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17 Nov 2017
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ABC random forests for Bayesian parameter inference

Machine learning methods are useful for Approximate Bayesian Computation in evolution and ecology

Recommended by Michael Blum based on reviews by Dennis Prangle and Michael Blum

It is my pleasure to recommend the paper by Raynal et al. [1] about using random forest for parameter inference. There are two reviews about the paper, one review written by Dennis Prangle and another review written by myself. Both reviews were positive and included comments that have been addressed in the current version of the preprint.

The paper nicely shows that modern machine learning approaches are useful for Approximate Bayesian Computation (ABC) and more generally for simulation-driven parameter inference in ecology and evolution.

The authors propose to consider the random forest approach, proposed by Meinshausen [2] to perform quantile regression. The numerical implementation of ABC with random forest, available in the abcrf package, is based on the RANGER R package that provides a fast implementation of random forest for high-dimensional data.

According to my reading of the manuscript, there are 3 main advantages when using random forest (RF) for parameter inference with ABC. The first advantage is that RF can handle many summary statistics and that dimension reduction is not needed when using RF.

The second advantage is very nicely displayed in Figure 5, which shows the main result of the paper. If correct, 95% posterior credibility intervals (C.I.) should contain 95% of the parameter values used in simulations. Figure 5 shows that posterior C.I. obtained with rejection are too large compared to other methods. By contrast, C.I. obtained with regression methods have been shrunken. However, the shrinkage can be excessive for the smallest tolerance rates, with coverage values that can be equal to 85% instead of the expected 95% value. The attractive property of RF is that C.I. have been shrunken but the coverage is of 100% resulting in a conservative decision about parameter values.

The last advantage is that no hyperparameter should be chosen. It is a parameter free approach, which is desirable because of the potential difficulty of choosing an appropriate acceptance rate.

The main drawback of the proposed approach concerns joint parameter inference. There are many settings where the joint parameter distribution is of interest and the proposed RF approach cannot handle that. In population genetics for example, estimation of the severity and of the duration of the bottleneck should be estimated jointly because of identifiability issues. The challenge of performing joint parameter inference with RF might constitute a useful research perspective.
 

References
 

[1] Raynal L, Marin J-M, Pudlo P, Ribatet M, Robert CP, Estoup A. 2017. ABC random forests for Bayesian parameter inference. arXiv 1605.05537v4, https://arxiv.org/pdf/1605.05537
[2] Meinshausen N. 2006. Quantile regression forests. Journal of Machine Learning Research 7: 983-999. http://www.jmlr.org/papers/v7/meinshausen06a.html

ABC random forests for Bayesian parameter inferenceLouis Raynal, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet, Christian P. Robert, Arnaud EstoupThis preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http:// dx.doi.org/ 10.24072/ pci.evolbiol.100036). Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian infer...Bioinformatics & Computational Biology, Evolutionary Applications, Other, Population Genetics / GenomicsMichael Blum 2017-07-06 07:42:00 View
07 Sep 2018
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Parallel pattern of differentiation at a genomic island shared between clinal and mosaic hybrid zones in a complex of cryptic seahorse lineages

Genomic parallelism in adaptation to orthogonal environments in sea horses

Recommended by based on reviews by 2 anonymous reviewers

Studies in speciation genomics have revealed that gene flow is quite common, and that despite this, species can maintain their distinct environmental adaptations. Although researchers are still elucidating the genomic mechanisms by which species maintain their adaptations in the face of gene flow, this often appears to involve few diverged genomic regions in otherwise largely undifferentiated genomes. In this preprint [1], Riquet and colleagues investigate the genetic structuring and patterns of parallel evolution in the long-snouted seahorse.
Before investigating specific SNPs plausibly associated with adaptation, the authors first describe genome-wide population structure in the long-snouted seahorse. This species is split into five phenotypically similar, but genetically distinct populations. Two populations reside in the Atlantic Ocean and are geographically structured with one north of the Iberian peninsula and the other around the Iberian peninsula. Two other populations are found in the Mediterranean Sea and are structured by the environment as they correspond to marine and lagoon environments. The genetic clustering of lagoon populations in the Mediterranean, despite the substantial geographic distance between them is quite impressive, and worthy of further study. Finally, a fifth population resides in a lagoon-like habitat in the Black Sea.
The authors then investigate patterns of extreme genomic differentiation among populations, and uncover a remarkable pattern of parallel differentiation in these populations. In an outlier scan, Riquet and colleagues find numerous SNPs in one genomic region that separates northern and southern Atlantic populations. Quiet surprisingly, this same genomic region appears to differentiate populations living in marine and lagoon habitats in the Mediterranean. The idea that parallel patterns of genomic differentiation may underlie adaptation to differing environmental scenarios has not yet received much attention. This paper should change that. This paper is particularity impressive in that the authors uncovered this intriguing pattern with under three hundred SNPs. Future genome scale studies will uncover the genomic basis behind this unusual case of parallelism.

