“Are certain developmental processes especially likely to be disrupted in hybrids? This question has been surprisingly neglected given that hybrid defects provide a rare window on those developmental processes and pathways that diverge rapidly between taxa.’ – Coyne and Orr, 2004, p.309.

Distinct species are those separate collections of genomes that, if you were to put them together in the same cells, the mixture would create a broken organism. Development in the hybrid individuals would go awry in such a way as to cause inviability, infertility, or a phenotypic mismatch to ecology or mating interactions that compromises further reproductive success. Given common descent from an ancestor, how could evolution produce such disastrous phenotypic consequences of genetic change? Darwin recognized this dilemma (Darwin 1859), in that natural selection will oppose changes that confer a net fitness cost. But evolution can circumvent this problem through interactions between multiple genetic factors, as intuited by W. Bateson, T. Dobzhansky and H.J. Muller and made explicit in the genetic mechanism of Dobzhansky-Muller incompatibility (DMI) for post-zygotic disruption in hybrids (Box 1A,B). Put simply: when evolution substitutes an allele at one locus, it makes no guarantee that this new derived genetic background will be compatible with allele substitutions occurring in other populations at other loci. Such inter-locus incompatibilities could involve two, or three, or many more interacting loci to create a DMI to genetically enforce species boundaries.

Box 1.

Visualizing, conceptualizing, and modeling Dobzhansky-Muller incompatibilities.

Dobzhansky-Muller Incompatibilities (DMIs) can be viewed as disrupted gene networks and as valleys (or holes) in fitness landscapes (Gavrilets, 2003; Fragata et al., 2019). Distinct biological species comprise separate groups of individuals that fail to successfully interbreed, though ‘good’ biological species may still yield hybrid F1 and F2 offspring, so long as they suffer clear fitness deficits (Coyne and Orr, 2004). Of special interest for development are genetically intrinsic post-zygotic species barriers that manifest as DMIs in hybrid individuals and do not depend on extrinsic circumstances, reflecting disruptive changes to developmental programs. DMIs between populations can evolve from a single common ancestral population through the independent substitution of two or more mutations distinguishing the descendant lineages. The substitutions may fix due to positive selection or genetic drift. This DM model also encapsulates the essence of developmental system drift (DSD): independent evolution in distinct lineages that causes divergence in genetic architectures while retaining within-lineage fitness (True and Haag, 2001).

Box 1—figure 1

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In this way, the mutational substitutions that accumulate in one population are indifferent to the substitutions that accumulate in other populations, but only so long as the two populations do not share alleles with one another through gene flow. When the populations of incipient species do intermingle genetically, then developmental programs in hybrid individuals must confront whether alleles derived from such ‘out-of-sight, out-of-mind’ evolution interact in a way that allows ontogeny to proceed normally (Box 1A,B). Incompatible combinations of alleles in hybrid individuals at two or more loci can be thought of as negative epistasis or as a kind of non-linear genetic perturbation. Put this way, the genetics of speciation sound eerily similar to notions of cryptic genetic variation (Wade et al., 1997; Ledón-Rettig et al., 2014; Paaby and Rockman, 2014) and developmental system drift in the literature on the evolution of development (True and Haag, 2001; Pavlicev and Wagner, 2012; Tulchinsky et al., 2014a). The regulatory pathways that govern development are an important component of how and whether divergence will lead to genetic incompatibility and speciation (Porter and Johnson, 2002). Our aim here is to draw these connections more explicitly, to place speciation in a firmer developmental context (Sucena and Stern, 2000; Johnson and Porter, 2001), and to emphasize the relevance of the evolution of reproductive isolation to problems in developmental biology.

Distinguishing detectable versus undetectable differences in phenotype between species is key to thinking about divergence in the genes and genetic networks that underlie the developmental pathways that create an organism. Detectable differences in the development of traits between species, often due to adaptive divergence, clearly involve genetic changes. The converse, however, is not true: conservation of phenotype does not imply conservation of genetic controls. For example, stabilizing selection on expression levels leads to similar expression levels between species, despite evidence of widespread compensatory effects of molecular evolution to both cis- and trans-acting regulators of gene expression (Mack and Nachman, 2017). This idea of genetic change despite phenotypic stasis is known in evo-devo as developmental system drift (DSD), and in population genetics as evolution along a ridge in a fitness landscape (Box 1C,D). Adaptive evolution at the molecular level also can contribute to DSD for a given trait, particularly if the genetic changes affect fitness through pleiotropic effects with phenotypic consequences for only a subset of traits or from subsequent compensatory evolution (Box 1C).

When molecular evolutionary differences between species involve two or more loci, they may not interact properly in hybrid individuals that have copies of both genomes. This can happen regardless of whether those differences induce detectable phenotypic differences that distinguish the species and regardless of whether selection or genetic drift caused their fixation. Each mutational substitution that distinguishes species in protein coding sequence or gene regulatory control thus has some probability of contributing to the formation of a DMI in hybrid individuals. The more substitutions, the more chances for incompatibilities to create hybrid dysfunction at some point in development (Orr, 1995). In this way, inter-species hybrids can reveal genetic divergence in the control of even those developmental programs that yield seemingly equivalent phenotypic outputs. Hybrids can also reveal the incidence and role of different kinds of changes, such as cis- vs trans-acting regulators of gene expression (Box 2; Wittkopp et al., 2004; Mack et al., 2016).

Box 2.

Developmental disruption and regulatory inference in inter-species hybrids.

Box 2—figure 1

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Divergence in developmental genetic programs and intrinsic post-zygotic reproductive isolation in the speciation process are thus close conceptual brethren, despite their largely separate research traditions. But how do features of genetic networks and evolutionary forces intersect to create such profound developmental genetic divergence revealed as DMIs in hybrids? Are some stages of ontogeny predisposed to genetic architectures and evolutionary pressures to be more likely to yield dysfunctional development in hybrid organisms? Applying a timepiece metaphor: what gears and springs in the genetic clockwork will set the developmental alarm clock to ring at one time versus another during ontogeny to signal speciation? The more molecular evolutionary change, the more likely are genetic incompatibilities to manifest in inter-species hybrids. We must therefore set our goal to define the factors that make some genes evolve more quickly than others. The answers will help us to determine ways in which the molecular evolution that underpins development is predictable and, consequently, in what ways the genetics of speciation is predictable.

To address this goal, here we first consider how molecular evolution is influenced by the properties of genes and genetic networks, such as pleiotropy, modularity, robustness, and cis/trans regulation. These are the ‘gears and springs’ in the genetic architecture of development. We then explore how genetic architecture may sensitize some phases of ontogeny disproportionately to disruptive effects of misexpression in genetic networks. By integrating genetic architecture and ontogenetic timing, we arrive at distinct predictions for how molecular evolution and hybrid dysfunction manifest across developmental time. Finally, we summarize the literature on these issues for three case study systems (Caenorhabditis nematodes, Drosophila fruit flies, and anuran Bufo and Xenopus). In the present state of the field, we find that few general answers emerge from these factors considered in isolation, motivating deeper attention from theory and empirical study.

Speciation and development: divergence in genetic networks

As we explore how the evolution of development intersects with speciation, it is valuable to consider some key aspects of genetic architecture from the perspective of multi-gene networks that, in turn, control organismal development (Johnson and Porter, 2007; Palmer and Feldman, 2009). Here, we focus on how pleiotropy, network modularity, and robustness can influence the molecular evolution of coding sequences and non-coding regulatory elements. These links will help ground our expectations for incorporating ontogenetic time into our thinking to then consider predictions for molecular evolution and the production of incompatibilities in genetic networks of hybrid individuals.

Pleiotropic roles and effects

The mapping of genotype on phenotype and fitness leads us to predict that evolution will proceed along genetic lines of least resistance (Schluter, 1996). Genetic ‘resistance’ to evolutionary change is affected by the mutability and covariation of traits within and across developmental stages, in addition to natural selection. That is, the net rate of evolution integrates the likelihood and accumulation of mutational input (genetic variance) with the consequences of genetic variants for the ensemble of traits that comprise an organism (genetic covariances between traits) and fitness (natural selection). From the perspective of developmental biologists, this means that genetic hot-spots of evolutionary change over the long term ought to favor mutations with minimally pleiotropic effects (Carroll, 2005; Stern and Orgogozo, 2008), a form of developmental bias. As more molecular evolutionary change occurs, genetic incompatibilities become more likely to form from dysfunctional interactions between diverged sequences in inter-species hybrids. Our goal, then, is to enumerate the factors that facilitate molecular evolution to make some genes evolve more quickly than others. The structure of genes and gene regulatory networks provide clues to predict what these factors are (Garfield et al., 2013). These clues about molecular evolution can help us in thinking how to connect the temporal dynamics of developmental genetic networks to genetic incompatibilities between species.

The extent of pleiotropic effects induced by mutation to a gene is a major determinant of the likelihood of evolutionary change to that gene (Carroll, 2005; Stern and Orgogozo, 2008). This logic follows from the idea that most traits experience stabilizing selection on short timescales, and so the effects of mutation on most traits will be detrimental and offset any fitness benefit conferred to a particular trait by a mutation. On longer timescales, conserved traits are thought to track a moving but bounded fitness landscape (Estes and Arnold, 2007), which can facilitate compensatory evolution among the collection of interacting genes of the genetic network to result in sequence divergence and reproductive isolation between populations (Haag and Molla, 2005; Tulchinsky et al., 2014a; Tulchinsky et al., 2014b). Consequently, we might expect that more rapid evolution should occur in genes with fewer pleiotropic roles or for classes of changes that perturb fewer of a gene’s roles. The position of a gene within gene regulatory networks defines the pleiotropic roles it plays in development. Genes in more central and highly connected network positions can influence a greater fraction of the genome, and so changes to those genes have the potential to induce greater pleiotropic effects (Promislow, 2004; Hahn and Kern, 2005; He and Zhang, 2006; Kahali et al., 2009). However, the precise relationship between centrality and pleiotropy remains complex (Siegal et al., 2006). And, despite the idea that network ‘kernels’ may perdure through evolution (Erwin and Davidson, 2009), the molecular interactions among kernel components may nevertheless coevolve in ways that could generate DMIs (Haag, 2007; Tulchinsky et al., 2014a). Genes that are expressed at high levels also are more likely to have a large number of interaction partners and therefore to occupy central network positions (Bloom and Adami, 2003).

