Genetic variation is the raw material for selection to act upon and the amount of genetic variation present within a population is a pivotal determinant of a population’s evolutionary potential. A large effective population size, i.e., the ideal number of individuals experiencing the same amount of genetic drift and inbreeding as an actual population, Ne (Wright 1931, Crow 1954), thus increases the probability of long-term survival of a population. However, natural populations, as opposed to theoretical ones, rarely adhere to the requirements of an ideal panmictic population (Sjödin et al. 2005). A range of circumstances can reduce Ne, including the structuring of populations (through space and time, as well as age and developmental stages) and inbreeding (Charlesworth 2009). In mammals, species with a larger body mass (as a proxy for lower Ne) were found to have a higher rate of nonsynonymous nucleotide substitutions (that alter the amino acid sequence of a protein), as well as radical amino acid substitutions (altering the physicochemical properties of a protein) (Popadin et al. 2007). In general, low effective population sizes increase the chance of mutation accumulation and drift, while reducing the strength of selection (Sjödin et al. 2005).
In this paper, Weyna and Romiguier (2021) set out to test if parasitism, body size, geographic range, and/or eusociality affect the strength of selection in Hymenoptera. Hymenoptera include the bees, wasps and ants and is an extraordinarily diverse order within the insects. It was recently estimated that Hymenoptera is the most speciose order of the animal kingdom (Forbes et al. 2018). Hymenoptera are further characterized by an impressive radiation of parasitic species, mainly parasitoids, that feed in or on a single host individual to complete their own development (Godfray 1994). All hymenopterans share the same sex determination system: haplo-diploidy, where unfertilized eggs are haploid males and fertilized eggs are diploid females. Compared to other animals, Hymenoptera further contain an impressive number of clades that evolved eusociality (Rehan and Toth 2015), in which societies show a clear division of labor for reproduction (i.e., castes) and cooperative brood care. Hymenopterans thus represent a diverse and interesting group of insects to investigate potential factors affecting strength of selection and Ne.
Using a previously published phylogenomic dataset containing 3256 genes and 169 hymenopteran species (Peters et al. 2017), Weyna and Romiguier (2021) estimated mean genomic dN/dS ratios (nonsynonymous to synonymous substitution rates) for each species and compared these values between parasitic and non-parasitic species, eusocial and solitary species, and in relation to body size, parasitoid-specific traits and geographic range, thought to affect the effective population size and strength of selection. The use of a large number of species, as well as several distinct traits is a clear asset of this study. The authors found no effect of body size, geographic range or parasitism (including a range of parasitoid-specific traits). There was an effect, however, of eusociality where dN/dS increased in three out of four eusocial lineages. Future studies including more independent evolutionary transitions to eusociality can lend further support that eusocial species indeed reduces the efficiency of selection. The most intriguing result was that for solitary and social bees, with high dN/dS ratios and a strong signature of relaxed selection (i.e., the elimination or reduction of a source of selection (Lahti et al. 2009). The authors suggest that the pollen-collecting behaviors of these species can constrain Ne, as pollen availability varies at both a spatial and temporal scale, requiring a large investment in foraging that may in turn limit reproductive output. It would be interesting to see if other pollen feeders, such as certain beetles, flies, butterflies and moths, as well as mites and spiders, experience relaxed selection as a consequence of the trade-off between energy investment in pollen foraging versus fecundity.

