In most animal species, males and females display distinct survival prospect, a phenomenon known as sex gap in longevity (SGL, Marais et al. 2018). The study of SGLs is crucial not only for having a full picture of the causes underlying organisms’ health, aging and death but also to initiate the development of sex-specific anti-aging interventions in humans (Austad and Bartke 2015). Three non-mutually evolutionary causes have been proposed to underlie SGLs (Marais et al. 2018). First, SGLs could be the consequences of sex-differences in life history strategies. For example, evolving dimorphic traits (e.g. body size, ornaments or armaments) may imply unequal physiological costs (e.g. developmental, maintenance) between the sexes and this may result in differences in longevity and aging. Second, mitochondria are usually transmitted by the mother and thus selection is blind to mitochondrial deleterious mutations affecting only males. Such mutations can freely accumulate in the mitochondrial genome and may reduce male longevity, a phenomenon called the mother’s curse (Frank and Hurst 1996). Third, in species with sex chromosomes, all recessive deleterious mutations will be expressed on the single X chromosome in XY males and may reduce their longevity (the unguarded X effect). In addition, the numerous transposable elements (TEs) on the Y chromosome may affect aging. TE activity is normally repressed by epigenetic regulation (DNA methylation, histone modifications and small RNAs). However, it is known that this regulation is disrupted with increasing age. Because of the TE-rich Y chromosome, more TEs may become active in old males than in old females, generating more somatic mutations, accelerating aging and reducing longevity in males (the toxic Y effect, Marais et al. 2018).
The relative contributions of these different effects to SGLs remain unknown. Sex-differences in life history strategies have been considered as the most important cause of SGLs for long (Tidière et al. 2015) but this effect remain equivocal (Lemaître et al. 2020) and cannot explain alone the diversity of patterns observed across species (Marais et al. 2018). Similarly, while studies in Drosophila and humans have shown that the mother’s curse contributes to SGLs in those organisms (e.g. Milot et al. 2007), its contribution may not be strong. Recently, two large-scale comparative analyses have shown that in species with XY chromosomes males show a shorter lifespan compared to females, while in species with ZW chromosomes (a system in which the female are the heterogametic sex and are ZW, and the males ZZ) the opposite pattern is observed (Pipoly et al. 2015; Xirocostas et al. 2020). Apart from these correlational studies, very little experimental tests of the effect of sex chromosomes on longevity have been conducted. In Drosophila, the evidence suggests that the unguarded X effect does not contribute to SGLs (Brengdahl et al. 2018). Whether a toxic Y effect exists in this species was unknown.
In a very elegant study, Brown et al. (2020) provided strong evidence for such a toxic Y effect in Drosophila melanogaster. First, they checked that in the D. melanogaster strain that they were studying (Canton-S), males were indeed dying younger than females. They also confirmed that in this strain, as in others, the male genomes include more repeats and heterochromatin than the female ones using cytometry. A careful analysis of the heterochromatin (using H3K9me2, a repressive histone modification typical of heterochromatin, as a proxy) in old flies revealed that heterochromatin loss was much more important in males than in females, in particular on the Y chromosome (but also to a lesser extent at the pericentromric regions of the autosomes). This change in heterochromatin had two outputs, they found. First, the expression of the genes in those regions was affected. They highlighted that many of such genes are involved in immunity and regulation with a potential impact on longevity. Second, they found a striking TE reactivation. These two effects were stronger in males. While females showed clear reactivation of 6 TEs, with the total fraction of repeats in the transcriptome going from 2% (young females) to 4.6% (old females), males experienced the reactivation of 32 TEs, with the total fraction of repeats in the transcriptome going from 1.6% (young males) to 5.8% (old males). It appeared that most of these TEs are Y-linked. And when focusing on Y-linked repeats, they found that 32 Y-linked TEs became upregulated during male aging and the fraction of Y-linked TEs in the transcriptome increased ninefold.
All these observations clearly suggested that male longevity was decreased because of a toxic Y effect. To really uncover a causal relationship between having a Y chromosome and shorter longevity, Brown et al. (2020) artificially produced flies with atypical karyotypes: X0 males, XXY females and XYY males. This is very interesting as they could uncouple the effect of the phenotypical sex (being male or female) and having a Y chromosome or not, as in fruit flies sex is determined not by the Y chromosome but by the X/autosome ratio. Their results are striking. They found that longevity of the X0 males was the highest (higher than XX females in fact), and that of the XYY males the lowest. Females XXY had intermediate longevities. Importantly, this was found to be robust to genomic background as results were the same using crosses from different strains. When analysing TEs of these flies, they found a particularly strong expression of the Y-linked TEs in old XXY and XYY flies. Interestingly, in young XXY and XYY flies Y-linked TEs expression was also strong, suggesting the chromatin regulation of the Y chromosome is disrupted in these flies.
This work points to the idea that SGLs in D. melanogaster are mainly explained by the toxic Y effect. The molecular details however remain to be elucidated. The effect of the Y chromosome on aging might be more complex than envisioned in the toxic Y model presented above. Brown et al. (2020) indeed found that heterochromatin loss was globally faster in males, both at the Y chromosome and the autosomes. The organisation of the nucleus, in particular of the nucleolus, which is involved in heterochromatin maintenance, involves the sex chromosomes in D. melanogaster as discussed in the paper, and may explain this observation. The epigenetic status of the Y chromosome is known to affect that of all the autosomes in Drosophila (Lemos et al. 2008). Also, in Brown et al. (2020) most of the work (in particular the genomic part) has been done on Canton-S. Only D. melanogaster was studied but limited data suggest different Drosophila species may have different SGLs. The TE analysis is known to be tricky, different tools to analyse TE expression exist (e.g. Lerat et al. 2017; Lanciano and Cristofari 2020). Future work should focus on testing the toxic Y effect on other D. melanogaster strains and other Drosophila species, using different tools to study TE expression, and on dissecting the molecular details of the toxic Y effect.

