Reconstructing ancestral character states is an exciting but difficult problem. The fossil record carries a great deal of information, but it is incomplete and not always easy to connect to data from modern species. Alternatively, ancestral states can be estimated by modelling trait evolution across a phylogeny, and fitting to values observed in extant species. This approach, however, is heavily dependent on the underlying assumptions, and typically results in wide confidence intervals.

An alternative approach is to gain information on ancestral character states from DNA sequence data. This can be done directly when the trait of interest is known to be determined by a single, or a small number, of major effect genes. In some of these cases it can even be possible to investigate an ancestral trait of interest by inferring and resurrecting ancestral sequences in the laboratory. Examples where this has been successfully used to address evolutionary questions range from the nocturnality of early mammals [1], to the loss of functional uricases in primates, leading to high rates of gout, obesity and hypertension in present day humans [2]. Another possibility is to rely on correlations between species traits and the genome average substitution rate/process. For instance, it is well established that the ratio of nonsynonymous to synonymous substitution rate, dN/dS, is generally higher in large than in small species of mammals, presumably due to a reduced effective population size in the former. By estimating ancestral dN/dS, one can therefore gain information on ancestral body mass (e.g. [3-4]).

The interesting paper by Wu et al. [5] further develops this second possibility of incorporating information on rate variation derived from genomic data in the estimation of ancestral traits. The authors analyse a large set of 1185 genes in 89 species of mammals, without any prior information on gene function. The substitution rate is estimated for each gene and each branch of the mammalian tree, and taken as an indicator of the selective constraint applying to a specific gene in a specific lineage – more constraint, slower evolution. Rate variation is modelled as resulting from a gene effect, a branch effect, and a gene X branch interaction effect, which captures lineage-specific peculiarities in the distribution of functional constraint across genes. The interaction term in terminal branches is regressed to observed trait values, and the relationship is used to predict ancestral traits from interaction terms in internal branches. The power and accuracy of the estimates are convincingly assessed via cross validation. Using this method, the authors were also able to use an unbiased approach to determine which genes were the main contributors to the evolution of the life-history traits they reconstructed.

The ancestors to current placental mammals are predicted to have been insectivorous - meaning that the estimated distribution of selective constraint across genes in basal branches of the tree resembles that of extant insectivorous taxa - consistent with the mainstream palaeontological hypothesis. Another interesting result is the prediction that only nocturnal lineages have passed the Cretaceous/Tertiary boundary, so that the ancestors of current orders of placentals would all have been nocturnal. This suggests that the so-called "nocturnal bottleneck hypothesis" should probably be amended. Similar reconstructions are achieved for seasonality, sociality and monogamy – with variable levels of uncertainty.

The beauty of the approach is to analyse the variance, not only the mean, of substitution rate across genes, and their methods allow for the identification of the genes contributing to trait evolution without relying on functional annotations. This paper only analyses discrete traits, but the framework can probably be extended to continuous traits as well.

References

[1] Bickelmann C, Morrow JM, Du J, Schott RK, van Hazel I, Lim S, Müller J, Chang BSW, 2015. The molecular origin and evolution of dim-light vision in mammals. Evolution 69: 2995-3003. doi: https://doi.org/10.1111/evo.12794

[2] Kratzer, JT, Lanaspa MA, Murphy MN, Cicerchi C, Graves CL, Tipton PA, Ortlund EA, Johnson RJ, Gaucher EA, 2014. Evolutionary history and metabolic insights of ancient mammalian uricases. Proceedings of the National Academy of Science, USA 111:3763-3768. doi: https://doi.org/10.1073/pnas.1320393111

[3] Lartillot N, Delsuc F. 2012. Joint reconstruction of divergence times and life-history evolution in placental mammals using a phylogenetic covariance model. Evolution 66:1773-1787. doi: https://doi.org/10.1111/j.1558-5646.2011.01558.x

[4] Romiguier J, Ranwez V, Douzery EJ, Galtier N. 2013. Genomic evidence for large, long-lived ancestors to placental mammals. Molecular Biology and Evolution 30:5-13. doi: https://doi.org/10.1093/molbev/mss211

[5] Wu J, Yonezawa T, Kishino H. 2016. Rates of Molecular Evolution Suggest Natural History of Life History Traits and a Post-K-Pg Nocturnal Bottleneck of Placentals. Current Biology 27: 3025-3033. doi: https://doi.org/10.1016/j.cub.2017.08.043

Sterols and hopanoids are sometimes used as biomarkers to infer the origin of certain groups of organisms. Traditionally, hopanoid-derived products in ancient rocks have been considered to indicate the presence of bacteria, whereas sterol derivatives have been considered to be exclusive to eukaryotes. However, a closer look at the topic reveals a rather complex distribution of either compound in both bacteria and eukaryotes. (1). The known biosynthetic pathways for sterols and hopanoids are similar but diverge at a critical step where two different enzymes are used: squalene-hopene cyclase (SHC) and oxidosqualene cyclase (OSC), the latter requiring oxygen. These two enzymes belong to the same gene family, whose complex evolutionary history is difficult to reconcile with the known species phylogeny.