References

[1] Riquet, F., Liautard-Haag, C., Woodall, L., Bouza, C., Louisy, P., Hamer, B., Otero-Ferrer, F., Aublanc, P., Béduneau, V., Briard, O., El Ayari, T., Hochscheid, S. Belkhir, K., Arnaud-Haond, S., Gagnaire, P.-A., Bierne, N. (2018). Parallel pattern of differentiation at a genomic island shared between clinal and mosaic hybrid zones in a complex of cryptic seahorse lineages. bioRxiv, 161786, ver. 4 recommended and peer-reviewed by PCI Evol Biol. doi: 10.1101/161786

Parallel pattern of differentiation at a genomic island shared between clinal and mosaic hybrid zones in a complex of cryptic seahorse lineagesFlorentine Riquet, Cathy Liautard-Haag, Lucy Woodall, Carmen Bouza, Patrick Louisy, Bojan Hamer, Francisco Otero-Ferrer, Philippe Aublanc, Vickie Béduneau, Olivier Briard, Tahani El Ayari, Sandra Hochscheid, Khalid Belkhir, Sophie Arnaud-Haond, Pi...<p>Diverging semi-isolated lineages either meet in narrow clinal hybrid zones, or have a mosaic distribution associated with environmental variation. Intrinsic reproductive isolation is often emphasized in the former and local adaptation in the la...Hybridization / Introgression, Molecular Evolution, Population Genetics / Genomics, SpeciationYaniv Brandvain Kathleen Lotterhos, Sarah Fitzpatrick2017-07-11 13:12:40 View
21 Nov 2018
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Convergent evolution as an indicator for selection during acute HIV-1 infection

Is convergence an evidence for positive selection?

Recommended by based on reviews by Jeffrey Townsend and 1 anonymous reviewer

The preprint by Bertels et al. [1] reports an interesting application of the well-accepted idea that positively selected traits (here variants) can appear several times independently; think about the textbook examples of flight capacity. Hence, the authors assume that reciprocally convergence implies positive selection. The methodology becomes then, in principle, straightforward as one can simply count variants in independent datasets to detect convergent mutations.
In this preprint, the authors have applied this counting strategy on 95 available sequence alignments of the env gene of HIV-1 [2,3] that corresponds to samples taken in different patients during the early phase of infection, at the very beginning of the onset of the immune system. They have compared the number and nature of the convergent mutations to a "neutral" model that assumes (a) a uniform distribution of mutations and (b) a substitution matrix estimated from the data. They show that there is an excess of convergent mutations when compared to the “neutral” expectations, especially for mutations that have arisen in 4+ patients. They also show that the gp41 gene is enriched in these convergent mutations. The authors then discuss in length the potential artifacts that could have given rise to the observed pattern.
I think that this preprint is remarkable in the proposed methodology. Samples are taken in different individuals, whose viral populations were founded by a single particle. Thus, there is no need for phylogenetic reconstruction of ancestral states that is the typical first step of trait convergent analyses. It simply becomes counting variants. This simple counting procedure needs nonetheless to be compared to a “neutral” expectation (a reference model), which includes the mutational process. In this article, the poor predictions of a specifically designed reference model is interpreted as an evidence for positive selection.
Whether the few mutations that are convergent in 4-7 samples out of 95 were selected or not is hard to assess with certainty. The authors have provided good evidence that they are, but only experimental validations will strengthen the claim. Nonetheless, beyond a definitive clue to the implication of selection on these particular mutations, I found the methodological strategy and the discussions on the potential biases highly stimulating. This article is an excellent starting point for further methodological developments that could be then followed by large-scale analyses of convergence in many different organisms and case studies.

References

[1] Bertels, F., Metzner, K. J., & Regoes R. R. (2018). Convergent evolution as an indicator for selection during acute HIV-1 infection. BioRxiv, 168260, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/168260
[2] Keele, B. F., Giorgi, E. E., Salazar-Gonzalez, J. F., Decker, J. M., Pham, K.T., Salazar, M. G., Sun, C., Grayson, T., Wang, S., Li, H. et al. (2008). Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection. Proc Natl Acad Sci USA 105: 7552–7557. doi: 10.1073/pnas.0802203105
[3] Li, H., Bar, K. J., Wang, S., Decker, J. M., Chen, Y., Sun, C., Salazar-Gonzalez, J.F., Salazar, M.G., Learn, G.H., Morgan, C. J. et al. (2010). High multiplicity infection by HIV-1 in men who have sex with men. PLoS Pathogens 6:e1000890. doi: 10.1371/journal.ppat.1000890

Convergent evolution as an indicator for selection during acute HIV-1 infectionFrederic Bertels, Karin J Metzner, Roland R Regoes<p>Convergent evolution describes the process of different populations acquiring similar phenotypes or genotypes. Complex organisms with large genomes only rarely and only under very strong selection converge to the same genotype. In contrast, ind...Bioinformatics & Computational Biology, Evolutionary Applications, Genome Evolution, Molecular EvolutionGuillaume Achaz2017-07-26 08:39:17 View
28 Feb 2018
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Insects and incest: sib-mating tolerance in natural populations of a parasitoid wasp

Incestuous insects in nature despite occasional fitness costs

Recommended by and based on reviews by 2 anonymous reviewers

Inbreeding, or mating between relatives, generally lowers fitness [1]. Mating between genetically similar individuals can result in higher levels of homozygosity and consequently a higher frequency with which recessive disease alleles may be expressed within a population. Reduced fitness as a consequence of inbreeding, or inbreeding depression, can vary between individuals, sexes, populations and species [2], but remains a pervasive challenge for many organisms with small local population sizes, including humans [3]. But all is not lost for individuals within small populations, because an array of mechanisms can be employed to evade the negative effects of inbreeding [4], including sib-mating avoidance and dispersal [5, 6].

