are updated since April of 2019, according to Genome and Gene databases of NCBI ( In total, 44 species were included in this analysis.

Gene analysis

Official gene symbols were used for comparison among species. A custom algorithm was developed for finding all common genes in the LF species group and in the R species group. Next, the two lists of common genes were compared. This was performed through the “unique values” function of Excel 2016. After comparison, two gene lists were created: genes that are common in LF but not found in R and genes that are common in R and not found in LF. We considered that these genes are probably associated with a special type of evolutionary process. Genes were analyzed under the concept of presence/absence. Copy numbers were not considered. All gene lists can be found in Additional file 1: Table S1.

Pathway analysis and DNA repair gene analysis

Panther 14.1 online software [16, 17] was used for pathway analysis of the two LF and R unique gene lists. The software analyzes the submitted gene lists with reference to the human genome. Two algorithms of the software were used: pathway and reactome profile analysis. Results were compared between LF and R to find any pathways that are unique in any of the two evolutionary processes. False discovery rate (FDR) is the statistical outcome that is a special type of adjusted p value. Significant level alpha was set to 0.0001 for highly reliable results.

To confirm if DNA repair genes represent a major genetic difference between the two vertebrate categories, all 44 species’ genomes were analyzed for their content in DNA repair genes. An updated list of all 151 known DNA repair genes was used [18]. Content analysis (presence/absence) was performed using the official gene symbols. An extra search was performed using the gene aliases for any missed misnamed genes. Content analysis was performed through the “duplicate values” function of Excel 2016. Results in detail can be found in Additional file 2: Table S2.

Statistical analysis

All statistical analysis needed for this work was performed through the statistical package STATAv.13 (StataCorp LLC, Texas, USA). The basic statistical analysis included univariate linear regression and independent t test (two-tailed). The heat map was performed through the “color gradient” function of Excel 2016. Significant level alpha was set to 0.01 for identifying the most significant categories of DNA repair genes.

Results and discussion

Species analyzed

Strict inclusion criteria were applied for the 44 species analyzed in this study. Several fossil and molecular studies that are cited below justify the classification “living fossil” or “radiated.” A more detailed description of “living fossil” species can be found in the book Living Fossils of [19]. Additionally, the 20 LF species satisfy the very accurate living fossil quantification system of [9]. Genome projects information can be found in Table 1.

Table 1 Living fossil (LF) vertebrate species and radiated (R) vertebrate species analyzed in this study, with genome and proteome information

The 20 LF species or taxa are as follows (common names, scientific names are found in Table 1): aardvark [20], platypus [21, 22], opossum [23, 24], elephant shrew [25], giant panda [26], koala [23, 27], Philippine tarsier [28], pelican [29], New Zealand wren [30, 31], speckled mousebird [32], red-legged seriema [33], tinamou [34], hoatzin [35,36,37], crocodilians [38], arowana [39], spotted gar [40], and coelacanth [12, 41].

The 24 R species or taxa are as follows (common names, scientific names are found in Table 1): bats [42,43,44], dolphins and whales [45, 46], lemurs [47,48,49], medium ground finch [50, 51], great tit [51], Carolina anole [52,53,54,55], black rockcod [56,57,58], and three cichlid species [59,60,61,62].

Gene and pathway analysis

Evolutionary stasis and rapid evolutionary speciation can be characterized as opposite evolutionary procedures or at least very different evolutionary phenomena. This is the first study that compares genetically those two very different categories of vertebrate species. Gene or annotation information was inadequate for most invertebrate LF or R species, so they were not included in this study.

The procedure we followed is very simple. We downloaded the annotated genome information for all 44 species. Then, we found the common genes in LF species and the common genes in R species, creating two separate gene lists (Additional file 1: Table S1). The next step was to compare the two lists to find any genes that are common in LF but not found in R species and genes that are common in R but not found in LF species. We consider that these genes may be under selection since they are found only in species with a special evolutionary profile. In total, 1534 genes were found to be specific for LF species and 2263 genes to be specific for R species.

Analysis of the two final gene lists was performed by Panther 14.1 software, under two algorithms: pathways (biological processes) and reactome. We looked for unique biological processes and reactomes in LF- and R-specific genes respectively. Using the strict criterion of FDR ≤ 0.0001, only one process/pathway was found to be significant in R-specific genes by both algorithms, this being DNA repair (DNA repair and cellular response to DNA damage; FDR = 8.35 × 10−5 and 7.15 × 10−6, respectively). Not any common significant pathways came out in the biological processes and reactome analyses for LF-specific genes. Step by step analysis and all analytical output can be found in Additional file 1: Table S1. The flowchart of analysis can be found in Table 2.