In this study (2), Dr. Warren R. Francis revisits the evolution of this gene family using an extended dataset with a broader taxonomic representation. In contrast to the traditional representation of the tree rooted between SHC and OSC paralogs (i.e., based on function), the author proposes that rooting the tree within bacterial SHCs and assuming a secondary origin of OSC is more parsimonious. This postulates SHC to be the ancestral function –retained in many extant bacteria and some eukaryotes– and OSC to have emerged later within bacteria –currently being mostly present in eukaryotes–. The reconstructed evolutionary history is arguably complex and can only be reconciled with the species' phylogeny by invoking many secondary losses. These losses are considered likely because many extant species acquire sterols and hopanoids by diet and lack one or both enzymes. Some cases of recent horizontal gene transfer are also proposed.

In contrast to the dichotomy between bacterial SHCs and eukaryote OSCs, the new proposed scenario suggests that the eukaryote ancestor likely inherited both enzymes from bacteria and thus could be able to synthesize both sterols and hopanoids. Under this hypothesis, not only bacteria but also eukaryotes could be responsible for the hopane found in old rocks. This agrees with eukaryote fossils dating back to more than 1 billion years ago (3). Also, the observed increase of sterane levels in rocks ~600-700 million years old cannot be associated with the origin of eukaryotes, which is a much older event, but could rather reflect changes in atmospheric oxygen levels because oxygen is required for the synthesis of sterols by OSC.

References

1. Santana-Molina C, Rivas-Marin E, Rojas AM, Devos DP (2020) Origin and Evolution of Polycyclic Triterpene Synthesis. Molecular Biology and Evolution, 37, 1925–1941. https://doi.org/10.1093/molbev/msaa054

2. Francis WR (2022) The Eukaryotic Last Common Ancestor Was Bifunctional for Hopanoid and Sterol Production. Preprints, 2020040186, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology.  https://doi.org/10.20944/preprints202004.0186.v5

3. Butterfield NJ (2000) Bangiomorpha pubescens n. gen., n. sp.: implications for the evolution of sex, multicellularity, and the Mesoproterozoic/Neoproterozoic radiation of eukaryotes. Paleobiology, 26, 386–404.  https://doi.org/10.1666/0094-8373(2000)026<0386:BPNGNS>2.0.CO;2

Antimalarial resistance is a major hurdle to malaria eradication efforts. The spread of drug resistance follows basic evolutionary principles, with competitive interactions between resistant and susceptible malaria strains being central to the fitness of resistant parasites. These competitive interactions can be used to design resistance management strategies, whereby a fitness cost of resistant parasites can be exploited through maintaining competitive suppression of the more fit drug-susceptible parasites. This can potentially be achieved using lower drug dosages or lower frequency of drug treatments. This approach has been demonstrated to work empirically in a rodent malaria model [1,2] and has been demonstrated to have clinical success in cancer treatments [3]. However, these resistance management approaches assume a fitness cost of the resistant pathogen, and, in the case of malaria parasites in general, and for artemisinin resistant parasites in particular, there is limited information on the presence of such fitness cost. The best suggestive evidence for the presence of fitness costs comes from the discontinuation of the use of the drug, which, in the case of chloroquine, was followed by a gradual drop in resistance frequency over the following decade [see e.g. 4,5]. However, with artemisinin derivative drugs still in use, alternative ways to study the presence of fitness costs need to be undertaken. 
There are several good in vitro studies demonstrating artemisinin resistant parasites being competitively suppressed by wildtype parasites [see e.g. 6–9], however, these have the limitation that they will only be able to detect the fitness cost during the blood stage of the infection and in an artificial environment. So far, there have not been animal models that have thoroughly studied the presence of resistance fitness costs for artemisinin resistant parasites. Moreover, in these types of studies, the focus is mostly on the fitness cost as detected in the vertebrate host. However, malaria parasites spent a significant portion of their life cycle in the mosquito host, where fitness costs could also be expressed. Overall, it is the fitness over the entire life cycle of the parasite that would determine if and to what extent a reduction in resistance frequency would be observed when the use of a drug is stopped. 
Here, Villa and colleagues present a study to quantify such fitness cost of artesunate-resistant parasites, not only in a vertebrate host, but also in the mosquito vector [10]. They used the underutilized model system of avian malaria species Plasmodium relictum in canaries. Villa and colleagues selected for several different resistance strains, which had a similar delayed clearance phenotype as observed in the field. Interestingly, they did not find evidence of a fitness cost in the vertebrate host. In fact, the resistant strains reached greater parasitaemia than the susceptible strains. From this set of experiments it is unclear whether this is an anomaly or a relevant result. Future work should establish this, though fitness benefits associated with drug resistance have been seen before in Leishmania parasites [11]. An important caveat to the present study is that the parasites were grown in the absence of competition and it is feasible that a cost is not detected when growing by themselves, but would become apparent when in competition. However, these types of experiments are technologically more challenging to perform as it would require an accurate quantification methodology able to distinguish based on one SNP. This problem has been circumvented by either using relative peak height in sanger sequencing [12], or via the likely more accurate route of pyrosequencing [7–9], though these methodologies only give relative frequencies rather than absolute densities. 
 
