Pleiotropy increases parallel selection signatures during adaptation from standing genetic variation

eLife Assessment

This study makes the important finding that pleiotropy is positively associated with parallelism of evolutionary responses in gene expression. This finding, if true, runs counter to current expectations in the field. The analysis uses state-of-the art experimental evolution approach to study the genetic basis of adaptation of Drosophila simulans to a hot environment. Although the experimental results are convincing, the theoretical model is incomplete, due to several unusual assumptions. It remains to be seen whether the main conclusion can be replicated in other contexts.

https://doi.org/10.7554/eLife.102321.3.sa0

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Abstract
Introduction
Methods
Results
Discussion
Appendix 1
Data availability
References
Article and author information
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Abstract

The phenomenon of parallel evolution, whereby similar genomic and phenotypic changes occur across replicated pairs of populations or species, is widely studied. Nevertheless, the determining factors of parallel evolution remain poorly understood. Theoretical studies have proposed that pleiotropy, the influence of a single gene on multiple traits, is an important factor. In order to gain a deeper insight into the role of pleiotropy for parallel evolution from standing genetic variation, we characterized the interplay between parallelism, polymorphism, and pleiotropy. The present study examined the parallel gene expression evolution in 10 replicated populations of Drosophila simulans, which were adapted from standing variation to the same new temperature regime. The data demonstrate that the parallel evolution of gene expression from standing genetic variation is positively correlated with the strength of pleiotropic effects. The ancestral variation in gene expression is, however, negatively correlated with parallelism. Given that pleiotropy is also negatively correlated with gene expression variation, we conducted a causal analysis to distinguish cause and correlation and evaluate the role of pleiotropy. The causal analysis indicated that both direct (causative) and indirect (correlational) effects of pleiotropy contribute to parallel evolution. The indirect effect is mediated by historic selective constraint in response to pleiotropy. This results in parallel selection responses due to the reduced standing variation of pleiotropic genes. The direct effect of pleiotropy is likely to reflect a genetic correlation among adaptive traits, which in turn gives rise to synergistic effects and higher parallelism.

Introduction

Distinguishing selection from stochastic changes continues to be a major challenge in evolutionary research. The evidence for selection mostly comes from parallel evolution, where similar genes, traits, or functions are identified in replicate populations exposed to similar environments (Colosimo et al., 2005; Westram et al., 2021; van der Zee et al., 2022; Zong et al., 2021). Given that non-parallel evolution does not preclude the possibility of selection (Barghi et al., 2019; Barghi et al., 2020), there has been a sustained interest in elucidating the factors that influence the extent of genetic parallelism (i.e. how similar is the response of the same gene to selection in multiple populations from the same/similar ecological niche(s)) (Bolnick et al., 2018).

It has been proposed that the degree of pleiotropy, the number of traits affected by a single gene, maybe a potential factor associated with parallelism (Bolnick et al., 2018; Conte et al., 2015; Rennison and Peichel, 2022; Stern, 2013). The majority of hypotheses regarding the influence of pleiotropy on parallel selection responses are (implicitly) related to the ideas of Fisher, 1930, later modified by Orr, 2000, focusing on the cost of pleiotropy. This concept posits that any new mutation in a pleiotropic gene results in a shift in trait values away from their optimum across multiple traits. New mutations in highly pleiotropic genes are more likely to have negative fitness effects than mutations in less pleiotropic genes as more traits are displaced from their optima. This ‘cost of pleiotropy’ model (Orr, 2000) is evidenced by the lack of sequence divergence observed in genes with a higher level of pleiotropy (Fraser et al., 2002; Hahn and Kern, 2005; Masalia et al., 2017; Josephs et al., 2017). Consequently, it can be postulated that less pleiotropic genes are more likely to contribute to adaptation over long evolutionary timescales, given that they have a higher probability of accumulating beneficial genetic variants. This results in a more parallel selection response in different populations/species (Hansen, 2003).

Fisher’s model of pleiotropy is simplistic in its assumption of uncorrelated traits and uncorrelated selection. It has been proposed that the influence of pleiotropy on the selection response strongly depends on the genetic correlation of the affected traits and the underlying fitness function which connects phenotypic values with fitness (Blows, 2007; Lande and Arnold, 1983; Lande, 1979). If the fitness effects of the affected traits are positively correlated (synergistic pleiotropy), this may result in stronger net selection (Thorhölludottir et al., 2023) and consequently in more parallel selection signatures (MacPherson and Nuismer, 2017). This implies that the correlation of fitness effects determines whether pleiotropy increases or decreases parallel selection signatures.

The impact of pleiotropy on parallel evolution has typically been considered in the context of long-term adaptation via de novo mutations. However, many studies of parallel evolution have focused on short-term adaptation [e.g. Rivas-Sánchez et al., 2023; Huang et al., 2021; Heckley et al., 2022; Fischer et al., 2021; Elmer et al., 2014; Butlin et al., 2014; Hsu et al., 2021; Otte et al., 2021], where standing genetic variation plays a more significant role in selection responses than de novo mutations. Nevertheless, it remains unclear whether a similar impact of pleiotropy on evolutionary parallelism is to be expected for standing genetic variation.

Indeed, the empirical evidence regarding the role of pleiotropy for intra-specific adaptive responses is somewhat inconclusive. A gene expression study in European grayling populations adapted to different temperature regimes provided compelling evidence for the constraining effect of pleiotropy (Papakostas et al., 2014), which supports the cost of complexity concept. A comparison of gene expression changes in natural Drosophila populations along a temperature cline and populations evolved in the laboratory to different temperature regimes demonstrated the importance of correlated fitness effects (Thorhölludottir et al., 2023). A direct test of the influence of pleiotropy on parallel evolution was conducted on 19 independent contrasts of stickleback populations (Rennison and Peichel, 2022). The authors discovered that pleiotropic genes were more likely to exhibit parallel adaptation signatures (Rennison and Peichel, 2022), thereby supporting the importance of synergistic pleiotropy.

Previous studies on pleiotropy and parallelism did not account for polymorphism (standing genetic variation), which plays a central role in selection responses during short-term adaptation. Although natural variation provides the basis for adaptive responses, it is not well-documented how ancestral variation (polymorphism) determines the level of genetic parallelism during short-term adaptation in replicate populations. Moreover, polymorphism of a gene is also affected by its pleiotropic level while the expectations regarding the influence of pleiotropy on the extent of polymorphism varies. The cost of complexity predicts that pleiotropic genes with uncorrelated fitness effects will be subject to stronger purifying selection, which will result in reduced polymorphism. In the case of multiple selected traits with aligned fitness effects, synergistic pleiotropy may facilitate the occurrence of selective sweeps, and thereby reducing variation in pleiotropic genes. Alternatively, it may be possible that once pleiotropic alleles have been established in a population, their multivariate fitness effects lead to a balanced polymorphism, which in turn maintains higher levels of variation. Empirical studies have observed a negative correlation between the level of pleiotropy and sequence/expression polymorphism (Lemos et al., 2004; Mähler et al., 2017). This suggests that pleiotropy may preferentially reduce variation, either by purifying selection, synergistic effects, or a combination of both.

In this study, we employed gene expression analysis to investigate the interplay between polymorphism and pleiotropy on the degree of parallelism of adaptive responses. We assessed the selective response by measuring gene expression differences between populations from different environments, a widely employed approach [e.g. Nourmohammad et al., 2017; Hsu et al., 2020; Yu et al., 2023; Velotta et al., 2017; Tang et al., 2017; O’Neil et al., 2014; Margres et al., 2017]. Specifically, we compared gene expression in the ancestral D. simulans population with that of flies that had evolved for 100 generations in a novel hot temperature regime. To infer parallel evolution, the gene expression change was measured in 10 replicate populations. By focusing on the evolutionary response, we circumvent the challenge of connecting the phenotype to an unknown fitness landscape. Furthermore, we take advantage of well-established pleiotropy estimates (McShea, 2000; Proulx et al., 2005; He and Zhang, 2006; Mank et al., 2008) to evaluate the influence of pleiotropy on parallel gene expression evolution. The natural variation in gene expression was determined by examining the differences in expression levels among individual flies from the ancestral D. simulans population.

We observed significant interplays between pleiotropy, ancestral variation, and parallel selection responses among genes. The parallelism of gene expression evolution is positively associated with pleiotropy but negatively associated with the ancestral variation in gene expression. Given that pleiotropy was also negatively associated with ancestral variation, it is crucial to differentiate between statistical causality and correlation. Causal analysis revealed that pleiotropy affects parallel evolution both directly and indirectly via ancestral variation. It is likely that the indirect effects operate via the reduction of ancestral variation in pleiotropic genes. The direct effects of pleiotropy on parallelism are probably best explained by synergistic pleiotropy which results in a stronger selection response.

Methods

Estimating gene pleiotropy

We approximated the pleiotropy of each gene with two alternative estimators, network connectivity, and tissue specificity (τ).

τ indicates how specific the expression of a gene is across different tissues. τ was estimated for each gene using the adult male expression profiles on flyatlas2 (Leader et al., 2018) as: $τ = \frac{\sum_{i} [1 - \frac{g e n e e x p r e s s i o n_{i}}{g e n e e x p r e s s i o n_{m a x}}]}{N - 1}$ , where N is the number of tissues examined and i indicates each of them (Dean and Mank, 2016). If a gene is only expressed in one tissue, τ will equal to 1 while it equals 0 when a gene is expressed at the same level across all tissues. The relationship between τ and pleiotropy is based on the idea that the genes expressed in many tissues are more likely to affect multiple traits than genes expressed in fewer tissues (McShea, 2000; Mank et al., 2008; Allen et al., 2018; Watanabe et al., 2019). Hence, we used 1-τ to indicate the pleiotropic effect of a gene.