References

Charlesworth, B. (2009). Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics, 10(3), 195-205. doi: https://doi.org/10.1038/nrg2526
Crow, J. F. (1954) Statistics and Mathematics in Biology (eds Kempthorne, O., Bancroft, T. A., Gowen, J. W. & Lush, J. L.) 543–556 (Iowa State Univ. Press, Ames, Iowa)
Forbes, A. A., Bagley, R. K., Beer, M. A., Hippee, A. C., and Widmayer, H. A. (2018). Quantifying the unquantifiable: why Hymenoptera, not Coleoptera, is the most speciose animal order. BMC ecology, 18(1), 1-11. doi: https://doi.org/10.1186/s12898-018-0176-x
Godfray, H. C. J. (1994) Parasitoids: Behavioral and Evolutionary Ecology. Vol. 67, Princeton University Press, 1994. doi: https://doi.org/10.2307/j.ctvs32rmp
Lahti et al. (2009). Relaxed selection in the wild. Trends in ecology & evolution, 24(9), 487-496. doi: https://doi.org/10.1016/j.tree.2009.03.010
Peters et al. (2017). Evolutionary history of the Hymenoptera. Current Biology, 27(7), 1013-1018. doi: https://doi.org/10.1016/j.cub.2017.01.027
Popadin, K., Polishchuk, L. V., Mamirova, L., Knorre, D., and Gunbin, K. (2007). Accumulation of slightly deleterious mutations in mitochondrial protein-coding genes of large versus small mammals. Proceedings of the National Academy of Sciences, 104(33), 13390-13395. doi: https://doi.org/10.1073/pnas.0701256104
Rehan, S. M., and Toth, A. L. (2015). Climbing the social ladder: the molecular evolution of sociality. Trends in ecology & evolution, 30(7), 426-433. doi: https://doi.org/10.1016/j.tree.2015.05.004
Sjödin, P., Kaj, I., Krone, S., Lascoux, M., and Nordborg, M. (2005). On the meaning and existence of an effective population size. Genetics, 169(2), 1061-1070. doi: https://doi.org/10.1534/genetics.104.026799
Weyna, A., and Romiguier, J. (2021) Relaxation of purifying selection suggests low effective population size in eusocial Hymenoptera and solitary pollinating bees. bioRxiv, 2020.04.14.038893, ver. 5 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.04.14.038893
Wright, S. (1931). Evolution in Mendelian populations. Genetics, 16(2), 97-159.

It is a classical result in the virulence evolution literature that treatments decreasing parasite replication within the host should select for higher replication rates, thus driving increased levels of virulence if the two are correlated. There is some evidence for this in vitro but very little in the field. HIV infections in humans offer a unique opportunity to go beyond the simple predictions that treatments should favour more virulent strains because many details of this host-parasite system are known, especially the link between set-point virus load, transmission rate and virulence.

To tackle this question, Herbeck et al. [1] used a detailed individual-based model. This is original because it allows them to integrate existing knowledge from the epidemiology and evolution of HIV (e.g. recent estimates of the ‘heritability’ of set-point virus load from one infection to the next). This detailed model allows them to formulate predictions regarding the effect of different treatment policies; especially regarding the current policy switch away from treatment initiation based on CD4 counts towards universal treatment.

The results show that, perhaps as expected from the theory, treatments based on the level of remaining host target cells (CD4 T cells) do not affect virulence evolution because they do not strongly affect the virulence level that maximizes HIV’s transmission potential. However, early treatments can lead to moderate increase in virulence within several years if coverage is high enough. These results seem quite robust to variation of all the parameters in realistic ranges.

The great step forward in this model is the ability to obtain quantitative prediction regarding how a virus may evolve in response to public health policies. Here the main conclusion is that given our current knowledge in HIV biology, the risk of virulence evolution is perhaps more limited than expected from a direct application of virulence evolution model. Interestingly, the authors also conclude that recently observed increased in HIV virulence [2-3] cannot be explained by the impact of antiretroviral therapy alone; which raises the question about the main mechanism behind this increase. Finally, the authors make the interesting suggestion that “changing virulence is amenable to being monitored alongside transmitted drug resistance in sentinel surveillance”.