The most interesting observation in the study by Villa et al is that the authors detected a fitness cost being played out in the mosquito vector, where the resistant strains had a decreased infectivity compared to the susceptible strain. This finding is important because 1) it demonstrates that the whole life cycle needs to be taken into account when understanding fitness costs, 2) resistance management strategies that are based on treatment within the vertebrate host may not have the intended effect if the cost does not play out in this host, and 3) it opens new research avenues to explore the possibility of exploiting fitness costs in mosquito vector. Future research should focus on incorporating these assays on fitness costs in mosquitoes, particularly for P. falciparum parasites. Additionally, it would be interesting to expand this work in a competitive environment, both in the vertebrate host as in the mosquito host. Finally, it would be important to establish the generalizability of such fitness cost in mosquitoes. If it is a significant factor, mathematical models could incorporate this effect in predictions on the spread of resistance.

References

[1] Huijben S, Bell AS, Sim DG, Tomasello D, Mideo N, Day T, et al. 2013. Aggressive chemotherapy and the selection of drug resistant pathogens. PLoS Pathog. 9(9): e1003578. https://doi.org/10.1371/journal.ppat.1003578
 
[2] Huijben S, Nelson WA, Wargo AR, Sim DG, Drew DR, Read AF. 2010. Chemotherapy, within-host ecology and the fitness of drug-resistant malaria parasites. Evolution (N Y). 64(10): 2952-68. https://doi.org/10.1111/j.1558-5646.2010.01068.x
 
[3] Zhang J, Cunningham JJ, Brown JS, Gatenby RA. 2017. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat Commun. 8(1). https://doi.org/10.1038/s41467-017-01968-5
 
[4] Laufer MK, Takala-Harrison S, Dzinjalamala FK, Stine OC, Taylor TE, Plowe C v. 2010. Return of chloroquine-susceptible falciparum malaria in Malawi was a reexpansion of diverse susceptible parasites. J Infect Dis. 202(5): 801-808. https://doi.org/10.1086/655659 

[5] Mharakurwa S, Matsena-Zingoni Z, Mudare N, Matimba C, Gara TX, Makuwaza A, et al. 2021. Steep rebound of chloroquine-sensitive Plasmodium falciparum in Zimbabwe. J Infect Dis. 223(2): 306-9. https://doi.org/10.1093/infdis/jiaa368
 
[6] Tirrell AR, Vendrely KM, Checkley LA, Davis SZ, McDew-White M, Cheeseman IH, et al. 2019. Pairwise growth competitions identify relative fitness relationships among artemisinin resistant Plasmodium falciparum field isolates. Malar J. 18: 295. https://doi.org/10.1186/s12936-019-2934-4
 
[7] Hott A, Tucker MS, Casandra D, Sparks K, Kyle DE. 2015. Fitness of artemisinin-resistant Plasmodium falciparum in vitro. J Antimicrob Chemother. 70(10): 2787-2796. https://doi.org/10.1093/jac/dkv199
 