Coding sequence changes can affect protein activity everywhere they get expressed, and so ought to have greater potential for pleiotropic effects than regulatory changes (Wray et al., 2003; Carroll, 2005; Stern and Orgogozo, 2008). Alterations of cis-regulatory elements, in contrast, generally influence only a subset of a gene’s expression, and so will exert the fewest pleiotropic effects relative to coding sequence and trans-regulatory changes. Consequently, trans-regulatory evolution and coding sequence divergence ought generally to be slower for genes with high centrality and connectivity in genetic networks. We should expect such genes to have especially high ratios of cis:trans regulatory divergence, provided that those genes also have reasonably complex arrays of cis-regulatory elements controlling their expression that can facilitate divergence. Because trans-acting regulation tends to show greater condition-dependence than does cis-regulation (Smith and Kruglyak, 2008; Tirosh et al., 2009), however, pleiotropy-based predictions for their evolution may be tempered by the fact that varying conditions experienced by organisms could limit the net negative pleiotropic effects of trans-regulatory changes. Moreover, trans-regulatory evolution may often be coupled to subsequent cis-regulatory compensatory evolution that ameliorates negative pleiotropic effects of a population having fixed a beneficial trans-regulatory change. Such compensatory evolution involving both cis- and trans-acting regulatory evolution may be an especially important contributor to DMI formation (Landry et al., 2005; Ortíz-Barrientos et al., 2006; Takahasi et al., 2011; Mack et al., 2016).

Experiments show that trans-acting regulatory factors contribute disproportionately to segregating genetic variation for gene expression levels within species, whereas cis-acting regulatory differences disproportionately underlie fixed genetic differences in expression between species (Wittkopp and Kalay, 2012). The loci controlling DMIs also can be polymorphic or fixed (Cutter, 2012), and so to the extent that regulatory evolution is responsible for creating DMIs, we ought to expect polymorphic DMIs to often be controlled by trans-acting factors. Because trans-regulatory mutations tend to arise more readily due to larger mutational target size in the genome, and to be more recessive, than cis-acting mutations (Landry et al., 2007; Gruber et al., 2012), they are expected to be a common and persistent kind of polymorphism within populations.

Modularity of genetic network architecture

Modularity of genetic networks reduces the scope for genes to have highly pleiotropic roles (Wagner and Altenberg, 1996). By constraining the neighborhood of partner interactions, greater modularity limits the pleiotropic effects of changes to gene expression or function for any given gene. Like tissue-specific expression in the spatial modularity of gene networks (Larracuente et al., 2008; Packer et al., 2019), we can also consider temporal modularity for those portions of gene regulatory networks that show stage-specific expression. Modularity of genetic network structure will increase over ontogeny as cells differentiate and tissues establish autonomous regulatory programs (Packer et al., 2019). Some transient phases of development are thought to reverse this trend, however, as when tissues integrate during gastrulation or reorganize during metamorphosis (Chin-Sang and Chisholm, 2000). Consequently, if pleiotropy at the scale of the whole organism constrains evolution of individual genes, then we might expect faster molecular evolution for genes that experience highly modular genetic network structures due to spatially- or temporally restricted expression. This logic provides one way to frame the ‘early conservation’ and ‘hourglass’ models in the evolution of development (Figure 1).

Figure 1

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Despite a trend of increasing genetic network modularity from single-celled zygote to adult, some genes will experience the modularity more than others at any given place or time in ontogeny. Genes with greater breadth of expression across tissues, and genes with greater temporal persistence of expression, will experience more scope to contribute to different genetic networks and their phenotypic outputs. Consequently, genes most likely to produce pleiotropic effects when genetically perturbed are those expressed at high levels across many tissues throughout a long span of developmental time (high expression breadth and low temporal expression bias; Larracuente et al., 2008; Coronado-Zamora et al., 2019). Even a modest role in any given tissue or at any single stage of development, however, may compound when integrated over space and time.

The appropriate weighting of the importance of spatial versus temporal expression breadth and modularity, however, is not entirely obvious. Therefore, empirically, it will be valuable to partition the ontogenetic trajectories of genes with similar expression breadths across tissues, or, complementarily, partition the spatial profiles of expression for genes with similar ontogenetic expression dynamics (Box 3). The transcriptome analysis of distinct cell lineages, as conducted for C. elegans precursors of endoderm, mesoderm and ectoderm (Hashimshony et al., 2015) or for single-cell transcriptomes throughout embryogenesis (Hashimshony et al., 2012; Tintori et al., 2016; Packer et al., 2019), provides one intriguing scheme for approaching this issue. Another approach could incorporate developmental time into transcriptome analysis of serial-sectioned samples to access four-dimensional expression profiles through developmental space and time (Ebbing et al., 2018).

Box 3.

Measuring molecular divergence across ontogeny.

Incorporating ontogeny into molecular evolution requires developmental time series of gene expression, which necessarily involves more experimental effort and sophistication of analysis than studies using a single developmental timepoint. This idea can be explored in several ways. First, one may quantify divergence in gene expression as a phenotypic output that closely maps to genotype. Studies in diverse organisms have compared transcriptome profiles over ontogeny with this approach (Table 1). Second, the contrast of gene expression levels between two parental species and their F1 hybrids provides a way to infer whether changes in expression result from cis- or trans-regulatory evolution (Box 1; Wittkopp et al., 2004; Signor and Nuzhdin, 2018). This approach has challenges and opportunities: environmental conditions also can influence transcriptome expression and the balance of cis:trans controls of the set of expressed genes (Tirosh et al., 2009), and it may be difficult to deconvolve maternal from zygotic trans-effects in F1 embryos. The technique also has not yet been applied in a developmentally dynamic way, making it ripe for future studies. Third, rates of coding sequence evolution (dN/dS or K_A/K_S) for genes expressed differentially across development provide a means of assessing selection on the encoded protein sequences used in genetic networks. A simple way of testing for trends in purifying selection and positive selection across ontogeny is to compare the average dN/dS value for the set of genes with peak expression at a given developmental timepoint (Cutter and Ward, 2005; Davis et al., 2005) or an expression-weighted mean dN/dS value for all genes expressed at a given timepoint (Quint et al., 2012; Liu and Robinson-Rechavi, 2018b). When a multi-species phylogeny is used, or if polymorphism data are incorporated in a McDonald-Kreitman testing framework, then positive selection may be distinguished from relaxed purifying selection (Liu and Robinson-Rechavi, 2018a). Genes also can be grouped according to patterns of co-expression to analyze for consistent differences in rates of molecular evolution among them or among parameters of mathematical functions that describe the temporal profiles of expression (Coronado-Zamora et al., 2019; Cutter et al., 2019). Spatial definition of expression can enhance such approaches using tissue- and sex-specificity (Kim et al., 2016), restricted cell-lineages (Hashimshony et al., 2015), or tomographic expression profiling (Ebbing et al., 2018). While mRNA expression is most commonly accessed, all these approaches can be extended to protein expression; protein levels appear to differ less between species than do transcriptomes (Leducq et al., 2012; Khan et al., 2013). Quantifying chromatin misregulation in a developmental time-course also could help discern the influence of epigenetic factors in hybrid dysfunction.

Box 3—figure 1

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Robustness to genetic change in gene regulatory networks

Selection on traits within a species often is stabilizing, favoring a particular phenotypic value and meaning that factors that lead to alternative phenotypic values will have lower fitness. A genetically ‘robust’ phenotype is insensitive to genetic perturbations, which permits an organism to produce the same phenotype and to maintain fitness despite mutational disruption to a gene or genetic network that contributes to the trait’s developmental program (Félix and Wagner, 2008). That is, a greater fraction of mutations to genes in more robust networks have effectively neutral effects (Ohta, 2011). A gene network’s robustness therefore ought to influence the mutations that can accumulate, which in turn affects rates of molecular evolutionary divergence and the manifestation of genetic incompatibilities in hybrids. Differences across gene networks in genetic robustness could arise from differential selection on robustness itself (adaptive robustness or canalization) or could simply arise as a byproduct of differences in their epistatic genetic architectures (Hermisson and Wagner, 2004). Genes and genetic networks that are more robust to genetic perturbation can be thought of as larger phenotypic capacitors (Paaby and Testa, 2018), letting more cryptic genetic divergence in developmental controls accumulate to be revealed upon the formation of inter-species hybrids. This effect is analogous to how specific environmental circumstances or genetic backgrounds can expose so-called cryptic genetic variation or conditionally-neutral variants within a species (Ledón-Rettig et al., 2014; Paaby and Rockman, 2014).

As a consequence, traits with greater genetic robustness ought to accumulate more cryptic genetic variation within a species that can then can contribute to greater developmental system drift between species as substitutions accrue (Félix and Wagner, 2008). This process will, in turn, lead separate populations to travel independently along a fitness ridge in the landscape of genotype space (Box 1D; Félix and Wagner, 2008; Fragata et al., 2019). More robust networks are more likely to contain more cryptic genetic variation, but the presence of cryptic genetic variation alone is insufficient to conclude whether or not selection directly favored greater robustness, and characterizing the robustness of genetic networks empirically can be difficult. The selective regime that shapes the fitness landscape will influence the speed at which developmental system drift can evolve for any given trait (Tulchinsky et al., 2014b). DSD is especially likely to result from directional selection on traits, perhaps facilitated by a role for cryptic genetic variation in adaptation to environmental shifts (Félix and Wagner, 2008), and from selection that drives molecular co-evolution among genetic elements (Tulchinsky et al., 2014b).

Phenotypes with a larger underlying genetic network are expected to be more robust to perturbation (Fragata et al., 2019), implying that they should more readily tolerate the accumulation of genetic changes. Genes holding more central positions in genetic networks also appear to be associated with greater robustness upon perturbation (Proulx et al., 2007), although this effect may depend more on expression level than connectivity per se (Siegal et al., 2006). This means that changes to any given gene in a network that is shared with other networks, and that confers a beneficial effect to that other network, are less likely to manifest negative pleiotropic effects for those genetic networks that are larger (Pavlicev and Wagner, 2012). Consequently, cryptic variation and DSD may be disproportionately prevalent in developmental programs comprised of larger genetic networks. Genetic networks with low modularity, by definition, have all constituent genes interacting, and networks with low modularity indeed are most robust to genetic perturbation (Tran and Kwon, 2013).