Despite thorough investigation of inbreeding and sib-mating avoidance in the laboratory, only very few studies have ventured into the field besides studies on vertebrates and eusocial insects. The study of Collet et al. [7] is a surprising exception, where the effect of male density and frequency of relatives on inbreeding avoidance was tested in the laboratory, after which robust field collections and microsatellite genotyping were used to infer relatedness and dispersal in natural populations. The parasitic wasp Venturia canescens is an excellent model system to study inbreeding, because mating success was previously found to decrease with increasing relatedness between mates in the laboratory [8] and this species thus suffers from inbreeding depression [9–11]. The authors used an elegant design combining population genetics and model simulations to estimate relatedness of mating partners in the field and compared that with a theoretical distribution of potential mate encounters when random mating is assumed. One of the most important findings of this study is that mating between siblings is not avoided in this species in the wild, despite negative fitness effects when inbreeding does occur. Similar findings were obtained for another insect species, the field cricket Gryllus campestris [12], which leaves us to wonder whether inbreeding tolerance could be more common in nature than currently appreciated.

The authors further looked into sex-specific dispersal patterns between two patches located a few hundred meters apart. Females were indeed shown to be more related within a patch, but no genetic differences were observed between males, suggesting that V. canescens males more readily disperse. Moreover, microsatellite data at 18 different loci did not reveal genetic differentiation between populations approximately 300 kilometers apart. Gene flow is thus occurring over considerable distances, which could play an important role in the ability of this species to avoid negative fitness consequences of inbreeding in nature.

Another interesting aspect of this work is that discrepancies were found between laboratory- and field-based data. What is the relevance of laboratory-based experiments if they cannot predict what is happening in the wild? Many, if not most, biologists (including us) bring our model system into the laboratory to control, at least to some extent, the plethora of environmental factors that could potentially affect our system (in ways that we do not want). Most behavioral studies on mating patterns and sexual selection are conducted in standardized laboratory conditions, but sexual selection is in essence social selection, because an individual’s fitness is partly determined by the phenotype of its social partners (i.e. the social environment) [13]. The social environment may actually dictate the expression of female mate choice and it is unclear how potential laboratory-induced social biases affect mating outcome. In V. canescens, findings using field-caught individuals paint a completely opposite picture of what was previously shown in the laboratory, i.e. sib-avoidance is not taking place in the field. It is likely that density, level of relatedness, sex ratio in the field, and/or the size of experimental arenas in the lab are all factors affecting mate selectivity, as we have previously shown in a butterfly [14–16]. If females, for example, typically only encounter a few males in sequence in the wild, it may be problematic for them to express choosiness when confronted simultaneously with two or more males in the laboratory. A recent study showed that, in the wild, female moths take advantage of staying in groups to blur male choosiness [17]. It is becoming more and more clear that what we observe in the laboratory may not actually reflect what is happening in nature [18]. Instead of ignoring the species-specific life history and ecological features of our favorite species when conducting lab experiments, we suggest that it is time to accept that we now have the theoretical foundations to tease apart what in this “environmental noise” actually shapes sexual selection in nature. Explicitly including ecology in studies on sexual selection will allow us to make more meaningful conclusions, i.e. rather than “this is what may happen in the wild”, we would be able to state “this is what often happens in nature”.

References

[1] Charlesworth D & Willis JH. 2009. The genetics of inbreeding depression. Nat. Rev. Genet. 10: 783–796. doi: 10.1038/nrg2664
[2] Hedrick PW & Garcia-dorado A. 2016. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31: 940–952. doi: 10.1016/j.tree.2016.09.005
[3] Bittles AH & Black ML. 2010. Consanguinity, human evolution, and complex diseases. Proc. Natl. Acad. Sci. United States Am. 107: 1779–1786. doi: 10.1073/pnas.0906079106
[4] Pusey A & Wolf M. 1996. Inbreeding avoidance in animals. Trends Ecol. Evol. 11: 201–206. doi: 10.1016/0169-5347(96)10028-8
[5] Greenwood PJ & Harvey PH. 1978. Inbreeding and dispersal in the great tit. Nature 271: 52–54. doi: 10.1038/271052a0
[6] Szulkin M & Sheldon BC. 2008. Dispersal as a means of inbreeding avoidance in a wild bird population. Proc. R. Soc. B 275: 703–711. doi: 10.1098/rspb.2007.0989
[7] Collet M, Amat I, Sauzet S, Auguste A, Fauvergue X, Mouton L, Desouhant E. 2018. Insects and incest: sib-mating tolerance in natural populations of a parasitoid wasp. bioRxiv 169268, ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/169268
[8] Metzger M, Bernstein C, Hoffmeister TS & Desouhant E. 2010. Does kin recognition and sib-mating avoidance limit the risk of genetic incompatibility in a parasitic wasp ? PLoS One 5: e13505. doi: 10.1371/journal.pone.0013505
[9] Beukeboom LW. 2001. Single-locus complementary sex determination in the Ichneumonid Venturia canescens. Netherlands J. Zool. 51: 1–15. doi: 10.1163/156854201X00017
[10] Vayssade C, de Fazio C, Quaglietti B, Auguste A, Ris N, Fauvergue X. 2014. Inbreeding depression in a parasitoid wasp with single- locus complementary sex determination. PLoS One 9: 1–8. doi: 10.1371/journal.pone.0097733
[11] Chuine A, Sauzet S, Debias F & Desouhant E. 2015. Consequences of genetic incompatibility on fitness and mate choice: the male point of view. Biol. J. Linn. Soc. 114: 279–286. doi: 10.1111/bij.12421
[12] Bretman A, Rodri R & Tregenza T. 2011. Fine-scale population structure , inbreeding risk and avoidance in a wild insect population. Mol. Ecol. 20: 3045–3055. doi: 10.1111/j.1365-294X.2011.05140.x
[13] West-Eberhard MJ. 2014. Darwin’s forgotten idea: The social essence of sexual selection. Neurosci. Biobehav. Rev. 46: 501–508. doi: 10.1016/j.neubiorev.2014.06.015
[14] Holveck M-J, Gauthier A-L & Nieberding CM 2015. Dense, small and male-biased cages exacerbate male-male competition and reduce female choosiness in Bicyclus anynana. Anim. Behav. 104: 229–245. doi: 10.1016/j.anbehav.2015.03.025
[15] Nieberding, CM & Holveck M-J 2017. Laboratory social environment biases mating outcome: a first quantitative synthesis in a butterfly. Behav. Ecol. Sociobiol. 71: 117. doi: 10.1007/s00265-017-2346-9
[16] Nieberding CM & Holveck M-J. (In prep). Comentary on Kehl et al. 2018: "Young male mating success is associated with sperm number but not with male sex pheromone titres". Front. Ecol. Evol.
[17] Wijk M Van, Heath J, Lievers R, Schal C & Groot AT. 2017. Proximity of signallers can maintain sexual signal variation under stabilizing selection. Sci. Rep. 7: 18101. doi: 10.1038/s41598-017-17327-9
[18] Miller CW & Svensson EI. 2014. Sexual selection in complex environments. Annu. Rev. Entomol. 59: 427–445. doi: 10.1146/annurev-ento-011613-162044