Table 2 Flowchart and main outcomes of each analysis performed in this study

DNA repair gene analysis

In order to confirm the pathway analysis results, we analyzed the 44 genomes for their content in DNA repair genes, using a list of all known DNA repair genes since presently (updated list of Wood et al. [18]). Subcategories of DNA repair genes were also considered in the analysis. Results in detail can be found in Additional file 2: Table S2. The results highly confirmed the previously performed pathway analysis (Table 3). R species’ genomes are significantly enriched in DNA repair genes (p = 5.3 × 10−3). The most significant subcategories are the nucleotide excision repair (p = 5.00 × 10−4) and base excision repair (p = 9.80 × 10−3). Many other subcategories seem to be significantly enriched in R species under the criterion of p < 0.05. Conserved DNA damage response and non-homologousend-joining are not significant at all (Table 3). A heat map diagram shows that indeed the R species’ genomes are enriched in DNA repair genes in comparison with the LF species, especially for mammals, reptiles, and birds (Fig. 1).

Table 3 Mean comparison (independent t test, two-tailed) between living fossil (LF) and radiated species (R), for each category of DNA repair genes and altogether (degrees of freedom, 42)
Fig. 1

Heat map showing the quantity of DNA repair genes, from red to blue in ascending order, per species’ genome (numbers at the top of the figure represent the species code that is found in Table 1). Each DNA repair gene pathway was analyzed separately in rows. Radiated species’ genomes are richer in DNA repair genes. Analytical data can be found in Additional file 2: Table S2. M mammals, B&R birds and reptiles, BF bony fishes

The top 20 genes with the highest existence rate in R species in relation to LF species can be found in Additional file 2: Table S2. Eleven out of the top 20 (55%) are genes related with nucleotide excision repair and base excision repair. All gene rates are available in Additional file 2: Table S2.

Genome and proteome size analysis

Interestingly, the number of DNA repair genes is linearly related with the genome size and the number of proteins (p < 1.00 × 10−4). We used genome and proteome data ( of the 44 vertebrate species (Fig. 2). The two linear associations are independently significant since genome size is not linearly related with the number of proteins (Fig. 2). It is well known that genome size is not related with organism complexity [63]; thus, we consider that this association is not due to increased complexity of large genomes. Not any association was found when genome size means of LF and R species were compared (results not shown).

Fig. 2

Linear regression analysis. The number of DNA repair genes is linearly related to genome size and protein number. As a negative control, we show that genome size is not linearly related with protein number

This result may also explain Drake’s rule. This is about the density of accumulated mutations per generation (mutagenesis rate) that is roughly inversely proportional to genome size [64,65,66]. Here, we found that larger genomes have more DNA repair genes (and possibly lower mutagenesis rate, if DNA errors are corrected at a higher rate) that may explain Drake’s rule, being unexplained for years.

Why DNA repair genes

There is evidence that LF species are evolving slower than R species. Additionally, some data show that mutagenesis and nucleotide diversity [59, 67] may be higher in R species than in LF species and that some R species with huge bodies (whales) have duplicated DNA repair genes to be protected by cancer [68, 69]. According to these data, we could hypothesize that R species may be at risk due to high mutation load. This could be balanced with more DNA repair genes, repairing as much DNA damages as possible. It seems that DNA repair at the nucleotide level (nucleotide excision repair and base excision repair) is more important than other DNA repair pathways (Table 3, Additional file 2: Table S2). Another explanation is that LF species are probably more protected from spontaneous DNA changes since due to the vast evolutionary time that they exist, stabilizing selection has formed their genome in a way that they are protected from random DNA changes that could change their general morphological features. Certain genes in LF genomes may act in a canalizing way that keeps these species in a narrow state of development and evolution since they are evolutionary successful. R species are not characterized by those features, and probably they need more or certain DNA repair genes to continue to diversify under a non-deleterious mutagenesis rate. We could consider that this is the first evidence for genes related with punctuated equilibrium evolution (long evolutionary stasis followed by short speciation explosions) [14, 15].

The fact that the number of DNA repair genes is related with the genome and proteome size is quite logical since larger genomes need more protection from spontaneous mutagenesis. This is the first time that a class of genes has been associated with genome size and number of proteins in animals.


A big number of genomes have been compared under the prism of evolutionary stasis and adaptive radiation. The analysis concluded that DNA repair genes might play a previously unknown significant role in evolution. It seems that more DNA repair genes are found in vertebrate taxa that have experienced recent adaptive radiation. Additionally, DNA repair genes were found to be statistically associated with the genome size and protein number in vertebrates. DNA repair genes are considered as tumor suppressor genes. There is evidence that tumor suppressor genes are related to environmental adaptation in humans [70, 71] and selective pressures along the evolution of mammals [72]. We can imagine that certain evolutionary procedures may be DNA repair-dependent, this showing the way for future analyses and experiments.