References

Austad, S. N., and Bartke, A. (2015). Sex differences in longevity and in responses to anti-aging interventions: A Mini-Review. Gerontology, 62(1), 40–46. 10.1159/000381472
Brengdahl, M., Kimber, C. M., Maguire-Baxter, J., and Friberg, U. (2018). Sex differences in life span: Females homozygous for the X chromosome do not suffer the shorter life span predicted by the unguarded X hypothesis. Evolution; international journal of organic evolution, 72(3), 568–577. 10.1111/evo.13434
Brown, E. J., Nguyen, A. H., and Bachtrog, D. (2020). The Y chromosome may contribute to sex-specific ageing in Drosophila. Nature ecology and evolution, 4(6), 853–862. 10.1038/s41559-020-1179-5 or preprint link on bioRxiv
Frank, S. A., and Hurst, L. D. (1996). Mitochondria and male disease. Nature, 383(6597), 224. 10.1038/383224a0
Lanciano, S., and Cristofari, G. (2020). Measuring and interpreting transposable element expression. Nature reviews. Genetics, 10.1038/s41576-020-0251-y. Advance online publication. 10.1038/s41576-020-0251-y
Lemaître, J. F., Ronget, V., Tidière, M., Allainé, D., Berger, V., Cohas, A., Colchero, F., Conde, D. A., Garratt, M., Liker, A., Marais, G., Scheuerlein, A., Székely, T., and Gaillard, J. M. (2020). Sex differences in adult lifespan and aging rates of mortality across wild mammals. Proceedings of the National Academy of Sciences of the United States of America, 117(15), 8546–8553. 10.1073/pnas.1911999117
Lemos, B., Araripe, L. O., and Hartl, D. L. (2008). Polymorphic Y chromosomes harbor cryptic variation with manifold functional consequences. Science (New York, N.Y.), 319(5859), 91–93. 10.1126/science.1148861
Lerat, E., Fablet, M., Modolo, L., Lopez-Maestre, H., and Vieira, C. (2017). TEtools facilitates big data expression analysis of transposable elements and reveals an antagonism between their activity and that of piRNA genes. Nucleic acids research, 45(4), e17. 10.1093/nar/gkw953
Marais, G., Gaillard, J. M., Vieira, C., Plotton, I., Sanlaville, D., Gueyffier, F., and Lemaitre, J. F. (2018). Sex gap in aging and longevity: can sex chromosomes play a role?. Biology of sex differences, 9(1), 33. 10.1186/s13293-018-0181-y
Milot, E., Moreau, C., Gagnon, A., Cohen, A. A., Brais, B., and Labuda, D. (2017). Mother's curse neutralizes natural selection against a human genetic disease over three centuries. Nature ecology and evolution, 1(9), 1400–1406. 10.1038/s41559-017-0276-6
Pipoly, I., Bókony, V., Kirkpatrick, M., Donald, P. F., Székely, T., and Liker, A. (2015). The genetic sex-determination system predicts adult sex ratios in tetrapods. Nature, 527(7576), 91–94. 10.1038/nature15380
Tidière, M., Gaillard, J. M., Müller, D. W., Lackey, L. B., Gimenez, O., Clauss, M., and Lemaître, J. F. (2015). Does sexual selection shape sex differences in longevity and senescence patterns across vertebrates? A review and new insights from captive ruminants. Evolution; international journal of organic evolution, 69(12), 3123–3140. 10.1111/evo.12801
Xirocostas, Z. A., Everingham, S. E., and Moles, A. T. (2020). The sex with the reduced sex chromosome dies earlier: a comparison across the tree of life. Biology letters, 16(3), 20190867. 10.1098/rsbl.2019.0867