[8] Straimer J, Gnädig NF, Stokes BH, Ehrenberger M, Crane AA, Fidock DA. 2017. Plasmodium falciparum K13 mutations differentially impact ozonide susceptibility and parasite fitness in vitro. mBio. 8(2): e00172-17. https://doi.org/10.1128/mBio.00172-17
 
[9] Nair S, Li X, Arya GA, McDew-White M, Ferrari M, Nosten F, et al. 2018. Fitness costs and the rapid spread of kelch13-C580Y substitutions conferring artemisinin resistance. Antimicrob Agents Chemother. 62(9). https://doi.org/10.1128/AAC.00605-18
 
[10] Villa M, Berthomieu A, Rivero A. Fitness costs and benefits in response to artificial artesunate selection in Plasmodium. 2022. bioRxiv, 20220128478164, ver 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.01.28.478164
 
[11] Vanaerschot M, Decuypere S, Berg M, Roy S, Dujardin JC. 2013. Drug-resistant microorganisms with a higher fitness--can medicines boost pathogens? Crit Rev Microbiol. 39(4): 384-394. https://doi.org/10.3109/1040841X.2012.716818
 
[12] Hassett MR, Roepe PD. In vitro growth competition experiments that suggest consequences of the substandard artemisinin epidemic that may be accelerating drug resistance in P. falciparum malaria. 2021. PLoS One. 16(3): e0248057. https://doi.org/10.1371/journal.pone.0248057

The Ebola virus (EBOV) epidemic that started in December 2013 resulted in around 28,000 cases and more than 11,000 deaths. Since the emergence of the disease in Zaire in 1976 the virus had produced a number of outbreaks in Africa but until 2013 the reported numbers of human cases had never risen above 500. Could this exceptional epidemic size be due to the spread of a human-adapted form of the virus?

The large mutation rate of the virus [1-2] may indeed introduce massive amounts of genetic variation upon which selection may act. Several earlier studies based on the accumulation of genome sequences sampled during the epidemic led to contrasting conclusions. A few studies discussed evidence of positive selection on the glycoprotein that may be linked to phenotypic variations on infectivity and/or immune evasion [3-4]. But the heterogeneity in the transmission of some lineages could also be due to environmental heterogeneity and/or stochasticity. Most studies could not rule out the null hypothesis of the absence of positive selection and human adaptation [1-2 and 5].

In a recent experimental study, Urbanowicz et al. [6] chose a different method to tackle this question. A phylogenetic analysis of genome sequences from viruses sampled in West Africa revealed the existence of two main lineages (one with a narrow geographic distribution in Guinea, and the other with a wider geographic distribution) distinguished by a single amino acid substitution in the glycoprotein of the virus (A82V), and of several sub-lineages characterised by additional substitutions. The authors used this phylogenetic data to generate a panel of mutant pseudoviruses and to test their ability to infect human and fruit bat cells. These experiments revealed that specific amino acid substitutions led to higher infectivity of human cells, including A82V. This increased infectivity on human cells was associated with a decreased infectivity in fruit bat cell cultures. Since fruit bats are likely to be the reservoir of the virus, this paper indicates that human adaptation may have led to a specialization of the virus to a new host.

An accompanying paper in the same issue of Cell by Diehl et al. [7] reports results that confirm the trend identified by Urbanowicz et al. [6] and further indicate that the increased infectivity of A82V is specific for primate cells. Diehl et al. [7] also report some evidence for higher virulence of A82V in humans. In other words, the evolution of the virus may have led to higher abilities to infect and to kill its novel host. This work thus confirms the adaptive potential of RNA virus and the ability of Ebola to specialize to a novel host. In this context, the availability of an effective vaccine against the disease is particularly welcome [8].

The study of Urbanowicz et al. [6] is also remarkable because it illustrates the need of experimental approaches for the study of phenotypic variation when inference methods based on phylodynamics fail to extract a clear biological message. The analysis of genomic evolution is still in its infancy and there is a need for new theoretical developments to help detect more rapidly candidate mutations involved in adaptations to new environmental conditions.