Recall, however, that we argued how such low modularity conditions maximizes the pleiotropic roles of genes, which should impede rather than tolerate molecular evolution. These effects thus appear to present a contradiction. The resolution may lie in assumptions about network size, such that the implications of low modularity for pleiotropy may be secondary to robustness for large genetic networks. Analysis of networks in high-resolution time series of expression showing distinct tissues with distinct transcriptomic profiles that vary in size may help in testing this idea (Packer et al., 2019). The resolution may also depend on whether we consider coding or regulatory sequence evolution, as coding sequence evolution does not appear to depend strongly on how connected the gene is in the genetic network (Jordan et al., 2003; Batada et al., 2006). The way that connections define the network output also can be crucial. Simple connectivity metrics may just poorly summarize functional and evolutionary properties of networks, making the contradiction more apparent than real (Siegal et al., 2006). For example, genetic networks with switch-like behavior may be more capable of accumulating cryptic variation, as appears to occur disproportionately among genes with early zygotic expression in sea urchin embryogenesis (Garfield et al., 2013).

Ontogenetic stages that have an especially high incidence of mutationally-robust phenotypes within species should thus be especially prone to experience developmental system drift. Such DSD can get revealed in the novel genomic environment of inter-species hybrids, taking the form of DMIs. A counter-argument to this idea is that mutationally-robust traits might also be predisposed to being robust to the genomic perturbation experienced in hybrid individuals, and therefore be less likely to show dysfunction. The logic of this counter-argument might be more pertinent at the very early stages of divergence between populations, becoming less and less applicable as substitutions accrue in the speciation process. It will be interesting for future research to resolve this question. In general, it remains unclear how network robustness within species versus between species might scale differently with the number of genetic changes. Measurements of mutational variance for traits shared across ontogenetic stages provide another possible way to test for stage-dependent changes in mutational robustness, for example, using mutation accumulation experiments that, so far, point to this possibility (Farhadifar et al., 2016; Zalts and Yanai, 2017). It will be interesting to see in future studies whether the genetic robustness of traits truly can predict the incidence of cryptic genetic variation, developmental system drift, and the propensity to reveal DMIs in hybrids.

Degeneracy via partially redundant genetic network pathways provides another way that phenotypes could be robust to genetic perturbation, potentially allowing DSD to accumulate without yielding DMIs in F1 hybrids. Indeed, genes associated with redundant networks evolve more quickly (Wagner, 2000; Kitami and Nadeau, 2002). Robustness mediated by distributed, rather than redundant (Félix and Wagner, 2008), genetic networks may thus more readily experience DSD in a way that would foster DMIs. Moreover, translational buffering of gene expression leads to the inference that protein production shows much less misexpression than do mRNA transcripts in F1 hybrids (Leducq et al., 2012; Khan et al., 2013; Artieri and Fraser, 2014; McManus et al., 2014). And, it is fitness as a phenotype that is the ultimate readout of robustness. Consequently, genetic networks important for development may be less disrupted in hybrids than transcriptome analyses might otherwise suggest. Overall, discerning the relative incidence of distinct mechanisms conferring phenotypic robustness will be important in defining whether different genetic networks and different stages of development will be more or less likely to contribute to post-zygotic reproductive isolation as divergence between species accumulates.

Evolvability of distinct genetic components

Mode of selection

Traits and sequences with greater propensity to diverge are said to be more evolvable (Wagner and Zhang, 2011). One way to detect evolvability is when traits and sequences diverge as a result of natural selection, because adaptive divergence in phenotypes between species makes it easy to conclude that there must have been changes to the underlying genetic networks. Such selection can involve multiple changes on an adaptive path, for example, if first a large-effect trans-regulatory mutation gets fixed and is followed by subsequent compensatory cis-regulatory substitutions that ameliorate suboptimal pleiotropic effects of the initial trans-acting substitution (Box 1C; Goncalves et al., 2012); similar logic can also apply to coding sequence changes (Clark et al., 2009). Moreover, positive selection and multi-locus antagonistic coevolution will drive molecular evolution that is much more rapid than will genetic drift. Repeated adaptive evolution of orthologous genes in disparate lineages is implicated as a general feature of some types of phenotypic change, including aggregate effects of multiple mutations affecting the same locus (Stern and Orgogozo, 2008; Martin and Orgogozo, 2013). The genes contributing to such adaptive divergence may thus be especially likely to contribute to post-zygotic DMIs (Presgraves, 2010b), in addition to pre-mating, gametic, or ecological reproductive isolation barriers.

Alternately, some genes may evolve faster than others simply because they experience weaker purifying selection or greater mutational target size. The evolutionary accumulation of changes in this way is rate-limited by the input of mutation and the speed of genetic drift, and so will be faster in species with small population sizes.

Haldane’s rule as developmentally predictable evolution

One developmentally predictable rule in evolution says something about sex: Haldane’s rule, in which the heterogametic sex is more likely to suffer inviability or sterility in inter-species hybrids (Haldane, 1922; Delph and Demuth, 2016). Dominance theory provides one explanation for this pattern: incompatibility loci linked to sex chromosomes will reveal themselves in F1 hybrids disproportionately for the sex that has only one copy of a given sex chromosome (Turelli and Orr, 1995). This tells us that genomic compartmentalization of how traits are genetically encoded also is important for the implications of molecular evolution; when genes contributing to a given developmental process are biased in genomic location, it may confer greater or lesser tendency to yield DMIs in hybrids.

When males are heterogametic (X/Y or X/O sex-determination), other factors may also contribute to Haldane’s rule (Delph and Demuth, 2016). Genes that control male-biased traits may evolve especially rapidly due to sexual selection or mutational biases (‘faster male’ theory), or gene regulatory networks controlling male traits may be unusually sensitive to genetic perturbation in hybrids (‘fragile males’), or genes linked the X-chromosome may evolve especially fast (‘faster X’) (Charlesworth et al., 1987; Wu and Davis, 1993). Consequently, X-linked genes may show distinct patterns of misexpression in hybrids (Moehring et al., 2007; Turner et al., 2014; Civetta, 2016; Sanchez-Ramirez et al., 2020). Developmental pathways related to spermatogenesis seem especially sensitive to disruption in hybrids, perhaps being predisposed to disproportionate compensatory cis-trans regulatory coevolution (Mack et al., 2016; Mack and Nachman, 2017). Sex-limited genetic networks also may differ for males versus females in size, location of genomic encoding, or predominant mechanism of regulatory control, and so influence the relative accumulation of cryptic genetic variation and developmental system drift.

Coding vs regulatory evolution

Both coding sequences and regulatory sequences diverge between species, despite the fact that most selection on each of them is expected to be purifying (Casillas et al., 2007). The rate of evolution for a coding sequence and its cis-regulatory regions, however, correlate only weakly (Castillo-Davis, 2004; Liao and Zhang, 2006; Tirosh and Barkai, 2008). This observation implies that the strength and mode of selection affecting mutations to protein structure tells us little about the strength and mode of selection on mutations affecting expression, and vice versa. Consequently, molecular evolution associated with ontogenetic timing may show distinct patterns for coding and regulatory sequences, and so also yield different implications for when DMIs manifest over ontogeny.

The type of regulatory change may be key, however, in understanding the relation between regulatory and coding sequence divergence (i.e. evolvability), as well as to the likelihood of contributing to a DMI. For example, genes showing evidence of trans-regulatory divergence appear to evolve slower in their coding sequences than do genes with cis-regulatory divergence (Goncalves et al., 2012). And, DMIs due to misregulation generally involve non-compensatory evolution of both cis- and trans-regulators of expression (Ortíz-Barrientos et al., 2006; Mack and Nachman, 2017). Specifically, regulatory divergence involving reinforcing cis+trans changes (thought to result from directional selection within species) may actually be less likely to create DMIs than changes with opposing cis-trans effects (thought to result from stabilizing selection within species) (Mack et al., 2016) (but see Tulchinsky et al., 2014b). Consequently, positive selection affecting regulatory versus coding sequences may differ in their propensity to yield DMIs.

Might the functional role of genes also represent an axis of predictability to factors that instigate DMIs in inter-species hybrids? The relatively few known ‘speciation genes’ in animals give little clue to whether particular molecular functions may be predisposed to involvement in inter-species incompatibilities (Blackman, 2016). At a coarse level, because DMIs require interaction, we should expect sequences that affect interactions between DNA, RNA, and proteins to be more prevalent among DMI loci than, say, enzymes that interact predominantly with metabolites. From the perspective of gene regulatory networks, transcription factors experience faster sequence evolution than other genes in the genome (Gilad et al., 2006; Haerty et al., 2008). piRNA genes also turnover rapidly and are implicated in hybrid dysfunction (Assis and Kondrashov, 2009; Bagijn et al., 2012; Kelleher et al., 2012), along with other classes of regulatory endogenous small RNAs (Li et al., 2016). Genes that act cell non-autonomously, as for secreted proteins or diffusible signaling molecules, may tend to evolve slower if they also tend to exhibit greater expression breadth and lower modularity, unless they are predisposed to co-evolutionary dynamics. Reproductive isolation between species need not only involve classic regulators of development, however, as attested by incompatibilities that commonly seem to involve chromosome segregation and cyto-nuclear interactions (Blackman, 2016; Lima et al., 2019). Despite the fundamental role of the mitochondrial genome in energy metabolism, mitochondrial genes experience adaptive molecular evolution (Bazin et al., 2006) and mito-nuclear incompatibilities can provide important reproductive barriers between species (Hill, 2015; Lima et al., 2019). To the extent that some developmental stages may be more sensitive to the disruption of chromosome segregation or mitochondrial function, such as phases of heightened cell division or metabolism, such developmentally tangential genetic pathways might nevertheless contribute to ontogenetic patterns of hybrid dysfunction.

