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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
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
31 Jan 2018
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Identifying drivers of parallel evolution: A regression model approach

A new statistical tool to identify the determinant of parallel evolution

Recommended by based on reviews by Bastien Boussau and 1 anonymous reviewer

In experimental evolution followed by whole genome resequencing, parallel evolution, defined as the increase in frequency of identical changes in independent populations adapting to the same environment, is often considered as the product of similar selection pressures and the parallel changes are interpreted as adaptive.
However, theory predicts that heterogeneity both in mutation rate and selection intensity across the genome can trigger patterns of parallel evolution. It is thus important to evaluate and quantify the contribution of both mutation and selection in determining parallel evolution to interpret more accurately experimental evolution genomic data and also potentially improve our capacity to predict the genes that will respond to selection.
In their manuscript, Bailey, Guo and Bataillon [1] derive a framework of statistical models to partition the role of mutation and selection in determining patterns of parallel evolution at the gene level. The rationale is to use the synonymous mutations dataset as a baseline to characterize the mutation rate heterogeneity, assuming a negligible impact of selection on synonymous mutations and then analyse the non-synonymous dataset to identify additional source(s) of heterogeneity, by examining the proportion of the variation explained by a number of genomic variables.
This framework is applied to a published data set of resequencing of 40 Saccharomyces cerevisiae populations adapting to a laboratory environment [2]. The model explaining at best the synonymous mutations dataset is one of homogeneous mutation rate along the genome with a significant positive effect of gene length, likely reflecting variation in the size of the mutational target. For the non-synonymous mutations dataset, introducing heterogeneity between sites for the probability of a change to increase in frequency is improving the model fit and this heterogeneity can be partially explained by differences in gene length, recombination rate and number of functional protein domains.
The application of the framework to an experimental data set illustrates its capacity to disentangle the role of mutation and selection and to identify genomic variables explaining heterogeneity in parallel evolution probability but also points to potential limits, cautiously discussed by the authors: first, the number of mutations in the dataset analysed needs to be sufficient, in particular to establish the baseline on the synonymous dataset. Here, despite a high replication (40 populations evolved in the exact same conditions), the total number of synonymous mutations that could be analysed was not very high and there was only one case of a gene with synonymous mutation in two independent populations. Second, although the models are able to identify factors affecting the mutation counts, the proportion of the variation explained is quite low. The consequence is that the models correctly predicts the mutation count distribution but the objective of predicting on which genes the response to selection will occur still seems quite far away.
The framework developed in this manuscript [1] clearly represents a very useful tool for the analysis of large “evolve and resequence” data sets and to gain a better understanding of the determinants of parallel evolution in general. The extension of its application to mutations others than SNPs would provide the possibility to get a more complete picture of the differences in contributions of mutation and selection intensity heterogeneities depending on the mutation types.

References

[1] Bailey SF, Guo Q and Bataillon T (2018) Identifying drivers of parallel evolution: A regression model approach. bioRxiv 118695, ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/118695

[2] Lang GI, Rice DP, Hickman, MJ, Sodergren E, Weinstock GM, Botstein D, and Desai MM (2013) Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500: 571–574. doi: 10.1038/nature12344

Identifying drivers of parallel evolution: A regression model approachSusan F Bailey, Qianyun Guo, Thomas Bataillon<p>This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100045). Parallel evolution, defined as identical changes arising in independent populations, is often attributed...Experimental Evolution, Molecular EvolutionStephanie Bedhomme2017-03-22 14:54:48 View
20 Dec 2017
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Renewed diversification following Miocene landscape turnover in a Neotropical butterfly radiation

The influence of environmental change over geological time on the tempo and mode of biological diversification, revealed by Neotropical butterflies

Recommended by based on reviews by Delano Lewis and 1 anonymous reviewer

The influence of environmental change over geological time on the tempo and mode of biological diversification is a hot topic in biogeography. Of central interest are questions about where, when, and how fast lineages proliferated, suffered extinction, and migrated in response to tectonic events, the waxing and waning of dominant biomes, etc. In this context, the dynamic conditions of the Miocene have received much attention, from studies of many clades and biogeographic regions. Here, Chazot et al. [1] present an exemplary analysis of butterflies (tribe Ithomiini) in the Neotropics, examining their diversification across the Andes and Amazon. They infer sharp contrasts between these regions in the late Miocene: accelerated diversification during orogeny of the Andes, and greater extinction in the Amazon associated during the Pebas system, with interchange and local diversification increasing following the Pebas during the Pliocene.
Two features of this study stand out. First is the impressive taxon sampling (340 out of 393 extant species). Second is the use of ancestral range reconstructions to compute per-lineage rates of colonization between regions, and rates of speciation within regions, through time. The latter allows for relatively fine-grained comparisons across the 2 fundamental dimensions of historical biogeography, space and time, and is key to the main results. The method resonated with me because I performed a similar analysis in a study showing evidence for uplift-driven diversification in the Hengduan Mountains of China [2]. This analysis is complemented by a variety of other comparative methods for inferring variable diversification across clades, through time, and in response to external factors. Overall, it represents a very nice contribution to our understanding of the effects of Miocene/Pliocene environmental change on the evolution of Neotropical biodiversity.

