The observed levels of genomic diversity in contemporary populations are the result of changes imposed by several evolutionary processes. Among them, natural selection is known to dramatically shape the genetic diversity of loci associated with phenotypes which affect the fitness of carriers. As such, many efforts have been dedicated towards developing methods to detect signatures of natural selection from genomes of contemporary samples [1].
Recent technological advances made the generation of large-scale genomic data from temporal samples, either from experimental populations or historical or ancient samples, accessible to a wide scientific community [2]. Notably, temporal population genomic data allow for a direct observation and study of how, for instance, allele frequencies change through time in response to evolutionary stimuli. Such information can be exploited to detect loci under natural selection, either via mathematical modelling or by investigating empirical distributions [3].
However, most of current methods to detect selection from temporal genomic data have largely ignored selfing populations, despite the latter comprising a significant proportion of species with social and economic importance. Selfing changes genomic patterns by reducing the effective recombination rate, which makes distinguishing between neutral evolution and natural selection even more challenging than for the case of outcrossing populations [4]. Nevertheless, an outlier-approach based on temporal genomic data for the selfing Arabidopsis thaliana population revealed loci under selection [5].
This study suggested the promise of detecting selection for selfing populations and encouraged further investigations to test the power of selection scans under different mating systems.
To address this question, Navascués et al. [6] extended a previously proposed approach for temporal genome scan [7] to incorporate partial self-fertilization. In the original implementation [7], it is assumed that, under neutrality, all loci provide levels of genetic differentiation drawn from the same distribution. If some of the loci are under selection, such distribution should show heterogeneity. Navascués et al. [6] proposed a test for the homogeneity between loci-specific and genome-wide differentiation by deriving a null distribution of FST via simulations using SLiM [8]. After filtering for low-frequency variants and correct for multiple tests, authors derived a statistical test for selection and assess its power under a wide range of scenarios of selfing rate, selection coefficient, duration and type of selection [6].
The newly proposed test achieved good performance to distinguish between neutral and selected loci in most tested scenarios.
As expected, the test's performance significantly drops for scenarios of high selfing rates and selection from standing variation. Additionally, the probability to correctly detect selection decreases with increasing distance from the causal variant. Intriguingly, the test showed high power when the selected ancestral allele had an initial low frequency, and when the selected derived allele had a high initial frequency. When applied to a data set of around 1,000 SNPs from the highly selfing Medicago truncatula population, an annual plant of the legume family [9], the test did not provide any candidate loci under selection [6].
In summary, the detection of loci under selection in selfing populations is and largely remains a challenging task even when explictly account for the different mating system. However, recombination events that occurred before the selective pressure allow ancestral beneficial alleles to exhibit a detectable pattern of non-neutrality. As such, in partially selfing populations, the strength of the footprint of selection depends on several factors, mostly on the selfing rate, the time of onset and type of selection.
One major assumption of this study is that the model implies unstructured population and continuity between samples obtained from the same geographical location over time. As such assumptions are typically violated in real populations, further research into the effect of more complex demographic scenarios is desired to fully understand the power to detect selection in selfing populations. Furthermore, more power could be gained by including additional genomic information at each time point. In this context, recent approaches that make full use of genomic data based on deep learning [10] may contribute significantly towards this goal. Similarly, the effect of data filtering on the power to detect selection should be further explored, especially in the context of DNA resequencing experiments. These analyses will help elucidate the power offered by selection scans from temporal genomic data in selfing populations.