The advent of genome-wide association studies (GWAS) has been a great promise for our understanding of the connection between genotype and phenotype. Today, the NHGRI-EBI GWAS catalog contains 251,401 associations from 4,961 studies (1). This wealth of studies has also generated interest to use the summary statistics beyond the few top hits in order to make predictions for individuals without known phenotype, e.g. to predict polygenic risk scores or to study polygenic selection by comparing different groups. For instance, polygenic selection acting on the most studied polygenic trait, height, has been subject to multiple studies during the past decade (e.g. 2–6). They detected north-south gradients in Europe which were consistent with expectations. However, their GWAS summary statistics were based on the GIANT consortium data set, a meta-analysis of GWAS conducted in different European cohorts (7,8). The availability of large data sets with less stratification such as the UK Biobank (9) has led to a re-evaluation of those results. The nature of the GIANT consortium data set was realized to represent a potential problem for studies of polygenic adaptation which led several of the authors of the original articles to caution against the interpretations of polygenic selection on height (10,11). This was a great example on how the scientific community assessed their own earlier results in a critical way as more data became available. At the same time it left the question whether there is detectable polygenic selection separating populations more open than ever.

Generally, recent years have seen several articles critically assessing the portability of GWAS results and risk score predictions to other populations (12–14). Refoyo-Martínez et al. (15) are now presenting a systematic assessment on the robustness of cross-population signatures of polygenic adaptation in humans. They compiled GWAS results for complex traits which have been studied in more than one cohort and then use allele frequencies from the 1000 Genomes Project data (16) set to detect signals of polygenic score overdispersion. As the source for the allele frequencies is kept the same across all tests, differences between the signals must be caused by the underlying GWAS. The results are concerning as the level of overdispersion largely depends on the choice of GWAS cohort. Cohorts with homogenous ancestries show little to no overdispersion compared to cohorts of mixed ancestries such as meta-analyses. It appears that the meta-analyses fail to fully account for stratification in their data sets.

The authors based most of their analyses on the heavily studied trait height. Additionally, they use educational attainment (measured as the number of school years of an individual) as an example. This choice was due to the potential over- or misinterpretation of results by the media, the general public and by far right hate groups. Such traits are potentially confounded by unaccounted cultural and socio-economic factors. Showing that previous results about polygenic selection on educational attainment are not robust is an important result that needs to be communicated well. This forms a great example for everyone working in human genomics. We need to be aware that our results can sometimes be misinterpreted. And we need to make an effort to write our papers and communicate our results in a way that is honest about the limitations of our research and that prevents the misuse of our results by hate groups.

This article represents an important contribution to the field. It is cruicial to be aware of potential methodological biases and technical artifacts. Future studies of polygenic adaptation need to be cautious with their interpretations of polygenic score overdispersion. A recommendation would be to use GWAS results obtained in homogenous cohorts. But even if different biobank-scale cohorts of homogeneous ancestry are employed, there will always be some remaining risk of unaccounted stratification. These conclusions may seem sobering but they are part of the scientific process. We need additional controls and new, different methods than polygenic score overdispersion for assessing polygenic selection. Last year also saw the presentation of a novel approach using sequence data and GWAS summary statistics to detect directional selection on a polygenic trait (17). This new method appears to be robust to bias stemming from stratification in the GWAS cohort as well as other confounding factors. Such new developments show light at the end of the tunnel for the use of GWAS summary statistics in the study of polygenic adaptation.