Gene duplication and gene origination

Gene duplication is a powerful factor in the evolution of phenotypic novelty (Kaessmann, 2010). Moreover, regulatory or structural subfunctionalization in the evolution of gene copies following duplication could lead to dysfunction in hybrids and so contribute to a DMI (Mack and Nachman, 2017). Similarly, a DMI could result from ‘divergent resolution’ in the loss of alternate copies in different species for functionally equivalent gene duplicates (Lynch and Force, 2000). The de novo origin of new genes also can instigate species differences in gene network structure (Neme and Tautz, 2014), as could divergent use of alternative splice forms of a gene (Ortíz-Barrientos et al., 2006). Thus, the phenotypic evolvability promoted by gene duplication and gene origination also confers the potential to induce reproductive isolation as gene copies evolve through distinct trajectories in different evolutionary lineages.

C. elegans ontogenetic profiles of gene expression, molecular evolution, and hybrid dysfunction

Taking C. elegans development as a point of reference, embryonic cell numbers grow approximately exponentially until ~520 cells upon which cell count increases relatively slowly to the 959 somatic and ~2000 germline cells that comprise the adult hermaphrodite animal (Giurumescu et al., 2012; Figure 2A). Cell lineages derived from five blastomeres exhibit distinct transcriptome profiles in ontogenetic timecourses (Hashimshony et al., 2015), with single-cell transcriptome sequencing of embryos in a time series showing even finer resolution (Packer et al., 2019). The number of genes expressed increases over most of embryogenesis, with relatively similar numbers of genes expressed at stages post-hatching (Boeck et al., 2016; Figure 2B). The change in identity of expressed genes, however, is greatest early in embryogenesis (primarily due to down-regulation) and late in embryogenesis (primarily due to up-regulation) (Boeck et al., 2016; Figure 2C). Gene connectivity peaks in early embryogenesis, declining until a spike in adulthood (Liu and Robinson-Rechavi, 2018a). Cellular development in embryos is conserved across species, being nearly indistinguishable at least to the 350-cell stage (Zhao et al., 2008; Memar et al., 2019). In hybrid crosses of most species pairs, however, embryos arrest around gastrulation (Baird and Yen, 2000; Baird and Seibert, 2013), near the time that expression divergence appears minimized across Caenorhabditis (Levin et al., 2012) and that expression-weighted coding sequence divergence is lowest (Cutter et al., 2019). Coding sequences with fastest molecular evolution show peak expression very early in embryogenesis or toward adulthood (Cutter and Ward, 2005; Cutter et al., 2019; Box 3). Hybrids of C. briggsae and C. nigoni show high embryonic inviability, but those genetically identical individuals that hatch successfully exhibit little larval mortality (Bundus et al., 2015). Gene misexpression is widespread in adults of both sexes for these hybrids (Sanchez-Ramirez et al., 2020) and misregulation of small-RNAs in hybrid genetic backgrounds is implicated in spermatogenic dysfunction (Li et al., 2016). Together, these observations suggest the possibility that developmental system drift may accrue more readily and generate hybrid incompatibility disproportionately at stages showing greatest selective constraint on gene expression levels and coding sequences.

Figure 2

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1. 1. Artieri CG
   2. Fraser HB

   (2014) Evolution at two levels of gene expression in yeast

   Genome Research 24:411–421.

   https://doi.org/10.1101/gr.165522.113

   • PubMed
   • Google Scholar
2. 1. Assis R
   2. Kondrashov AS

   (2009) Rapid repetitive element-mediated expansion of piRNA clusters in mammalian evolution

   PNAS 106:7079–7082.

   https://doi.org/10.1073/pnas.0900523106

   • PubMed
   • Google Scholar
3. 1. Baack E
   2. Melo MC
   3. Rieseberg LH
   4. Ortiz-Barrientos D

   (2015) The origins of reproductive isolation in plants

   New Phytologist 207:968–984.

   https://doi.org/10.1111/nph.13424

   • PubMed
   • Google Scholar
4. 1. Bagijn MP
   2. Goldstein LD
   3. Sapetschnig A
   4. Weick EM
   5. Bouasker S
   6. Lehrbach NJ
   7. Simard MJ
   8. Miska EA

   (2012) Function, targets, and evolution of Caenorhabditis elegans piRNAs

   Science 337:574–578.

   https://doi.org/10.1126/science.1220952

   • PubMed
   • Google Scholar
5. 1. Baird SE
   2. Seibert SR

   (2013) Reproductive isolation in the Elegans-Group of Caenorhabditis

   Natural Science 5:18–25.

   https://doi.org/10.4236/ns.2013.54A004

   • Google Scholar
6. 1. Baird SE
   2. Yen WC

   (2000) Reproductive isolation in Caenorhabditis: terminal phenotypes of hybrid embryos

   Evolution and Development 2:9–15.

   https://doi.org/10.1046/j.1525-142x.2000.00031.x

   • PubMed
   • Google Scholar
7. 1. Barbash DA
   2. Siino DF
   3. Tarone AM
   4. Roote J

   (2003) A rapidly evolving MYB-related protein causes species isolation in Drosophila

   PNAS 100:5302–5307.

   https://doi.org/10.1073/pnas.0836927100

   • PubMed
   • Google Scholar
8. 1. Barreto FS
   2. Watson ET
   3. Lima TG
   4. Willett CS
   5. Edmands S
   6. Li W
   7. Burton RS

   (2018) Genomic signatures of mitonuclear coevolution across populations of Tigriopus californicus

   Nature Ecology & Evolution 2:1250–1257.

   https://doi.org/10.1038/s41559-018-0588-1

   • PubMed
   • Google Scholar
9. 1. Batada NN
   2. Hurst LD
   3. Tyers M

   (2006) Evolutionary and physiological importance of hub proteins

   PLOS Computational Biology 2:e88.

   https://doi.org/10.1371/journal.pcbi.0020088

   • PubMed
   • Google Scholar
10. 1. Bazin E
    2. Glémin S
    3. Galtier N

    (2006) Population size does not influence mitochondrial genetic diversity in animals

    Science 312:570–572.

    https://doi.org/10.1126/science.1122033

    • PubMed
    • Google Scholar
11. 1. Bedinger PA
    2. Chetelat RT
    3. McClure B
    4. Moyle LC
    5. Rose JK
    6. Stack SM
    7. van der Knaap E
    8. Baek YS
    9. Lopez-Casado G
    10. Covey PA
    11. Kumar A
    12. Li W
    13. Nunez R
    14. Cruz-Garcia F
    15. Royer S

    (2011) Interspecific reproductive barriers in the tomato clade: opportunities to decipher mechanisms of reproductive isolation

    Sexual Plant Reproduction 24:171–187.

    https://doi.org/10.1007/s00497-010-0155-7

    • PubMed
    • Google Scholar
12. Book

    1. Blackman BK

    (2016)

    Speciation genes

    In: Kliman R. M, editors. Encyclopedia of Evolutionary Biology. Oxford:  Academic Press. pp. 166–175.

    • Google Scholar
13. Book

    1. Blair WF

    (1972)

    Evolution in the Genus Bufo

    University of Texas Press.

    • Google Scholar
14. 1. Bloom JD
    2. Adami C

    (2003) Apparent dependence of protein evolutionary rate on number of interactions is linked to biases in protein-protein interactions data sets

    BMC Evolutionary Biology 3:1–10.

    https://doi.org/10.1186/1471-2148-3-21

    • PubMed
    • Google Scholar
15. 1. Boeck ME
    2. Huynh C
    3. Gevirtzman L
    4. Thompson OA
    5. Wang G
    6. Kasper DM
    7. Reinke V
    8. Hillier LW
    9. Waterston RH

    (2016) The time-resolved transcriptome of C. elegans

    Genome Research 26:1441–1450.

    https://doi.org/10.1101/gr.202663.115

    • PubMed
    • Google Scholar
16. 1. Bolnick DI
    2. Turelli M
    3. López-Fernández H
    4. Wainwright PC
    5. Near TJ

    (2008) Accelerated mitochondrial evolution and "Darwin's corollary": asymmetric viability of reciprocal F1 hybrids in Centrarchid fishes

    Genetics 178:1037–1048.

    https://doi.org/10.1534/genetics.107.081364

    • PubMed
    • Google Scholar
17. 1. Brandvain Y
    2. Haig D

    (2005) Divergent mating systems and parental conflict as a barrier to hybridization in flowering plants

    The American Naturalist 166:330–338.

    https://doi.org/10.1086/432036

    • PubMed
    • Google Scholar
18. 1. Bundus JD
    2. Alaei R
    3. Cutter AD

    (2015) Gametic selection, developmental trajectories and extrinsic heterogeneity in Haldane's rule

    Evolution 69:2005–2017.

    https://doi.org/10.1111/evo.12708

    • Google Scholar
19. 1. Carroll SB

    (2005) Evolution at two levels: on genes and form

    PLOS Biology 3:e245.

    https://doi.org/10.1371/journal.pbio.0030245

    • PubMed
    • Google Scholar
20. 1. Casillas S
    2. Barbadilla A
    3. Bergman CM

    (2007) Purifying selection maintains highly conserved noncoding sequences in Drosophila

    Molecular Biology and Evolution 24:2222–2234.

    https://doi.org/10.1093/molbev/msm150

    • PubMed
    • Google Scholar
21. 1. Castillo-Davis CI

    (2004) cis-Regulatory and protein evolution in Orthologous and duplicate genes

    Genome Research 14:1530–1536.

    https://doi.org/10.1101/gr.2662504

    • Google Scholar
22. 1. Charlesworth B
    2. Coyne JA
    3. Barton NH

    (1987) The relative rates of evolution of sex chromosomes and autosomes

    The American Naturalist 130:113–146.

    https://doi.org/10.1086/284701

    • Google Scholar
23. 1. Chin-Sang ID
    2. Chisholm AD

    (2000) Form of the worm: genetics of epidermal morphogenesis in C. elegans

    Trends in Genetics : TIG 16:544–551.

    https://doi.org/10.1016/s0168-9525(00)02143-0

    • PubMed
    • Google Scholar
24. 1. Church SH
    2. Donoughe S
    3. de Medeiros BAS
    4. Extavour CG

    (2019) Insect egg size and shape evolve with ecology but not developmental rate

    Nature 571:58–62.

    https://doi.org/10.1038/s41586-019-1302-4

    • PubMed
    • Google Scholar
25. 1. Civetta A

    (2016) Misregulation of gene expression and sterility in interspecies hybrids: causal links and alternative hypotheses

    Journal of Molecular Evolution 82:176–182.

    https://doi.org/10.1007/s00239-016-9734-z

    • PubMed
    • Google Scholar
26. 1. Clark NL
    2. Gasper J
    3. Sekino M
    4. Springer SA
    5. Aquadro CF
    6. Swanson WJ

    (2009) Coevolution of interacting fertilization proteins

    PLOS Genetics 5:e1000570.

    https://doi.org/10.1371/journal.pgen.1000570

    • PubMed
    • Google Scholar
27. 1. Coronado-Zamora M
    2. Salvador-Martínez I
    3. Castellano D
    4. Barbadilla A
    5. Salazar-Ciudad I

    (2019) Adaptation and conservation throughout the Drosophila melanogaster life-Cycle

    Genome Biology and Evolution 11:1463–1482.

    https://doi.org/10.1093/gbe/evz086

    • PubMed
    • Google Scholar
28. 1. Coyne JA

    (1989) Patterns of speciation in Drosophila

    Evolution 43:362–381.

    https://doi.org/10.1111/j.1558-5646.1989.tb04233.x.