References

[1] Stern AJ, Nielsen R (2019) Detecting Natural Selection. In: Handbook of Statistical Genomics , pp. 397–40. John Wiley and Sons, Ltd. https://doi.org/10.1002/9781119487845.ch14
[2] Leonardi M, Librado P, Der Sarkissian C, Schubert M, Alfarhan AH, Alquraishi SA, Al-Rasheid KAS, Gamba C, Willerslev E, Orlando L (2017) Evolutionary Patterns and Processes: Lessons from Ancient DNA. Systematic Biology, 66, e1–e29. https://doi.org/10.1093/sysbio/syw059
[3] Dehasque M, Ávila‐Arcos MC, Díez‐del‐Molino D, Fumagalli M, Guschanski K, Lorenzen ED, Malaspinas A-S, Marques‐Bonet T, Martin MD, Murray GGR, Papadopulos AST, Therkildsen NO, Wegmann D, Dalén L, Foote AD (2020) Inference of natural selection from ancient DNA. Evolution Letters, 4, 94–108. https://doi.org/10.1002/evl3.165
[4] Vitalis R, Couvet D (2001) Two-locus identity probabilities and identity disequilibrium in a partially selfing subdivided population. Genetics Research, 77, 67–81. https://doi.org/10.1017/S0016672300004833
[5] Frachon L, Libourel C, Villoutreix R, Carrère S, Glorieux C, Huard-Chauveau C, Navascués M, Gay L, Vitalis R, Baron E, Amsellem L, Bouchez O, Vidal M, Le Corre V, Roby D, Bergelson J, Roux F (2017) Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time. Nature Ecology and Evolution, 1, 1551–1561. https://doi.org/10.1038/s41559-017-0297-1
[6] Navascués M, Becheler A, Gay L, Ronfort J, Loridon K, Vitalis R (2020) Power and limits of selection genome scans on temporal data from a selfing population. bioRxiv, 2020.05.06.080895, ver. 4 peer-reviewed and recommended by PCI Evol Biol. https://doi.org/10.1101/2020.05.06.080895
[7] Goldringer I, Bataillon T (2004) On the Distribution of Temporal Variations in Allele Frequency: Consequences for the Estimation of Effective Population Size and the Detection of Loci Undergoing Selection. Genetics, 168, 563–568. https://doi.org/10.1534/genetics.103.025908
[8] Messer PW (2013) SLiM: Simulating Evolution with Selection and Linkage. Genetics, 194, 1037–1039. https://doi.org/10.1534/genetics.113.152181
[9] Siol M, Prosperi JM, Bonnin I, Ronfort J (2008) How multilocus genotypic pattern helps to understand the history of selfing populations: a case study in Medicago truncatula. Heredity, 100, 517–525. https://doi.org/10.1038/hdy.2008.5
[10] Sanchez T, Cury J, Charpiat G, Jay F Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation. Molecular Ecology Resources, n/a. https://doi.org/10.1111/1755-0998.13224

Studying the effects of speciation, hybridisation, and evolutionary outcomes following reproduction from divergent populations is a major research area in evolutionary genetics [1]. There are two phenomena that have been the focus of contemporary research. First, a classic concept is the formation of ‘Bateson-Dobzhansky-Muller’ incompatibilities (BDMi) [2–4] that negatively affect hybrid fitness. Here, two diverging populations accumulate mutations over time that are unique to that subpopulation. If they subsequently meet, then these mutations might negatively interact, leading to a loss in fitness or even a complete lack of reproduction. BDMi formation can be complex, involving multiple genes and the fitness changes can depend on the direction of introgression [5]. Second, such secondary contact can instead lead to heterosis, where offspring are fitter than their parental progenitors [6].

Understanding which outcomes are likely to arise require one to know the potential fitness effects of mutations underlying reproductive isolation, to determine whether they are likely to reduce or enhance fitness when hybrids are formed. This is far from an easy task, as it requires one to track mutations at several loci, along with their effects, across a fitness landscape.

The work of De Sanctis et al. [7] neatly fills in this knowledge gap, by creating a general mathematical framework for describing the consequences of a cross from two divergent populations. The derivations are based on Fisher’s Geometric Model, which is widely used to quantify selection acting on a general fitness landscape that is affected by several biological traits [8,9], and has previously been used in theoretical studies of hybridisation [10–12]. By doing so, they are able to decompose how divergence at multiple loci affects offspring fitness through both additive and dominance effects.

A key result arising from their analyses is demonstrating how offspring fitness can be captured by two main functions. The first one is the ‘net effect of evolutionary change’ that, broadly defined, measures how phenotypically divergent two populations are. The second is the ‘total amount of evolutionary change’, which reflects how many mutations contribute to divergence and the effect sizes captured by each of them. The authors illustrate these measurements using simulations covering different scenarios, demonstrating how different parental states can lead to similar fitness outcomes. They also propose experimental methods to measure the underlying mutational effects.

This study neatly demonstrates how complex genetic phenomena underlying hybridisation can be captured using fairly simple mathematical formulae. This powerful approach will thus open the door for future research to investigate hybridisation in more detail, whether it is by expanding on these theoretical models or using the elegant outcomes to quantify fitness effects in experiments.