References

1. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Research. 2019 Jan 8;47(D1):D1005–12. doi: https://doi.org/10.1093/nar/gky1120

2. Turchin MC, Chiang CW, Palmer CD, Sankararaman S, Reich D, Hirschhorn JN. Evidence of widespread selection on standing variation in Europe at height-associated SNPs. Nature Genetics. 2012 Sep;44(9):1015–9. doi: https://doi.org/10.1038/ng.2368

3. Berg JJ, Coop G. A Population Genetic Signal of Polygenic Adaptation. PLOS Genetics. 2014 Aug 7;10(8):e1004412. doi: https://doi.org/10.1371/journal.pgen.1004412

4. Robinson MR, Hemani G, Medina-Gomez C, Mezzavilla M, Esko T, Shakhbazov K, et al. Population genetic differentiation of height and body mass index across Europe. Nature Genetics. 2015 Nov;47(11):1357–62. doi: https://doi.org/10.1038/ng.3401

5. Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA, et al. Genome-wide patterns of selection in 230 ancient Eurasians. Nature. 2015 Dec;528(7583):499–503. doi: https://doi.org/10.1038/nature16152

6. Racimo F, Berg JJ, Pickrell JK. Detecting polygenic adaptation in admixture graphs. Genetics. 2018. Arp;208(4):1565–1584. doi: https://doi.org/10.1534/genetics.117.300489

7. Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010 Oct;467(7317):832–8. doi: https://doi.org/10.1038/nature09410

8. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014 Nov;46(11):1173–86. doi: https://doi.org/10.1038/ng.3097

9. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018 Oct;562(7726):203–9. doi: https://doi.org/10.1038/s41586-018-0579-z

10. Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H, Field Y, et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife. 2019 Mar 21;8:e39725. doi: https://doi.org/10.7554/eLife.39725

11. Sohail M, Maier RM, Ganna A, Bloemendal A, Martin AR, Turchin MC, et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife. 2019 Mar 21;8:e39702. doi: https://doi.org/10.7554/eLife.39702

12. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics. 2019 Apr;51(4):584–91. doi: https://doi.org/10.1038/s41588-019-0379-x

13. Bitarello BD, Mathieson I. Polygenic Scores for Height in Admixed Populations. G3: Genes, Genomes, Genetics. 2020 Nov 1;10(11):4027–36. doi: https://doi.org/10.1534/g3.120.401658

14. Uricchio LH, Kitano HC, Gusev A, Zaitlen NA. An evolutionary compass for detecting signals of polygenic selection and mutational bias. Evolution Letters. 2019;3(1):69–79. doi: https://doi.org/10.1002/evl3.97

15. Refoyo-Martínez A, Liu S, Jørgensen AM, Jin X, Albrechtsen A, Martin AR, Racimo F. How robust are cross-population signatures of polygenic adaptation in humans? bioRxiv, 2021, 2020.07.13.200030, version 5 peer-reviewed and recommended by Peer community in Evolutionary Biology. doi: https://doi.org/10.1101/2020.07.13.200030

16. Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, et al. A global reference for human genetic variation. Nature. 2015 Sep 30;526(7571):68–74. doi: https://doi.org/10.1038/nature15393

17. Stern AJ, Speidel L, Zaitlen NA, Nielsen R. Disentangling selection on genetically correlated polygenic traits using whole-genome genealogies. bioRxiv. 2020 May 8;2020.05.07.083402. doi: https://doi.org/10.1101/2020.05.07.083402