Network connectivity has also been used as a proxy for pleiotropy, as more connected genes are likely to be more pleiotropic (Josephs et al., 2017; Proulx et al., 2005; He and Zhang, 2006; Hämälä et al., 2020a). We used published information about the transcriptional regulatory network of Drosophila, which was inferred from several sources, including genome-wide chromatin immuno-precipitation, conserved transcription factor binding motifs, gene expression profiles across different development stages, and chromatin modification profiles among several cell types (Marbach et al., 2012). The connectivity for each gene was estimated as the sum of adjacencies between the focal gene and other genes in the network.

The significant positive correlation (rho = 0.54) between 1-τ and network connectivity suggests that both estimates capture a similar, but not identical, signature of the pleiotropic properties among the genes (Appendix 1—figure 1).

We note that approximating pleiotropic effects in D. simulans by using data-rich estimates from the close relative Drosophila melanogaster could potentially compromise the interpretability of our results. However, comparative analyses of tissue-specific gene expression have shown an extremely high consistency across species in animals (Mao et al., 2024) and plants (Davidson et al., 2012; Julca et al., 2021). Hence, these data suggest that at least for tissue specificity, one of the pleiotropy measures used, estimates from one species can be safely used for close relatives.

Experimental evolution and common garden experiment

The procedures of the evolution and common garden experiments were described in Barghi et al., 2019; Hsu et al., 2020; Jakšić et al., 2020. Briefly, 202 isofemale lines collected from Florida, USA were used to constitute 10 outbred Drosophila simulans populations, which have been exposed for more than 100 generations to a laboratory experiment at 28/18 °C with 12 hr light/12 hr dark photoperiod. The census population size of each replicate population is 1000–1250 adult flies.

Each founder isofemale line was maintained at a small population size (typically less than 50 individuals) at 18 °C on standard laboratory food. Adaptation to the laboratory environment with the residual heterogeneity or de novo mutation is unlikely due to the small effective population size during the maintenance of each line (Barghi et al., 2019). This is supported by the observation that 50 generations of maintenance as isofemale lines does not result in significant genetic differentiation from the ancestral population (Nouhaud et al., 2016). Furthermore, mutations that occur in the laboratory are mostly recessive (Charlesworth and Charlesworth, 2010), and the effects are likely to be masked because, after two generations of random mating during common garden maintenance, most individuals will be heterozygous for isofemale line-specific variants.

The collection of samples for RNA-Seq was preceded by two generations of common garden experiments (CGE). As described previously (Barghi et al., 2019; Hsu et al., 2020; Jakšić et al., 2020), at generation 103 of the evolution experiment, five biological replicates of reconstituted ancestral populations were generated by pooling five mated females from 184 founder isofemale lines (Nouhaud et al., 2018) and reared together with 10 independently evolved populations for two generations with controlled egg density (400 eggs/bottle) at the same temperature regime as in the evolution experiment. Three biological replicates were included for each independently evolved population (each biological replicate was cultivated separately for the two generations of the common garden). From each biological replicate, a sample of 50 5-d-old adult males was collected (Figure 1a).

Figure 1

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Overview of the experimental procedures (a) and the variation in parallel gene expression evolution across genes (**b,c**).

(a) Experimental Evolution: ten replicated populations seeded from one common founder population have been evolving for >100 generations in a hot laboratory environment at 18 and 28°C. Common Garden Experiment: at the 103^rd generation of the evolution experiment, the ten evolved populations (each with three biological replicates) were maintained together with the reconstituted ancestral population (with five biological replicates) for two generations in the same environment as in the evolution experiment. After two generations in the common garden, 50 males from each biological replicates were pooled and subjected to RNA-Seq. (b) Replicate frequency spectrum. Number of populations (x-axis) in which a given gene experienced a significant change in gene expression. The y-axis indicates the number of genes in each category. Most of the genes experienced a significant change in gene expression in a few evolved populations while much fewer genes were significant in all 10 populations. This pattern suggests that the parallelism in gene expression evolution differs across genes. (c) The distribution of gene expression evolution parallelism (log(1 /F)) across genes. Larger log(1 /F) values indicate more parallel evolution of a gene. The exhibit variation suggests that genes varied in their parallelism of its expression evolution.

Quantifying the evolutionary parallelism in gene expression across ten replicate populations

The raw RNA-Seq count table of five replicates of the reconstituted ancestral population and 10 independently evolved populations, each with three biological samples, was taken from Jakšić et al., 2020. The raw read counts of each gene were normalized with the TMM method implemented in edgeR (Robinson et al., 2010). Only genes with at least 0.1 normalized counts per million base (CPM) across all samples were considered for further analysis. Because we are interested in the evolutionary response in each independently evolved population, we contrasted the three biological samples from each evolved population to the five biological samples from the ancestral population. The differential expression (DE) analysis was done separately in each of the ten evolved populations. For DE analysis, we utilized negative binomial generalized linear modeling implemented in edgeR to fit the expression to the model $Y = E + ε$ , in which Y stands for gene expression, E is the effect of evolution and $ε$ is the random error. Likelihood ratio tests were performed to test the effect of evolution. p-values were adjusted using Benjamini-Hochberg false discovery rate (FDR) correction (Benjamini and Hochberg, 1995). After we identified DE genes in each evolved population, we constructed a replicate-frequency spectrum (RFS; Barghi et al., 2019). We considered genes with significant changes in the same direction in at least three evolved populations as putatively adaptive genes.

To quantify the degree of parallel gene expression evolution, we calculated the expression changes in response to evolution (log₂FC; fold change in expression between ancestral and evolved sample; see below) of each putatively adaptive gene (n=1775) in each evolved sample.

\log_{2} F C = \log (\frac{y_{e v o}}{\bar{y_{a n c}}}),

where $y_{e v o}$ is the expression value (CPM) per gene per evolved sample, and $\bar{y_{a n c}}$ is the overall mean expression value (CPM) per gene across five ancestral samples.

We calculated the ratio of between-evolution population variation (MS_pop) and residual (MS_e) among the expression changes (log₂FC), which is denoted as F.

F = \frac{M S_{p o p}}{M S_{e}}

M S_{p o p} = \frac{\sum_{i = 1}^{10} {({\bar{X}}_{i} - \bar{X})}^{2}}{10 - 1}

M S_{e} = \frac{\sum_{i = 1}^{10} \sum_{j = 1}^{3} {(X_{i j} - {\bar{X}}_{i})}^{2}}{30 - 10}

Where $X_{i j}$ represent the log₂FC value for the j^th biological sample in the i^th evolution population. i=1, 2, …,10, j=1, 2, 3.

As F reflects the heterogeneity of the evolutionary response for a gene across 10 evolutionary populations, we used the reciprocal of F (i.e. 1 /F) to quantify the degree of parallelism in gene expression evolution.

Estimating gene expression variation in an outbred ancestral population

The variation in expression of each gene within an outbred ancestral population was estimated from 20 adult males sampled from an outbred ancestral population (Lai and Schlötterer, 2022). We distinguished biological variation and measurement error of each gene using the statistical method implemented in edgeR Robinson et al., 2010, where the biological variance across individual (biological coefficient of variation; BCV; defined in Robinson et al., 2010) of each gene can be estimated (tag-wised dispersion). BCV² was used to represent the expression variance relative to the mean of each gene within the population. To illustrate that the gene expression variation is not explained by random measurement error, we took the data from two ancestral population replicates, which are considered to be genetically identical, and calculated BCV² of expression levels across individuals for each population replicates separately. We found that both mean and variance in expression for each gene were similar in two ancestral population replicates (mean: rho >0.99; variance: rho = 0.8, p-value <2.2e-16), suggesting that sequencing noise does not mask the biological variability.

Causal analysis

To infer the causal relationship between three factors: pleiotropy (Pl), ancestral variation (A), and parallelism (Pa), we applied causal analysis. The causal analysis is built upon a previously published statistical framework (Schadt et al., 2005). We consider five possible causal relationships between pleiotropic effects, ancestral variation, and parallelism (Figure 5). Our goal is to determine which of the five models is best supported by the data. The selection is based on Bayesian information criteria (BIC) which are calculated from the maximum likelihood of the respective models. The likelihood of each model is represented as:

L (M o d e l I) = p (P a | A) p (A | P l) p (P l)

L (M o d e l I I) = p (P a | P l) p (A | P l) p (P l)

L (M o d e l I I I) = p (P a | A, P l) p (A | P l) p (P l)

L (M o d e l I V) = p (P a | A, P l) p (A) p (P l)

L (M o d e l V) = p (P a) p (A) p (P l)

p(Pl) is the probability distribution of the pleiotropic effect. To utilize the normal probability density function in the derivation of the likelihoods for each joint probability distribution, we transformed the random variables Pa and A to be normally distributed (natural log transformation on 1 /F and BCV²). The exact forms of these likelihoods are given in the Appendix.

For each model, we maximized the likelihood and estimated the parameters using standard maximum likelihood methods. Based on the maximum likelihood, we computed the BICs for each model; the model with the smallest BIC is the one best supported by the data.

