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

In this instructive review, Stritt and Gagneux offer a balanced perspective on the evolutionary forces shaping Mycobacterium tuberculosis and make the case that our instinct for storytelling be balanced with quantitative models. M. tuberculosis is perhaps the best-known clonal bacterial pathogen – evolving largely in the absence of horizontal gene transfer. Its genome is full of puzzling patterns, including much higher GC content than most intracellular pathogens (which suggests efficient selection to resist AT-skewed mutational bias) but a very high ratio of nonsynonymous to synonymous substitution rates (dN/dS ~ 0.5, typically interpreted as weak selection against deleterious amino acid changes). 

The authors offer alternative explanations for these patterns, framing the question: is M. tuberculosis evolution shaped mainly by drift or by efficient selection? They propose that this question can only be answered by accounting for the pathogen’s extreme clonality. A clonal lifestyle can have its advantages, for example when adaptations must arise in a particular order (Kondrashov and Kondrashov 2001). An important disadvantage highlighted by the authors are linkage effects: without recombination to shuffle them up, beneficial mutations are linked to deleterious mutations in the same genome (hitchhiking) and purging deleterious mutations also purges neutral diversity across the genome (background selection). The authors propose the latter – efficient purifying selection and strong linkage – as an explanation for the low genetic diversity observed in M. tuberculosis. This is of course not exclusive of other related explanations, such as clonal interference (Gerrish and Lenski 1998). They also champion the use of forward evolutionary simulations (Haller and Messer 2019) to model the interplay between selection, recombination, and demography as a powerful alternative to traditional backward coalescent models.

At times, Stritt and Gagneux are pessimistic about our existing methods – arguing that dN/dS and homoplasies “tell us little about the frequency and strength of selection.” Even though I favour a more optimistic view, I fully agree that our traditional population genetic metrics are sensitive to a slew of different deviations from a standard neutral evolution model and must be interpreted with caution. As I and others have argued, the extent of recombination (measured as the amount of linkage in a genome) is a key factor in determining how best to test for natural selection (Shapiro et al. 2009) and to conduct genotype-phenotype association studies (Chen and Shapiro 2021) in microbes. While this article is focused on the well-studied M. tuberculosis complex, there are many parallels with other clonal bacteria, including pathogens and symbionts. Whatever your favourite bug, we must all be careful to make sure the stories we tell about them are not “just so tales” but are supported, to the extent possible, by data and quantitative models.

References

Chen, Peter E., and B. Jesse Shapiro. 2021. "Classic Genome-Wide Association Methods Are Unlikely to Identify Causal Variants in Strongly Clonal Microbial Populations." bioRxiv. 
https://doi.org/10.1101/2021.06.30.450606
 
Gerrish, P. J., and R. E. Lenski. 1998. "The Fate of Competing Beneficial Mutations in an Asexual Population." Genetica 102-103 (1-6): 127-44.
https://doi.org/10.1023/A:1017067816551
 
Haller, Benjamin C., and Philipp W. Messer. 2019. "SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model." Molecular Biology and Evolution 36 (3): 632-37.
https://doi.org/10.1093/molbev/msy228
 
Kondrashov, F. A., and A. S. Kondrashov. 2001. "Multidimensional Epistasis and the Disadvantage of Sex." Proceedings of the National Academy of Sciences of the United States of America 98 (21): 12089-92.
https://doi.org/10.1073/pnas.211214298
 
Shapiro, B. Jesse, Lawrence A. David, Jonathan Friedman, and Eric J. Alm. 2009. "Looking for Darwin's Footprints in the Microbial World." Trends in Microbiology 17 (5): 196-204.
https://doi.org/10.1016/j.tim.2009.02.002 

Stritt, C., Gagneux, S. (2023). How do monomorphic bacteria evolve? The Mycobacterium tuberculosis complex and the awkward population genetics of extreme clonality. EcoEvoRxiv, ver.3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.32942/X2GW2P

Sirtuin proteins are class III histone deacetylases that are involved in a variety of fundamental biological functions mostly related to aging. These proteins are located in different subcellular compartments and are associated with different biological functions such as metabolic regulation, stress response, and cell cycle control [1]. In mammals, the sirtuin gene family is composed of seven paralogs (SIRT1-7) grouped into four classes [2]. Due to their involvement in maintaining cell cycle integrity, sirtuins have been studied as a way to understand fundamental mechanisms governing longevity [1]. Indeed, the downregulation of sirtuin genes with aging seems to explain much of the pathophysiology that accumulates with aging [3]. Biomedical studies have thus explored the potential therapeutic implications of sirtuins [4] but whether they can effectively be used as molecular targets for the treatment of human diseases remains to be demonstrated [1]. Despite this biomedical interest and some phylogenetic analyses of sirtuin paralogs mostly conducted in mammals, a comprehensive evolutionary analysis of the sirtuin gene family at the scale of vertebrates was still lacking.