In spite of its name, the effective population size, Ne, has a complex and often distant relationship to census population size, as we usually understand it. In truth, it is primarily an abstract concept aimed at measuring the amount of genetic drift occurring in a population at any given time. The standard way to model random genetic drift in population genetics is the Wright-Fisher model and, with a few exceptions, definitions of the effective population size stems from it: “a certain model has effective population size, Ne, if some characteristic of the model has the same value as the corresponding characteristic for the simple Wright-Fisher model whose actual size is Ne” (Ewens 2004). Since Sewall Wright introduced the concept of effective population size in 1931 (Wright 1931), it has flourished and there are today numerous definitions of it depending on the process being examined (genetic diversity, loss of alleles, efficacy of selection) and the characteristic of the model that is considered. These different definitions of the effective population size were generally introduced to address specific aspects of the evolutionary process. One aspect that has been hotly debated since the first estimates of genetic diversity in natural populations were published is the so-called Lewontin’s paradox (1974). Lewontin noted that the observed variation in heterozygosity across species was much smaller than one would expect from the neutral expectations calculated with the actual size of the species.
In essence, what Galtier and Rousselle propose in their clever paper is to introduce a new approach to compare effective population sizes across species and thereby a new way to address Lewontin’s paradox. Classically, the effective population size in this type of comparative genomic studies is simply estimated from nucleotide diversity at putatively neutral sites using the equation relating levels of diversity (θ) to mutation rate per generation (μ) and effective population size, Ne, θ = 4Neμ. As Galtier and Rousselle point out there are many issues with this approach. In particular, although we can now estimate θ very precisely, we generally do not have a reliable estimate of the mutation rate, and the method rests on many, unwarranted, assumptions; for example that the population is at mutation-drift equilibrium. Instead they propose to estimate the effective population size from the load of segregating deleterious mutations which can be summarized by the ratio of nonsynonymous to synonymous mutations, πN/πS: small-Ne species are expected to accumulate more deleterious mutations and carry a higher load than large-Ne ones at selection/drift equilibrium (Ohta et al. 1973; Welch et al. 2008). At first glance, this suggestion seems counterintuitive since considering sites under selection undoubtedly adds a new layer of complexity to an already intricate situation. Indeed, one is now bringing to the brew another elusive object, namely, the Distribution of Fitness Effect of mutations (DFE). However, estimating Ne from the load of segregating deleterious mutations may actually simplify the situation in two important ways. First, using πN/πS does not require assumption about μ (as the mutation rate will cancel out in the ratio). Second, the ratio of nonsynonymous to synonymous nucleotide diversity, reaches equilibrium faster than the nucleotide diversity at synonymous site after a change in population size, so we can hope for less sensitivity to (often unknown) recent demographic history (Brandvain and Wright 2016).
Extending recent developments in the estimation of the DFE, Galtier and Rousselle eventually obtain estimates of the average deleterious effect, , where Ne is the effective population size and is the mean fitness effect of non-synonymous mutations. Assuming further that distinct species share a common DFE and therefore a common , they obtain estimates of the between species ratio of Ne from the between species ratio of . Applying their newly developed approach to various datasets they conclude that the power of drift varies by a factor of at least 500 between large-Ne (Drosophila) and small-Ne species (H. sapiens). This is an order of magnitude larger than what would be obtained by comparing estimates of the variation in neutral diversity. Hence the proposed approach seems to have gone some way in making Lewontin’s paradox less paradoxical. But, perhaps more importantly, as the authors tersely point out at the end of the abstract their results further questions the meaning of Ne parameters in population genetics. And arguably this could well be the most important contribution of their study and something that is badly needed.

References

Brandvain Y, Wright SI (2016) The Limits of Natural Selection in a Nonequilibrium World. Trends in Genetics, 32, 201–210. doi: 10.1016/j.tig.2016.01.004
Ewens WJ (2010) Mathematical Population Genetics: Theoretical Introduction. Springer-Verlag New York Inc., New York, NY. doi: 10.1007/978-0-387-21822-9
Galtier N, Rousselle M (2020) How much does Ne vary among species? bioRxiv, 861849, ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/861849
Lewontin RC (1974) The genetic basis of evolutionary change. Columbia University Press, New York. Ohta T (1973) Slightly Deleterious Mutant Substitutions in Evolution. Nature, 246, 96–98. doi: 10.1038/246096a0
Welch JJ, Eyre-Walker A, Waxman D (2008) Divergence and Polymorphism Under the Nearly Neutral Theory of Molecular Evolution. Journal of Molecular Evolution, 67, 418–426. doi: 10.1007/s00239-008-9146-9
Wright S (1931) Evolution in Mendelian Populations. Genetics, 16, 97–159. url: https://www.genetics.org/content/16/2/97