References

[1] Herbeck JT, Mittler JE, Gottlieb GS, Goodreau SM, Murphy JT, Cori A, Pickles M, Fraser C. 2016. Evolution of HIV virulence in response to widespread scale up of antiretroviral therapy: a modeling study. Virus Evolution 2:vew028. doi: 10.1093/ve/vew028

[2] Herbeck JT, Müller V, Maust BS, Ledergerber B, Torti C, et al. 2012. Is the virulence of HIV changing? A meta-analysis of trends in prognostic markers of HIV disease progression and transmission. AIDS 26:193-205. doi: 10.1097/QAD.0b013e32834db418

[3] Pantazis N, Porter K, Costagliola D, De Luca A, Ghosn J, et al. 2014. Temporal trends in prognostic markers of HIV-1 virulence and transmissibility: an observational cohort study. Lancet HIV 1:e119-26. doi: 10.1016/s2352-3018(14)00002-2

The study by Pfenninger (2025) addresses the critical need to understand the genomic basis of ecologically important traits to better predict and respond to the impacts of global change on biodiversity (Gienapp et al. 2017). It introduces the popGWAS, a novel GWAS approach, which utilizes phenotypic population means and genome-wide allele frequency data, obtainable through methods like Pool-sequencing (Pool-Seq), to identify the genetic loci underlying quantitative polygenic traits in natural populations and predict their mean. The core idea is that trait-increasing alleles should exhibit higher frequencies in populations with higher mean trait values. popGWAS then maps mean allele frequencies across populations to their trait means. Working with as many allele frequency values as populations sampled, popGWAS potentially has more power to find significant associations at genomic loci than individual-based GWAS working with three genotypes at a locus. This new method addresses some of the problems faced by traditional genome-wide association studies (GWAS), which require extensive resources and large sample sizes, posing challenges for biodiversity research on non-model species in natural populations. 

To evaluate the effectiveness of popGWAS, Pfenninger (2025) conducted extensive population genetic forward simulations, examining scenarios with varying numbers of populations, ranging from 12 to 60. The results indicated that popGWAS performance improved with increasing sample size, showing a diminishing return above 36 populations. In a direct comparison across all simulation scenarios, popGWAS consistently outperformed individual-based GWAS (iGWAS). On average, popGWAS identified more true positive loci than iGWAS. In addition, when combined with minimum entropy feature selection (MEFS), popGWAS achieved large predictive accuracy of population means of 0.8 or better in over 97% of simulations with 36 or more populations, regardless of other parameters. In contrast, iGWAS failed to generate valid phenotypic predictions in over 70% of the simulations. Also, unlike iGWAS, popGWAS did not suffer from p-value inflation. Yet, population structure or varying levels of relatedness among individuals were not fully accounted for in the simulations. The extent to which popGWAS would be sensitive to such individual covariates remains to be shown. Finally, popGWAS was relatively insensitive to low trait heritability because random individual variation gets averaged out when calculating the population mean trait value. 

The study demonstrates that popGWAS is a promising approach, particularly for oligogenic and moderately polygenic traits. The method performs more poorly for polygenic traits with large genetic redundancy, where different alleles contribute to the same trait mean in different populations. The method thus performs better when large-effect loci contribute to genetic differentiation in parallel across populations, as expected when gene flow is moderate to high (Yeaman & Whitlock 2011). Low genomic predictability is reached when drift dominates or when genetic architectures are highly polygenic.

The popGWAS method proved effective with a moderate number of sampled populations and, when combined with machine learning for genomic prediction, exhibited strong performance in predicting population means, even for low-heritability traits. Notably, popGWAS consistently outperformed iGWAS in terms of identifying true positive loci and prediction accuracy. This suggests that popGWAS can make GWAS studies more accessible for biodiversity genomics research, providing a valuable tool for dissecting the genetic basis of complex traits in natural populations. A key aspect contributing to the efficiency of popGWAS is its compatibility with pooled sequencing (Pool-Seq). Pool-Seq provides estimates of allele frequencies within a population by sequencing a mixed DNA sample representing multiple individuals from that population (Futschik & Schlötterer 2010). This approach is significantly more cost-effective than sequencing each individual separately, allowing researchers to obtain genome-wide allele frequency data across multiple populations with a substantially reduced budget. This data efficiency makes GWAS more accessible to a wider range of researchers, particularly those working in biodiversity genomics where financial resources may be limited. Furthermore, popGWAS can be coupled with bulk phenotyping methods, such as automatic video recording, remote sensing, metabolomics/transcriptomics, etc., to efficiently obtain population-level phenotypic data, further streamlining the research process. Ultimately, popGWAS represents a valuable addition to the geneticist's toolkit, offering a complementary approach to iGWAS that can be particularly advantageous in specific research contexts where predicting trait mean is more important than resolving the precise genetic basis of a trait.