References

[1] Chazot N, Willmott KR, Lamas G, Freitas AVL, Piron-Prunier F, Arias CF, Mallet J, De-Silva DL and Elias M. 2017. Renewed diversification following Miocene landscape turnover in a Neotropical butterfly radiation. BioRxiv 148189, ver 4 of 19th December 2017. doi: 10.1101/148189

[2] Xing Y, and Ree RH. 2017. Uplift-driven diversification in the Hengduan Mountains, a temperate biodiversity hotspot. Proceedings of the National Academy of Sciences of the United States of America, 114: E3444-E3451. doi: 10.1073/pnas.1616063114

Renewed diversification following Miocene landscape turnover in a Neotropical butterfly radiationNicolas Chazot, Keith R. Willmott, Gerardo Lamas, André V.L. Freitas, Florence Piron-Prunier, Carlos F. Arias, James Mallet, Donna Lisa De-Silva, Marianne EliasThe Neotropical region has experienced a dynamic landscape evolution throughout the Miocene, with the large wetland Pebas occupying western Amazonia until 11-8 my ago and continuous uplift of the Andes mountains along the western edge of South Ame...Macroevolution, Phylogenetics / Phylogenomics, Phylogeography & BiogeographyRichard H Ree2017-06-12 11:55:14 View
18 Dec 2017
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Co-evolution of virulence and immunosuppression in multiple infections

Two parasites, virulence and immunosuppression: how does the whole thing evolve?

Recommended by based on reviews by 2 anonymous reviewers

How parasite virulence evolves is arguably the most important question in both the applied and fundamental study of host-parasite interactions. Typically, this research area has been progressing through the formalization of the problem via mathematical modelling. This is because the question is a complex one, as virulence is both affected and affects several aspects of the host-parasite interaction. Moreover, the evolution of virulence is a problem in which ecology (epidemiology) and evolution (changes in trait values through time) are tightly intertwined, generating what is now known as eco-evolutionary dynamics. Therefore, intuition is not sufficient to address how virulence may evolve.
In their classical model, Anderson and May [1] predict that the optimal virulence level results from a trade-off between increasing parasite load within hosts and promoting transmission between hosts. Although very useful and foundational, this model incurs into several simplifying assumptions. One of the most obvious is that it considers that hosts are infected by a single parasite strain/species. Some subsequent models have thus accounted for multiple infections, generally predicting that this will select for higher virulence, because it increases the strength of selection in the within-host compartment.
Usually, when attacked, hosts deploy defences to combat their parasites. In many systems, however, parasites can suppress the immune response of their hosts. This leads to prolonged infection, which is beneficial for the parasite. However, immunosuppressed hosts are also more prone to infection. Thus, multiple infections are more likely in a population of immunosuppressed hosts, leading to higher virulence, hence a shorter infection period. Thus, the consequences of immunosuppression for the evolution of virulence in a system allowing for multiple infections are not straightforward.
Kamiya et al.[2] embrace this challenge. They create an epidemiological model in which the probability of co-infection trades off with the rate of recovery from infection, via immunosuppression. They then use adaptive dynamics to study how either immunosuppression or virulence evolve in response to one another, to then establish what happens when they both coevolve. They find that when virulence only evolves, its evolutionary equilibrium increases as immunosuppression levels increase. In the reverse case, that is, when virulence is set to a fixed value, the evolutionarily stable immunosuppression varies non-linearly with virulence, with first a decrease, but then an increase at high levels of virulence. The initial decrease of immunosuppression may be due to (a) a decrease in infection duration and/or (b) a decrease in the proportion of double infections, caused by increased levels of virulence. However, as virulence increases, the probability of double infections decreases even in non-immunosuppressed hosts, hence increased immunosuppression is selected for.
The combination of both Evolutionary Stable Strategies (ESSs) yields intermediate levels of virulence and immunosuppression. The authors then address how this co-ESS varies with host mortality and with the shape of the trade-off between the probability of co-infection and the rate of recovery. They find that immunosuppression always decreases with increased host mortality, as it becomes not profitable to invest on this trait. In contrast, virulence peaks at intermediate values of host mortality, unlike the monotonical decrease that is found in absence of immunosuppression. Also, this relationship is predicted to vary with the shape of the trade-off underlying the costs and benefits of immunosuppression.
In sum, Kamiya et al. [2] provide a comprehensive analysis of an important problem in the evolution of host-parasite interactions. The model provides clear predictions, and thus can now be tested using the many systems in which immunosuppression has been detected, provided that the traits that compose the model can be measured.