Sex chromosomes are special in the genome because they are often highly differentiated over much of their lengths and marked by degenerative evolution of their gene content. Understanding why sex chromosomes differentiate requires deciphering the forces driving their recombination patterns. Suppression of recombination may be subject to selection, notably because of functional effects of locking together variation at different traits, as well as longer-term consequences of the inefficient purge of deleterious mutations, both of which may contribute to patterns of differentiation [1]. As an example, male and female functions may reveal intrinsic antagonisms over the optimal genotypes at certain genes or certain combinations of interacting genes. As a result, selection may favour the recruitment of rearrangements blocking recombination and maintaining the association of sex-antagonistic allele combinations with the sex-determining locus.
The hypothesis that sexually antagonistic selection might drive recombination suppression along the sex chromosomes is not new, but there are surprisingly few studies examining this empirically [1]. Support mainly comes from the study of guppy populations Poecilia reticulata in which the level of sexual dimorphism (notably due to male ornaments, subject to sexual selection) varies among populations, and was found to correlate with the length of the non-recombining region on the sex chromosome [2]. But the link is not always that clear. For instance in the fungus Microbotryum violaceum, the mating type loci is characterized by adjacent segments with recombination suppression, despite the near absence of functional differentiation between mating types [3].
In this study, Shearn and colleagues [4] explore the patterns of recombination suppression on the sex chromosomes of primates. X and Y chromosomes are strongly differentiated, except in a small region where they recombine with each other, the pseudoautosomal region (PAR). In the clade of apes and monkeys, including humans, large rearrangements have extended the non recombining region stepwise, eroding the PAR. Could this be driven by sexually antagonistic selection in a clade showing strong sexual differentiation?
To evaluate this idea, Shearn et al. have compared the structure of recombination in apes and monkeys to their sister clade with lower levels of sexual dimorphism, the lemurs and the lorises. If sexual antagonism was important in shaping recombination suppression, and assuming lower measures of sexual dimorphism reflect lower sexual antagonism [5], then lemurs and lorises would be predicted to show a shorter non-recombining region than apes and monkeys.
Lemurs and lorises were terra incognita in terms of genomic research on the sex chromosomes, so Shearn et al. have sequenced the genomes of males and females of different species. To assess whether sequences came from a recombining or non-recombining segment, they used coverage information in males vs females to identify sequences on the X whose copy on the Y is absent or too divergent to map, indicating long-term differentiation (absence of recombination). This approach reveals that the two lineages have undergone different recombination dynamics since they split from their common ancestor: regions which have undergone further structural rearrangements extending the non-recombining region in apes and monkeys, have continued to recombine normally in lemurs and lorises. Consistent with the prediction, macroevolutionary variation in the differentiation of males and females is indeed accompanied by variation in the size of the non-recombining region on the sex chromosome.
Sex chromosomes are excellent examples of how genomes are shaped by selection. By directly exploring recombination patterns on the sex chromosome across all extant primate groups, this study comes as a nice addition to the short series of empirical studies evaluating whether sexual antagonism may drive certain aspects of genome structure. The sexual selection causing sometimes spectacular morphological or behavioural differences between sexes in many animals may be the visible tip of the iceberg of all the antagonisms that characterise male vs. female functions generally [5]. Further research should bring insight into how different flavours or intensities of antagonistic selection can contribute to shape genome variation.

References

[1] Charlesworth D (2017) Evolution of recombination rates between sex chromosomes. Philosophical Transactions of the Royal Society B: Biological Sciences, 372, 20160456. https://doi.org/10.1098/rstb.2016.0456
[2] Wright AE, Darolti I, Bloch NI, Oostra V, Sandkam B, Buechel SD, Kolm N, Breden F, Vicoso B, Mank JE (2017) Convergent recombination suppression suggests role of sexual selection in guppy sex chromosome formation. Nature Communications, 8, 14251. https://doi.org/10.1038/ncomms14251
[3] Branco S, Badouin H, Vega RCR de la, Gouzy J, Carpentier F, Aguileta G, Siguenza S, Brandenburg J-T, Coelho MA, Hood ME, Giraud T (2017) Evolutionary strata on young mating-type chromosomes despite the lack of sexual antagonism. Proceedings of the National Academy of Sciences, 114, 7067–7072. https://doi.org/10.1073/pnas.1701658114
[4] Shearn R, Wright AE, Mousset S, Régis C, Penel S, Lemaitre J-F, Douay G, Crouau-Roy B, Lecompte E, Marais GAB (2020) Evolutionary stasis of the pseudoautosomal boundary in strepsirrhine primates. bioRxiv, 445072. https://doi.org/10.1101/445072
[5] Connallon T, Clark AG (2014) Evolutionary inevitability of sexual antagonism. Proceedings of the Royal Society B: Biological Sciences, 281, 20132123. https://doi.org/10.1098/rspb.2013.2123