    • Google Scholar
29. 1. Coyne JA
    2. Orr HA

    (1997) Patterns of speciation in Drosophila" revisited

    Evolution 51:295–303.

    https://doi.org/10.1111/j.1558-5646.1997.tb02412.x

    • Google Scholar
30. Book

    1. Coyne JA
    2. Orr HA

    (2004)

    Speciation

    Sunderland, MA: Sinauer Associates, Inc.

    • Google Scholar
31. 1. Crespi B
    2. Nosil P

    (2013) Conflictual speciation: species formation via genomic conflict

    Trends in Ecology & Evolution 28:48–57.

    https://doi.org/10.1016/j.tree.2012.08.015

    • PubMed
    • Google Scholar
32. 1. Cutter AD

    (2012) The polymorphic prelude to Bateson-Dobzhansky-Muller incompatibilities

    Trends in Ecology & Evolution 27:209–218.

    https://doi.org/10.1016/j.tree.2011.11.004

    • PubMed
    • Google Scholar
33. 1. Cutter AD

    (2018) X exceptionalism in Caenorhabditis speciation

    Molecular Ecology 27:3925–3934.

    https://doi.org/10.1111/mec.14423

    • PubMed
    • Google Scholar
34. 1. Cutter AD
    2. Garrett RH
    3. Mark S
    4. Wang W
    5. Sun L

    (2019) Molecular evolution across developmental time reveals rapid divergence in early embryogenesis

    Evolution Letters 3:359–373.

    https://doi.org/10.1002/evl3.122

    • PubMed
    • Google Scholar
35. 1. Cutter AD
    2. Ward S

    (2005) Sexual and temporal dynamics of molecular evolution in C. elegans development

    Molecular Biology and Evolution 22:178–188.

    https://doi.org/10.1093/molbev/msh267

    • PubMed
    • Google Scholar
36. 1. Dapper AL
    2. Wade MJ

    (2016) The evolution of sperm competition genes: the effect of mating system on levels of genetic variation within and between species

    Evolution 70:502–511.

    https://doi.org/10.1111/evo.12848

    • Google Scholar
37. 1. Davis JC
    2. Brandman O
    3. Petrov DA

    (2005) Protein evolution in the context of Drosophila development

    Journal of Molecular Evolution 60:774–785.

    https://doi.org/10.1007/s00239-004-0241-2

    • PubMed
    • Google Scholar
38. 1. Delph LF
    2. Demuth JP

    (2016) Haldane's Rule: Genetic Bases and Their Empirical Support

    Journal of Heredity 107:383–391.

    https://doi.org/10.1093/jhered/esw026

    • PubMed
    • Google Scholar
39. 1. Demuth JP
    2. Wade MJ

    (2007) Maternal expression increases the rate of bicoid evolution by relaxing selective constraint

    Genetica 129:37–43.

    https://doi.org/10.1007/s10709-006-0031-4

    • PubMed
    • Google Scholar
40. 1. Ebbing A
    2. Vértesy Á
    3. Betist MC
    4. Spanjaard B
    5. Junker JP
    6. Berezikov E
    7. van Oudenaarden A
    8. Korswagen HC

    (2018) Spatial transcriptomics of C. elegans Males and Hermaphrodites Identifies Sex-Specific Differences in Gene Expression Patterns

    Developmental Cell 47:801–813.

    https://doi.org/10.1016/j.devcel.2018.10.016

    • PubMed
    • Google Scholar
41. 1. Ehrenreich IM
    2. Torabi N
    3. Jia Y
    4. Kent J
    5. Martis S
    6. Shapiro JA
    7. Gresham D
    8. Caudy AA
    9. Kruglyak L

    (2010) Dissection of genetically complex traits with extremely large pools of yeast segregants

    Nature 464:1039–1042.

    https://doi.org/10.1038/nature08923

    • PubMed
    • Google Scholar
42. 1. Erwin DH
    2. Davidson EH

    (2009) The evolution of hierarchical gene regulatory networks

    Nature Reviews Genetics 10:141–148.

    https://doi.org/10.1038/nrg2499

    • PubMed
    • Google Scholar
43. 1. Estes S
    2. Arnold SJ

    (2007) Resolving the paradox of stasis: models with stabilizing selection explain evolutionary divergence on all timescales

    The American Naturalist 169:227–244.

    https://doi.org/10.1086/510633

    • PubMed
    • Google Scholar
44. 1. Farhadifar R
    2. Ponciano JM
    3. Andersen EC
    4. Needleman DJ
    5. Baer CF

    (2016) Mutation is a sufficient and robust predictor of genetic variation for mitotic spindle traits in Caenorhabditis elegans

    Genetics 203:1859–1870.

    https://doi.org/10.1534/genetics.115.185736

    • PubMed
    • Google Scholar
45. 1. Félix MA
    2. Wagner A

    (2008) Robustness and evolution: concepts, insights and challenges from a developmental model system

    Heredity 100:132–140.

    https://doi.org/10.1038/sj.hdy.6800915

    • PubMed
    • Google Scholar
46. 1. Flatt T
    2. Schmidt PS

    (2009) Integrating evolutionary and molecular genetics of aging

    Biochimica Et Biophysica Acta (BBA) - General Subjects 1790:951–962.

    https://doi.org/10.1016/j.bbagen.2009.07.010

    • Google Scholar
47. 1. Fouquette MJ

    (1960) Isolating mechanisms in three sympatric treefrogs in the canal zone

    Evolution 14:484–497.

    https://doi.org/10.1111/j.1558-5646.1960.tb03115.x

    • Google Scholar
48. 1. Fragata I
    2. Blanckaert A
    3. Dias Louro MA
    4. Liberles DA
    5. Bank C

    (2019) Evolution in the light of fitness landscape theory

    Trends in Ecology & Evolution 34:69–82.

    https://doi.org/10.1016/j.tree.2018.10.009

    • PubMed
    • Google Scholar
49. 1. Frost JS
    2. Platz JE

    (1983) Comparative assessment of modes of reproductive isolation among four species of leopard frogs (rana pipiens complex)

    Evolution 37:66–78.

    https://doi.org/10.2307/2408175

    • Google Scholar
50. 1. Garfield DA
    2. Runcie DE
    3. Babbitt CC
    4. Haygood R
    5. Nielsen WJ
    6. Wray GA

    (2013) The impact of gene expression variation on the robustness and evolvability of a developmental gene regulatory network

    PLOS Biology 11:e1001696.

    https://doi.org/10.1371/journal.pbio.1001696

    • PubMed
    • Google Scholar
51. 1. Gavrilets S

    (2003) Models of speciation: what have we learned in 40 years?

    Evolution 57:2197–2215.

    https://doi.org/10.1111/j.0014-3820.2003.tb00233.x

    • Google Scholar
52. 1. Gilad Y
    2. Oshlack A
    3. Smyth GK
    4. Speed TP
    5. White KP

    (2006) Expression profiling in primates reveals a rapid evolution of human transcription factors

    Nature 440:242–245.

    https://doi.org/10.1038/nature04559

    • PubMed
    • Google Scholar
53. 1. Giurumescu CA
    2. Kang S
    3. Planchon TA
    4. Betzig E
    5. Bloomekatz J
    6. Yelon D
    7. Cosman P
    8. Chisholm AD

    (2012) Quantitative semi-automated analysis of Morphogenesis with single-cell resolution in complex embryos

    Development 139:4271–4279.

    https://doi.org/10.1242/dev.086256

    • PubMed
    • Google Scholar
54. 1. Goncalves A
    2. Leigh-Brown S
    3. Thybert D
    4. Stefflova K
    5. Turro E
    6. Flicek P
    7. Brazma A
    8. Odom DT
    9. Marioni JC

    (2012) Extensive compensatory cis-trans regulation in the evolution of mouse gene expression

    Genome Research 22:2376–2384.

    https://doi.org/10.1101/gr.142281.112

    • PubMed
    • Google Scholar
55. 1. Gruber JD
    2. Vogel K
    3. Kalay G
    4. Wittkopp PJ

    (2012) Contrasting properties of gene-specific regulatory, coding, and copy number mutations in Saccharomyces cerevisiae: frequency, effects, and dominance

    PLOS Genetics 8:e1002497.

    https://doi.org/10.1371/journal.pgen.1002497

    • PubMed
    • Google Scholar
56. 1. Guerrero RF
    2. Muir CD
    3. Josway S
    4. Moyle LC

    (2017) Pervasive antagonistic interactions among hybrid incompatibility loci

    PLOS Genetics 13:e1006817.

    https://doi.org/10.1371/journal.pgen.1006817

    • PubMed
    • Google Scholar
57. 1. Haag ES

    (2007) Compensatory vs. pseudocompensatory evolution in molecular and developmental interactions