References

[1] Anderson RM and May RM. 1982. Coevolution of hosts and parasites. Parasitology, 1982. 85: 411–426. doi: 10.1017/S0031182000055360

[2] Kamiya T, Mideo N and Alizon S. 2017. Coevolution of virulence and immunosuppression in multiple infections. bioRxiv, ver. 7 peer-reviewed by PCI Evol Biol, 149211. doi: 10.1101/139147

Co-evolution of virulence and immunosuppression in multiple infectionsTsukushi Kamiya, Nicole Mideo, Samuel AlizonMany components of the host-parasite interaction have been shown to affect the way virulence, that is parasite induced harm to the host, evolves. However, co-evolution of multiple traits is often neglected. We explore how an immunosuppressive mech...Evolutionary Applications, Evolutionary Dynamics, Evolutionary Ecology, Evolutionary Epidemiology, Evolutionary TheorySara Magalhaes2017-06-13 16:49:45 View
05 Dec 2017
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Reconstruction of body mass evolution in the Cetartiodactyla and mammals using phylogenomic data

Predicting small ancestors using contemporary genomes of large mammals

Recommended by based on reviews by Bruce Rannala and 1 anonymous reviewer

Recent methodological developments and increased genome sequencing efforts have introduced the tantalizing possibility of inferring ancestral phenotypes using DNA from contemporary species. One intriguing application of this idea is to exploit the apparent correlation between substitution rates and body size to infer ancestral species' body sizes using the inferred patterns of substitution rate variation among species lineages based on genomes of extant species [1].
The recommended paper by Figuet et al. [2] examines the utility of such approaches by analyzing the Cetartiodactyla, a clade of large mammals that have mostly well resolved phylogenetic relationships and a reasonably good fossil record. This combination of genomic data and fossils allows a direct comparison between body size predictions obtained from the genomic data and empirical evidence from the fossil record. If predictions seem good in groups such as the Cetartiodactyla, where there is independent evidence from the fossil record, this would increase the credibility of predictions made for species with less abundant fossils.
Figuet et al. [2] analyze transcriptome data for 41 species and report a significant effect of body mass on overall substitution rate, synonymous vs. non-synonymous rates, and the dynamics of GC-content, thus allowing a prediction of small ancestral body size in this group despite the fact that the extant species that were analyzed are nearly all large.
A comparative method based solely on morphology and phylogenetic relationships would be very unlikely to make such a prediction. There are many sources of uncertainty in the variables and parameters associated with these types of approaches: phylogenetic uncertainty (topology and branch lengths), uncertainty about inferred substitution rates, and so on. Although the authors do not account for all these sources of uncertainty the fact that their predicted body sizes appear sensible is encouraging and undoubtedly the methods will become more statistically sophisticated over time.

References

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

[2] Figuet E, Ballenghien M, Lartillot N and Galtier N. 2017. Reconstruction of body mass evolution in the Cetartiodactyla and mammals using phylogenomic data. bioRxiv, ver. 3 of 4th December 2017. 139147. doi: 10.1101/139147

Reconstruction of body mass evolution in the Cetartiodactyla and mammals using phylogenomic dataEmeric Figuet, Marion Ballenghien, Nicolas Lartillot, Nicolas Galtier<p>Reconstructing ancestral characters on a phylogeny is an arduous task because the observed states at the tips of the tree correspond to a single realization of the underlying evolutionary process. Recently, it was proposed that ancestral traits...Genome Evolution, Life History, Macroevolution, Molecular Evolution, Phylogenetics / PhylogenomicsBruce Rannala2017-05-18 15:28:58 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
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
13 Nov 2017
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Epidemiological trade-off between intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen population

The pace of pathogens’ adaptation to their host plants

Recommended by based on reviews by Benoit Moury and 1 anonymous reviewer

Because of their shorter generation times and larger census population sizes, pathogens are usually ahead in the evolutionary race with their hosts. The risks linked to pathogen adaptation are still exacerbated in agronomy, where plant and animal populations are not freely evolving but depend on breeders and growers, and are usually highly genetically homogeneous. As a consequence, the speed of pathogen adaptation is crucial for agriculture sustainability. Unraveling the time scale required for pathogens’ adaptation to their hosts would notably greatly improve our estimation of the risks of pathogen emergence, the efficiency of disease control strategies and the design of epidemiological surveillance schemes. However, the temporal scale of pathogen evolution has received much less attention than its spatial scale [1]. In their study of a wheat fungal disease, Suffert et al. [2] reached contrasting conclusions about the pathogen adaptation depending on the time scale (intra- or inter-annual) and on the host genotype (sympatric or allopatric) considered, questioning the experimental assessment of this important problem.