References

Futschik, A. and Schlötterer, C. 2010. The Next Generation of Molecular Markers From Massively Parallel Sequencing of Pooled DNA Samples. Genetics 186(1): 207-218. https://doi.org/10.1534/genetics.110.114397

Gienapp, P., Fior, S., Guillaume, F., Lasky, J. R., Sork, V. L. and Csilléry, K. 2017. Genomic Quantitative Genetics to Study Evolution in the Wild. Trends Ecol. Evol. 32(12): 897-908. https://doi.org/10.1016/j.tree.2017.09.004

Markus Pfenninger (2025) On the potential for GWAS with phenotypic population means and allele-frequency data (popGWAS). bioRxiv, ver.3 peer-reviewed and recommended by PCI Evol Biol https://doi.org/10.1101/2024.06.12.598621

Yeaman, S. and Whitlock, M. C. 2011. The genetic architecture of adaptation under migration-selection balance. Evolution 65(7): 1897-1911. https://doi.org/10.1111/j.1558-5646.2011.01269.x

The present-day distribution of extant species is the result of the interplay between their past population demography (e.g., expansion, contraction, isolation, and migration) and adaptation to the environment. Shedding light on the timing and magnitude of key demographic events helps identify potential drivers of such events and interaction of those drivers, such as life history traits and past episodes of environmental shifts.

The understanding of the key factors driving species evolution gives important insights into how the species may respond to changing conditions, which can be particularly relevant for the management of harmful species, such as agricultural pests (e.g. [1]). Meaningful demographic inferences present major challenges. These include formulating evolutionary scenarios fitting species biology and the eco-geographical context and choosing informative molecular markers and accurate quantitative approaches to statistically compare multiple demographic scenarios and estimate the parameters of interest. A further issue comes with result interpretation. Accurately dating the inferred events is far from straightforward since reliable calibration points are necessary to translate the molecular estimates of the evolutionary time into absolute time units (i.e. years). This can be attempted in different ways, such as by using fossil and archaeological records, heterochronous samples (e.g. ancient DNA), and/or mutation rate estimated from independent data (e.g. [2], [3] for review). Nonetheless, most experimental systems rarely meet these conditions, hindering the comprehensive interpretation of results.

The contribution of Chapuis et al. [4] addresses these issues to investigate the recent history of the African insect pest Schistocerca gregaria (desert locust). They apply Approximate Bayesian Computation-Random Forest (ABC-RF) approaches to microsatellite markers. Owing to their fast mutation rate microsatellite markers offer at least two advantages: i) suitability for analyzing recently diverged populations, and ii) direct estimate of the germline mutation rate in pedigree samples. The work of Chapuis et al. [4] benefits of both these advantages, since they have estimates of mutation rate and allele size constraints derived from germline mutations in the species [5].

The main aim of the study is to infer the history of divergence of the two subspecies of the desert locust, which have spatially disjoint distribution corresponding to the dry regions of North and West-South Africa. They first use paleo-vegetation maps to formulate hypotheses about changes in species range since the last glacial maximum. Based on them, they generate 12 divergence models. For the selection of the demographic model and parameter estimation, they apply the recently developed ABC-RF approach, a powerful inferential tool that allows optimizing the use of summary statistics information content, among other advantages [6]. Some methodological novelties are also introduced in this work, such as the computation of the error associated with the posterior parameter estimates under the best scenario. The accuracy of timing estimate is assured in two ways: i) by the use of microsatellite markers with known evolutionary dynamics, as underlined above, and ii) by assessing the divergence time threshold above which posterior estimates are likely to be biased by size homoplasy and limits in allele size range [7]. The best-supported model suggests a recent divergence event of the subspecies of S. gregaria (around 2.6 kya) and a reduction of populations size in one of the subspecies (S. g. flaviventris) that colonized the southern distribution area. As such, results did not support the hypothesis that the southward colonization was driven by the expansion of African dry environments associated with the last glacial maximum, as it has been postulated for other arid-adapted species with similar African disjoint distributions [8]. The estimated time of divergence points at a much more recent origin for the two subspecies, during the late Holocene, in a period corresponding to fairly stable arid conditions similar to current ones [9,10].