Insects and incest: sib-mating tolerance in natural populations of a parasitoid waspMarie Collet, Isabelle Amat, Sandrine Sauzet, Alexandra Auguste, Xavier Fauvergue, Laurence Mouton, Emmanuel Desouhant<p>This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100047) 1. Sib-mating avoidance is a pervasive behaviour that likely evolves in species subject to inbreeding dep...Behavior & Social Evolution, Evolutionary Ecology, Sexual SelectionCaroline Nieberding2017-07-28 09:23:20 View
03 Aug 2017
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Fisher's geometrical model and the mutational patterns of antibiotic resistance across dose gradients

What doesn’t kill us makes us stronger: can Fisher’s Geometric model predict antibiotic resistance evolution?

Recommended by and ORCID_LOGO

The increasing number of reported cases of antibiotic resistance is one of today’s major public health concerns. Dealing with this threat involves understanding what drives the evolution of antibiotic resistance and investigating whether we can predict (and subsequently avoid or circumvent) it [1].
One of the most illustrative and common models of adaptation (and, hence, resistance evolution) is Fisher’s Geometric Model (FGM). The original model maps phenotypes to fitness, meaning that each point in the fitness landscape corresponds to a phenotype rather than a genotype. However, it has been shown that when mutations are numerous enough, FGM can also describe adaptive walks in genotype space [2]. Nevertheless, limitations have been highlighted, particularly when trying to study complex scenarios such as antibiotic resistance evolution [3].
Harmand et al. [4] incorporated three extensions to the FGM, which allowed them to match the mutational patterns of antibiotic resistance that they obtained from a screen across a gradient of drug concentrations. The implemented extensions took into account that: 1) only a subset of mutations may contribute to traits under selection, reflecting that not all regions in the genome affect the ability to resist antibiotics; 2) mutations that confer a fitness increase in one environment may not reflect a similar increase in others, if the selective constraints are different; and 3) different antibiotic concentrations may either constrain the maximum fitness that populations can reach (changing the height of the fitness peak) or change the rate of fitness increase with each mutation (changing the width/slope of the peak).
Traditionally, most empirical fitness landscape studies have focused on a subset of mutations obtained after laboratory evolution in specific conditions [5, 6]. The results obtained in Harmand et al. [4] indicate a potential shortcoming of studying these small fitness landscapes: rather than having a constrained evolutionary path to a resistant phenotype, as previously observed, their results suggest that antibiotic resistance can be the product of mutations in different regions of the genome. Returning to the fitness landscape perspective, this indicates that there are many alternative paths that can lead to the evolution of antibiotic resistance. This comparison points at a difficult challenge when aiming at developing a predictive framework for evolution: real-time experiments may indicate that evolution is likely to take similar and predictable paths because the strongest and most frequent mutations dictate the outcome, whereas systematic screens of mutants potentially indicate several paths, that may, however, not be relevant in nature. Only a combination of different experimental approaches with motivated theory as presented in Harmand et al. [4] will allow for a better understanding of where in this continuum evolution is taking place in nature, and to which degree we are able to interfere with it in order to slow down adaptation.