Estimating the absolute age of diversification events is challenging, because molecular sequences provide timing information in units of substitutions, not years. Additionally, the rate of molecular evolution (in substitutions per year) can vary widely across lineages. Accurate dating of speciation events traditionally relies on non-molecular data. For very fast-evolving organisms such as SARS-CoV-2, for which samples are obtained over a time span, the collection times provide this external information from which we can learn the rate of molecular evolution and date past events (Boni et al. 2020). In groups for which the fossil record is abundant, state-of-the-art dating methods use fossil information to complement molecular data, either in the form of a prior distribution on node ages (Nguyen & Ho 2020), or as data modelled with a fossilization process (Heath et al. 2014).

Dating is a challenge in groups that lack fossils or other geological evidence, such as very old lineages and microbial lineages. In these groups, horizontal gene transfer (HGT) events have been identified as informative about relative dates: the ancestor of the gene's donor must be older than the descendants of the gene's recipient. Previous work using HGTs to date phylogenies have used methodologies that are ad-hoc (Davín et al 2018) or employ a small number of HGTs only (Magnabosco et al. 2018, Wolfe & Fournier 2018).

Szöllősi et al. (2021) present and validate a Bayesian approach to estimate the age of diversification events based on relative information on these ages, such as implied by HGTs. This approach is flexible because it is modular: constraints on relative node ages can be combined with absolute age information from fossil data, and with any substitution model of molecular evolution, including complex state-of-art models. To ease the computational burden, the authors also introduce a two-step approach, in which the complexity of estimating branch lengths in substitutions per site is decoupled from the complexity of timing the tree with branch lengths in years, accounting for uncertainty in the first step. Currently, one limitation is that the tree topology needs to be known, and another limitation is that constraints need to be certain. Users of this method should be mindful of the latter when hundreds of constraints are used, as done by Szöllősi et al. (2021) to date the trees of Cyanobacteria and Archaea.

Szöllősi et al. (2021)'s method is implemented in RevBayes, a highly modular platform for phylogenetic inference, rapidly growing in popularity (Höhna et al. 2016). The RevBayes tutorial page features a step-by-step tutorial "Dating with Relative Constraints", which makes the method highly approachable.

References:

Boni MF, Lemey P, Jiang X, Lam TT-Y, Perry BW, Castoe TA, Rambaut A, Robertson DL (2020) Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic. Nature Microbiology, 5, 1408–1417. https://doi.org/10.1038/s41564-020-0771-4

Davín AA, Tannier E, Williams TA, Boussau B, Daubin V, Szöllősi GJ (2018) Gene transfers can date the tree of life. Nature Ecology & Evolution, 2, 904–909. https://doi.org/10.1038/s41559-018-0525-3

Heath TA, Huelsenbeck JP, Stadler T (2014) The fossilized birth–death process for coherent calibration of divergence-time estimates. Proceedings of the National Academy of Sciences, 111, E2957–E2966. https://doi.org/10.1073/pnas.1319091111

Höhna S, Landis MJ, Heath TA, Boussau B, Lartillot N, Moore BR, Huelsenbeck JP, Ronquist F (2016) RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language. Systematic Biology, 65, 726–736. https://doi.org/10.1093/sysbio/syw021

Magnabosco C, Moore KR, Wolfe JM, Fournier GP (2018) Dating phototrophic microbial lineages with reticulate gene histories. Geobiology, 16, 179–189. https://doi.org/10.1111/gbi.12273

Nguyen JMT, Ho SYW (2020) Calibrations from the Fossil Record. In: The Molecular Evolutionary Clock: Theory and Practice  (ed Ho SYW), pp. 117–133. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-60181-2_8

Szollosi, G.J., Hoehna, S., Williams, T.A., Schrempf, D., Daubin, V., Boussau, B. (2021) Relative time constraints improve molecular dating. bioRxiv, 2020.10.17.343889, ver. 8  recommended and peer-reviewed by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.10.17.343889

Wolfe JM, Fournier GP (2018) Horizontal gene transfer constrains the timing of methanogen evolution. Nature Ecology & Evolution, 2, 897–903. https://doi.org/10.1038/s41559-018-0513-7