The human genome not only encodes for biological functions and for what makes us human, it also encodes the population history of our ancestors. Changes in past population sizes, for example, affect the distribution of times to the most recent common ancestor (tMRCA) of genomic segments, which in turn can be inferred by sophisticated modelling along the genome.
A key framework for such modelling of local tMRCA tracts along genomes is the Sequentially Markovian Coalescent (SMC) (McVean and Cardin 2005, Marjoram and Wall 2006) . The problem that the SMC solves is that the mosaic of local tMRCAs along the genome is unknown, both in their actual ages and in their positions along the genome. The SMC allows to effectively sum across all possibilities and handle the uncertainty probabilistically. Several important tools for inferring the demographic history of a population have been developed built on top of the SMC, including PSMC (Li and Durbin 2011), diCal (Sheehan et al 2013), MSMC (Schiffels and Durbin 2014), SMC++ (Terhorst et al 2017), eSMC (Sellinger et al. 2020) and others.
In this paper, Sellinger, Abu Awad and Tellier (2020) review these SMC-based methods and provide a coherent simulation design to comparatively assess their strengths and weaknesses in a variety of demographic scenarios (Sellinger, Abu Awad and Tellier 2020). In addition, they used these simulations to test how breaking various key assumptions in SMC methods affects estimates, such as constant recombination rates, or absence of false positive SNP calls.
As a result of this assessment, the authors not only provide practical guidance for researchers who want to use these methods, but also insights into how these methods work. For example, the paper carefully separates sources of error in these methods by observing what they call “Best-case convergence” of each method if the data behaves perfectly and separating that from how the method applies with actual data. This approach provides a deeper insight into the methods than what we could learn from application to genomic data alone.
In the age of genomics, computational tools and their development are key for researchers in this field. All the more important is it to provide the community with overviews, reviews and independent assessments of such tools. This is particularly important as sometimes the development of new methods lacks primary visibility due to relevant testing material being pushed to Supplementary Sections in papers due to space constraints. As SMC-based methods have become so widely used tools in genomics, I think the detailed assessment by Sellinger et al. (2020) is timely and relevant.
In conclusion, I recommend this paper because it bridges from a mere review of the different methods to an in-depth assessment of performance, thereby addressing both beginners in the field who just seek an initial overview, as well as experienced researchers who are interested in theoretical boundaries and assumptions of the different methods.

References

[1] Li, H., and Durbin, R. (2011). Inference of human population history from individual whole-genome sequences. Nature, 475(7357), 493-496. doi: https://doi.org/10.1038/nature10231
[2] Marjoram, P., and Wall, J. D. (2006). Fast"" coalescent"" simulation. BMC genetics, 7(1), 16. doi: https://doi.org/10.1186/1471-2156-7-16
[3] McVean, G. A., and Cardin, N. J. (2005). Approximating the coalescent with recombination. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1459), 1387-1393. doi: https://doi.org/10.1098/rstb.2005.1673
[4] Schiffels, S., and Durbin, R. (2014). Inferring human population size and separation history from multiple genome sequences. Nature genetics, 46(8), 919-925. doi: https://doi.org/10.1038/ng.3015
[5] Sellinger, T. P. P., Awad, D. A., Moest, M., and Tellier, A. (2020). Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data. PLoS Genetics, 16(4), e1008698. doi: https://doi.org/10.1371/journal.pgen.1008698
[6] Sellinger, T. P. P., Awad, D. A. and Tellier, A. (2020) Limits and Convergence properties of the Sequentially Markovian Coalescent. bioRxiv, 2020.07.23.217091, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: https://doi.org/10.1101/2020.07.23.217091
[7] Sheehan, S., Harris, K., and Song, Y. S. (2013). Estimating variable effective population sizes from multiple genomes: a sequentially Markov conditional sampling distribution approach. Genetics, 194(3), 647-662. doi: https://doi.org/10.1534/genetics.112.149096
[8] Terhorst, J., Kamm, J. A., and Song, Y. S. (2017). Robust and scalable inference of population history from hundreds of unphased whole genomes. Nature genetics, 49(2), 303-309. doi: https://doi.org/10.1038/ng.3748

Although most mutations are deleterious, the strongly deleterious ones do not spread in a very large population as their chance of fixation is very small. Another mechanism via which the deleterious mutations can be eliminated is via recombination or sexual reproduction. However, in a finite asexual population, the subpopulation without any deleterious mutation will eventually acquire a deleterious mutation resulting in the reduction of the population size or in other words, an increase in the genetic drift. This, in turn, will lead the population to acquire deleterious mutations at a faster rate eventually leading to a mutational meltdown.