BIC is calculated as:

B I C = - 2 \ln (L_{i}) + k_{i} \ln (n)

Where $L_{i}$ is the likelihood for model I-V, $k_{i}$ is the corresponding number of free parameters and n is the total number of putatively selected genes. The analysis was performed independently on two measures of pleiotropy (tissue specificity and network connectivity).

Estimating the sizes of direct and indirect pleiotropic effects on parallelism

We used path analysis to estimate the effect sizes of direct and indirect pleiotropic effects on parallelism (Wright, 1920). We standardized all three transformed variables (Parallelism (Pa), Pleiotropy (Pl), and Ancestral variation (A); see Appendix) by subtracting the mean and dividing by the standard deviation for the following analysis.

First, we fit the regression model for all three variables across all putatively adaptive genes:

y_{P a} = β_{1} x_{A} + β_{2} x_{P l} + ε

where $y_{P a}$ stands for parallelism, $x_{A}$ is ancestral variation, $x_{P l}$ is pleiotropy measure and $ε$ is a random error. $β_{1}$ and $β_{2}$ are the regression coefficients corresponding to $x_{A}$ and $x_{P l}$ , respectively.

Second, we fit another regression model for ancestral variation and pleiotropy measure across all putatively adaptive genes:

y_{A} = β_{3} x_{P l} + ε

where $y_{A}$ stands for ancestral variation, $x_{P l}$ is pleiotropy measure and $ε$ is a random error. $β_{3}$ is the regression coefficient corresponding to $x_{p l}$ .

The sizes of the direct effect of pleiotropy on parallelism will be $β_{2}$ . The size of the indirect effect of pleiotropy on parallelism via ancestral variation will then be the product of $β_{1} \times β_{3}$ .

The analysis was performed independently on two different measures of pleiotropy.

Computer simulations

In our interpretation of the indirect effect of pleiotropy on parallelism, we assumed that ancestral variation in gene expression is negatively correlated with its parallel evolution. Because this has not been previously demonstrated, we used computer simulations to illustrate how the level of standing genetic variation impacts the parallelism of adaptive responses after a shift in trait optimum. For simplicity, we considered four redundant genes equally contributing to a fitness-associated trait. The phenotype (expression level) of each gene is controlled by a different number of genetic loci (5, 15, 30, and 50) with equal effect. The underlying assumption is that these four genes are redundant in terms of their fitness effect during the experimental evolution, but they exhibit different levels of pleiotropy and thus have historically experienced different levels of purifying selection, leaving different levels of genetic variation at the start of the experiment. Using mimicrEE2 (Vlachos and Kofler, 2018), we evolved 10 replicate populations (N=300) derived from the same set of 189 natural Drosophila haplotypes (Barghi et al., 2019) in the same selection regime with a shift in trait optimum of one standard deviation relative to the ancestral phenotypic distribution. After 100 generations, we determined the evolutionary parallelism of the four genes using the same approach as for the empirical data. Each simulation was repeated 100 times and one out of four genes was randomly picked from each simulation run to generate Figure 4 (i.e. we estimated variance in the ancestral population and parallelism for the evolved replicate populations).

Results

The parallelism of expression evolution differs among genes

We investigated the degree of parallelism in gene expression evolution, using an RNA-Seq dataset from an experimental evolution study in which 10 replicated populations with the same genetic variation were exposed to the same environment for more than 100 generations (Hsu et al., 2021; Jakšić et al., 2020; Lai and Schlötterer, 2022; Figure 1a). While previous studies have focused on the shared parallel response across replicated populations (Hsu et al., 2021; Jakšić et al., 2020; Lai and Schlötterer, 2022), we re-analyzed the RNA-Seq data to quantify the degree of evolutionary parallelism across different genes. We identified 1775 putatively selected genes with a significant change in gene expression in the same direction in response to the novel environment in at least three populations. This conditioning on multiple replicate populations with a significant change in the same direction reduces false positives due to drift (Rennison and Peichel, 2022; Bohutínská et al., 2021). Relaxing the threshold of responding populations provides qualitatively similar results.

Barghi et al., 2019 introduced the replicate frequency spectrum to describe the parallel genomic selection response in replicate populations. The replicate frequency spectrum of DE genes shows considerable heterogeneity among populations, with many genes displaying significant gene expression changes only in a few populations (Figure 1b). The replicate frequency spectrum is strongly influenced by the significance threshold used and provides an incomplete quantitative measure of parallelism. To obtain a more quantitative measure of parallelism, we evaluated the between-population variation in the evolutionary response (log₂FC) of each putatively adaptive gene (n=1775) while controlling for measurement error (F). The parallelism of evolution for each gene is then quantified as 1 /F (Figure 1b; see Materials and Methods). A higher value of 1 /F indicates a more parallel evolutionary response among replicated populations (Appendix 1—figure 2). Importantly, the measure of parallelism varies between genes (Figure 1c) and is positively correlated with the number of populations in which a significant change in gene expression is observed for each gene (Appendix 1—figure 3).

Pleiotropy is positively associated with the degree of parallelism in gene expression evolution

Pleiotropy is one of the factors that influence the degree of parallel evolution (Bolnick et al., 2018; Conte et al., 2015; Rennison and Peichel, 2022; Stern, 2013). Therefore, we tested whether and how the pleiotropic effect of a gene influences the degree of parallelism of its expression evolution in a new environment. We correlated two different measures of pleiotropy (1-τ (tissue specificity) and network connectivity; see materials and methods) of each putatively adaptive gene (n=1775) with the parallelism of its evolutionary responses (1 /F). We observed a significant positive correlation between the parallelism and the degree of pleiotropy, (rho = 0.26, p-value <2.2e-16 for 1-τ and rho = 0.11, p-value <5.6e-07 for network connectivity) (Figure 2 and Appendix 1—figure 4a). This suggests that pleiotropy may enhance parallel changes in gene expression during evolution.

Figure 2

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Association between the magnitude of evolution parallelism and the strength of pleiotropy.

The distribution of evolution parallelism (log(1 /F)) of putatively adaptive genes (n=1775) is shown in boxplots binned by their strength of pleiotropy (1- $τ$ ). Overall, the strength of pleiotropy was positively correlated with evolution parallelism (spearman correlation coefficient; $ρ$ =0.26; p-value <2.2e-16).

Pleiotropy constrains the ancestral (natural) variation in gene expression

The negative effect of pleiotropy on genetic variation over longer time scales, mostly between species, is well documented (Fraser et al., 2002; Hahn and Kern, 2005; Masalia et al., 2017; Josephs et al., 2017), but it is not yet clear whether the same effect is seen for adaptation from standing genetic variation. We reconstituted the ancestral population from 189 isofemale lines (Lai and Schlötterer, 2022) and estimated the expression variance (BCV²; see materials and methods) of each putatively adaptive gene (n=1775) in two replicate sets of 20 individuals. Since the variance in gene expression is positively correlated with genetic variation in cis-regulatory regions (Hämälä et al., 2020b) and the heritability of gene expression is high (Ayroles et al., 2009), we assumed that the observed variation in gene expression has a genetic basis, rather than being noise (see discussion). We observed a highly significant negative correlation between the level of pleiotropy and the magnitude of ancestral variation in gene expression (Figure 3a and Appendix 1—figure 4b; rho = −0.34, p-value <2.2e-16 for 1- $τ$ and rho = −0.37, p-value <2.2e-16 for network connectivity). To exclude that this correlation was driven by our filtering for significant expression changes in at least three populations, we repeated these analyses with all genes (n=9,882) and also observed a significant correlation (rho = −0.45, p-value <2.2e-16 for 1- $τ$ and rho = −0.38, p-value <2.2e-16 for network connectivity, data not shown). Hence, we suggest that the negative relationship between pleiotropy and polymorphism/diversity (Lemos et al., 2004; Mähler et al., 2017) (i.e. reduced variants, lower frequencies, and/or lower allelic effects) can be extended to gene expression.

Figure 3

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Association between ancestral variation in gene expression with the strength of pleiotropy (1- $τ$ ) (a) and with the evolution parallelism (b).

(a) The distribution of gene expression variation in the ancestral population (log (BCV²)) of putatively evolved genes (n=1,775) is shown in boxplots binned by their strength of pleiotropy (1- $τ$ ). Overall, the strength of pleiotropy was negatively correlated with ancestral variation in gene expression (spearman correlation coefficient; $ρ$ =−0.34, p-value <2.2e-16). (b) The distribution of evolutionary parallelism (log(1 /F)) of putatively adaptive genes (n=1775) is shown in boxplots binned by their strength of ancestral variation. The strength of parallelism is negatively correlated with the ancestral variation in gene expression (spearman correlation coefficient; ρ=−0.22, p-value <2.2e-16).

Evolutionary parallelism is negatively associated with ancestral variation

For a better understanding of the factors determining parallel evolution, we examined the influence of ancestral variation in gene expression on the parallel evolution of gene expression. We observed a significant negative correlation between ancestral variation and parallelism of the evolutionary response (rho = −0.22, p-value <2.2e-16; Figure 3b). We suggest that this pattern could be explained by genetic redundancy (Barghi et al., 2019; Láruson et al., 2020), which describes the phenomenon that a polygenic trait can shift its phenotypic value through different combinations of contributing loci: When the diversity of the contributing loci is reduced (either through fewer loci, lower ancestral frequencies or smaller variation in effect size), fewer combinations of the contributing loci are available for adaptation, resulting in more parallel adaptive responses. To confirm this, we performed computer simulations of replicated polygenic adaptation and found that lower ancestral phenotypic (i. e. expression) variation leads to more parallel trait evolution (Figure 4). Hence, that natural variation in the ancestral population could be one of the factor determining the parallel adaptive response in replicate populations. Although we focused on the phenotypic response to match gene expression, it is interesting to note that a similar result was obtained for parallelism at the genetic level with different levels of standing genetic variation (Thompson et al., 2019). If pleiotropy reduces variation, the positive correlation between pleiotropy and parallelism may be an indirect (correlative) effect, rather than a direct consequence of pleiotropy.