References

[1] Gire, S.K., Goba, A., Andersen, K.G., Sealfon, R.S.G., Park, D.J., Kanneh, L., Jalloh, S., Momoh, M., Fullah, M., Dudas, G., et al. (2014). Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345, 1369–1372. doi: 10.1126/science.1259657
[2] Hoenen, T., Safronetz, D., Groseth, A., Wollenberg, K.R., Koita, O.A., Diarra, B., Fall, I.S., Haidara, F.C., Diallo, F., Sanogo, M., et al. (2015). Mutation rate and genotype variation of Ebola virus from Mali case sequences. Science 348, 117–119. doi: 10.1126/science.aaa5646
[3] Liu, S.-Q., Deng, C.-L., Yuan, Z.-M., Rayner, S., and Zhang, B. (2015). Identifying the pattern of molecular evolution for Zaire ebolavirus in the 2014 outbreak in West Africa. Infection, Genetics and Evolution 32, 51–59. doi: 10.1016/j.meegid.2015.02.024
[4] Holmes, E.C., Dudas, G., Rambaut, A., and Andersen, K.G. (2016). The evolution of Ebola virus: Insights from the 2013–2016 epidemic. Nature 538, 193–200. doi: 10.1038/nature19790
[5] Azarian, T., Lo Presti, A., Giovanetti, M., Cella, E., Rife, B., Lai, A., Zehender, G., Ciccozzi, M., and Salemi, M. (2015). Impact of spatial dispersion, evolution, and selection on Ebola Zaire Virus epidemic waves. Scientific Reports. 5, 10170. doi: 10.1038/srep10170
[6] Urbanowicz, R.A., McClure, C.P., Sakuntabhai, A., Sall, A.A., Kobinger, G., Müller, M.A., Holmes, E.C., Rey, F.A., Simon-Loriere, E., and Ball, J.K. (2016). Human adaptation of Ebola virus during the West African outbreak. Cell 167, 1079–1087. doi: 10.1016/j.cell.2016.10.013
[7] Diehl, W.E., Lin, A.E., Grubaugh, N.D., Carvalho, L.M., Kim, K., Kyawe, P.P., McCauley, S.M., Donnard, E., Kucukural, A., McDonel, P., et al. (2016). Ebola virus glycoprotein with increased infectivity dominated the 2013-2016 epidemic. Cell 167, 1088–1098. doi: 10.1016/j.cell.2016.10.014
[8] Henao-Restrepo, A.M., Camacho, A., Longini, I.M., Watson, C.H., Edmunds, W.J., Egger, M., Carroll, M.W., Dean, N.E., Diatta, I., Doumbia, M., et al. (2016). Efficacy and effectiveness of an rVSV-vectored vaccine in preventing Ebola virus disease: final results from the Guinea ring vaccination, open-label, cluster-randomised trial (Ebola Ça Suffit!). The Lancet 389, 505-518. doi: 10.1016/S0140-6736(16)32621-6

The total amount of DNA utilized to store hereditary information varies immensely among eukaryotic organisms. Single copy genome sizes – disregarding differences due to ploidy - differ by more than three orders of magnitude ranging from a few million nucleotides (Mb) to hundreds of billions (Gb). With the ever-increasing availability of fully sequenced genomes we now know that most of the difference is due either to whole genome duplication or to variation in the abundance of repetitive elements. Regarding repetitive elements, the evolutionary forces underlying the large variation 'allowing' more or less elements in a genome remain largely elusive. A tentative correlation between an organism's complexity (however this may be adequately measured) and genome size, the so called C-value paradox [1], has long been dismissed. Studies testing for selection on secondary phenotypic effects associated with genome size (cell size, metabolic rates, nutrient availability) have yielded mixed results. Nonadaptive theories capitalizing on a role of deleterious insertion-deletion mutations and genetic drift as the main drivers have likewise received mixed support [2-3]. Overall, most evidence was derived from analyses across broad taxonomical scales [4-6].

Lefébure and colleagues [7] take a different approach. They confine their considerations to a homogeneous, restricted taxonomical group, isopod crustaceans of the superfamily Aselloidea. This taxonomic focus allows the authors to circumvent many of the confounding factors such as phylogenetic inertia, life history divergence and mutation rate variation that tend to trouble analyses across broad taxonomic timescales. Another important feature of the chosen system is the evolutionary independent transition of habitat use that has occurred at least 11 times. One group of species inhabits subterranean ecosystems (groundwater), another group thrives on surface water. Populations of the former live in low-energy habitats and are expected to be outnumbered by their surface dwelling relatives. Interestingly – and a precondition for the study - the groundwater species have significantly larger genomes (up to 137%). With this unique set-up, the authors are able to investigate the link between genome size and evolutionary forces related to a proxy of long-term population size by removing many of the confounding factors a priori.