This irreversible (or, at least over some long time scales) accumulation of deleterious mutations is especially relevant to RNA viruses due to their high mutation rate, and while the prior work has dealt with bacteriophages and RNA viruses, the study by Lafforgue et al. [1] makes an interesting contribution to the existing literature by focusing on plants.

In this study, the authors enquire how despite the repeated increase in the strength of genetic drift, how the RNA viruses manage to survive in plants. Following a series of experiments and some numerical simulations, the authors find that as expected, after severe bottlenecks, the fitness of the population decreases significantly. But if the bottlenecks are followed by population expansion, the Muller’s ratchet can be halted due to the genetic diversity generated during population growth. They hypothesize this mechanism as a potential way by which the RNA viruses can survive the mutational meltdown.

As a theoretician, I find this investigation quite interesting and would like to see more studies addressing, e.g., the minimum population growth rate required to counter the potential extinction for a given bottleneck size and deleterious mutation rate. Of course, it would be interesting to see in future work if the hypothesis in this article can be tested in natural populations.

References

[1] Guillaume Lafforgue, Marie Lefebvre, Thierry Michon, Santiago F. Elena (2024) How do plant RNA viruses overcome the negative effect of Muller s ratchet despite strong transmission bottlenecks? bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community In Evolutionary Biology
https://doi.org/10.1101/2023.08.01.550272

The study of sexual selection goes as far as Darwin himself. Since then, elaborate theories concerning both intra- and inter-sexual sexual have been developed, and elegant experiments have been designed to test this body of theory. It may thus come as a surprise that the community is still debating on the correct way to measure simple components of sexual selection, such as the Bateman gradient (i.e., the covariance between the number of matings and the number of offspring)[1,2], or to quantify complex behaviours such as mate choice (the non-random choice of individuals with particular characters as mates)[3,4] and their consequences.
One difficulty in the study of sexual selection is evaluating the consequences of non-random mating. Indeed, when non-random mating is observed in a population, it is often difficult to establish whether such mating pattern leads to i) sexual selection per se (selection pressures favouring certain phenotypes), and/or ii) the non-random association of parental genes in their offspring or not. These two processes differ. In particular, assortative (and disassortative) mating can shape genetic covariances without leading to changes in gene frequencies in the population. Their distinction matters because these two processes lead to different evolutionary outcomes, which can have large ripple effects in the evolution of sexual behaviours, sexual ornamentation, and speciation.
In his paper, entitled “Multi-model inference of non-random mating from an information theoretic approach” [5], Carvajal-Rodríguez tackled this issue. The author generated a simple model in which the consequences of non-random mating can be inferred from information on the population frequencies before and after mating. The procedure is as follows: from the initial population frequencies of phenotypes (or genotypes) of both sexes, the model generates predictions on the frequencies after mating, assuming that particular mating patterns have occurred. This leads to different predictions for the phenotypic (or genotypic) frequencies after mating. The particular mating pattern leading to the best fit with the real frequencies is then identified via a model selection procedure (performing model averaging to combine different mating patterns is also possible).
This study builds on a framework introduced by Carvajal-Rodríguez’s colleagues [6] and encompasses later methodological developments involving the author himself [7]. Compared to early work, the new method proposed by the author builds on the relationship between mating pattern and information [8] to distinguish among scenarios that would lead to non-random mating due to different underlying processes, using simple model selection criterion such as the AICc.
The great asset of the proposed method is that it can be applied to the study of natural populations in which the study of mate choice and sexual selection is notoriously difficult. In the manuscript, the procedure is tested on a population of marine gastropods (Littorina saxatilis). This allows the reader to grasp how the method can be applied to a real system. In fact, anyone can try out the method thanks to the freely available software InfoMating programmed by the author. One important assumption underlying the current method is that the frequencies of unmated individuals do not change during the mating season. If this is not the case, the reader may refer to another publication of the same author which relaxes this assumption [9]. These papers are both instrumental for empiricists interested in testing sexual selection theory.