Figure 4

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Computer simulations illustrate the negative effect of ancestral variation on parallel evolution.

Computer simulations assume a fitness-related trait determined by the expression of four genes. The expression of these four genes is determined by a different number of genetic loci, reflecting the influence of pleiotropy on ancestral genetic variation. A shift in trait optimum was used to illustrate the connection between ancestral variation in expression (x-axis) and parallelism of expression change across ten evolved populations (y-axis). The negative correlation between them (ρ = –0.26, p-value <1.77e-07) suggests that the genes with less ancestral variation are resulting in more parallel responses.

Testing causality

Our previous analyses indicated that parallelism is affected by pleiotropy and ancestral variation. Because pleiotropy is negatively correlated with ancestral variation for both pleiotropy measures (Figure 3a and Appendix 1—figure 4b), it is not clear whether pleiotropy affects parallelism directly or indirectly through the negative correlation with ancestral variation. We used causal analysis, which is based on a Bayesian statistical framework (Schadt et al., 2005), to disentangle the causal relationship between the three different parameters. Since the directionality of pleiotropic effects on ancestral variation has been previously demonstrated, and ancestral variation is unlikely to influence the pleiotropy of a gene, we considered five possible causal relationships between pleiotropic effects, ancestral variation, and parallelism (Figure 5). In the first model, parallelism is determined by ancestral variation, which is shaped by pleiotropic, but pleiotropy has no direct effect on parallelism. In model II, the pleiotropic effect affects ancestral variation and parallelism independently, but ancestral variation has no causal effect on parallelism. In model III, pleiotropy affects both ancestral variation and parallelism, and ancestral variation also determines parallelism. In model IV, parallelism is determined by ancestral variation and pleiotropy, but pleiotropic effects are assumed to have no effect on the ancestral variation. This serves as a null model to confirm the indirect effect of pleiotropy on parallelism via ancestral variation. We also add a global null model with no correlation between these three factors (model V, Figure 5). The analysis was performed independently for two measures of pleiotropy (tissue specificity and network connectivity).

Figure 5

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Schematic figure illustrating the causal models evaluated.

Five possible relationships between pleiotropic effects, ancestral variation, and evolutionary parallelism. $L$ denotes the likelihood of each model given the data. P is the probability or conditional probability of the measurements for each gene; Pa is the parallelism level and A is the level of ancestral variation in gene expression. Pl is the pleiotropic effects. See materials and methods for a more detailed description.

For tissue specificity, our data were better explained by model III (i.e. model III has the lowest BIC, Table 1), suggesting that both direct and indirect effects of pleiotropy determine the degree of parallelism of adaptive gene expression evolution. On the other hand, when pleiotropy was estimated by network connectivity, model I had a slightly lower BIC than model III (Table 1). Thus, our causal analysis does not provide evidence for a direct effect of network connectivity. We further quantified the strength of the direct and indirect effects of pleiotropy on parallelism using a path analysis (see materials and methods). The path analysis confirmed the significant direct effect of tissue specificity, but network connectivity has no statistically significant direct effect on the parallelism of gene expression evolution (Table 2, Appendix 1—table 1). Although the relevance of the direct effects of pleiotropy on parallel gene expression evolution differs for the two pleiotropy estimates, the similarity of the indirect effect sizes for both pleiotropy measures is striking (Table 2). Taken together, these results suggest that pleiotropy may enhance the parallel evolutionary response of gene expression directly and indirectly through its influence on ancestral variation.

Table 1

Bayesian Information Criteria (BIC) value for Model I-V.

The model with the smallest BIC is the one best support by the data.

Model	BIC value (Network connectivity)	BIC value (Tissue specificity)
I	9258.395	8958.125
II	9316.852	8972.626
III	9264.769	8939.342
IV	9490.017	9162.090
V	9550.309	9247.540

Table 2

The size of direct and indirect pleiotropic effects on the evolutionary parallelism.

	Direct effect	Indirect effect
1- $τ$	0.127	0.055 (−0.181, –0.332) *
Network connectivity	0.026	0.068 (−0.210, –0.337) *

*

Coefficient estimates (β2,β3) whose product provides the indirect effect.

Discussion

This study examined the influence of pleiotropy on the parallelism of the adaptive response from standing genetic variation. By examining the role of natural variation, which is important for parallelism of short-term evolution, we extended the scope of previous studies. Because differences in genetic background and selection strength among replicated populations cannot be controlled in natural populations and may affect the degree of parallelism, we relied on experimental evolution for our study. We took advantage of ten replicated populations that have evolved from the same genetic background in the same hot environment for more than 100 generations (Barghi et al., 2019).

The adaptive response during adaptation to the new hot temperature regime was measured by changes in gene expression. This approach has the advantage that pleiotropic effects and their associated fitness function, which are typically not known, are already included in the adaptive response. Furthermore, we were able to rely on two well-established measures of pleiotropy to study the interplay between parallelism and natural variation.

A key finding of our study is that adaptation from standing genetic variation results in complex interplays between pleiotropy, ancestral variation, and parallelism. Of particular interest is the observed negative correlation between pleiotropy and ancestral variation in gene expression, which confirms previous observations (Lemos et al., 2004; Mähler et al., 2017). This suggests that the primary effect of pleiotropy is not to maintain variation. Rather, stronger purifying selection removes pleiotropic variants from the population, resulting in a reduced standing genetic variation. The reduced variation in more pleiotropic genes could reflect historical hard sweeps or purifying selection. A similar pattern is seen over longer evolutionary timescales: less sequence divergence in pleiotropic genes (Fraser et al., 2002; Hahn and Kern, 2005; Masalia et al., 2017; Josephs et al., 2017).

An important difference between adaptation from de novo mutations and standing genetic variation concerns the implications for parallel evolution. While pleiotropic genes are less likely to contribute to parallel evolution from de novo mutations (Bolnick et al., 2018), our results show that adaptation from standing genetic variation favors parallel selection responses of pleiotropic genes (Figure 2, Appendix 1—figure 4a). It is important to note that many previous studies focused on sequence variation, whereas we used expression variation. While it is possible that these two types of variation behave differently, we do not believe that this is the case as sequence polymorphism and expression variation are correlated (Lawniczak et al., 2008). Furthermore, a recent study focusing on genomic changes (Rennison and Peichel, 2022) reached similar conclusions.

A very important assumption in our interpretation is that differences in gene expression variation in the ancestral population reflect regulatory sequence polymorphism rather than noise. The high correlation in the variance of gene expression between two replicate groups of individuals indicates that our measures do not reflect technical noise but have a biological basis. However, stochastic fluctuations in gene expression have also been observed among cells in single-celled and multicellular organisms and the variance in gene expression differs among genes (Swain et al., 2002; Elowitz et al., 2002; Blake et al., 2003). In this case, the differences in gene expression variance are biological in nature, but not associated with sequence variation. We do not believe that variation in the control of gene expression levels among genes can explain our pattern because Drosophila is a multicellular organism and we used whole body RNA-Seq data for our analysis. Therefore, even if expression is controlled to different extents among genes, by averaging across multiple cells, this does not affect our results. The only scenario we can imagine in which gene expression noise could propagate across the many cells of a multicellular organism is that during early development, differences in gene expression of a key regulator could affect downstream gene expression throughout the entire organism. However, we do not think this is a likely scenario, especially in the light of previous results showing that genetic variation in cis-regulatory regions correlates with expression variation (Hämälä et al., 2020b). Furthermore, whole-body gene expression in Drosophila is highly heritable, suggesting that gene expression is not stochastic (Ayroles et al., 2009). Nevertheless, we would like to point out that our observations are valid irrespective of whether the correlation between parallelism and expression variation is the result of genetic polymorphism or genetic control of expression noise.

Based on the significant correlation results, the question arose as to whether the positive correlation between pleiotropy and parallelism, is causal (direct effect) or merely correlational (indirect effect). Regardless of the measure of pleiotropy, our causal analysis showed a consistent indirect effect of pleiotropy on parallelism (Tables 1 and 2). The estimated indirect effects were very similar for both measures of pleiotropy (Table 2). This indirect effect of pleiotropy could be explained by the reduced variation in the expression of more pleiotropic genes in the ancestral population leading to a more parallel selection response of gene expression. For the direct effect, however, the results differed between the two measures of pleiotropy. Only tissue specificity had a significant direct effect, which was even larger than the indirect effect (Table 2). No significant direct effect was found for network connectivity. The discrepancy between the two measures of pleiotropy is particularly interesting given their significant correlation (Appendix 1—figure 1). This suggests that both measures capture aspects of pleiotropy that differ in their biological implications. The direct effects of pleiotropy, most likely arise from the positively correlated fitness effects of non-focal traits (synergistic pleiotropy). The synergistic pleiotropic effects result in a stronger fitness advantage, which translates into a more parallel selection response - as previously shown (MacPherson and Nuismer, 2017). Given that temperature is one of the most important abiotic factors, it is expected that positive genetic correlations between temperature-adaptive traits have been selected over long evolutionary timescales, probably prior to the speciation of D. simulans. Thus, it is conceivable that positive pleiotropy for temperature adaptation-related traits is common and thus determines the positive contribution of pleiotropy to the parallel evolution observed here.