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Change history

  • 02 July 2019

    In the original publication of this article [1], the Figure 1 and Figure 2 were wrong. The Figure 1 “Heat map showing the quantity of DNA repair genes, from red to blue in ascending order, per species’ genome (numbers at the top of the figure represent the species code that is found in Table 1). Each DNA repair gene pathway was analyzed separately in rows. Radiated species’ genomes are richer in DNA repair genes. Analytical data can be found in Additional file 2: Table S2. M mammals, B&R birds and reptiles, BF bony fishes” should be the picture of Figure 2. The figure 2 “Linear regression analysis. The number of DNA repair genes is linearly related to genome size and protein number. As a negative control, we show that genome size is not linearly related with protein number” should be the picture of figure 1.


  1. 1.

    Barlow N. The voyage of the beagle. Nature. 1932;129(3255):439.

    Google Scholar 

  2. 2.

    Emerson BC. Evolution on oceanic islands: molecular phylogenetic approaches to understanding pattern and process. Mol Ecol. 2002;11(6):951–66.

    CAS  PubMed  Google Scholar 

  3. 3.

    Gavrilets S, Losos JB. Adaptive radiation: contrasting theory with data. Science. 2009;323(5915):732–7.

    CAS  PubMed  Google Scholar 

  4. 4.

    Ecology. Book review: the ecology of adaptive radiation. Ecology. 2002;83(2):591–2.

    Google Scholar 

  5. 5.

    Simões M, Breitkreuz L, Alvarado M, Baca S, Cooper JC, Heins L, et al. The evolving theory of evolutionary radiations. Trends Ecol Evol. 2016;31(1):27–34.

    PubMed  Google Scholar 

  6. 6.

    Kapralov MV, Votintseva AA, Filatov DA. Molecular adaptation during a rapid adaptive radiation. Mol Biol Evol. 2013;30(5):1051–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Zhou T-C, Irwin DM, Shen Y-Y, Zhang Y-P, Liang L, Pan X-W, et al. Adaptive evolution of the Hox gene family for development in bats and dolphins. PLoS One. 2013;8(6):e65944.

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Combosch DJ, Lemer S, Ward PD, Landman NH, Giribet G. Genomic signatures of evolution in nautilus—an endangered living fossil. Mol Ecol. 2017;26(21):5923–38.

    PubMed  Google Scholar 

  9. 9.

    Bennett DJ, Sutton MD, Turvey ST. Quantifying the living fossil concept. Palaeontol Electron. 2018;21(1):1–25.

  10. 10.

    Kin A, Błazejowski B. The horseshoe crab of the genus Limulus: living fossil or stabilomorph? PLoS One. 2014;9(10):e108036.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Mans BJ, de Klerk D, Pienaar R, Latif AA. Nuttalliella namaqua: a living fossil and closest relative to the ancestral tick lineage: implications for the evolution of blood-feeding in ticks. PLoS One. 2011;6(8):e23675.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Schmutz J, Miyake T, Powers TP, Ruddle FH, Myers RM, Grimwood J, et al. Complete HOX cluster characterization of the coelacanth provides further evidence for slow evolution of its genome. Proc Natl Acad Sci. 2010;107(8):3622–7.

    PubMed  Google Scholar 

  13. 13.

    Hunt G, Hopkins MJ, Lidgard S. Simple versus complex models of trait evolution and stasis as a response to environmental change. Proc Natl Acad Sci. 2015;112(16):4885–90.

    CAS  PubMed  Google Scholar 

  14. 14.

    Gould SJ, Eldredge N. Punctuated equilibria: the tempo and mode of evolution reconsidered. Paleobiology. 1977;2:115–51.

    Google Scholar 

  15. 15.

    Bitterman ME. Phyletic differences in learning. Am Psychol. 1965;20:396–410.

    CAS  PubMed  Google Scholar 

  16. 16.

    Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, et al. PANTHER version 11: expanded annotation data from gene ontology and reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 2017;45(D1):D183–9.

    CAS  PubMed  Google Scholar 

  17. 17.

    Mi H, Muruganujan A, Huang X, Ebert D, Mills C, Guo X, Thomas PD. Protocol update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nat Protoc. 2019;14(3):703–21.

    CAS  PubMed  Google Scholar 

  18. 18.

    Wood RD, Mitchell M, Lindahl T. Human DNA repair genes, 2005. Mutat Res. 2005;577(1–2):275–83.

    CAS  PubMed  Google Scholar 

  19. 19.