Suffert et al. [2] sampled two pairs of Zymoseptoria tritici (the causal agent of septoria leaf blotch) sub-populations in a bread wheat field plot, representing (i) isolates collected at the beginning or at the end of an epidemic in a single growing season (2009-2010 intra-annual sampling scale) and (ii) isolates collected from plant debris at the end of growing seasons in 2009 and in 2015 (inter-annual sampling scale). Then, they measured in controlled conditions two aggressiveness traits of the isolates of these four Z. tritici sub-populations, the latent period and the lesion size on leaves, on two wheat cultivars. One of the cultivars was considered as "sympatric" because it was at the source of the studied isolates and was predominant in the growing area before the experiment, whereas the other cultivar was considered as "allopatric" since it replaced the previous one and became predominant in the growing area during the sampling period.

On the sympatric host, at the intra-annual scale, they observed a marginally-significant decrease in latent period and a significant decrease of the between-isolate variance for this trait, which are consistent with a selection of pathogen variants with an enhanced aggressiveness. In contrast, at the inter-annual scale, no difference in the mean or variance of aggressiveness trait values was observed on the sympatric host, suggesting a lack of pathogen adaptation. They interpreted the contrast between observations at the two time scales as the consequence of a trade-off for the pathogen between a gain of aggressiveness after several generations of asexual reproduction at the intra-annual scale and a decrease of the probability to reproduce sexually and to be transmitted from one growing season to the next. Indeed, at the end of the growing season, the most aggressive isolates are located on the upper leaves of plants, where the pathogen density and hence probably also the probability to reproduce sexually, is lower. On the allopatric host, the conclusion about the pathogen stability at the inter-annual scale was somewhat different, since a significant increase in the mean lesion size was observed (isolates corresponding to the intra-annual scale were not checked on the allopatric host). This shows the possibility for the pathogen to evolve at the inter-annual scale, for a given aggressiveness trait and on a given host.

In conclusion, Suffert et al.’s [2] study emphasizes the importance of the experimental design in terms of sampling time scale and host genotype choice to analyze the pathogen adaptation to its host plants. It provides also an interesting scenario, at the crossroad of the pathogen’s reproduction regime, niche partitioning and epidemiological processes, to interpret these contrasted results. Pathogen adaptation to plant cultivars with major-effect resistance genes is usually fast, including in the wheat-Z. tritici system [3]. Therefore, this study will be of great help for future studies on pathogen adaptation to plant partial resistance genes and on strategies of deployment of such resistance at the landscape scale.

References
[1] Penczykowski RM, Laine A-L and Koskella B. 2016. Understanding the ecology and evolution of host–parasite interactions across scales. Evolutionary Applications, 9: 37–52. doi: 10.1111/eva.12294

[2] Suffert F, Goyeau H, Sache I, Carpentier F, Gelisse S, Morais D and Delestre G. 2017. Epidemiological trade-off between intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen population. bioRxiv, 151068, ver. 3 of 12th November 2017. doi: 10.1101/151068

[3] Brown JKM, Chartrain L, Lasserre-Zuber P and Saintenac C. 2015. Genetics of resistance to Zymoseptoria tritici and applications to wheat breeding. Fungal Genetics and Biology, 79: 33–41. doi: 10.1016/j.fgb.2015.04.017

Epidemiological trade-off between intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen populationFrederic Suffert, Henriette Goyeau, Ivan Sache, Florence Carpentier, Sandrine Gelisse, David Morais, Ghislain DelestreThe efficiency of plant resistance to fungal pathogen populations is expected to decrease over time, due to its evolution with an increase in the frequency of virulent or highly aggressive strains. This dynamics may differ depending on the scale i...Adaptation, Evolutionary Applications, Evolutionary EpidemiologyBenoit Moury2017-06-23 21:04:54 View
10 Nov 2017
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POSTPRINT

Rates of Molecular Evolution Suggest Natural History of Life History Traits and a Post-K-Pg Nocturnal Bottleneck of Placentals

A new approach to DNA-aided ancestral trait reconstruction in mammals

Recommended by ORCID_LOGO and

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

Rates of Molecular Evolution Suggest Natural History of Life History Traits and a Post-K-Pg Nocturnal Bottleneck of PlacentalsWu J, Yonezawa T, Kishino H.Life history and behavioral traits are often difficult to discern from the fossil record, but evolutionary rates of genes and their changes over time can be inferred from extant genomic data. Under the neutral theory, molecular evolutionary rate i...Bioinformatics & Computational Biology, Life History, Molecular Evolution, Paleontology, Phylogenetics / PhylogenomicsNicolas Galtier2017-11-10 14:52:26 View