References

1. Coyne JA, Orr HA. Speciation. Sunderland, Mass: Sinauer Associates; 2004.
2. Bateson W, Seward A. Darwin and modern science. Heredity and variation in modern lights. 1909;85: 101. https://doi.org/10.1017/CBO9780511693953.007
3. Dobzhansky T. Genetics and the Origin of Species. Columbia university press; 1937.
4. Muller HJ. Isolating mechanisms, evolution and temperature. Biol Symp. 1942;6: 71-125.
5. Fraïsse C, Elderfield JAD, Welch JJ. The genetics of speciation: are complex incompatibilities easier to evolve? J Evol Biol. 2014;27: 688-699. https://doi.org/10.1111/jeb.12339
6. Birchler JA, Yao H, Chudalayandi S, Vaiman D, Veitia RA. Heterosis. The Plant Cell. 2010;22: 2105-2112. https://doi.org/10.1105/tpc.110.076133
7. De Sanctis B, Schneemann H, Welch JJ. How does the mode of evolutionary divergence affect reproductive isolation? bioRxiv. 2022. 2022.03.08.483443 version 4. https://doi.org/10.1101/2022.03.08.483443 
8. Fisher RA. The genetical theory of natural selection. Oxford: The Clarendon Press; 1930. https://doi.org/10.5962/bhl.title.27468 
9. Tenaillon O. The Utility of Fisher's Geometric Model in Evolutionary Genetics. Annu Rev Ecol Evol Syst. 2014;45: 179-201. https://doi.org/10.1146/annurev-ecolsys-120213-091846
10. Barton NH. The role of hybridization in evolution. Molecular Ecology. 2001;10: 551-568. https://doi.org/10.1046/j.1365-294x.2001.01216.x 
11. Chevin L-M, Decorzent G, Lenormand T. Niche Dimensionality and The Genetics of Ecological Speciation. Evolution. 2014;68: 1244-1256. https://doi.org/10.1111/evo.12346 
12. Fraïsse C, Gunnarsson PA, Roze D, Bierne N, Welch JJ. The genetics of speciation: Insights from Fisher's geometric model. Evolution. 2016;70: 1450-1464. https://doi.org/10.1111/evo.12968

Partitioning the effects of vicariance and low dispersal has been a long-standing problem in historical biogeography and phylogeography. While the term “vicariance” refers to divergence in allopatry, caused by some physical (geological, geographical) or climatic barriers (e.g. Rosen 1978), isolation by distance refers to the genetic differentiation of remote populations due to the physical distance separating them, when the latter surpasses the scale of dispersal (Wright 1938, 1940, 1943). 

Vicariance and dispersal have long been considered as separate forces leading to separate scenarii of speciation (e.g. reviewed in Hickerson and Meyer 2008). Nevertheless, these two processes are strongly linked, as, for example, vicariance theory relies on the assumption that ancestral lineages were once linked by dispersal prior to physical or climatic isolation (Rosen 1978). Low dispersal and vicariance are not mutually exclusive, and distinguishing these two processes in heterogeneous landscapes, especially for poor dispersers, remains therefore a severe challenge. For example, low dispersal (and/or small population size) can give rise to geographic patterns consistent with a phylogeographic break and be mistaken for geographic isolation (Irwin 2002, Kuo and Avise 2005).

The study of Rancilliac and colleagues (2023) is at the heart of this issue. It focuses on a nominal lizard species, the red-tailed spiny-footed lizard (Acanthodactylus erythrurus, Squamata: Lacertidae), which has a wide spatial distribution (from the Maghreb to the Iberian Peninsula), is found in a variety of different habitats, and has a wide range of morphological traits that do not always correlate with phylogeny. The main question is the following: have “the morphological and ecological diversification of this group been produced by vicariance and lineage diversification, or by local adaptation in the face of historical gene flow?” To tackle this question, the authors used sequence data from multiple mitochondrial and nuclear markers and a nested analysis workflow integrating phylogeography, multiple correspondence analyses and a relatively novel approach to IBD testing (Hausdorf & Henning, 2020). The latter is based on regression analysis and was shown to be less prone to error than the traditional (partial) Mantel test. 

While this set of methods allowed the partitioning of the effect of isolation by distance and vicariance in shaping contemporary genetic diversity in red-tailed spiny-footed lizards, some of the evolutionary history of this species complex remains blurred by ongoing gene flow and admixture, retention of ancestral polymorphism, or selection. The lack of congruence between mitochondrial and nuclear gene trees once again warns us that proposing evolutionary scenarii based on individual gene trees is a risky business. 