    Simon C. Living fossils. Eldredge N, Stanley SM, editors. Science news 2007;121(17):284.

  20. 20.

    Perelman PL, Yang F, Robinson TJ, Harrison WR, Graphodatsky AS, Pardini AT, et al. Reciprocal chromosome painting among human, aardvark, and elephant (superorder Afrotheria) reveals the likely eutherian ancestral karyotype. Proc Natl Acad Sci. 2003;100(3):1062–6.

    PubMed  Google Scholar 

  21. 21.

    Musser AM. Review of the monotreme fossil record and comparison of palaeontological and molecular data. Comp Biochem Physiol A Mol Integr Physiol. 2003;136(4):927–42.

    CAS  PubMed  Google Scholar 

  22. 22.

    Warren WC, Hillier LDW, Marshall Graves JA, Birney E, Ponting CP, Grützner F, et al. Genome analysis of the platypus reveals unique signatures of evolution. Nature. 2008;453(7192):175–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Archer M, Beck RMD, Hand SJ, Weisbecker V, Godthelp H. Australia’s oldest marsupial fossils and their biogeographical implications. PLoS One. 2008;3(3):e1858.

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Palma RE, Spotorno AE. Molecular systematics of marsupials based on the rRNA 12S mitochondrial gene: the phylogeny of Didelphimorphia and of the living fossil Microbiotheriid Dromiciops gliroides Thomas. Mol Phylogenet Evol. 1999;13(3):525–35.

    CAS  PubMed  Google Scholar 

  25. 25.

    Robinson TJ, Fu B, Ferguson-Smith MA, Yang F. Cross-species chromosome painting in the golden mole and elephant-shrew: support for the mammalian clades Afrotheria and Afroinsectiphillia but not Afroinsectivora. Proc R Soc B Biol Sci. 2004;271(1547):1477–84.

    CAS  Google Scholar 

  26. 26.

    Hunt RM, Jaeger M, Zhu Q, Dong W, Liu J, Ciochon RL, et al. The first skull of the earliest giant panda. Proc Natl Acad Sci. 2007;104(26):10932–7.

    PubMed  Google Scholar 

  27. 27.

    Black KH, Archer M, Hand SJ, Godthelp H. First comprehensive analysis of cranial ontogeny in a fossil marsupial-from a 15-million-year-old cave deposit in northern Australia. J Vertebr Paleontol. 2010;30(4):993–1011.

    Google Scholar 

  28. 28.

    Rossie JB, Ni X, Beard KC. Cranial remains of an Eocene tarsier. Proc Natl Acad Sci USA. 2006;103(12):4381–5.

    CAS  Google Scholar 

  29. 29.

    Louchart A, Tourment N, Carrier J. The earliest known pelican reveals 30 million years of evolutionary stasis in beak morphology. J Ornithol. 2011;152(1):15–20.

    Google Scholar 

  30. 30.

    Taylor Smith BL, McComish BJ, Hartig G, England R, Penny D, McLenachan PA (Trish), et al. New Zealand passerines help clarify the diversification of major songbird lineages during the Oligocene. Genome Biol Evol 2015;7(11):2983–2995.

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Worthy TH, Hand SJ, Nguyen JMT, Tennyson AJD, Worthy JP, Scofield RP, et al. Biogeographical and phylogenetic implications of an early Miocene wren (Aves: Passeriformes: Acanthisittidae) from New Zealand. J Vertebr Paleontol. 2010;30(2):479–98.

    Google Scholar 

  32. 32.

    Ksepka DT, Stidham TA, Williamson TE. Early Paleocene landbird supports rapid phylogenetic and morphological diversification of crown birds after the K–Pg mass extinction. Proc Natl Acad Sci. 2017;114(30):8047–52.

    CAS  PubMed  Google Scholar 

  33. 33.

    Mayr G, Noriega J. A well-preserved partial skeleton of the poorly known early Miocene seriema Noriegavis santacrucensis (Aves, Cariamidae). Acta Palaeontol Pol. 2013;60(3):589–98.

  34. 34.

    Mitchell KJ, Llamas B, Soubrier J, Rawlence NJ, Worthy TH, Wood J, et al. Ancient DNA reveals elephant birds and kiwi are sister taxa and clarifies ratite bird evolution. Science. 2014;344(6186):898–900.

    CAS  PubMed  Google Scholar 

  35. 35.

    Mayr G, Alvarenga H, Mourer-Chauviré C. Out of Africa: fossils shed light on the origin of the hoatzin, an iconic Neotropic bird. Naturwissenschaften. 2011;98(11):961–6.

    CAS  PubMed  Google Scholar 

  36. 36.