References

[1] Palmer AC, and Kishony R. 2013. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nature Review Genetics 14: 243—248. doi: 10.1038/nrg3351

[2] Tenaillon O. 2014. The utility of Fisher’s geometric model in evolutionary genetics. Annual Review of Ecology, Evolution and Systematics 45: 179—201. doi: 10.1146/annurev-ecolsys-120213-091846

[3] Blanquart F and Bataillon T. 2016. Epistasis and the structure of fitness landscapes: are experimental fitness landscapes compatible with Fisher’s geometric model? Genetics 203: 847—862. doi: 10.1534/genetics.115.182691

[4] Harmand N, Gallet R, Jabbour-Zahab R, Martin G and Lenormand T. 2017. Fisher’s geometrical model and the mutational patterns of antibiotic resistance across dose gradients. Evolution 71: 23—37. doi: 10.1111/evo.13111

[5] de Visser, JAGM, and Krug J. 2014. Empirical fitness landscapes and the predictability of evolution. Nature 15: 480—490. doi: 10.1038/nrg3744

[6] Palmer AC, Toprak E, Baym M, Kim S, Veres A, Bershtein S and Kishony R. 2015. Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes. Nature Communications 6: 1—8. doi: 10.1038/ncomms8385

Fisher's geometrical model and the mutational patterns of antibiotic resistance across dose gradientsNoémie Harmand, Romain Gallet, Roula Jabbour-Zahab, Guillaume Martin, Thomas LenormandFisher's geometrical model (FGM) has been widely used to depict the fitness effects of mutations. It is a general model with few underlying assumptions that gives a large and comprehensive view of adaptive processes. It is thus attractive in sever...AdaptationInês Fragata2017-08-01 16:06:02 View
20 Nov 2017
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Effects of partial selfing on the equilibrium genetic variance, mutation load and inbreeding depression under stabilizing selection

Understanding genetic variance, load, and inbreeding depression with selfing

Recommended by based on reviews by Frédéric Guillaume and 1 anonymous reviewer

A classic problem in evolutionary biology is to understand the genetic variance in fitness. The simplest hypothesis is that variation exists, even in well-adapted populations, as a result of the balance between mutational input and selective elimination. This variation causes a reduction in mean fitness, known as the mutation load. Though mutation load is difficult to quantify empirically, indirect evidence of segregating genetic variation in fitness is often readily obtained by comparing the fitness of inbred and outbred offspring, i.e., by measuring inbreeding depression. Mutation-selection balance models have been studied as a means of understanding the genetic variance in fitness, mutation load, and inbreeding depression. Since their inception, such models have increased in sophistication, allowing us to ask these questions under more realistic and varied scenarios. The new theoretical work by Abu Awad and Roze [1] is a substantial step forward in understanding how arbitrary levels of self-fertilization affect variation, load and inbreeding depression under mutation-selection balance.
It has never been entirely clear how selfing should affect these population genetic properties in a multi-locus model. From the single-locus perspective, selfing increases homozygosity, which allows for more efficient purging leading to a prediction of less variance and lower load. On the other hand, selfing directly and indirectly affects several types of multilocus associations, which tend to make selection less efficient. Though this is certainly not the first study to consider mutation-selection balance in species with selfing (e.g., [2-5]), it is perhaps the most biologically realistic. The authors consider a model where n traits are under stabilizing selection and where each locus affects an arbitrary subset of these traits. As others have argued [6-7], this type of fitness landscape model “naturally” gives rise to dominance and epistatic effects. Abu Awad and Roze [1] thoroughly investigate this model both with analytical approximations and stochastic simulations (incorporating the effects of drift).
Their analysis reveals three major parameter regimes. The first regime occurs under low mutation rates, when segregating deleterious alleles are sufficiently rare across the genome that multi-locus genetic associations (disequilibria) can be ignored. As expected, in this regime, increased selfing facilitates purging, thereby leading to less standing genetic variation, lower load and less inbreeding depression.
In the second regime, mutation rates are higher and segregating deleterious alleles are more common. Though the effects of multilocus genetic associations cannot be ignored, Abu Awad and Roze [1] show that a good approximation can be obtained by considering only two-locus associations (ignoring the multitude of higher order associations). This is where the sophistication of their analysis yields the greatest insights. Their analysis shows that two different types of interlocus associations are important. First, selfing directly generates identity disequilibrium (correlation in homozygosity between two loci) that occurs because individuals produced through outbreeding tend to be heterozygous across multiple loci whereas individuals produced by selfing tend to be homozygous across multiple loci. These correlations reduce the efficiency of selection when deleterious effects are partially recessive [5]. Second, selfing indirectly affects traditional linkage disequilibrium. Epistatic selection resulting from the fitness landscape generates negative linkage disequilibrium between alleles at different loci that cause the same direction of deviation in a trait from its optimum. Because selfing reduces the effective rate of recombination, linkage disequilibrium reaches higher levels. Because selection tends to generate compensatory combinations of alleles, partially masking their deleterious effects, these associations also make purging less efficient. Their analysis shows the strength of the effect from identity disequilibrium scales with U, the genome-wide rate of deleterious mutations, but the effect of linkage disequilibrium scales with U/n because with more traits (higher n) two randomly chosen alleles are less likely to affect the same trait and so be subject to epistatic selection. Together, the effects of multilocus associations increase the load and can, in some cases, cause the load to increase as selfing increase from moderate to high levels.
However, their analytical approximations become inaccurate under conditions when the number of epistatically interacting segregating mutations (proportional to U/n) becomes large relative to the effective recombination rate (dependent on outcrossing and recombination rates). In this third regime, higher order genetic associations become important. In the limit of no recombination, model behaves as if the whole genome is a single locus with a very large number of alleles, becoming equivalent to previous studies [2-3].
The study by Abu Awad and Roze [1] helps us better understand the “simplest” explanation for genetic variance in fitness—mutation-selection balance—in a model of considerable complexity involving multiple traits under stabilizing selection, which ‘naturally’ allows for pleiotropy and epistasis. Their model tends to confirm the classic prediction of lower variation in fitness, less load, and inbreeding depression in species with higher levels of selfing. However, their careful analysis provides a clearer picture of how (and by how much) epistasis and selfing affect key population genetic properties.