References

Hausdorf B, Hennig C (2020) Species delimitation and geography. Molecular Ecology Resources, 20, 950–960. https://doi.org/10.1111/1755-0998.13184

Hickerson MJ, Meyer CP (2008) Testing comparative phylogeographic models of marine vicariance and dispersal using a hierarchical Bayesian approach. BMC Evolutionary Biology, 8, 322. https://doi.org/10.1186/1471-2148-8-322

Irwin DE (2002) Phylogeographic breaks without geographic barriers to gene flow. Evolution, 56, 2383–2394. https://doi.org/10.1111/j.0014-3820.2002.tb00164.x

Kuo C-H, Avise JC (2005) Phylogeographic breaks in low-dispersal species: the emergence of concordance across gene trees. Genetica, 124, 179–186. https://doi.org/10.1007/s10709-005-2095-y

Rancilhac L, Miralles A, Geniez P, Mendez-Aranda D, Beddek M, Brito JC, Leblois R, Crochet P-A (2023) Phylogeographic breaks and how to find them: An empirical attempt at separating vicariance from isolation by distance in a lizard with restricted dispersal. bioRxiv, 2022.09.30.510256, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.09.30.510256

Rosen DE (1978) Vicariant Patterns and Historical Explanation in Biogeography. Systematic Biology, 27, 159–188. https://doi.org/10.2307/2412970

Wright, S (1938) Size of population and breeding structure in relation to evolution. Science 87:430-431.

Wright S (1940) Breeding Structure of Populations in Relation to Speciation. The American Naturalist, 74, 232–248. https://doi.org/10.1086/280891

Wright S (1943) Isolation by distance. Genetics, 28, 114–138. https://doi.org/10.1093/genetics/28.2.114

The long-distance transport and introduction of new species by humans is increasingly leading divergent lineages to interact, and sometimes interbreed, even after thousands or millions of years of separation. It is thus of prime importance to understand the consequences of these contemporary admixture events on the evolutionary fitness of interacting organisms, and their ecological implications.

Ciona robusta and Ciona intestinalis are two species of sea squirts that diverged between 1.5 and 2 million years ago and recently came into contact again. This occurred through human-mediated introduction of C. robusta (native to the Northwest Pacific) into the range of C. intestinalis (the English channeled Northeast Atlantic). In this study, Fraïsse et al. (2022) follow up on earlier work by Le Moan et al. (2021), who had identified a long genomic hotspot of introgression of C. robusta ancestry segments in chromosome 5 of C. intestinalis. The hotspot bears suggestive evidence of positive selection and the authors aimed to investigate this further using fully phased whole-genome sequences.

The authors narrow down on the exact boundaries of the introgressed region, and make a compelling case that it has been the likely target of positive selection after introgression, using various complementary approaches based on patterns of population differentiation, haplotype structure and local levels of diversity in the region. Using extensive demographic modeling, they also show that the introgression event was likely recent (approximately 75 years ago), and distinct from other tracts in the C. intestinalis genome that are likely a product of more ancient episodes of interbreeding in the past 30,000 years. Narrowing down on the potential drivers of selection, the authors show that candidate SNPs in the region overlap with the cytochrome family 2 subfamily U gene - involved in the detoxification of exogenous compounds - potentially reflecting adaptation to chemicals encountered in the sea squirt's environment. There also appears to be copy number variation at the candidate SNPs, which provides clues into the adaptation mechanism in the region.

All reviewers agreed that the work carried out by the authors is elegant and the results are robustly supported and well presented. In a round of reviews, various clarifications of the manuscript were suggested by the reviewers, including on the quality of the newly generated sequencing data, and some suggestions for qualifications on the conclusions reached by the authors as well as changes in the figures to increase their clarity. The authors addressed the different concerns of the reviewers, and the new version is much improved. 

This study into human-mediated introgression and its consequences for adaptation is, in my view, both well thought-out and executed. I therefore provide an enthusiastic recommendation of this manuscript.

References

Fraïsse C, Le Moan A, Roux C, Dubois G, Daguin-Thiébaut C, Gagnaire P-A, Viard F and Bierne N (2022) Introgression between highly divergent sea squirt genomes: an adaptive breakthrough? bioRxiv, 2022.03.22.485319, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.03.22.485319

Le Moan A, Roby C, Fraïsse C, Daguin-Thiébaut C, Bierne N, Viard F (2021) An introgression breakthrough left by an anthropogenic contact between two ascidians. Molecular Ecology, 30, 6718–6732. https://doi.org/10.1111/mec.16189