    Genetica 129:45–55.

    https://doi.org/10.1007/s10709-006-0032-3

    • PubMed
    • Google Scholar
58. 1. Haag ES
    2. Molla MN

    (2005) Compensatory evolution of interacting gene products through multifunctional intermediates

    Evolution 59:1620–1632.

    https://doi.org/10.1554/04-543.1

    • Google Scholar
59. 1. Haerty W
    2. Artieri C
    3. Khezri N
    4. Singh RS
    5. Gupta BP

    (2008) Comparative analysis of function and interaction of transcription factors in Nematodes: extensive conservation of orthology coupled to rapid sequence evolution

    BMC Genomics 9:399.

    https://doi.org/10.1186/1471-2164-9-399

    • PubMed
    • Google Scholar
60. 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

    • PubMed
    • Google Scholar
61. 1. Haldane JBS

    (1922) Sex ratio and unisexual sterility in hybrid animals

    Journal of Genetics 12:101–109.

    https://doi.org/10.1007/BF02983075

    • Google Scholar
62. 1. Hashimshony T
    2. Wagner F
    3. Sher N
    4. Yanai I

    (2012) CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification

    Cell Reports 2:666–673.

    https://doi.org/10.1016/j.celrep.2012.08.003

    • PubMed
    • Google Scholar
63. 1. Hashimshony T
    2. Feder M
    3. Levin M
    4. Hall BK
    5. Yanai I

    (2015) Spatiotemporal transcriptomics reveals the evolutionary history of the endoderm germ layer

    Nature 519:219–222.

    https://doi.org/10.1038/nature13996

    • PubMed
    • Google Scholar
64. 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
65. 1. Hermisson J
    2. Wagner GP

    (2004) The population genetic theory of hidden variation and genetic robustness

    Genetics 168:2271–2284.

    https://doi.org/10.1534/genetics.104.029173

    • PubMed
    • Google Scholar
66. 1. Hill GE

    (2015) Mitonuclear ecology

    Molecular Biology and Evolution 32:1917–1927.

    https://doi.org/10.1093/molbev/msv104

    • PubMed
    • Google Scholar
67. 1. Hutter P
    2. Roote J
    3. Ashburner M

    (1990)

    A genetic basis for the inviability of hybrids between sibling species of Drosophila

    Genetics 124:909.

    • Google Scholar
68. 1. Irie N
    2. Kuratani S

    (2014) The developmental hourglass model: a predictor of the basic body plan?

    Development 141:4649–4655.

    https://doi.org/10.1242/dev.107318

    • PubMed
    • Google Scholar
69. 1. Johnson NA
    2. Porter AH

    (2001) Toward a new synthesis: population genetics and evolutionary developmental biology

    Genetica 113:45–58.

    https://doi.org/10.1023/A:1013371201773

    • Google Scholar
70. 1. Johnson NA
    2. Porter AH

    (2007) Evolution of branched regulatory genetic pathways: directional selection on pleiotropic loci accelerates developmental system drift

    Genetica 129:57–70.

    https://doi.org/10.1007/s10709-006-0033-2

    • PubMed
    • Google Scholar
71. 1. Jordan IK
    2. Wolf YI
    3. Koonin EV

    (2003) No simple dependence between protein evolution rate and the number of protein-protein interactions: only the most prolific interactors tend to evolve slowly

    BMC Evolutionary Biology 3:1.

    https://doi.org/10.1186/1471-2148-3-1

    • PubMed
    • Google Scholar
72. 1. Kaessmann H

    (2010) Origins, evolution, and phenotypic impact of new genes

    Genome Research 20:1313–1326.

    https://doi.org/10.1101/gr.101386.109

    • PubMed
    • Google Scholar
73. 1. Kahali B
    2. Ahmad S
    3. Ghosh TC

    (2009) Exploring the evolutionary rate differences of party hub and date hub proteins in Saccharomyces cerevisiae protein-protein interaction network

    Gene 429:18–22.

    https://doi.org/10.1016/j.gene.2008.09.032

    • PubMed
    • Google Scholar
74. 1. Kalinka AT
    2. Varga KM
    3. Gerrard DT
    4. Preibisch S
    5. Corcoran DL
    6. Jarrells J
    7. Ohler U
    8. Bergman CM
    9. Tomancak P

    (2010) Gene expression divergence recapitulates the developmental hourglass model

    Nature 468:811–814.

    https://doi.org/10.1038/nature09634

    • PubMed
    • Google Scholar
75. 1. Kalinka AT
    2. Tomancak P

    (2012) The evolution of early animal embryos: conservation or divergence?

    Trends in Ecology & Evolution 27:385–393.

    https://doi.org/10.1016/j.tree.2012.03.007

    • PubMed
    • Google Scholar
76. 1. Kawamura T
    2. Nishioka M
    3. Ueda H

    (1990) Reproductive isolation in treefrogs distributed in Japan, Korea, Europe and America

    Scientific Reports of the Laboratory Amphibian Biology of Hiroshima University 10:255–293.

    https://doi.org/10.15027/300

    • Google Scholar
77. 1. Kelleher ES
    2. Edelman NB
    3. Barbash DA

    (2012) Drosophila interspecific hybrids phenocopy piRNA-Pathway mutants

    PLOS Biology 10:e1001428.

    https://doi.org/10.1371/journal.pbio.1001428

    • Google Scholar
78. 1. Khan Z
    2. Ford MJ
    3. Cusanovich DA
    4. Mitrano A
    5. Pritchard JK
    6. Gilad Y

    (2013) Primate transcript and protein expression levels evolve under compensatory selection pressures

    Science 342:1100–1104.

    https://doi.org/10.1126/science.1242379

    • PubMed
    • Google Scholar
79. 1. Kim B
    2. Suo B
    3. Emmons SW

    (2016) Gene function prediction based on developmental transcriptomes of the two sexes in C. elegans

    Cell Reports 17:917–928.

    https://doi.org/10.1016/j.celrep.2016.09.051

    • PubMed
    • Google Scholar
80. 1. Kitami T
    2. Nadeau JH

    (2002) Biochemical networking contributes more to genetic buffering in human and mouse metabolic pathways than does gene duplication

    Nature Genetics 32:191–194.

    https://doi.org/10.1038/ng945

    • PubMed
    • Google Scholar
81. 1. Kuramoto M

    (1974) Experimental hybridization between the Brown frogs of Taiwan, the ryukyu islands and Japan

    Copeia 1974:815–822.

    https://doi.org/10.2307/1442577

    • Google Scholar
82. 1. Kuramoto M

    (1984) Systematic implications of hybridization experiments with some eurasian treefrogs (Genus Hyla)

    Copeia 1984:609.

    https://doi.org/10.2307/1445141

    • Google Scholar
83. 1. Landry CR
    2. Wittkopp PJ
    3. Taubes CH
    4. Ranz JM
    5. Clark AG
    6. Hartl DL

    (2005) Compensatory cis-trans evolution and the dysregulation of gene expression in Interspecific hybrids of Drosophila

    Genetics 171:1813–1822.

    https://doi.org/10.1534/genetics.105.047449

    • PubMed
    • Google Scholar
84. 1. Landry CR
    2. Lemos B
    3. Rifkin SA
    4. Dickinson WJ
    5. Hartl DL

    (2007) Genetic properties influencing the evolvability of gene expression

    Science 317:118–121.

    https://doi.org/10.1126/science.1140247

    • PubMed
    • Google Scholar
85. 1. Larracuente AM
    2. Sackton TB
    3. Greenberg AJ
    4. Wong A
    5. Singh ND
    6. Sturgill D
    7. Zhang Y
    8. Oliver B
    9. Clark AG

    (2008) Evolution of protein-coding genes in Drosophila

    Trends in Genetics : TIG 24:114–123.

    https://doi.org/10.1016/j.tig.2007.12.001

    • PubMed
    • Google Scholar
86. 1. Ledón-Rettig CC
    2. Pfennig DW
    3. Chunco AJ
    4. Dworkin I

    (2014) Cryptic genetic variation in natural populations: a predictive framework

    Integrative and Comparative Biology 54:783–793.

    https://doi.org/10.1093/icb/icu077

    • PubMed
    • Google Scholar
87. 1. Leducq JB
    2. Charron G
    3. Diss G
    4. Gagnon-Arsenault I
    5. Dubé AK
    6. Landry CR

    (2012) Evidence for the robustness of protein complexes to inter-species hybridization

    PLOS Genetics 8:e1003161.

    https://doi.org/10.1371/journal.pgen.1003161

    • PubMed
    • Google Scholar
88. 1. Levin M
    2. Hashimshony T
    3. Wagner F
    4. Yanai I

    (2012) Developmental milestones punctuate gene expression in the Caenorhabditis embryo

    Developmental Cell 22:1101–1108.

    https://doi.org/10.1016/j.devcel.2012.04.004

    • PubMed
    • Google Scholar
89. 1. Li R
    2. Ren X
    3. Bi Y
    4. Ho VW
    5. Hsieh CL
    6. Young A
    7. Zhang Z
    8. Lin T
    9. Zhao Y
    10. Miao L
    11. Sarkies P
    12. Zhao Z

    (2016) Specific down-regulation of spermatogenesis genes targeted by 22G RNAs in hybrid sterile males associated with an X-Chromosome introgression

    Genome Research 26:1219–1232.

    https://doi.org/10.1101/gr.204479.116

    • PubMed
    • Google Scholar
90. 1. Liao BY
    2. Zhang J

    (2006) Evolutionary conservation of expression profiles between human and mouse orthologous genes

    Molecular Biology and Evolution 23:530–540.

    https://doi.org/10.1093/molbev/msj054

    • PubMed
    • Google Scholar
91. 1. Lima TG
    2. Burton RS
    3. Willett CS

    (2019) Genomic scans reveal multiple mito‐nuclear incompatibilities in population crosses of the copepod Tigriopus californicus

    Evolution 73:609–620.

    https://doi.org/10.1111/evo.13690

    • Google Scholar
92. 1. Liu J
    2. Robinson-Rechavi M

    (2018a) Adaptive evolution of animal proteins over development: support for the darwin selection opportunity hypothesis of Evo-Devo