    Miller AH. A fossil hoatzin from the Miocene of Colombia. Auk. 2012;70(4):484–9.

    Google Scholar 

  37. 37.

    Mayr G, De Pietri VL. Earliest and first Northern Hemispheric hoatzin fossils substantiate Old World origin of a “neotropic endemic”. Naturwissenschaften. 2014;101(2):143–8.

    CAS  PubMed  Google Scholar 

  38. 38.

    Jarvis ED, McCormack J, Ray DA, Ramakodi MP, Lyons E, McCarthy FM, et al. Three crocodilian genomes reveal ancestral patterns of evolution among archosaurs. Science. 2014;346(6215):1254449.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Tan MH, Austin CM, Hammer MP, Gan HM, Croft LJ. Whole genome sequencing of the Asian Arowana (Scleropages formosus ) provides insights into the evolution of Ray-finned fishes. Genome Biol ution. 2015;7(10):2885–95.

    Google Scholar 

  40. 40.

    Sun Y, Volff J-N, Venkatesh B, Ravi V, Holland PWH, Barrell D, et al. The spotted gar genome illuminates vertebrate evolution and facilitates human-teleost comparisons. Nat Genet. 2016;48(4):427–37.

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Qiao T, Lu J, Jia L, Zhu M, Zhao W, Yu X. Earliest known coelacanth skull extends the range of anatomically modern coelacanths to the early Devonian. Nat Commun. 2012;3(1):1–8.

  42. 42.

    Monteiro LR, Nogueira MR. Evolutionary patterns and processes in the radiation of phyllostomid bats. BMC Evol Biol. 2011;11(1):1–23.

  43. 43.

    Jones KE, Bininda-Emonds ORP, Gittleman JL. Bats, clocks, and rocks: diversification patterns in Chiroptera. Evolution. 2006;59(10):2243.

    Google Scholar 

  44. 44.

    Shi JJ, Rabosky DL. Speciation dynamics during the global radiation of extant bats. Evolution. 2015;69(6):1528–45.

    PubMed  Google Scholar 

  45. 45.

    Nielsen R, Ho SYW, Rabosky DL, Fordyce RE, Steeman ME, Willerslev E, et al. Radiation of extant cetaceans driven by restructuring of the oceans. Syst Biol. 2009;58(6):573–85.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Thewissen JGM, Williams EM. The early radiations of Cetacea (Mammalia): evolutionary pattern and developmental correlations. Annu Rev Ecol Syst. 2002;33(1):73–90.

    Google Scholar 

  47. 47.

    Martin RD. Adaptive radiation and behaviour of the Malagasy lemurs. Philos Trans R Soc Lond Ser B Biol Sci. 1972;264(862):295–352.

    CAS  Google Scholar 

  48. 48.

    Thalmann U. Biodiversity, phylogeography, biogeography and conservation: lemurs as an example. Folia Primatol. 2007;78(5–6):420–43.

    PubMed  Google Scholar 

  49. 49.

    Herrera JP. Testing the adaptive radiation hypothesis for the lemurs of Madagascar. R Soc Open Sci. 2017;4(1):1–12.

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Tebbich S, Stereln K, Teschke I. The tale of the finch: adaptive radiation and behavioural flexibility. Philos Trans R Soc B Biol Sci. 2010;365(1543):1099–109.

    Google Scholar 

  51. 51.

    Cooney CR, Bright JA, Capp EJR, Chira AM, Hughes EC, Moody CJA, et al. Mega-evolutionary dynamics of the adaptive radiation of birds. Nature. 2017;542(7641):344–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Pinto G, Mahler DL, Harmon LJ, Losos JB. Testing the island effect in adaptive radiation: rates and patterns of morphological diversification in Caribbean and mainland Anolis lizards. Proc R Soc B Biol Sci. 2008;275(1652):2749–57.

    Google Scholar 

  53. 53.

    Yoder JB, Clancey E, Des Roches S, Eastman JM, Gentry L, Godsoe W, et al. Ecological opportunity and the origin of adaptive radiations. J Evol Biol. 2010;23(8):1581–96.

    CAS  PubMed  Google Scholar 

  54. 54.

    Surget-Groba Y. Lizards in an evolutionary tree: ecology and adaptive radiation of anoles. Amphibia-Reptilia. 2010;32(1):141–2.

    Google Scholar 

  55. 55.

    Joseph T. Ecology and evolution of Darwin’s finches. J Evol Biol. 1986;1(3):281–3.

    Google Scholar 

  56. 56.

    Hu Y, Ghigliotti L, Vacchi M, Pisano E, Detrich HW, Albertson RC. Evolution in an extreme environment: developmental biases and phenotypic integration in the adaptive radiation of antarctic notothenioids. BMC Evol Biol. 2016;16(1):142.