We focused our analysis on genes that exhibited evidence of adaptive responses, defined as significant changes in the same direction across at least three populations. This approach allowed us to investigate the extent of parallel adaptive responses and how pleiotropy contributes to variation in parallelism among genes. Interestingly, when comparing pleiotropy between DE genes (those showing adaptive responses) and non-DE genes (those that did not change their response during evolution), we observed significantly higher pleiotropy in the non-DE gene group (Appendix 1—figure 5). This finding suggests that extremely high levels of pleiotropy may constrain evolutionary responses. Overall, the pattern of increased pleiotropy associated with evolutionary parallelism supports the idea that low to intermediate levels of pleiotropy are more favorable for adaptation (Rennison and Peichel, 2022; Frachon et al., 2017).

In a concluding remark, we would like to emphasize that although pleiotropy and ancestral variation were found to be significantly associated with the degree of parallelism of gene expression evolution, it is likely that additional factors also contribute. For example, diminishing return epistasis, population size (drift), and the contribution of gene expression to the selected traits may also affect the parallelism of gene evolution. Further investigation of other potential factors will continue to advance our understanding of parallelism and the predictability of evolution.

Appendix 1

Data transformation and standardization

As described in the main text, we quantified the evolutionary parallelism with 1 /F and the estimates were log-transformed to fit normal assumption.

P a = \log (1 / F)

We measured the ancestral variation of gene expression with a log-transformed squared biological coefficient of variation based on the individual expression data of each gene for the ancestral population.

A = \log (B C V^{2})

The pleiotropy of a gene is approximated with the tissue-specificity of its expression and network connectivity. For each measure, 10 bins of equal sizes were derived based on deciles, and genes in different bins are considered to exhibit different extent of pleiotropy.

P l = j, w h e n \overset{⌢}{P} l_{10 (j - 1) %} < \overset{⌢}{P} l_{i} \leq \overset{⌢}{P} l_{10 (j) %}

Where j=1, 2, 3…, 10; ${\hat{P l}}_{i}$ is 1-τ or connectivity of a gene; i=1, 2, 3…, n; n is the total number of genes.

We standardized all three transformed variables (Parallelism (Pa), Pleiotropy (Pl), Ancestral variation (A)) by subtracting the mean and dividing by the standard deviation for the causal and regression analysis.

Exact forms of the likelihood for a causal model

To uncover the causal relationship between the genetic variables of interest (i.e. evolutionary parallelism (Pa), ancestral variation (A), and pleiotropy (Pl)), we represent the correlation structures in five different models considered in the main text (Figure 4) with Gaussian likelihoods and use Bayesian Information Criteria (BIC) to determine which model is best supported by the data. Similar approach has been taken previously to infer causality among genetic features of interest (Schadt et al., 2005).

The parameterization and likelihoods for each model over all putatively adaptive genes are given by:

\begin{aligned} L (M o d e l I) = & \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) l (θ; A_{i} | P l_{j}) l (θ; P a_{i} | A_{i}) \\ = & \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) \frac{1}{\sqrt{2 π σ_{A}^{2}}} e x p (- \frac{{(A_{i} - μ_{A_{P l_{j}}})}^{2}}{2 σ_{A}^{2}}) \\ \frac{1}{\sqrt{2 π σ_{A}^{2} (1 - ρ^{2})}} e x p (- \frac{{(P a_{i} - μ_{P a} - ρ \frac{σ_{A}}{σ_{P a}} (A_{i} - μ_{A}))}^{2}}{2 σ_{P a}^{2} (1 - ρ^{2})}) \end{aligned}

\begin{array}{ll} L (M o d e l I I) = \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) l (θ; A_{i} | P l_{j}) l (θ; P a_{i} | P l_{j}) \\ = \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) \frac{1}{\sqrt{2 π σ_{A}^{2}}} e x p (- \frac{{(A_{i} - μ_{A_{P l_{j}}})}^{2}}{2 σ_{A}^{2}}) \\ \frac{1}{\sqrt{2 π σ_{P a}^{2}}} e x p (- \frac{{(A_{i} - μ_{P a_{P l_{j}}})}^{2}}{2 σ_{P a}^{2}}) \end{array}

\begin{aligned} L (M o d e l I I I) = & \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) l (θ; A_{i} | P l_{j}) l (θ; P a_{i} ∣ A_{i}, P l_{j}) \\ = & \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) \frac{1}{\sqrt{2 π σ_{A}^{2}}} e x p (- \frac{{(A_{i} - μ_{A_{P l_{j}}})}^{2}}{2 σ_{A}^{2}}) \\ \frac{1}{\sqrt{2 π σ_{P a}^{2} (1 - ρ^{2})}} e x p (- \frac{{(P a_{i} - μ_{P a_{P l_{j}}} - ρ \frac{σ_{A}}{σ_{P a}} (A_{i} - μ_{A}))}^{2}}{2 σ_{A}^{2} (1 - ρ^{2})}) \end{aligned}

\begin{aligned} L (M o d e l I V) = & \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) l (θ; A_{i}) l (θ; P a_{i} ∣ A_{i}, P l_{j}) \\ = & \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) \frac{1}{\sqrt{2 π σ_{A}^{2}}} e x p (- \frac{{(A_{i} - μ_{A})}^{2}}{2 σ_{A}^{2}}) \\ \frac{1}{\sqrt{2 π σ_{P a}^{2} (1 - ρ^{2})}} e x p (- \frac{{(P a_{i} - μ_{P a_{P l_{j}}} - ρ \frac{σ_{A}}{σ_{P a}} (A_{i} - μ_{A}))}^{2}}{2 σ_{A}^{2} (1 - ρ^{2})}) \end{aligned}

\begin{aligned} L (M o d e l V) = & \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) l (θ; A_{i}) l (θ; P a_{i}) \\ = & \prod_{i = 1}^{n} \sum_{j = 1}^{k} P (P l_{j}) \frac{1}{\sqrt{2 π σ_{A}^{2}}} e x p (- \frac{{(A_{i} - μ_{A})}^{2}}{2 σ_{A}^{2}}) \\ \frac{1}{\sqrt{2 π σ_{P a}^{2}}} e x p (- \frac{{(P a_{i} - μ_{P a})}^{2}}{2 σ_{P a}^{2}}) \end{aligned}

Where i indicates each gene, j represents different pleiotropic levels, n is the total number of putatively selected genes and k equals to 10 in this study. For each likelihood model, the corresponding likelihood is maximized, and parameters are estimated using standard maximum likelihood methods.

The BICs are then computed for each model as follows:

B I C = - 2 l n (L_{i}) + k_{i} l n (n)

Where $L_{i}$ are the likelihood for model I-III, $k_{i}$ is the corresponding number of free parameters and n is a total number of putatively selected genes.

The model with the smallest BIC value is identified as the best-supported model.

Regression models including the effects of two measures of pleiotropy on ancestral variation/parallelism

To examine whether two measures of pleiotropy explain ancestral variation/parallelism through common or independent features, we performed two additional regression analyses (Equation A10; A11). All variables were transformed and standardized (see Appendix).

First, to understand whether two pleiotropy measures explain the parallelism directly through common or independent features, we fit the regression model across all putatively adaptive genes as follows:

y_{P a} = β_{A} x_{A} + β_{T S} x_{T S} + β_{N C} x_{N C} + ε

Where $y_{P a}$ stands for parallelism, $x_{A}$ is ancestral variation, $x_{T S}$ is 1- $τ$ , $x_{N C}$ is network connectivity and $ε$ is random error. $β_{A}$ , $β_{T S}$ and $β_{N C}$ are the regression coefficients corresponding to $x_{A}$ , $x_{T S}$ and $x_{N C}$ , respectively. The regression coefficients for each corresponding variable are shown in Appendix 1—table 1.

Second, to understand whether two pleiotropy measures explain ancestral variation through the common or independent feature, we fit another regression model across all putatively adaptive genes as follows:

y_{A} = β_{T S} x_{T S} + β_{N C} x_{N C} + ε

Where $y_{A}$ stands for ancestral variation, $x_{T S}$ is 1- $τ$ , $x_{N C}$ is network connectivity and $ε$ is random error. $β_{T S}$ and $β_{N C}$ are the regression coefficients corresponding to $x_{T S}$ and $x_{N C}$ , respectively. The regression coefficients for each corresponding variable are shown in Appendix 1—table 2.

Appendix 1—figure 1

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Positive correlation between two measures of pleiotropy.

The substantial correlation (rho = 0.45, p-value <2.2e-16) between network connectivity (x-axis) and 1- $τ$ (y-axis) suggests that both estimates are capturing similar information representing the pleiotropy.

Appendix 1—figure 2

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Evolutionary changes of gene expression across 10 evolved populations.

The figure compares the log₂FC of the top 10 genes with the most parallel evolutionary responses (genes with the top 10 highest 1 /F values, shown in pink) and the top 10 genes with the most heterogeneous evolutionary responses (genes with the lowest 1 /F values, shown in orange). As illustrated in the figure, genes with higher 1 /F values display more parallel evolutionary responses (log₂FC) across the 10 evolved populations, while genes with lower 1 /F values exhibit more variation in their evolutionary responses.