References

[1] Abu Awad D and Roze D. 2017. Effects of partial selfing on the equilibrium genetic variance, mutation load and inbreeding depression under stabilizing selection. bioRxiv, 180000, ver. 4 of 17th November 2017. doi: 10.1101/180000

[2] Lande R. 1977. The influence of the mating system on the maintenance of genetic variability in polygenic characters. Genetics 86: 485–498.

[3] Charlesworth D and Charlesworth B. 1987. Inbreeding depression and its evolutionary consequences. Annual Review of Ecology and Systematics. 18: 237–268. doi: 10.1111/10.1146/annurev.es.18.110187.001321

[4] Lande R and Porcher E. 2015. Maintenance of quantitative genetic variance under partial self-fertilization, with implications for the evolution of selfing. Genetics 200: 891–906. doi: 10.1534/genetics.115.176693

[5] Roze D. 2015. Effects of interference between selected loci on the mutation load, inbreeding depression, and heterosis. Genetics 201: 745–757. doi: 10.1534/genetics.115.178533

[6] Martin G and Lenormand T. 2006. A general multivariate extension of Fisher's geometrical model and the distribution of mutation fitness effects across species. Evolution 60: 893–907. doi: 10.1111/j.0014-3820.2006.tb01169.x

[7] Martin G, Elena SF and Lenormand T. 2007. Distributions of epistasis in microbes fit predictions from a fitness landscape model. Nature Genetics 39: 555–560. doi: 10.1038/ng1998

Effects of partial selfing on the equilibrium genetic variance, mutation load and inbreeding depression under stabilizing selectionDiala Abu Awad and Denis RozeThe mating system of a species is expected to have important effects on its genetic diversity. In this paper, we explore the effects of partial selfing on the equilibrium genetic variance Vg, mutation load L and inbreeding depression δ under stabi...Evolutionary Theory, Population Genetics / Genomics, Quantitative Genetics, Reproduction and SexAneil F. Agrawal2017-08-26 09:29:20 View
11 Sep 2017
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Less effective selection leads to larger genomes

Colonisation of subterranean ecosystems leads to larger genome in waterlouse (Aselloidea)

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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

Less effective selection leads to larger genomesTristan Lefébure, Claire Morvan, Florian Malard, Clémentine François, Lara Konecny-Dupré, Laurent Guéguen, Michèle Weiss-Gayet, Andaine Seguin-Orlando, Luca Ermini, Clio Der Sarkissian, N. Pierre Charrier, David Eme, Florian Mermillod-Blondin, Lau...The evolutionary origin of the striking genome size variations found in eukaryotes remains enigmatic. The effective size of populations, by controlling selection efficacy, is expected to be a key parameter underlying genome size evolution. However...Evolutionary Theory, Genome Evolution, Molecular Evolution, Population Genetics / GenomicsBenoit Nabholz2017-09-08 09:39:23 View
19 Feb 2018
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Genomic imprinting mediates dosage compensation in a young plant XY system

Dosage compensation by upregulation of maternal X alleles in both males and females in young plant sex chromosomes

Recommended by and based on reviews by 3 anonymous reviewers

Sex chromosomes evolve as recombination is suppressed between the X and Y chromosomes. The loss of recombination on the sex-limited chromosome (the Y in mammals) leads to degeneration of both gene expression and gene content for many genes [1]. Loss of gene expression or content from the Y chromosome leads to differences in gene dose between males and females for X-linked genes. Because expression levels are often correlated with gene dose [2], these hemizygous genes have a lower expression levels in the heterogametic sex. This in turn disrupts the stoichiometric balance among genes in protein complexes that have components on both the sex chromosomes and autosomes [3], which could have serious deleterious consequences for the heterogametic sex.
To overcome these deleterious effects of degeneration, the expression levels of dosage sensitive X-linked genes, and in some organisms, entire X chromosomes, are compensated, the expression of the single copy of in the heterogametic sex being increased. Dosage compensation for such genes has evolved in several species, restoring similar expression levels as in the ancestral state in males and/or equal gene expression in males and females [4-8]. The mechanisms for dosage compensation are variable among species and their evolutionary paths are not fully understood, as the few model sex chromosomes studied so far have old, and highly degenerate sex chromosomes [4-7].
Muyle et al. [9] studied the young sex chromosomes of the plant Silene latifolia, which has young sex chromosomes (4 MY) and highly variable dosage compensation [10, 11]. The authors used both an outgroup species without sex chromosomes for obtaining a proxy for ancestral expression levels before Y degeneration, and implemented methods to identify sex-linked genes and disentangle paternal versus maternal allele expression [12]. Using these elements, Muyle et al. [9] reveal upregulation of maternal X alleles in both males and females in the young S. latifolia sex chromosomes [9], possibly by genomic imprinting. The upregulation in both sexes of the maternal X alleles likely yields non-optimal gene expression in females, which is strikingly consistent with the theoretical first step of dosage compensation as postulated by Ohno [8], which predicts restoration of ancestral expression in males, over-expression in females, and unequal expression in the two sexes. These findings provide surprising insight into the earliest stages of dosage compensation, one of the most intriguing aspects of evolutionary biology.