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Matschiner M, Hanel R, Salzburger W. On the origin and trigger of the notothenioid adaptive radiation. PLoS One. 2011;6(4):e18911.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Colombo M, Damerau M, Hanel R, Salzburger W, Matschiner M. Diversity and disparity through time in the adaptive radiation of Antarctic notothenioid fishes. J Evol Biol. 2015;28(2):376–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Galibert F, Fan S, Sanchez-Pulido L, Nikaido M, Przybylski D, Simakov O, et al. The genomic substrate for adaptive radiation in African cichlid fish. Nature. 2014;513(7518):375–81.

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Seehausen O. African cichlid fish: a model system in adaptive radiation research. Proc R Soc B Biol Sci. 2006;273(1597):1987–98.

    Google Scholar 

  61. 61.

    Takahashi T, Koblmüller S. The adaptive radiation of cichlid fish in Lake Tanganyika: a morphological perspective. Int J Evol Biol. 2011;2011:1–14.

    Google Scholar 

  62. 62.

    Kocher TD. Adaptive evolution and explosive speciation: the cichlid fish model. Nat Rev Genet. 2004;5(4):288–98.

    CAS  PubMed  Google Scholar 

  63. 63.

    Patrushev LI, Minkevich IG. The problem of the eukaryotic genome size. Biochem Mosc. 2009;73(13):1519–52.

    Google Scholar 

  64. 64.

    Drake JW. A constant rate of spontaneous mutation in DNA-based microbes. Proc Natl Acad Sci. 2006;88(16):7160–4.

    Google Scholar 

  65. 65.

    Yamaguchi O, Mukai T. Variation of spontaneous occurrence rates of chromosomal aberrations in the second chromosomes of Drosophila melanogaster. Genetics. 1974;78(4):1209–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Sung W, Lynch M, Miller SF, Doak TG, Ackerman MS. Drift-barrier hypothesis and mutation-rate evolution. Proc Natl Acad Sci. 2012;109(45):18488–92.

    CAS  PubMed  Google Scholar 

  67. 67.

    Rubin C-J, Berglund J, Grabherr M, Martinez-Barrio A, Wang C, Webster MT, et al. Evolution of Darwin’s finches and their beaks revealed by genome sequencing. Nature. 2015;518(7539):371–5.

    PubMed  Google Scholar 

  68. 68.

    Tollis M, Robbins J, Webb AE, Kuderna LFK, Caulin AF, Garcia JD, et al. Return to the sea, get huge, beat cancer: an analysis of cetacean genomes including an assembly for the humpback whale (Megaptera novaeangliae). Mol Biol Evol. 2019.

  69. 69.

    Lander ES, Haussler D, Castoe TA, Glor RE, Organ CL, Grabherr M, et al. The genome of the green anole lizard and a comparative analysis with birds and mammals. Nature. 2011;477(7366):587–91.

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Voskarides K. Group selection may explain cancer predisposition and other human traits’ evolution. J Mol Evol. 2018;86(3–4):184–6.

    CAS  PubMed  Google Scholar 

  71. 71.

    Voskarides K. Combination of 247 genome-wide association studies reveals high cancer risk as a result of evolutionary adaptation. Mol Biol Evol. 2018;35(2):473–85.

    CAS  PubMed  Google Scholar 

  72. 72.

    Vicens A, Posada D. Selective pressures on human cancer genes along the evolution of mammals. Genes. 2018;9(12):1–13.

    PubMed Central  Google Scholar 

  73. 73.

    Martin HC, Batty EM, Hussin J, Westall P, Daish T, Kolomyjec S, et al. Insights into platypus population structure and history from whole-genome sequencing. Mol Biol Evol. 2018;35(5):1238–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Mikkelsen TS, Wakefield MJ, Aken B, Amemiya CT, Chang JL, Duke S, et al. Genome of the marsupial Monodelphis domestica reveals innovation in non-coding sequences. Nature. 2007;447(7141):167–77.

  75. 75.

    Li R, Fan W, Tian G, Zhu H, He L, Cai J, et al. The sequence and de novo assembly of the giant panda genome. Nature. 2010;463(7279):311–7.

    CAS  PubMed  Google Scholar 

  76. 76.

    Shan L, Nie Y, Hu Y, Wang X, Xiu Y, Ma T, et al. Comparative genomics reveals convergent evolution between the bamboo-eating giant and red pandas. Proc Natl Acad Sci. 2017;114(5):1081–6.

    PubMed  Google Scholar 

  77. 77.