    Molecular Biology and Evolution 35:2862–2872.

    https://doi.org/10.1093/molbev/msy175

    • PubMed
    • Google Scholar
93. 1. Liu J
    2. Robinson-Rechavi M

    (2018b) Developmental constraints on genome evolution in four bilaterian model species

    Genome Biology and Evolution 10:2266–2277.

    https://doi.org/10.1093/gbe/evy177

    • PubMed
    • Google Scholar
94. 1. Lynch M
    2. Force AG

    (2000) The origin of interspecific genomic incompatibility via gene duplication

    The American Naturalist 156:590–605.

    https://doi.org/10.1086/316992

    • PubMed
    • Google Scholar
95. 1. Mack KL
    2. Campbell P
    3. Nachman MW

    (2016) Gene regulation and speciation in house mice

    Genome Research 26:451–461.

    https://doi.org/10.1101/gr.195743.115

    • PubMed
    • Google Scholar
96. 1. Mack KL
    2. Nachman MW

    (2017) Gene regulation and speciation

    Trends in Genetics 33:68–80.

    https://doi.org/10.1016/j.tig.2016.11.003

    • Google Scholar
97. 1. Malone JH
    2. Fontenot BE

    (2008) Patterns of reproductive isolation in toads

    PLOS ONE 3:e3900.

    https://doi.org/10.1371/journal.pone.0003900

    • Google Scholar
98. 1. Mank JE

    (2017) Population genetics of sexual conflict in the genomic era

    Nature Reviews Genetics 18:721–730.

    https://doi.org/10.1038/nrg.2017.83

    • PubMed
    • Google Scholar
99. 1. Martin A
    2. Orgogozo V

    (2013) The loci of repeated evolution: a catalog of genetic hotspots of phenotypic variation

    Evolution 22:1235–1250.

    https://doi.org/10.1111/evo.12081

    • Google Scholar
100. 1. Masly JP
     2. Presgraves DC

     (2007) High-resolution genome-wide dissection of the two rules of speciation in Drosophila

     PLOS Biology 5:e243.

     https://doi.org/10.1371/journal.pbio.0050243

     • PubMed
     • Google Scholar
101. 1. McManus CJ
     2. Coolon JD
     3. Duff MO
     4. Eipper-Mains J
     5. Graveley BR
     6. Wittkopp PJ

     (2010) Regulatory divergence in Drosophila revealed by mRNA-seq

     Genome Research 20:816–825.

     https://doi.org/10.1101/gr.102491.109

     • PubMed
     • Google Scholar
102. 1. McManus CJ
     2. May GE
     3. Spealman P
     4. Shteyman A

     (2014) Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast

     Genome Research 24:422–430.

     https://doi.org/10.1101/gr.164996.113

     • PubMed
     • Google Scholar
103. 1. Mecham JS

     (1960) Introgressive hybridization between two southeastern treefrogs

     Evolution 14:445–457.

     https://doi.org/10.1111/j.1558-5646.1960.tb03112.x

     • Google Scholar
104. 1. Mecham JS

     (1965) Genetic relationships and reproductive isolation in southeastern frogs of the genera Pseudacris and Hyla

     American Midland Naturalist 74:269.

     https://doi.org/10.2307/2423259

     • Google Scholar
105. Book

     1. Medawar PB

     (1952)

     An Unsolved Problem of Biology

     London: H.K. Lewis & Co.

     • Google Scholar
106. 1. Memar N
     2. Schiemann S
     3. Hennig C
     4. Findeis D
     5. Conradt B
     6. Schnabel R

     (2019) Twenty million years of evolution: the embryogenesis of four Caenorhabditis species are indistinguishable despite extensive genome divergence

     Developmental Biology 447:182–199.

     https://doi.org/10.1016/j.ydbio.2018.12.022

     • PubMed
     • Google Scholar
107. 1. Moehring AJ
     2. Teeter KC
     3. Noor MA

     (2007) Genome-wide patterns of expression in Drosophila pure species and hybrid males. II. examination of multiple-species hybridizations, platforms, and life cycle stages

     Molecular Biology and Evolution 24:137–145.

     https://doi.org/10.1093/molbev/msl142

     • PubMed
     • Google Scholar
108. 1. Neme R
     2. Tautz D

     (2014) Evolution: dynamics of de novo gene emergence

     Current Biology 24:R238–R240.

     https://doi.org/10.1016/j.cub.2014.02.016

     • PubMed
     • Google Scholar
109. 1. Ohta T

     (2011) Near-neutrality, robustness, and epigenetics

     Genome Biology and Evolution 3:1034–1038.

     https://doi.org/10.1093/gbe/evr012

     • PubMed
     • Google Scholar
110. 1. Orr HA

     (1995)

     The population genetics of speciation: the evolution of hybrid incompatibilities

     Genetics 139:1805–1813.

     • PubMed
     • Google Scholar
111. 1. Orr HA
     2. Madden LD
     3. Coyne JA
     4. Goodwin R
     5. Hawley RS

     (1997)

     The developmental genetics of hybrid inviability: a mitotic defect in Drosophila hybrids

     Genetics 145:1031.

     • PubMed
     • Google Scholar
112. 1. Ortíz-Barrientos D
     2. Counterman BA
     3. Noor MA

     (2006) Gene expression divergence and the origin of hybrid dysfunctions

     Genetica 129:71–81.

     https://doi.org/10.1007/s10709-006-0034-1

     • PubMed
     • Google Scholar
113. 1. Paaby AB
     2. Rockman MV

     (2014) Cryptic genetic variation: evolution's hidden substrate

     Nature Reviews Genetics 15:247–258.

     https://doi.org/10.1038/nrg3688

     • PubMed
     • Google Scholar
114. Book

     1. Paaby AB
     2. Testa ND

     (2018)

     Developmental Plasticity and Evolution

     In: de la Rosa L, Müller G, editors. Evolutionary Developmental Biology: A Reference Guide. Springer International Publishing, Cham. pp. 1–14.

     • Google Scholar
115. 1. Packer JS
     2. Zhu Q
     3. Huynh C
     4. Sivaramakrishnan P
     5. Preston E
     6. Dueck H
     7. Stefanik D
     8. Tan K
     9. Trapnell C
     10. Kim J
     11. Waterston RH
     12. Murray JI

     (2019) A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution

     Science 365:eaax1971.

     https://doi.org/10.1126/science.aax1971

     • PubMed
     • Google Scholar
116. 1. Palmer ME
     2. Feldman MW

     (2009) Dynamics of hybrid incompatibility in gene networks in a constant environment

     Evolution 63:418–431.

     https://doi.org/10.1111/j.1558-5646.2008.00577.x

     • Google Scholar
117. 1. Partridge L

     (2001) Evolutionary theories of ageing applied to long-lived organisms

     Experimental Gerontology 36:641–650.

     https://doi.org/10.1016/S0531-5565(00)00232-1

     • PubMed
     • Google Scholar
118. 1. Pavlicev M
     2. Wagner GP

     (2012) A model of developmental evolution: selection, pleiotropy and compensation

     Trends in Ecology & Evolution 27:316–322.

     https://doi.org/10.1016/j.tree.2012.01.016

     • Google Scholar
119. 1. Porter AH
     2. Johnson NA

     (2002) Speciation despite gene flow when developmental pathways evolve

     Evolution 56:2103–2111.

     https://doi.org/10.1111/j.0014-3820.2002.tb00136.x

     • Google Scholar
120. 1. Presgraves DC

     (2010a) Darwin and the origin of interspecific genetic incompatibilities

     The American Naturalist 176 Suppl 1:S45–S60.

     https://doi.org/10.1086/657058

     • PubMed
     • Google Scholar
121. 1. Presgraves DC

     (2010b) The molecular evolutionary basis of species formation

     Nature Reviews Genetics 11:175–180.

     https://doi.org/10.1038/nrg2718

     • PubMed
     • Google Scholar
122. 1. Promislow DEL

     (2004) Protein networks, pleiotropy and the evolution of senescence

     Proceedings of the Royal Society of London. Series B: Biological Sciences 271:1225–1234.

     https://doi.org/10.1098/rspb.2004.2732

     • Google Scholar
123. 1. Promislow DEL
     2. Tatar M

     (1998) Mutation and senescence: where genetics and demography meet

     Genetica 103:299–314.

     https://doi.org/10.1023/A:1017047212008

     • Google Scholar
124. 1. Proulx SR
     2. Nuzhdin S
     3. Promislow DE

     (2007) Direct selection on genetic robustness revealed in the yeast transcriptome

     PLOS ONE 2:e911.

     https://doi.org/10.1371/journal.pone.0000911

     • PubMed
     • Google Scholar
125. 1. Quint M
     2. Drost HG
     3. Gabel A
     4. Ullrich KK
     5. Bönn M
     6. Grosse I

     (2012) A transcriptomic hourglass in plant embryogenesis

     Nature 490:98–101.

     https://doi.org/10.1038/nature11394

     • PubMed
     • Google Scholar
126. 1. Raff RA

     (1996) The shape of life: genes, and the evolution of animal forms

     Genetics Research 63:34261.

     https://doi.org/10.1017/S0016672300034261

     • Google Scholar
127. 1. Rieseberg LH
     2. Blackman BK

     (2010) Speciation genes in plants

     Annals of Botany 106:439–455.

     https://doi.org/10.1093/aob/mcq126

     • Google Scholar
128. 1. Roux J
     2. Robinson-Rechavi M

     (2008) Developmental constraints on vertebrate genome evolution

     PLOS Genetics 4:e1000311.

     https://doi.org/10.1371/journal.pgen.1000311

     • PubMed
     • Google Scholar
129. 1. Rowe L
     2. Chenoweth SF
     3. Agrawal AF

     (2018) The genomics of sexual conflict

     The American Naturalist 192:274–286.

     https://doi.org/10.1086/698198

     • PubMed
     • Google Scholar
130. Preprint

     1. Sanchez-Ramirez S
     2. Weiss JG
     3. Thomas CG
     4. Cutter AD

     (2020) Sex-specific and sex-chromosome regulatory evolution underlie widespread misregulation of inter-species hybrid transcriptomes

     bioRxiv.

     https://doi.org/10.1101/2020.05.04.076505

     • Google Scholar
131. 1. Satyaki PR
     2. Cuykendall TN
     3. Wei KH
     4. Brideau NJ
     5. Kwak H
     6. Aruna S
     7. Ferree PM
     8. Ji S
     9. Barbash DA

     (2014) The hmr and lhr hybrid incompatibility genes suppress a broad range of heterochromatic repeats

     PLOS Genetics 10:e1004240.

     https://doi.org/10.1371/journal.pgen.1004240

     • PubMed
     • Google Scholar
132. 1. Schluter D

     (1996) Adaptive radiation along genetic lines of least resistance

     Evolution 50:1766–1774.

     https://doi.org/10.1111/j.1558-5646.1996.tb03563.x

     • Google Scholar
133. 1. Siegal ML
     2. Promislow DE
     3. Bergman A

     (2006) Functional and evolutionary inference in gene networks: does topology matter?