Appendix 1—figure 3

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Association between parallelism of a gene (log(1 /F); y-axis) and the number of evolved populations in which it is significantly differentiated from the ancestral population (x-axis).

A significant positive correlation between them (rho = 0.22, p-value <5.7e-10) suggests that genes being detected in more evolved populations have higher expression parallelism and vice versa.

Appendix 1—figure 4

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Correlation between the strength of pleiotropy (network connectivity) with the evolutionary parallelism (a) and with the ancestral variation (b).

(a) The distribution of evolutionary parallelism of different genes is shown in boxplots binned by their strength of pleiotropy (network connectivity). The strength of pleiotropy was positively correlated with evolutionary parallelism (rho = 0.11, p-value <5.6e-07). (b) The distribution of expression variance in ancestral populations of different genes is shown in boxplots binned by their strength of pleiotropy. The strength of pleiotropy is negatively correlated with the ancestral variation in gene expression (rho = −0.37, p-value <2.2e-16). Both results were consistent with the finding of the other measure of pleiotropy, tissue specificity.

Appendix 1—figure 5

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Strength of pleiotropy between DE and non-DE gene groups using 1-tissue specificity (a) and network connectivity (b) as proxies, respectively.

Genes without an evolutionary response (non-DE) exhibit greater pleiotropic strength compared to genes with evolutionary responses (differential expression, DE), defined here as genes showing significant expression changes between ancestral and evolved populations in the same direction in at least three populations. This pattern is consistent when using both tissue specificity and network connectivity as measures of pleiotropy, suggesting that extremely high levels of pleiotropy may constrain evolutionary responses.

Appendix 1—table 1

Regression of evolutionary parallelism (response) on both measures of pleiotropy (1- $τ$ and network connectivity) and ancestral variation.

Coefficient estimates and hypothesis test ( $β = 0$ ) results are provided.

Variable	$β$	F-statistics	p-value
Ancestral variation	–0.16	76.7227	<2.2e-16***
1- $τ$	0.13	26.4172	3.055e-07***
Network connectivity	–0.03	1.1288	0.2882

Appendix 1—table 2

Regression of ancestral variation (response) on both measures of pleiotropy (1- $τ$ and network connectivity).

Coefficient estimates and hypothesis test ( $β = 0$ ) results are provided.

Variable	$β$	F-statistics	p-value
1- $τ$	–0.236	258.581	<2.2e-16***
Network connectivity	–0.239	95.079	<2.2e-16***

Data availability

All sequencing data are available in European Nucleotide Archive (ENA) under the accessionnumber PRJEB35504, PRJEB35506 and PRJEB37011. Other raw data and scripts are available on GitHub (https://github.com/cloudweather34/pleiotropy, copy archived at cloudweather34, 2025).

The following previously published data sets were used

1. Jakšić AM
2. Karner J
3. Nolte V
4. Hsu SK
5. Barghi N
6. Mallard F
7. Otte KA
8. Svečnjak L
9. Senti KA
10. Schlötterer C
(2020) EBI European Nucleotide Archive
ID PRJEB35504. Neuronal function and dopamine signaling evolve at high temperature in Drosophila.

https://www.ebi.ac.uk/ena/browser/view/PRJEB35504
1. Hsu SK
2. Jakšić AM
3. Nolte V
4. Lirakis M
5. Kofler R
6. Barghi N
7. Versace E
8. Schlötterer C
(2020) EBI European Nucleotide Archive
ID PRJEB35506. Rapid sex-specific adaptation to high temperature in Drosophila.

https://www.ebi.ac.uk/ena/browser/view/PRJEB35506
1. Lai WY
2. Schlötterer C
(2020) EBI European Nucleotide Archive
ID PRJEB37011. Evolution of phenotypic variance in response to a novel hot environment.

https://www.ebi.ac.uk/ena/browser/view/PRJEB37011

References

(2018) Genetic constraints on microevolutionary divergence of sex-biased gene expression
Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 373:20170427.

https://doi.org/10.1098/rstb.2017.0427
- PubMed
- Google Scholar
1. Ayroles JF
2. Carbone MA
3. Stone EA
4. Jordan KW
5. Lyman RF
6. Magwire MM
7. Rollmann SM
8. Duncan LH
9. Lawrence F
10. Anholt RRH
11. Mackay TFC
(2009) Systems genetics of complex traits in Drosophila melanogaster
Nature Genetics 41:299–307.

https://doi.org/10.1038/ng.332
- PubMed
- Google Scholar
1. Barghi N
2. Tobler R
3. Nolte V
4. Jakšić AM
5. Mallard F
6. Otte KA
7. Dolezal M
8. Taus T
9. Kofler R
10. Schlötterer C
(2019) Genetic redundancy fuels polygenic adaptation in Drosophila
PLOS Biology 17:e3000128.

https://doi.org/10.1371/journal.pbio.3000128
- Google Scholar
(2020) Polygenic adaptation: a unifying framework to understand positive selection
Nature Reviews. Genetics 21:769–781.

https://doi.org/10.1038/s41576-020-0250-z
- PubMed
- Google Scholar
1. Benjamini Y
2. Hochberg Y
(1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing
Journal of the Royal Statistical Society Series B 57:289–300.

https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
- Google Scholar
1. Blake WJ
2. KAErn M
3. Cantor CR
4. Collins JJ
(2003) Noise in eukaryotic gene expression
Nature 422:633–637.

https://doi.org/10.1038/nature01546
- PubMed
- Google Scholar
1. Blows MW
(2007) A tale of two matrices: multivariate approaches in evolutionary biology
Journal of Evolutionary Biology 20:1–8.

https://doi.org/10.1111/j.1420-9101.2006.01164.x
- PubMed
- Google Scholar
1. Bohutínská M
2. Vlček J
3. Yair S
4. Laenen B
5. Konečná V
6. Fracassetti M
7. Slotte T
8. Kolář F
(2021) Genomic basis of parallel adaptation varies with divergence in Arabidopsis and its relatives
PNAS 118:e2022713118.

https://doi.org/10.1073/pnas.2022713118
- PubMed
- Google Scholar
(2018) (Non)Parallel evolution
Annual Review of Ecology, Evolution, and Systematics 49:303–330.

https://doi.org/10.1146/annurev-ecolsys-110617-062240
- Google Scholar
(2014) Parallel evolution of local adaptation and reproductive isolation in the face of gene flow
Evolution; International Journal of Organic Evolution 68:935–949.

https://doi.org/10.1111/evo.12329
- PubMed
- Google Scholar
Book
1. Charlesworth B
2. Charlesworth D
(2010)
Elements of Evolutionary Genetics

Roberts and Company Publishers.
- Google Scholar
Software
1. cloudweather34
(2025) Pleiotropy, version swh:1:rev:7a7106f5853b35fda68c530403e42fe42ec7c0be
Software Heritage.

https://archive.softwareheritage.org/swh:1:dir:f85a4376eeb107a91bacd75e6eeb30920a050b69;origin=https://github.com/cloudweather34/pleiotropy;visit=swh:1:snp:6b2cc0ad1b5e1bfb40fcb70c52f2670961022f82;anchor=swh:1:rev:7a7106f5853b35fda68c530403e42fe42ec7c0be
(2005) Widespread parallel evolution in sticklebacks by repeated fixation of Ectodysplasin alleles
Science 307:1928–1933.

https://doi.org/10.1126/science.1107239
- PubMed
- Google Scholar
1. Conte GL
2. Arnegard ME
3. Best J
4. Chan YF
5. Jones FC
6. Kingsley DM
7. Schluter D
8. Peichel CL
(2015) Extent of QTL reuse during repeated phenotypic divergence of sympatric threespine stickleback
Genetics 201:1189–1200.

https://doi.org/10.1534/genetics.115.182550
- PubMed
- Google Scholar
1. Davidson RM
2. Gowda M
3. Moghe G
4. Lin H
5. Vaillancourt B
6. Shiu S-H
7. Jiang N
8. Robin Buell C
(2012) Comparative transcriptomics of three Poaceae species reveals patterns of gene expression evolution
The Plant Journal 71:492–502.

https://doi.org/10.1111/j.1365-313X.2012.05005.x
- PubMed
- Google Scholar
1. Dean R
2. Mank JE
(2016) Tissue specificity and sex-specific regulatory variation permit the evolution of sex-biased gene expression
The American Naturalist 188:E74–E84.

https://doi.org/10.1086/687526
- PubMed
- Google Scholar
1. Elmer KR
2. Fan S
3. Kusche H
4. Spreitzer ML
5. Kautt AF
6. Franchini P
7. Meyer A
(2014) Parallel evolution of Nicaraguan crater lake cichlid fishes via non-parallel routes
Nature Communications 5:5168.

https://doi.org/10.1038/ncomms6168
- PubMed
- Google Scholar
(2002) Stochastic gene expression in a single cell
Science 297:1183–1186.

https://doi.org/10.1126/science.1070919
- PubMed
- Google Scholar
1. Fischer EK
2. Song Y
3. Hughes KA
4. Zhou W
5. Hoke KL
(2021) Nonparallel transcriptional divergence during parallel adaptation
Molecular Ecology 30:1516–1530.

https://doi.org/10.1111/mec.15823
- PubMed
- Google Scholar
Book
1. Fisher RA
(1930) The Genetical Theory of Natural Selection
Oxford: Clarendon Press.