Artificial selection experiments are key experiments in evolutionary biology. The demonstration that application of selective pressure across multiple generations results in heritable phenotypic changes is a tangible and reproducible proof of the evolution by natural selection.
Artificial selection experiments are used to evaluate the joint effects of selection on multiple traits, their genetic covariances and differences in responses in different environments. Most studies on artificial selection experiments report and base their analyses on phenotypic changes [1]. More recently, changes in allele frequency and other patterns of molecular genetic diversity have been used to identify genomic locations where selection has had an effect. However, so far the changes in gene expression have not been in the focus of artificial selection experiment studies (see [2] for an example though).
In plants, one of the most famous artificial selection experiments is the Illinois Corn Experiment where maize (Zea mays) is selected for oil and protein content [3], but in addition, similar experiments have been conducted also for other traits in maize. In Saclay divergent selection experiment [4] two maize inbred lines (F252 and MBS847) have been selected for early and late flowering for 13 generations, resulting in two week difference in flowering time.
In ”Transcriptomic response to divergent selection for flowering time in maize reveals convergence and key players of the underlying gene regulatory network ” [5] Maud Tenaillon and her coworkers study the gene expression differences among these two independently selected maize populations. Their experiments cover two years in field conditions and they use samples of shoot apical meristem at three different developmental stages: vegetative, transitioning and reproductive. They use RNA-seq transcriptome level differences and qRT-PCR for gene expression pattern investigation. The work is continuation to earlier genetic and phenotypic studies on the same material [4, 6].
The reviewers and I agree that dataset is unique and its major benefit is that it has been obtained from field conditions similar to those that species may face under natural setting during selection. Their tissue sampling is supported by flowering time phenotypic observations and covers the developmental transition stage, making a good effort to identify key transcriptional and phenotypic changes and their timing affected by selection.
Tenaillon et al. [5] identify more than 2000 genes that are differentially expressed among early and late flowering populations. Expectedly, they are enriched for known flowering time genes. As they point out, differential expression of thousands of genes does not mean that they all were independently affected by selection, but rather that the whole transcriptional network has shifted, possibly due to just few upstream or hub-genes. Also, the year-to-year variation had smaller effect in gene expression compared to developmental stage or genetic background, possibly indicating selection for stability across environmental fluctuation for such an important phenotype as flowering time.
Another noteworthy observation is that they find convergent patterns of transcriptional changes among the two selected lines. 115 genes expression patterns are shifted due to selection in both genetic backgrounds. This convergent pattern can be a result of either selection on standing variation or de novo mutations. The data does not allow testing which process is underlying the observed convergence. However, their results show that this is an interesting future question that can be addressed using genotype and gene expression data from the same ancestral and derived material and possibly their hybrids.

References

[1] Hill, W. G., & Caballero, A. (1992). Artificial selection experiments. Annual Review of Ecology and Systematics, 23(1), 287-310. doi: 10.1146/annurev.es.23.110192.001443
[2] Konczal, M., Babik, W., Radwan, J., Sadowska, E. T., & Koteja, P. (2015). Initial molecular-level response to artificial selection for increased aerobic metabolism occurs primarily through changes in gene expression. Molecular biology and evolution, 32(6), 1461-1473. doi: 10.1093/molbev/msv038
[3] Moose, S. P., Dudley, J. W., & Rocheford, T. R. (2004). Maize selection passes the century mark: a unique resource for 21st century genomics. Trends in plant science, 9(7), 358-364. doi: 10.1016/j.tplants.2004.05.005
[4] Durand, E., Tenaillon, M. I., Ridel, C., Coubriche, D., Jamin, P., Jouanne, S., Ressayre, A., Charcosset, A. and Dillmann, C. (2010). Standing variation and new mutations both contribute to a fast response to selection for flowering time in maize inbreds. BMC evolutionary biology, 10(1), 2. doi: 10.1186/1471-2148-10-2
[5] Tenaillon, M. I., Seddiki, K., Mollion, M., Le Guilloux, M., Marchadier, E., Ressayre, A. and Dillmann C. (2019). Transcriptomic response to divergent selection for flowering time in maize reveals convergence and key players of the underlying gene regulatory network. BioRxiv, 461947 ver. 5 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/461947
[6] Durand, E., Tenaillon, M. I., Raffoux, X., Thépot, S., Falque, M., Jamin, P., Bourgais A., Ressayre, A. and Dillmann, C. (2015). Dearth of polymorphism associated with a sustained response to selection for flowering time in maize. BMC evolutionary biology, 15(1), 103. doi: 10.1186/s12862-015-0382-5