     Genetica 129:83–103.

     https://doi.org/10.1007/s10709-006-0035-0

     • PubMed
     • Google Scholar
134. 1. Signor SA
     2. Nuzhdin SV

     (2018) The evolution of gene expression in Cis and trans

     Trends in Genetics 34:532–544.

     https://doi.org/10.1016/j.tig.2018.03.007

     • PubMed
     • Google Scholar
135. 1. Smith EN
     2. Kruglyak L

     (2008) Gene-environment interaction in yeast gene expression

     PLOS Biology 6:e83.

     https://doi.org/10.1371/journal.pbio.0060083

     • PubMed
     • Google Scholar
136. 1. Stern DL
     2. Orgogozo V

     (2008) The loci of evolution: how predictable is genetic evolution?

     Evolution 62:2155–2177.

     https://doi.org/10.1111/j.1558-5646.2008.00450.x

     • Google Scholar
137. 1. Sucena E
     2. Stern DL

     (2000) Divergence of larval morphology between Drosophila sechellia and its sibling species caused by cis-regulatory evolution of ovo/shaven-baby

     PNAS 97:4530–4534.

     https://doi.org/10.1073/pnas.97.9.4530

     • PubMed
     • Google Scholar
138. 1. Sumida M
     2. Ueda H
     3. Nishioka M

     (2003) Reproductive isolating mechanisms and molecular phylogenetic relationships among palearctic and oriental Brown frogs

     Zoological Science 20:567–580.

     https://doi.org/10.2108/zsj.20.567

     • PubMed
     • Google Scholar
139. 1. Swanson WJ
     2. Vacquier VD

     (2002) The rapid evolution of reproductive proteins

     Nature Reviews Genetics 3:137–144.

     https://doi.org/10.1038/nrg733

     • PubMed
     • Google Scholar
140. 1. Takahasi KR
     2. Matsuo T
     3. Takano-Shimizu-Kouno T

     (2011) Two types of cis-trans compensation in the evolution of transcriptional regulation

     PNAS 108:15276–15281.

     https://doi.org/10.1073/pnas.1105814108

     • PubMed
     • Google Scholar
141. 1. Tang S
     2. Presgraves DC

     (2009) Evolution of the Drosophila nuclear pore complex results in multiple hybrid incompatibilities

     Science 323:779–782.

     https://doi.org/10.1126/science.1169123

     • PubMed
     • Google Scholar
142. 1. Tintori SC
     2. Osborne Nishimura E
     3. Golden P
     4. Lieb JD
     5. Goldstein B

     (2016) A transcriptional lineage of the early C. elegans Embryo

     Developmental Cell 38:430–444.

     https://doi.org/10.1016/j.devcel.2016.07.025

     • PubMed
     • Google Scholar
143. 1. Tirosh I
     2. Reikhav S
     3. Levy AA
     4. Barkai N

     (2009) A yeast hybrid provides insight into the evolution of gene expression regulation

     Science 324:659–662.

     https://doi.org/10.1126/science.1169766

     • PubMed
     • Google Scholar
144. 1. Tirosh I
     2. Barkai N

     (2008) Evolution of gene sequence and gene expression are not correlated in yeast

     Trends in Genetics 24:109–113.

     https://doi.org/10.1016/j.tig.2007.12.004

     • PubMed
     • Google Scholar
145. 1. Tran T-D
     2. Kwon Y-K

     (2013) The relationship between modularity and robustness in signalling networks

     Journal of the Royal Society Interface 10:20130771.

     https://doi.org/10.1098/rsif.2013.0771

     • Google Scholar
146. 1. True JR
     2. Haag ES

     (2001) Developmental system drift and flexibility in evolutionary trajectories

     Evolution and Development 3:109–119.

     https://doi.org/10.1046/j.1525-142x.2001.003002109.x

     • Google Scholar
147. 1. Tulchinsky AY
     2. Johnson NA
     3. Porter AH

     (2014a) Hybrid incompatibility despite pleiotropic constraint in a sequence-based bioenergetic model of transcription factor binding

     Genetics 198:1645–1654.

     https://doi.org/10.1534/genetics.114.171397

     • PubMed
     • Google Scholar
148. 1. Tulchinsky AY
     2. Johnson NA
     3. Watt WB
     4. Porter AH

     (2014b) Hybrid incompatibility arises in a sequence-based bioenergetic model of transcription factor binding

     Genetics 198:1155–1166.

     https://doi.org/10.1534/genetics.114.168112

     • PubMed
     • Google Scholar
149. 1. Turelli M
     2. Moyle LC

     (2007) Asymmetric Postmating Isolation: Darwin's Corollary to Haldane's Rule

     Genetics 176:1059–1088.

     https://doi.org/10.1534/genetics.106.065979

     • Google Scholar
150. 1. Turelli M
     2. Orr HA

     (1995)

     The dominance theory of Haldane's rule

     Genetics 140:389–402.

     • PubMed
     • Google Scholar
151. 1. Turner LM
     2. White MA
     3. Tautz D
     4. Payseur BA

     (2014) Genomic Networks of Hybrid Sterility

     PLOS Genetics 10:e1004162.

     https://doi.org/10.1371/journal.pgen.1004162

     • Google Scholar
152. 1. Wade MJ
     2. Johnson NA
     3. Jones R
     4. Siguel V
     5. McNaughton M

     (1997)

     Genetic variation segregating in natural populations of tribolium castaneum affecting traits observed in hybrids with T. freemani

     Genetics 147:1235–1247.

     • PubMed
     • Google Scholar
153. 1. Wagner A

     (2000) Robustness against mutations in genetic networks of yeast

     Nature Genetics 24:355–361.

     https://doi.org/10.1038/74174

     • PubMed
     • Google Scholar
154. 1. Wagner GP
     2. Altenberg L

     (1996) Perspective: complex adaptations and the evolution of evolvability

     Evolution 50:967–976.

     https://doi.org/10.2307/2410639

     • Google Scholar
155. 1. Wagner GP
     2. Zhang J

     (2011) The pleiotropic structure of the genotype–phenotype map: the evolvability of complex organisms

     Nature Reviews Genetics 12:204–213.

     https://doi.org/10.1038/nrg2949

     • Google Scholar
156. 1. Wang Z
     2. Sun X
     3. Zhao Y
     4. Guo X
     5. Jiang H
     6. Li H
     7. Gu Z

     (2015) Evolution of gene regulation during transcription and translation

     Genome Biology and Evolution 7:1155–1167.

     https://doi.org/10.1093/gbe/evv059

     • PubMed
     • Google Scholar
157. 1. Williams GC

     (1957) Pleiotropy, natural selection, and the evolution of senescence

     Evolution 11:398–411.

     https://doi.org/10.1111/j.1558-5646.1957.tb02911.x

     • Google Scholar
158. 1. Wittkopp PJ
     2. Haerum BK
     3. Clark AG

     (2004) Evolutionary changes in Cis and trans gene regulation

     Nature 430:85–88.

     https://doi.org/10.1038/nature02698

     • PubMed
     • Google Scholar
159. 1. Wittkopp PJ
     2. Kalay G

     (2012) Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence

     Nature Reviews Genetics 13:59–69.

     https://doi.org/10.1038/nrg3095

     • Google Scholar
160. 1. Wray GA
     2. Hahn MW
     3. Abouheif E
     4. Balhoff JP
     5. Pizer M
     6. Rockman MV
     7. Romano LA

     (2003) The evolution of transcriptional regulation in eukaryotes

     Molecular Biology and Evolution 20:1377–1419.

     https://doi.org/10.1093/molbev/msg140

     • PubMed
     • Google Scholar
161. 1. Wu CI
     2. Davis AW

     (1993) Evolution of postmating reproductive isolation: the composite nature of Haldane rule and its genetic bases

     The American Naturalist 142:187–212.

     https://doi.org/10.1086/285534

     • PubMed
     • Google Scholar
162. 1. Yanai I
     2. Peshkin L
     3. Jorgensen P
     4. Kirschner MW

     (2011) Mapping gene expression in two Xenopus species: evolutionary constraints and developmental flexibility

     Developmental Cell 20:483–496.

     https://doi.org/10.1016/j.devcel.2011.03.015

     • PubMed
     • Google Scholar
163. 1. Zalts H
     2. Yanai I

     (2017) Developmental constraints shape the evolution of the nematode mid-developmental transition

     Nature Ecology & Evolution 1:0113.

     https://doi.org/10.1038/s41559-017-0113

     • Google Scholar
164. 1. Zhao Z
     2. Boyle TJ
     3. Bao Z
     4. Murray JI
     5. Mericle B
     6. Waterston RH

     (2008) Comparative analysis of embryonic cell lineage between Caenorhabditis briggsae and Caenorhabditis elegans

     Developmental Biology 314:93–99.

     https://doi.org/10.1016/j.ydbio.2007.11.015

     • PubMed
     • Google Scholar

Author details

1. Asher D Cutter

   Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Canada

   For correspondence

   asher.cutter@utoronto.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7141-0013

2. Joanna D Bundus

   Department of Integrative Biology, University of Wisconsin – Madison, Madison, United States

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4715-8238

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