https://doi.org/10.5962/bhl.title.27468
- Google Scholar
1. Frachon L
2. Libourel C
3. Villoutreix R
4. Carrère S
5. Glorieux C
6. Huard-Chauveau C
7. Navascués M
8. Gay L
9. Vitalis R
10. Baron E
11. Amsellem L
12. Bouchez O
13. Vidal M
14. Le Corre V
15. Roby D
16. Bergelson J
17. Roux F
(2017) Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time
Nature Ecology & Evolution 1:1551–1561.

https://doi.org/10.1038/s41559-017-0297-1
- PubMed
- Google Scholar
(2002) Evolutionary rate in the protein interaction network
Science 296:750–752.

https://doi.org/10.1126/science.1068696
- PubMed
- Google Scholar
1. Hahn MW
2. Kern AD
(2005) Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks
Molecular Biology and Evolution 22:803–806.

https://doi.org/10.1093/molbev/msi072
- Google Scholar
(2020a) Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia)
PLOS Genetics 16:e1008707.

https://doi.org/10.1371/journal.pgen.1008707
- PubMed
- Google Scholar
(2020b) Gene expression modularity reveals footprints of polygenic adaptation in theobroma cacao
Molecular Biology and Evolution 37:110–123.

https://doi.org/10.1093/molbev/msz206
- PubMed
- Google Scholar
1. Hansen TF
(2003) Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability
Bio Systems 69:83–94.

https://doi.org/10.1016/s0303-2647(02)00132-6
- PubMed
- Google Scholar
1. He X
2. Zhang J
(2006) Toward a molecular understanding of pleiotropy
Genetics 173:1885–1891.

https://doi.org/10.1534/genetics.106.060269
- PubMed
- Google Scholar
1. Heckley AM
2. Pearce AE
3. Gotanda KM
4. Hendry AP
5. Oke KB
(2022) Compiling forty years of guppy research to investigate the factors contributing to (non)parallel evolution
Journal of Evolutionary Biology 35:1414–1431.

https://doi.org/10.1111/jeb.14086
- PubMed
- Google Scholar
1. Hsu S-K
2. Jakšić AM
3. Nolte V
4. Lirakis M
5. Kofler R
6. Barghi N
7. Versace E
8. Schlötterer C
(2020) Rapid sex-specific adaptation to high temperature in Drosophila
eLife 9:e53237.

https://doi.org/10.7554/eLife.53237
- PubMed
- Google Scholar
(2021) Parallel gene expression evolution in natural and laboratory evolved populations
Molecular Ecology 30:884–894.

https://doi.org/10.1111/mec.15649
- PubMed
- Google Scholar
1. Huang Y
2. Lack JB
3. Hoppel GT
4. Pool JE
(2021) Parallel and population-specific gene regulatory evolution in cold-adapted fly populations
Genetics 218:iyab077.

https://doi.org/10.1093/genetics/iyab077
- PubMed
- Google Scholar
1. Jakšić AM
2. Karner J
3. Nolte V
4. Hsu S-K
5. Barghi N
6. Mallard F
7. Otte KA
8. Svečnjak L
9. Senti K-A
10. Schlötterer C
(2020) Neuronal function and dopamine signaling evolve at high temperature in Drosophila
Molecular Biology and Evolution 37:2630–2640.

https://doi.org/10.1093/molbev/msaa116
- PubMed
- Google Scholar
(2017) The relationship between selection, network connectivity, and regulatory variation within a population of capsella grandiflora
Genome Biology and Evolution 9:1099–1109.

https://doi.org/10.1093/gbe/evx068
- Google Scholar
1. Julca I
2. Ferrari C
3. Flores-Tornero M
4. Proost S
5. Lindner A-C
6. Hackenberg D
7. Steinbachová L
8. Michaelidis C
9. Gomes Pereira S
10. Misra CS
11. Kawashima T
12. Borg M
13. Berger F
14. Goldberg J
15. Johnson M
16. Honys D
17. Twell D
18. Sprunck S
19. Dresselhaus T
20. Becker JD
21. Mutwil M
(2021) Comparative transcriptomic analysis reveals conserved programmes underpinning organogenesis and reproduction in land plants
Nature Plants 7:1143–1159.

https://doi.org/10.1038/s41477-021-00958-2
- PubMed
- Google Scholar
1. Lai WY
2. Schlötterer C
(2022) Evolution of phenotypic variance in response to a novel hot environment
Molecular Ecology 31:934–945.

https://doi.org/10.1111/mec.16274
- PubMed
- Google Scholar
1. Lande R
(1979) Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry
Evolution; International Journal of Organic Evolution 33:402–416.

https://doi.org/10.1111/j.1558-5646.1979.tb04694.x
- PubMed
- Google Scholar
1. Lande R
2. Arnold SJ
(1983) The measurement of selection on correlated characters
Evolution 37:2408842.

https://doi.org/10.2307/2408842
- Google Scholar
(2020) The importance of genetic redundancy in evolution
Trends in Ecology & Evolution 35:809–822.

https://doi.org/10.1016/j.tree.2020.04.009
- Google Scholar
(2008) Genomic analysis of the relationship between gene expression variation and DNA polymorphism in Drosophila simulans
Genome Biology 9:R125.

https://doi.org/10.1186/gb-2008-9-8-r125
- PubMed
- Google Scholar
1. Leader DP
2. Krause SA
3. Pandit A
4. Davies SA
5. Dow JAT
(2018) FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data
Nucleic Acids Research 46:D809–D815.

https://doi.org/10.1093/nar/gkx976
- PubMed
- Google Scholar
(2004) Regulatory evolution across the protein interaction network
Nature Genetics 36:1059–1060.

https://doi.org/10.1038/ng1427
- PubMed
- Google Scholar
1. MacPherson A
2. Nuismer SL
(2017) The probability of parallel genetic evolution from standing genetic variation
Journal of Evolutionary Biology 30:326–337.

https://doi.org/10.1111/jeb.13006
- PubMed
- Google Scholar
(2017) Gene co-expression network connectivity is an important determinant of selective constraint
PLOS Genetics 13:e1006402.

https://doi.org/10.1371/journal.pgen.1006402
- PubMed
- Google Scholar
(2008) Pleiotropic constraint hampers the resolution of sexual antagonism in vertebrate gene expression
The American Naturalist 171:35–43.

https://doi.org/10.1086/523954
- PubMed
- Google Scholar
1. Mao Y-X
2. Li Y
3. Yang Z
4. Xu N
5. Zhang S
6. Wang X
7. Yang X
8. Sun Q
9. Mao Y
(2024) Comparative transcriptome analysis between rhesus macaques (Macaca mulatta) and crab-eating macaques (M. fascicularis)
Zoological Research 45:299–310.

https://doi.org/10.24272/j.issn.2095-8137.2023.322
- PubMed
- Google Scholar
1. Marbach D
2. Roy S
3. Ay F
4. Meyer PE
5. Candeias R
6. Kahveci T
7. Bristow CA
8. Kellis M
(2012) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks
Genome Research 22:1334–1349.

https://doi.org/10.1101/gr.127191.111
- PubMed
- Google Scholar
(2017) Quantity, not quality: rapid adaptation in a polygenic trait proceeded exclusively through expression differentiation
Molecular Biology and Evolution 34:3099–3110.

https://doi.org/10.1093/molbev/msx231
- PubMed
- Google Scholar
(2017) Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants
PLOS ONE 12:e0182289.

https://doi.org/10.1371/journal.pone.0182289
- PubMed
- Google Scholar
1. McShea DW
(2000) Functional complexity in organisms: parts as proxies
Biology & Philosophy 15:641–668.

https://doi.org/10.1023/A:1006695908715
- Google Scholar
(2016) Ancestral population reconstitution from isofemale lines as a tool for experimental evolution
Ecology and Evolution 6:7169–7175.

https://doi.org/10.1002/ece3.2402
- PubMed
- Google Scholar
(2018) High-throughput fecundity measurements in Drosophila
Scientific Reports 8:4469.

https://doi.org/10.1038/s41598-018-22777-w
- PubMed
- Google Scholar
1. Nourmohammad A
2. Rambeau J
3. Held T
4. Kovacova V
5. Berg J
6. Lässig M
(2017) Adaptive evolution of gene expression in Drosophila
Cell Reports 20:1385–1395.

https://doi.org/10.1016/j.celrep.2017.07.033
- PubMed
- Google Scholar
1. O’Neil ST
2. Dzurisin JDK
3. Williams CM
4. Lobo NF
5. Higgins JK
6. Deines JM
7. Carmichael RD
8. Zeng E
9. Tan JC
10. Wu GC
11. Emrich SJ
12. Hellmann JJ
(2014) Gene expression in closely related species mirrors local adaptation: consequences for responses to a warming world
Molecular Ecology 23:2686–2698.

https://doi.org/10.1111/mec.12773
- PubMed
- Google Scholar
1. Orr HA
(2000) Adaptation and the cost of complexity
Evolution; International Journal of Organic Evolution 54:13–20.

https://doi.org/10.1111/j.0014-3820.2000.tb00002.x
- PubMed
- Google Scholar
(2021) The genetic architecture of temperature adaptation is shaped by population ancestry and not by selection regime
Genome Biology 22:211.

https://doi.org/10.1186/s13059-021-02425-9
- PubMed
- Google Scholar
(2014) Gene pleiotropy constrains gene expression changes in fish adapted to different thermal conditions
Nature Communications 5:4071.

https://doi.org/10.1038/ncomms5071
- PubMed
- Google Scholar
(2005) Network thinking in ecology and evolution
Trends in Ecology & Evolution 20:345–353.