    Johnson RN, O’Meally D, Chen Z, Etherington GJ, Ho SYW, Nash WJ, et al. Adaptation and conservation insights from the koala genome. Nat Genet. 2018;50(8):1102–1111. Available from:

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Voss R, Clawson H, Warren WC, Noll A, Minx P, Churakov G, et al. Genome sequence of the basal haplorrhine primate Tarsius syrichta reveals unusual insertions. Nat Commun. 2016;7(1):1–11.

  79. 79.

    Moskalev AA, Lee S-G, Lyapunov AN, Zhu Y, Sun Y, Chen G, et al. Genome analysis reveals insights into physiology and longevity of the Brandt’s bat Myotis brandtii. Nat Commun. 2013;4(1):1–8.

  80. 80.

    Zhang G, Cowled C, Shi Z, Huang Z, Bishop-Lilly KA, Fang X, et al. Comparative analysis of bat genomes provides insight into the evolution of flight and immunity. Science. 2013;339(6118):456–60.

    CAS  PubMed  Google Scholar 

  81. 81.

    Pavlovich SS, Lovett SP, Koroleva G, Guito JC, Arnold CE, Nagle ER, et al. The Egyptian rousette genome reveals unexpected features of bat antiviral immunity. Cell. 2018;173(5):1098–1110.e18.

    CAS  PubMed  Google Scholar 

  82. 82.

    Lei M, Pang E, Mu S, Hua P, Zheng G, Dong D, et al. The genomes of two bat species with long constant frequency echolocation calls. Mol Biol Evol. 2016;34(1):20–34.

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Jaffe DB, Massingham T, Clawson H, Zuk O, Kheradpour P, Wen J, et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature. 2011;478(7370):476–82.

    PubMed  PubMed Central  Google Scholar 

  84. 84.

    Eckalbar WL, Schlebusch SA, Mason MK, Gill Z, Parker AV, Booker BM, et al. Transcriptomic and epigenomic characterization of the developing bat wing. Nat Genet. 2016;48(5):528–36.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Alföldi J, Mancia A, Qin X, Gilbert MTP, Liu Y, Vinař T, et al. Convergent evolution of the genomes of marine mammals. Nat Genet. 2015;47(3):272–5.

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Kim HW, Vijay N, Park C, Jin S, Zhang J, Park J-K, et al. Population genomic analysis reveals contrasting demographic changes of two closely related dolphin species in the last glacial. Mol Biol Evol. 2018;35(8):2026–33.

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Yim HS, Cho YS, Guang X, Kang SG, Jeong JY, Cha SS, et al. Minke whale genome and aquatic adaptation in cetaceans. Nat Genet. 2014;46(1):88–92.

    CAS  PubMed  Google Scholar 

  88. 88.

    Malde K, Seliussen BB, Quintela M, Dahle G, Besnier F, Skaug HJ, et al. Whole genome resequencing reveals diagnostic markers for investigating global migration and hybridization between minke whale species. BMC Genomics. 2017;18(1):1–11.

  89. 89.

    Foote AD, Vijay N, Ávila-Arcos MC, Baird RW, Durban JW, Fumagalli M, et al. Genome-culture coevolution promotes rapid divergence of killer whale ecotypes. Nat Commun. 2016;7:11693.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Moura AE, Van Rensburg CJ, Pilot M, Tehrani A, Best PB, Thornton M, et al. Killer whale nuclear genome and mtdna reveal widespread population bottleneck during the last glacial maximum. Mol Biol Evol. 2014;31(5):1121–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Zhou X, Sun F, Xu S, Fan G, Zhu K, Liu X, et al. Baiji genomes reveal low genetic variability and new insights into secondary aquatic adaptations. Nat Commun. 2013;4:2708.

    PubMed  PubMed Central  Google Scholar 

  92. 92.

    Warren R, Marra M, Li I, Troussard A, Taylor G, Chan A, et al. The genome of the beluga whale (Delphinapterus leucas). Genes. 2017;8(12):378.

    PubMed Central  Google Scholar 

  93. 93.

    Fjeldsa J, Burge SW, Campos PF, Johnson WE, Schubert M, Jarvis ED, et al. Comparative genomics reveals insights into avian genome evolution and adaptation. Science. 2014;346(6215):1311–20.

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Chong AY, Braun EL, Castoe TA, Iguchi T, Khan S, Isberg SR, et al. Sequencing three crocodilian genomes to illuminate the evolution of archosaurs and amniotes. Genome Biol. 2013;13(1):415.

    Google Scholar 

  95. 95.

    Wan QH, Pan SK, Hu L, Zhu Y, Xu PW, Xia JQ, et al. Genome analysis and signature discovery for diving and sensory properties of the endangered Chinese alligator. Cell Res. 2013;23(9):1091–105.