References
[1] Bachtrog D. 2013. Y chromosome evolution: emerging insights into processes of Y-chromosome degeneration? Nature Reviews Genetics 14: 113–124. doi: 10.1038/nrg3366
[2] Malone JH, Cho D-Y, Mattiuzzo NR, Artieri CG, Jiang L, Dale RK, Smith HE, McDaniel J, Munro S, Salit M, Andrews J, Przytycka TM and Oliver B. 2012. Mediation of Drosophila autosomal dosage effects and compensation by network interactions. Genome Biology 13: R28. doi: 10.1186/gb-2012-13-4-r28
[3] Pessia E, Makino T, Bailly-Bechet M, McLysaght A and Marais GAB. 2012. Mammalian X chromosome inactivation evolved as a dosage-compensation mechanism for dosage-sensitive genes on the X chromosome. Proceedings of the National Academy of Sciences of the United States of America. 109: 5346–5351. doi: 10.1073/pnas.1116763109.
[4] Graves JAM. 2016. Evolution of vertebrate sex chromosomes and dosage compensation. Nature Reviews Genetics 17: 33–46. doi: 10.1038/nrg.2015.2
[5] Mank JE. 2013. Sex chromosome dosage compensation: definitely not for everyone. Trends in Genetics 12: 677–683. doi: 10.1016/j.tig.2013.07.005
[6] Pessia E and Engelstädter J. 2014. The evolution of X chromosome inactivation in mammals: the demise of Ohno’s hypothesis? Cellular and Molecular Life Sciences 71: 1383–1394. doi: 10.1007/s00018-013-1499-6
[7] Muyle A, Shearn R and Marais GAB. 2017. The evolution of sex chromosomes and dosage compensation in plants. Genome Biology and Evolution 9: 627–645. doi: 10.1093/gbe/evw282
[8] Ohno S. 1967. Sex chromosomes and sex linked genes. Springer, Berlin Heidelberg New York.
[9] Muyle A, Zemp N, Fruchard C, Cegan R, Vrana J, Deschamps C, Tavares R, Picard F, Hobza R, Widmer A and Marais GAB. 2018. Genomic imprinting mediates dosage compensation in a young plant XY system. bioRxiv 118695, ver. 6 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/179044
[10] Papadopulos AST, Chester M, Ridout K and Filatov DA. 2015. Rapid Y degeneration and dosage compensation in plant sex chromosomes. Proceedings of the National Academy of Sciences of the United States of America 112: 13021–13026. doi: 10.1073/pnas.1508454112
[11] Bergero R, Qiu S and Charlesworth D. 2015. Gene loss from a plant sex chromosome system. Current Biology 25: 1234–1240. doi: 10.1016/j.cub.2015.03.015
[12] Muyle A, Kafer J, Zemp N, Mousset S, Picard F and Marais GAB. 2016. SEX-DETector: a probabilistic approach to study sex chromosomes in non-model organisms. Genome Biology and Evolution 8: 2530–2543. doi: 10.1093/gbe/evw172

Genomic imprinting mediates dosage compensation in a young plant XY systemAline Muyle, Niklaus Zemp, Cecile Fruchard, Radim Cegan, Jan Vrana, Clothilde Deschamps, Raquel Tavares, Franck Picard, Roman Hobza, Alex Widmer, Gabriel Marais<p>During the evolution of sex chromosomes, the Y degenerates and its expression gets reduced relative to the X and autosomes. Various dosage compensation mechanisms that recover ancestral expression levels in males have been described in animals....Bioinformatics & Computational Biology, Expression Studies, Genome Evolution, Molecular Evolution, Reproduction and SexTatiana Giraud2017-09-20 20:39:46 View
19 Mar 2018
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Natural selection on plasticity of thermal traits in a highly seasonal environment

Is thermal plasticity itself shaped by natural selection? An assessment with desert frogs

Recommended by based on reviews by Dries Bonte, Wolf Blanckenhorn and Nadia Aubin-Horth

It is well known that climatic factors – most notably temperature, season length, insolation and humidity – shape the thermal niche of organisms on earth through the action of natural selection. But how is this achieved precisely? Much of thermal tolerance is actually mediated by phenotypic plasticity (as opposed to genetic adaptation). A prominent expectation is that environments with greater (daily and/or annual) thermal variability select for greater plasticity, i.e. better acclimation capacity. Thus, plasticity might be selected per se.

A Chilean group around Leonardo Bacigalupe assessed natural selection in the wild in one marginal (and extreme) population of the four-eyed frog Pleurodema thaul (Anura: Leptodactylidae) in an isolated oasis in the Atacama Desert, permitting estimation of mortality without much potential of confounding it with migration [1]. Several thermal traits were considered: CTmax – the critical maximal temperature; CTmin – the critical minimum temperature; Tpref – preferred temperature; Q10 – thermal sensitivity of metabolism; and body mass. Animals were captured in the wild and subsequently assessed for thermal traits in the laboratory at two acclimation temperatures (10° & 20°C), defining the plasticity in all traits as the difference between the traits at the two acclimation temperatures. Thereafter the animals were released again in their natural habitat and their survival was monitored over the subsequent 1.5 years, covering two breeding seasons, to estimate viability selection in the wild. The authors found and conclude that, aside from larger body size increasing survival (an unsurprising result), plasticity does not seem to be systematically selected directly, while some of the individual traits show weak signs of selection.