Upfront, we learn that the dN/dS ratio is higher in the groundwater species. This may either suggest prevalent positive selection or lower efficacy of purifying selection (relaxed constraint) in the group of species in which population sizes are expected to be low. Using a series of population genetic analyses the authors provide compelling evidence for the latter. Analyses are carefully conducted and include models for estimating the intensity and frequency of purifying and positive selection, the DoS (direction of selection) and α statistic. Next the authors also exclude the possibility that increased dN/dS of the subterranean groundwater species may be due to nonfunctionalization, which may result from the subterranean lifestyle.

Overall, these analyses suggest relaxed constraint in smaller populations as the most plausible alternative to explain increased dN/dS ratios. In addition to the efficacy of selection, the authors estimate the timing of the ecological transition under the rationale that the amount of time a species may have been exposed to the subterranean habitat may reflect long term population sizes. To calibrate the 'colonization clock' they apply a neat trick based on the degree of degeneration of the opsin gene (as vision tends to get lost in these habitats). When finally testing which parameters may explain differences in genome size all factors – ecological status, selection efficiency as measured by dN/dS and colonization time - turned out to be significant predictors. Direct estimates of the short term effective population size Ne from polymorphism data, however, did not correlate with genome size. Ruling out the effect of other co-variates such as body size and growth rate the authors conclude that genome size was overall best predicted by long-term population size change upon habitat shift. In that the authors provide convincing evidence that the increase in genome size is linked to a decrease in long-term reduction of selection efficiency of subterranean species. Assuming a bias for insertion mutations over deletion mutations (which is usually the case in eukaryotes) this result is in agreement with the theory of mutational hazard [4-6]. This theory proposed by Michael Lynch postulates that the accumulation of non-functional DNA has a weak deleterious effect that can only be efficiently opposed by natural selection in species with high Ne.

In conclusion, Lefébure and colleagues provide novel and welcome evidence supporting a 'neutralist' hypothesis of genome size evolution without the need to invoke an adaptive component. Methodologically, the study cautions against the common use of polymorphism-based estimates of Ne which are often obfuscated by transitory demographic change. Instead, alternative measures of selection efficacy linked to long-term population size may serve as better predictors of genome size. We hope that this study will stimulate additional work testing the link between Ne and genome size variation in other taxonomical groups [8-9]. Using genome sequences instead of the transcriptome approach applied here may concomitantly further our understanding of the molecular mechanisms underlying genome size change.

References

[1] Thomas, CA Jr. 1971. The genetic organization of chromosomes. Annual Review of Genetics 5: 237–256. doi: 10.1146/annurev.ge.05.120171.001321

[2] Ågren JA, Greiner S, Johnson MTJ, Wright SI. 2015. No evidence that sex and transposable elements drive genome size variation in evening primroses. Evolution 69: 1053–1062. doi: 10.1111/evo.12627

[3] Bast J, Schaefer I, Schwander T, Maraun M, Scheu S, Kraaijeveld K. 2016. No accumulation of transposable elements in asexual arthropods. Molecular Biology and Evolution 33: 697–706. doi: 10.1093/molbev/msv261

[4] Lynch M. 2007. The Origins of Genome Architecture. Sinauer Associates.

[5] Lynch M, Bobay LM, Catania F, Gout JF, Rho M. 2011. The repatterning of eukaryotic genomes by random genetic drift. Annual Review of Genomics and Human Genetics 12: 347–366. doi: 10.1146/annurev-genom-082410-101412

[6] Lynch M, Conery JS. 2003. The origins of genome complexity. Science 302: 1401–1404. doi: 10.1126/science.1089370

[7] Lefébure T, Morvan C, Malard F, François C, Konecny-Dupré L, Guéguen L, Weiss-Gayet M, Seguin-Orlando A, Ermini L, Der Sarkissian C, Charrier NP, Eme D, Mermillod-Blondin F, Duret L, Vieira C, Orlando L, and Douady CJ. 2017. Less effective selection leads to larger genomes. Genome Research 27: 1016-1028. doi: 10.1101/gr.212589.116

[8] Lower SS, Johnston JS, Stanger-Hall KF, Hjelmen CE, Hanrahan SJ, Korunes K, Hall D. 2017. Genome size in North American fireflies: Substantial variation likely driven by neutral processes. Genome Biolology and Evolution 9: 1499–1512. doi: 10.1093/gbe/evx097

[9] Sessegolo C, Burlet N, Haudry A. 2016. Strong phylogenetic inertia on genome size and transposable element content among 26 species of flies. Biology Letters 12: 20160407. doi: 10.1098/rsbl.2016.0407