References

[1] Bateman, A. J. (1948). Intra-sexual selection in Drosophila. Heredity, 2(3), 349-368. doi: 10.1038/hdy.1948.21
[2] Jones, A. G. (2009). On the opportunity for sexual selection, the Bateman gradient and the maximum intensity of sexual selection. Evolution: International Journal of Organic Evolution, 63(7), 1673-1684. doi: 10.1111/j.1558-5646.2009.00664.x
[3] Andersson, M., & Simmons, L. W. (2006). Sexual selection and mate choice. Trends in ecology & evolution, 21(6), 296-302. doi: 10.1016/j.tree.2006.03.015
[4] Kuijper, B., Pen, I., & Weissing, F. J. (2012). A guide to sexual selection theory. Annual Review of Ecology, Evolution, and Systematics, 43, 287-311. doi: 10.1146/annurev-ecolsys-110411-160245
[5] Carvajal-Rodríguez, A. (2019). Multi-model inference of non-random mating from an information theoretic approach. bioRxiv, 305730, ver. 5 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/305730
[6] Rolán‐Alvarez, E., & Caballero, A. (2000). Estimating sexual selection and sexual isolation effects from mating frequencies. Evolution, 54(1), 30-36. doi: 10.1111/j.0014-3820.2000.tb00004.x
[7] Carvajal-Rodríguez, A., & Rolan-Alvarez, E. (2006). JMATING: a software for the analysis of sexual selection and sexual isolation effects from mating frequency data. BMC Evolutionary Biology, 6(1), 40. doi: 10.1186/1471-2148-6-40
[8] Carvajal-Rodríguez, A. (2018). Non-random mating and information theory. Theoretical population biology, 120, 103-113. doi: 10.1016/j.tpb.2018.01.003
[9] Carvajal-Rodríguez, A. (2019). A generalization of the informational view of non-random mating: Models with variable population frequencies. Theoretical population biology, 125, 67-74. doi: 10.1016/j.tpb.2018.12.004

In evolutionary biology, sexual selection is hypothesized to increase speciation rates in animals, as theory predicts that sexual selection will contribute to phenotypic diversification and affect rates of species accumulation at macro-evolutionary time scales. However, testing this hypothesis and gathering convincing evidence have proven difficult. Although some studies have shown a strong correlation between proxies of sexual selection and species diversity (mostly in birds), this relationship relies on some assumptions on the link between these proxies and the strength of sexual selection and is not detected in some other taxa, making taxonomically widespread conclusions impossible.

In a recent study published in Current Biology [1], Ellis and Oakley provide strong evidence that bioluminescent sexual displays have driven high species richness in taxonomically diverse animal lineages, providing a crucial link between sexual selection and speciation.
It was known that bioluminescence has evolved independently more than 40 times, with males often using it as a mating signal but with also some other possible adaptive functions including anti-predator defense and predation. Moreover, it has been reported that small marine lanternfishes and sharks that use bioluminescence in mate identification had a greater concentration of species than other deep-sea fishes that use bioluminescence for defensive purposes [2-4]. But no one had ever determined whether this pattern is consistent across diverse and distantly related animal groups living on sea and land.

Ellis and Oakley [1] explored the scientific literature for well-resolved evolutionary trees with branches containing bioluminescent lineages and identified lineages that use light for courtship or camouflage in a wide range of marine and terrestrial taxa including insects, crustaceans, cephalopods, segmented worms, and fishes. The researchers counted the number of species in each bioluminescent clade and found that all groups with light-courtship displays had more species and faster rates of species accumulation than their non-luminous most closely related sister lineages or ancestors. In contrast, those groups that used bioluminescence for predator avoidance had a lower than expected rate of species richness on average.

Nicely encompassing a diversity of taxa and neatly controlling for the rate of species accumulation of the encompassing clade, the results of Ellis and Oakley are clear-cut and provide the most comprehensive evidence to date for the hypothesis that sexual displays can act as drivers of speciation. One question this study incites is what is happening in terms of sexual selection in species displaying defensive bioluminescence or no bioluminescence at all: do those lineages use no mating signals at all or other mating signals that are less apparent, and will those experience lower levels of sexual selection than bioluminescent mating signals, i.e. consistent with Ellis and Oakley results? It would also be interesting to investigate the diversification rates in animal species using other modalities, such as chemical, acoustic or any other type of signals used by males, females or both sexes, to determine what types of sexual signals may be more generally drivers of speciation.

References

[1] Ellis EA, Oakley TH. 2016. High Rates of Species Accumulation in Animals with Bioluminescent Courtship Displays. Current Biology 26:1916–1921. doi: 10.1016/j.cub.2016.05.043

[2] Davis MP, Holcroft NI, Wiley EO, Sparks JS, Smith WL. 2014. Species-specific bioluminescence facilitates speciation in the deep sea. Marine Biology 161:1139­1148. doi: 10.1007/s00227-014-2406-x

[3] Davis MP, Sparks JS, Smith WL. 2016. Repeated and Widespread Evolution of Bioluminescence in Marine Fishes. PLoS One 11:e0155154. doi: 10.1371/journal.pone.0155154

[4] Claes JM, Nilsson D-E, Mallefet J, Straube N. 2015. The presence of lateral photophores correlates with increased speciation in deep-sea bioluminescent sharks. Royal Society Open Science 2:150219. doi: 10.1098/rsos.150219

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