Despite limited sample size (ca. 80 frogs) investigated in only one marginal but very seasonal population, this study is interesting because selection on plasticity in physiological thermal traits, as opposed to selection on the thermal traits themselves, is rarely investigated. The study thus also addressed the old but important question of whether plasticity (i.e. CTmax-CTmin) is a trait by itself or an epiphenomenon defined by the actual traits (CTmax and CTmin) [2-5]. Given negative results, the main question could not be ultimately solved here, so more similar studies should be performed.

References

[1] Bacigalupe LD, Gaitan-Espitia, JD, Barria AM, Gonzalez-Mendez A, Ruiz-Aravena M, Trinder M & Sinervo B. 2018. Natural selection on plasticity of thermal traits in a highly seasonal environment. bioRxiv 191825, ver. 5 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/191825
[2] Scheiner SM. 1993. Genetics and evolution of phenotypic plasticity. Annual Review in Ecology and Systematics 24: 35–68. doi: 10.1146/annurev.es.24.110193.000343
[3] Scheiner SM. 1993. Plasticity as a selectable trait: Reply to Via. The American Naturalist. 142: 371–373. doi: 10.1086/285544
[4] Via S. 1993. Adaptive phenotypic plasticity - Target or by-product of selection in a variable environment? The American Naturalist. 142: 352–365. doi: 10.1086/285542
[5] Via S. 1993. Regulatory genes and reaction norms. The American Naturalist. 142: 374–378. doi: 10.1086/285542

Natural selection on plasticity of thermal traits in a highly seasonal environmentLeonardo Bacigalupe, Juan Diego Gaitan-Espitia, Aura M Barria, Avia Gonzalez-Mendez, Manuel Ruiz-Aravena, Mark Trinder, Barry Sinervo<p>For ectothermic species with broad geographical distributions, latitudinal/altitudinal variation in environmental temperatures (averages and extremes) are expected to shape the evolution of physiological tolerances and the acclimation capacity ...Adaptation, Evolutionary Ecology, Phenotypic PlasticityWolf Blanckenhorn2017-09-22 23:17:40 View
09 Feb 2018
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Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak

Simulating the effect of public health interventions using dated virus sequences and geographical data

Recommended by ORCID_LOGO based on reviews by Christian Althaus, Chris Wymant and 1 anonymous reviewer

Perhaps because of its deadliness, the 2013-2016 Ebola Virus (EBOV) epidemics in West-Africa has led to unprecedented publication and sharing of full virus genome sequences. This was both rapid (90 full genomes were shared within weeks [1]) and important (more than 1500 full genomes have been released overall [2]). Furthermore, the availability of the metadata (especially GPS location) has led to depth analyses of the geographical spread of the epidemics [3].
In their work, Dellicour et al. [4] pursue earlier phylogeographical investigations in an original and yet simple approach to address questions of key public health importance. The originality of the approach is dual. First, from a technical standpoint, they capture the spread of infectious diseases in a continuous framework using a novel model that allows for rare long-distance dispersal events. Second, in a more classical discrete meta-population framework, they simulate the effect of public health interventions by pruning the phylogenetic tree and assessing how this affects key parameters. For instance, to simulate the effect of closing borders they remove subsets of the phylogeny that involved dispersal between countries and to simulate the effect of protecting a region by quarantine they remove all the leaves (i.e. the infections sampled) from this region. This phylogeny pruning is both original and simple. It is however limited because it currently assumes that policies are 100% effective and earlier modelling work on human influenza showed that long distance travel bans had to be implemented with >99% efficiency in order to slow epidemic growth from a time scale of days to weeks [5].
From a biological standpoint, Dellicour et al. [4] corroborate earlier findings that highly populated locations (>1,000,000 inhabitants) were crucial in explaining the magnitude of the epidemics but also show the importance of the transmission between the three capital cities. They also show that rare long-distance dispersing events of the virus are not key to explaining the magnitude of the epidemics (even though they assume 100% efficiency of suppressing long-distance event). Finally, thanks to their continuous model they estimate the speed of spread of the epidemics and are able to detect the effect of border closing on this speed.
Overall, this study [4], which involves state-of-the-art Bayesian inference methods of infection phylogenies using MCMC, stands out because of its effort to simulate public health interventions. It stands as an encouragement for the development of intervention models with increased realism and for even faster and larger virus sequence data sharing.

References

[1] Gire 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] Holmes EC, Dudas G, Rambaut A and Andersen KG. 2016. The evolution of Ebola virus: insights from the 2013-2016 epidemic. Nature 538: 193–200. doi: 10.1038/nature19790.
[3] Dudas et al. 2017. Virus genomes reveal factors that spread and sustained the Ebola epidemic. Nature 544: 309–315 (2017). doi: 10.1038/nature22040.
[4] Dellicour S, Baele G, Dudas G, Faria NR, Pybus OG, Suchard MA, Rambaud A and Lemey P. 2018. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. bioRxiv, 163691, ver. 3 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/163691.
[5] Hollingsworth TD, Ferguson NM and Anderson RM. 2006. Will travel restrictions control the international spread of pandemic influenza? Nature Medicine 12, 497–499. doi: 10.1038/nm0506-497.

Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreakSimon Dellicour, Guy Baele, Gytis Dudas, Nuno R. Faria, Oliver G. Pybus, Marc A. Suchard, Andrew Rambaut, Philippe Lemey<p>This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (https://doi.org/10.24072/pci.evolbiol.100046). The recent Ebola virus (EBOV) outbreak in West Africa witnessed considerable efforts to obtain viral genom...Phylogenetics / Phylogenomics, Phylogeography & BiogeographySamuel Alizon2017-09-30 13:49:57 View