In this preprint, Opazo and collaborators [5] took advantage of the increasing availability of whole-genome sequences for species representing all main groups of vertebrates to unravel the evolution of the sirtuin gene family. To do so, they undertook a phylogenomic approach in its original sense aimed at improving functional predictions by evolutionary analysis [6] in order to inventory the full vertebrate sirtuin gene repertoire and reconstruct its precise duplication history. Harvesting genomic databases, they extracted all predicted sirtuin proteins and performed phylogenetic analyses based on probabilistic inference methods. Maximum likelihood and Bayesian analyses resulted in well-resolved and congruent phylogenetic trees dividing vertebrate sirtuin genes into three major clades. These analyses also revealed an additional eighth paralog that was previously overlooked because of its restricted phyletic distribution. This newly identified sirtuin family member (named SIRT8) was recovered with unambiguous statistical support as a sister-group to the SIRT3 clade. Comparative genomic analyses based on conserved gene synteny confirmed that SIRT8 was present in all sampled non-amniote vertebrate genomes (cartilaginous fish, bony fish, coelacanth, lungfish, and amphibians) except cyclostomes. SIRT8 has thus most likely been lost in the last common ancestor of amniotes (mammals, reptiles, and birds). Discovery of such previously unknown genes in vertebrates is not completely surprising given the plethora of high-quality genomes now available. However, this study highlights the importance of considering a broad taxonomic sampling to infer evolutionary patterns of gene families that have been mostly studied in mammals because of their potential importance for human biology.

Based on its phylogenetic position as closely related to SIRT3 within class I, it could be predicted that the newly identified SIRT8 paralog likely has a deacetylase activity and is probably located in mitochondria. To test these evolutionary predictions, Opazo and collaborators [5] conducted further bioinformatics analyses and functional experiments using the elephant shark (Callorhinchus milii) as a model species. RNAseq expression data were analyzed to determine tissue-specific transcription of sirtuin genes in vertebrates, including SIRT8 found to be mainly expressed in the ovary, which suggests a potential role in biological processes associated with reproduction. The elephant shark SIRT8 protein sequence was used with other vertebrates for comparative analyses of protein structure modeling and subcellular localization prediction both pointing to a probable mitochondrial localization. The protein localization and its function were further characterized by immunolocalization in transfected cells, and enzymatic and functional assays, which all confirmed the prediction that SIRT8 proteins are targeted to the mitochondria and have deacetylase activity. The extensive experimental efforts made in this study to shed light on the function of this newly discovered gene are both rare and highly commendable.

Overall, this work by Opazo and collaborators [5] provides a comprehensive phylogenomic study of the sirtuin gene family in vertebrates based on detailed evolutionary analyses using state-of-the-art phylogenetic reconstruction methods. It also illustrates the power of adopting an integrative comparative approach supplementing the reconstruction of the duplication history of the gene family with complementary functional experiments in order to elucidate the function of the newly discovered SIRT8 family member. These results provide a reference phylogenetic framework for the evolution of sirtuin genes and the further functional characterization of the eight vertebrate paralogs with potential relevance for understanding the cellular biology of aging and its associated diseases in human.

References

[1] Vassilopoulos A, Fritz KS, Petersen DR, Gius D (2011) The human sirtuin family: Evolutionary divergences and functions. Human Genomics, 5, 485. https://doi.org/10.1186/1479-7364-5-5-485

[2] Yamamoto H, Schoonjans K, Auwerx J (2007) Sirtuin Functions in Health and Disease. Molecular Endocrinology, 21, 1745–1755. https://doi.org/10.1210/me.2007-0079

[3] Morris BJ (2013) Seven sirtuins for seven deadly diseases ofaging. Free Radical Biology and Medicine, 56, 133–171. https://doi.org/10.1016/j.freeradbiomed.2012.10.525

[4] Bordo D Structure and Evolution of Human Sirtuins. Current Drug Targets, 14, 662–665. http://dx.doi.org/10.2174/1389450111314060007

[5] Opazo JC, Vandewege MW, Hoffmann FG, Zavala K, Meléndez C, Luchsinger C, Cavieres VA, Vargas-Chacoff L, Morera FJ, Burgos PV, Tapia-Rojas C, Mardones GA (2022) How many sirtuin genes are out there? evolution of sirtuin genes in vertebrates with a description of a new family member. bioRxiv, 2020.07.17.209510, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology.  https://doi.org/10.1101/2020.07.17.209510

[6] Eisen JA (1998) Phylogenomics: Improving Functional Predictions for Uncharacterized Genes by Evolutionary Analysis. Genome Research, 8, 163–167. https://doi.org/10.1101/gr.8.3.163

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

References

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

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