Although the authors cannot exclude that their microsatellite data bear limited information on older colonization events than the last one, they bring arguments in favour of alternative explanations. The hypothesis privileged does not involve climatic drivers, but the particularly efficient dispersal behaviour of the species, whose individuals are able to fly over long distances (up to thousands of kilometers) under favourable windy conditions. A single long-distance dispersal event by a few individuals would explain the genetic signature of the bottleneck. There is a growing number of studies in phylogeography in arid regions in the Southern hemisphere, but the impact of past climate changes on the species distribution in this region remains understudied relative to the Northern hemisphere [11,12].

The study presented by Chapuis et al. [4] offers several important insights into demographic changes and the evolutionary history of an agriculturally important pest species in Africa, which could also mirror the history of other organisms in the continent. As the authors point out, there are necessarily some uncertainties associated with the models of past ecosystems and climate, especially for Africa. Interestingly, the authors argue that the information on paleo-vegetation turnover was more informative than climatic niche modeling for the purpose of their study since it made them consider a wider range of bio-geographical changes and in turn a wider range of evolutionary scenarios (see discussion in Supplementary Material). Microsatellite markers have been offering a useful tool in population genetics and phylogeography for decades, but their popularity is perhaps being taken over by single nucleotide polymorphism (SNP) genotyping and whole-genome sequencing (WGS) (the peak year of the number of the publication with “microsatellite” is in 2012 according to PubMed).

This study reaffirms the usefulness of these classic molecular markers to estimate past demographic events, especially when species- and locus-specific microsatellite mutation features are available and a powerful inferential approach is adopted. Nonetheless, there are still hurdles to overcome, such as the limitations in scenario choice associated with the simulation software used (e.g. not allowing for continuous gene flow in this particular case), which calls for further improvement of simulation tools allowing for more flexible modeling of demographic events and mutation patterns. In sum, this work not only contributes to our understanding of the makeup of the African biodiversity but also offers a useful statistical framework, which can be applied to a wide array of species and molecular markers (microsatellites, SNPs, and WGS).

References

[1] Lehmann, P. et al. (2018). Complex responses of global insect pests to climate change. bioRxiv, 425488. doi: https://dx.doi.org/10.1101/425488

[2] Donoghue, P. C., & Benton, M. J. (2007). Rocks and clocks: calibrating the Tree of Life using fossils and molecules. Trends in Ecology & Evolution, 22(8), 424-431. doi: https://dx.doi.org/10.1016/j.tree.2007.05.005

[3] Ho, S. Y., Lanfear, R., Bromham, L., Phillips, M. J., Soubrier, J., Rodrigo, A. G., & Cooper, A. (2011). Time‐dependent rates of molecular evolution. Molecular ecology, 20(15), 3087-3101. doi: https://dx.doi.org/10.1111/j.1365-294X.2011.05178.x

[4] Chapuis, M.-P., Raynal, L., Plantamp, C., Meynard, C. N., Blondin, L., Marin, J.-M. and Estoup, A. (2020). A young age of subspecific divergence in the desert locust Schistocerca gregaria, inferred by ABC Random Forest. bioRxiv, 671867, ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. doi: https://dx.doi.org/10.1101/671867

5] Chapuis, M.-P., Plantamp, C., Streiff, R., Blondin, L., & Piou, C. (2015). Microsatellite evolutionary rate and pattern in Schistocerca gregaria inferred from direct observation of germline mutations. Molecular ecology, 24(24), 6107-6119. doi: https://dx.doi.org/10.1111/mec.13465