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Webster MT, Han F, Grant PR, Grant BR, Andersson L, Lamichhaney S. Rapid hybrid speciation in Darwin’s finches. Science. 2017;359(6372):224–8.

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Veronika N. Laine, Toni I. Gossmann, Kyle M. Schachtschneider, Colin J. Garroway, Ole Madsen, Koen J. F. Verhoeven, Victor de Jager, Hendrik-Jan Megens, Wesley C. Warren, Patrick Minx, Richard P. M. A. Evolutionary signals of selection on cognition from the great tit genome and methylome. Nat Commun. 2016;7(1):1–9.

  98. 98.

    Qu Y, Tian S, Han N, Zhao H, Gao B, Fu J, et al. Genetic responses to seasonal variation in altitudinal stress: whole-genome resequencing of great tit in eastern Himalayas. Sci Rep. 2015;5:14256.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Yu H, Kabilov M, Zhao X, Peng C, You X, Wang J, et al. The Asian arowana (Scleropages formosus) genome provides new insights into the evolution of an early lineage of teleosts. Sci Rep. 2016;6(1):1–17.

  100. 100.

    Li J, Bian C, Hu Y, Mu X, Shen X, Ravi V, et al. A chromosome-level genome assembly of the Asian arowana, Scleropages formosus. Sci Data. 2016;3:160105.

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Nikaido M, Noguchi H, Nishihara H, Toyoda A, Suzuki Y, Kajitani R, et al. Coelacanth genomes reveal signatures for evolutionary transition from water to land. Genome Res. 2013;23(10):1740–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Christoffels A, Przybylski D, Lander ES, Litman GW, Schartl M, Aken B, et al. The African coelacanth genome provides insights into tetrapod evolution. Nature. 2013;496(7445):311–6.

    PubMed  PubMed Central  Google Scholar 

  103. 103.

    Shin SC h, Ahn DH w, Kim SJ i, Pyo CW o, Lee H, Kim MK, et al. The genome sequence of the Antarctic bullhead notothen reveals evolutionary adaptations to a cold environment. Genome Biol. 2014;15(9):468.

    PubMed  PubMed Central  Google Scholar 

  104. 104.

    Conte MA, Kocher TD. An improved genome reference for the African cichlid, Metriaclima zebra. BMC Genomics. 2015;16(1):1–13.

  105. 105.

    Meier JI, Marques DA, Wagner CE, Excoffier L, Seehausen O. Genomics of parallel ecological speciation in Lake Victoria cichlids. Mol Biol Evol. 2018;35(6):1489–506.

    CAS  PubMed  Google Scholar 

  106. 106.

    Baldo L, Santos ME, Salzburger W. Comparative transcriptomics of eastern African cichlid fishes shows signs of positive selection and a large contribution of untranslated regions to genetic diversity. Genome Biol Evol. 2011;3(1):443–55.

    PubMed  Google Scholar 

Download references


Not applicable


Not any funding exists for this research.

Author information




KV conceived the study idea, analyzed and interpreted most of the data of this study, and prepared the first draft of the paper. HD contributed to genomic data retrieval and proofread the paper. CC performed a custom-made bioinformatics algorithm for the initial comparative genomics data analysis and proofread the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Konstantinos Voskarides.

Ethics declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original version of this article was revised: “The Figure 1 and Figure 2 were wrong. The Figure 1 “Heat map showing the quantity of DNA repair genes, from red to blue in ascending order, per species’ genome (numbers at the top of the figure represent the species code that is found in Table 1). Each DNA repair gene pathway was analyzed separately in rows. Radiated species’ genomes are richer in DNA repair genes. Analytical data can be found in Additional file 2: Table S2. M mammals, B&R birds and reptiles, BF bony fishes” should be the picture of Figure 2. The figure 2 “Linear regression analysis. The number of DNA repair genes is linearly related to genome size and protein number. As a negative control, we show that genome size is not linearly related with protein number” should be the picture of figure 1.”

Additional files

Additional file 1:

Table S1.Pathway analysis by PANTHER (XLSX 137 kb)

Additional file 2:

Table S2. DNA repair gene analysis (XLSX 95 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Voskarides, K., Dweep, H. & Chrysostomou, C. Evidence that DNA repair genes, a family of tumor suppressor genes, are associated with evolution rate and size of genomes. Hum Genomics 13, 26 (2019).

Download citation


  • Genomics
  • Evolutionary genetics
  • Natural selection
  • Rapid evolution
  • Speciation
  • Mutagenesis rate
  • Evolutionary medicine
  • Molecular evolution