https://doi.org/10.1016/j.tree.2005.04.004
- Google Scholar
1. Rennison DJ
2. Peichel CL
(2022) Pleiotropy facilitates parallel adaptation in sticklebacks
Molecular Ecology 31:1476–1486.

https://doi.org/10.1111/mec.16335
- PubMed
- Google Scholar
(2023) Parallel evolution of behavior, physiology, and life history associated with altitudinal shifts in forest type in Heliconius butterflies
Evolution; International Journal of Organic Evolution 77:1458–1467.

https://doi.org/10.1093/evolut/qpad062
- PubMed
- Google Scholar
(2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
Bioinformatics 26:139–140.

https://doi.org/10.1093/bioinformatics/btp616
- PubMed
- Google Scholar
1. Schadt EE
2. Lamb J
3. Yang X
4. Zhu J
5. Edwards S
6. Guhathakurta D
7. Sieberts SK
8. Monks S
9. Reitman M
10. Zhang C
11. Lum PY
12. Leonardson A
13. Thieringer R
14. Metzger JM
15. Yang L
16. Castle J
17. Zhu H
18. Kash SF
19. Drake TA
20. Sachs A
21. Lusis AJ
(2005) An integrative genomics approach to infer causal associations between gene expression and disease
Nature Genetics 37:710–717.

https://doi.org/10.1038/ng1589
- PubMed
- Google Scholar
1. Stern DL
(2013) The genetic causes of convergent evolution
Nature Reviews. Genetics 14:751–764.

https://doi.org/10.1038/nrg3483
- PubMed
- Google Scholar
(2002) Intrinsic and extrinsic contributions to stochasticity in gene expression
PNAS 99:12795–12800.

https://doi.org/10.1073/pnas.162041399
- PubMed
- Google Scholar
1. Tang Q
2. Gu Y
3. Zhou X
4. Jin L
5. Guan J
6. Liu R
7. Li J
8. Long K
9. Tian S
10. Che T
11. Hu S
12. Liang Y
13. Yang X
14. Tao X
15. Zhong Z
16. Wang G
17. Chen X
18. Li D
19. Ma J
20. Wang X
21. Mai M
22. Jiang A
23. Luo X
24. Lv X
25. Gladyshev VN
26. Li X
27. Li M
(2017) Comparative transcriptomics of 5 high-altitude vertebrates and their low-altitude relatives
GigaScience 6:1–9.

https://doi.org/10.1093/gigascience/gix105
- PubMed
- Google Scholar
(2019) Parallel genetic evolution and speciation from standing variation
Evolution Letters 3:129–141.

https://doi.org/10.1002/evl3.106
- PubMed
- Google Scholar
(2023) Temperature-driven gene expression evolution in natural and laboratory populations highlights the crucial role of correlated fitness effects for polygenic adaptation
Evolution; International Journal of Organic Evolution 77:2081–2089.

https://doi.org/10.1093/evolut/qpad132
- PubMed
- Google Scholar
1. van der Zee MJ
2. Whiting JR
3. Paris JR
4. Bassar RD
5. Travis J
6. Weigel D
7. Reznick DN
8. Fraser BA
(2022) Rapid genomic convergent evolution in experimental populations of Trinidadian guppies (Poecilia reticulata)
Evolution Letters 6:149–161.

https://doi.org/10.1002/evl3.272
- PubMed
- Google Scholar
1. Velotta JP
2. Wegrzyn JL
3. Ginzburg S
4. Kang L
5. Czesny S
6. O’Neill RJ
7. McCormick SD
8. Michalak P
9. Schultz ET
(2017) Transcriptomic imprints of adaptation to fresh water: parallel evolution of osmoregulatory gene expression in the Alewife
Molecular Ecology 26:831–848.

https://doi.org/10.1111/mec.13983
- PubMed
- Google Scholar
1. Vlachos C
2. Kofler R
(2018) MimicrEE2: genome-wide forward simulations of evolve and resequencing studies
PLOS Computational Biology 14:e1006413.

https://doi.org/10.1371/journal.pcbi.1006413
- PubMed
- Google Scholar
(2019) A global overview of pleiotropy and genetic architecture in complex traits
Nature Genetics 51:1339–1348.

https://doi.org/10.1038/s41588-019-0481-0
- PubMed
- Google Scholar
(2021) Using replicate hybrid zones to understand the genomic basis of adaptive divergence
Molecular Ecology 30:3797–3814.

https://doi.org/10.1111/mec.15861
- PubMed
- Google Scholar
1. Wright S
(1920)
Correlation and causation Part I. Method of path coefficients

Journal of Agricultural Research 20:557–585.
- Google Scholar
1. Yu X
2. Wei P
3. Zhao S
4. Chen Z
5. Li X
6. Zhang W
7. Liu C
8. Yang Y
9. Li X
10. Liu X
(2023) Population transcriptomics uncover the relative roles of positive selection and differential expression in Batrachium bungei adaptation to the Qinghai-Tibetan plateau
Plant Cell Reports 42:879–893.

https://doi.org/10.1007/s00299-023-03005-w
- PubMed
- Google Scholar
1. Zong S-B
2. Li Y-L
3. Liu J-X
(2021) Genomic architecture of rapid parallel adaptation to fresh water in a wild fish
Molecular Biology and Evolution 38:1317–1329.

https://doi.org/10.1093/molbev/msaa290
- PubMed
- Google Scholar

Article and author information

Author details

Wei-Yun Lai
1. Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
2. Vienna Graduate School of Population Genetics, Vetmeduni Vienna, Vienna, Austria
Contribution
Conceptualization, Formal analysis, Writing – original draft, Writing – review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-5101-8695
Sheng-Kai Hsu
1. Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
2. Vienna Graduate School of Population Genetics, Vetmeduni Vienna, Vienna, Austria
Contribution
Conceptualization, Formal analysis, Writing – review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-6942-7163
Andreas Futschik

Department of Applied Statistics, Johannes Kepler University Linz, Linz, Austria

Contribution
Writing – review and editing, Statistical advice

Competing interests
No competing interests declared
Christian Schlötterer

Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria

Contribution
Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

For correspondence
schlotc@gmail.com

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0003-4710-6526

Funding

Austrian Science Fund

https://doi.org/10.55776/W1225

Christian Schlötterer

European Research Council (Archadapt)

Christian Schlötterer

Austrian Science Fund

https://doi.org/10.55776/P32935

Christian Schlötterer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Viola Nolte for preparing all RNA-Seq libraries and supervising the maintenance of the evolution experiment. We thank Dagný Ásta Rúnarsdóttir for the suggestions on the early version of the manuscript and all members of the Institut für Populationsgenetik for discussion. Ana Marija Jakšić, Neda Barghi, François Mallard, and Kathrin Otte performed the common garden experiment. Illumina sequencing was done at the VBCF NGS Unit https://www.viennabiocenter.org/vbcf/. This research was funded in whole or in part by the Austrian Science Fund (FWF) [W1225, P32935] and the European Research Council (ERC) [ArchAdapt]. For open access purposes, the author has applied a CC BY public copyright license to any author-accepted manuscript version arising from this submission.

Version history

Sent for peer review: August 21, 2024
Preprint posted: August 25, 2024
Reviewed Preprint version 1: November 22, 2024
Reviewed Preprint version 2: March 6, 2025
Version of Record published: April 14, 2025

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Wei-Yun Lai
Sheng-Kai Hsu
Andreas Futschik
Christian Schlötterer

(2025)

Pleiotropy increases parallel selection signatures during adaptation from standing genetic variation

eLife 13:RP102321.

https://doi.org/10.7554/eLife.102321.3

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Overview of the experimental procedures (a) and the variation in parallel gene expression evolution across genes (b,c).

Association between the magnitude of evolution parallelism and the strength of pleiotropy.

Association between ancestral variation in gene expression with the strength of pleiotropy (1- τ) (a) and with the evolution parallelism (b).

Computer simulations illustrate the negative effect of ancestral variation on parallel evolution.

Schematic figure illustrating the causal models evaluated.

Bayesian Information Criteria (BIC) value for Model I-V.

The size of direct and indirect pleiotropic effects on the evolutionary parallelism.

Positive correlation between two measures of pleiotropy.

Evolutionary changes of gene expression across 10 evolved populations.

Association between parallelism of a gene (log(1 /F); y-axis) and the number of evolved populations in which it is significantly differentiated from the ancestral population (x-axis).

Correlation between the strength of pleiotropy (network connectivity) with the evolutionary parallelism (a) and with the ancestral variation (b).

Strength of pleiotropy between DE and non-DE gene groups using 1-tissue specificity (a) and network connectivity (b) as proxies, respectively.

Regression of evolutionary parallelism (response) on both measures of pleiotropy (1-τ and network connectivity) and ancestral variation.

Regression of ancestral variation (response) on both measures of pleiotropy (1-τ and network connectivity).

Author details

Wei-Yun Lai

Contribution

Competing interests

Sheng-Kai Hsu

Contribution

Competing interests

Andreas Futschik

Contribution

Competing interests

Christian Schlötterer

Contribution

For correspondence

Competing interests

Downloads (link to download the article as PDF)

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Further reading

Association between ancestral variation in gene expression with the strength of pleiotropy (1- $τ$ ) (a) and with the evolution parallelism (b).

Regression of evolutionary parallelism (response) on both measures of pleiotropy (1- $τ$ and network connectivity) and ancestral variation.

Regression of ancestral variation (response) on both measures of pleiotropy (1- $τ$ and network connectivity).