[6] Raynal, L., Marin, J. M., Pudlo, P., Ribatet, M., Robert, C. P., & Estoup, A. (2018). ABC random forests for Bayesian parameter inference. Bioinformatics, 35(10), 1720-1728. doi: https://dx.doi.org/10.1093/bioinformatics/bty867

[7] Estoup, A., Jarne, P., & Cornuet, J. M. (2002). Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Molecular ecology, 11(9), 1591-1604. doi: https://dx.doi.org/10.1046/j.1365-294X.2002.01576.x

[8] Moodley, Y. et al. (2018). Contrasting evolutionary history, anthropogenic declines and genetic contact in the northern and southern white rhinoceros (Ceratotherium simum). Proceedings of the Royal Society B, 285(1890), 20181567. doi: https://dx.doi.org/10.1098/rspb.2018.1567

[9] Kröpelin, S. et al. (2008). Climate-driven ecosystem succession in the Sahara: the past 6000 years. science, 320(5877), 765-768. doi: https://dx.doi.org/10.1126/science.1154913

[10] Maley, J. et al. (2018). Late Holocene forest contraction and fragmentation in central Africa. Quaternary Research, 89(1), 43-59. doi: https://dx.doi.org/10.1017/qua.2017.97

[11] Beheregaray, L. B. (2008). Twenty years of phylogeography: the state of the field and the challenges for the Southern Hemisphere. Molecular Ecology, 17(17), 3754-3774. doi: https://dx.doi.org/10.1111/j.1365-294X.2008.03857.x

[12] Dubey, S., & Shine, R. (2012). Are reptile and amphibian species younger in the Northern Hemisphere than in the Southern Hemisphere?. Journal of evolutionary biology, 25(1), 220-226. doi: https://dx.doi.org/10.1111/j.1420-9101.2011.02417.x

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A video about this preprint is available here:

Given the general importance of protein expression levels, in cells it is widely accepted that gene expression levels are often a target of natural selection and that most mutations affecting gene expression levels are therefore likely to be deleterious [1]. However, it is perhaps less obvious that the strength of selection on the regulated genes themselves may be influenced by their expression levels. This might be due to harmful effects of misfolded proteins, for example, when higher protein concentrations exist in cells [2]. Recent studies have suggested that highly expressed genes accumulate fewer deleterious mutations; thus a positive relationship appears to exist between gene expression levels and the relative strength of purifying selection [3].

The recommended paper by Trucchi et al. [4] examines the relationship between gene expression, purifying selection and a third variable -- effective population size -- in populations of two species of penguin with different population sizes, the Emperor penguin (Aptenodytes forsteri) and the King penguin (A. patagonicus). Using transcriptomic data and computer simulations modeling selection, they examine patterns of nonsynonymous and synonymous segregating polymorphisms (p) across genes in the two populations, concluding that even in relatively small populations purifying selection has an important effect in eliminating deleterious mutations. 

References

1] Gilad Y, Oshlack A, and Rifkin SA. 2006. Natural selection on gene expression. Trends in Genetics 22: 456-461. https://doi.org/10.1016/j.tig.2006.06.002
 
[2] Yang JR, Liao BY, Zhuang SM, and Zhang J. 2012. Protein misinteraction avoidance causes highly expressed proteins to evolve slowly. Proceedings of the National Academy of Sciences 109: E831-E840. https://doi.org/10.1073/pnas.1117408109
 
[3] Duret L, and Mouchiroud D (2000). Determinants of substitution rates in mammalian genes: expression pattern affects selection intensity but not mutation rate. Molecular Biology and Evolution 17; 68-070. https://doi.org/10.1093/oxfordjournals.molbev.a026239

[4] Trucchi E, Massa P, Giannelli F, Latrille T, Fernandes FAN, Ancona L, Stenseth NC, Obiol JF, Paris J, Bertorelle G, and Le Bohec, C. 2023. Gene expression is the main driver of purifying selection in large penguin populations. bioRxiv 2023.08.08.552445, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2023.08.08.552445