The nomenclature of “Modern synthesis” and “Population genetics” can be seen as two sides of the same coin, with the first one representing the theoretical framework and the latter the mathematical implementation of our current evolutionary theory. In the most recent version of one of the classic modern textbooks, the basic principles of the MS, i.e., the basic mechanisms driving evolution, were listed by and are summarized in Box1.

Box 1: Evolutionary mechanisms

Natural selection: This is the key process at the base of the modern synthesis. It refers to the differential survival and reproduction rate of individuals as a consequence of their individual phenotype. In a nutshell, individuals with higher fitness for a specific environment, have higher chances of survival and reproduction. Their genes, providing the higher fitness features, will be passed on to the next generation at a higher frequency.

Genetic drift: Genetic drift is a change in allele frequencies due to the random sampling of alleles for the next generation (Masel 2011). It results in random allele frequency changes and depends on population size. The smaller the population, the more likely alleles will be lost compared to the originally larger genepool due to stochasticity. A conceptual example is given in figure 1.

Figure 1: Schematic example of genetic drift. Simply by chance, in generation 1 only four individuals produce progeny and only one of them carries the blue allele. Consequently, in generation 2 only a minority of individuals carry the blue allele.

Genetic hitchhiking: Genetic hitchhiking occurs when an allele changes in frequency not for being under selection itself but for being in linkage with another allele which is under selection. An extreme case of genetic hitchhiking was observed in the model plant Arabidopsis thaliana: a glyphosate resistance mutation occurred in the chloroplast genome causing an increase in the occurrence of the entire nuclear genome associated with the mutation (Flood et al. 2016). The stronger the selection and the quicker it acts, the less recombination events will occur between the positive mutation and the rest of the genome, extending the hitchhiking to normally unlinked parts of the genome.

Epistasis: Epistasis occurs when a locus A is interfering with the phenotypic effect of another locus B. Selection on locus B will therefore be dependent on the allele present at locus A. A straightforward example is eye colour in many animals. For example, in the parasitic wasp Nasonia vitripennis, the compound determining eye colour is synthesized through a biosynthetic pathway. The last steps of the pathway are catalysed by two enzymes that define the eye colour depending on the alleles present on the loci coding the enzymes, which we will call A and B for simplicity (Figure 2).

Figure 2: Example of epistasis. Nasonia vitripennis is a wasp with diploid females and haploid males. This species has Wild Type (WT) individuals with dark eyes, but loss of function mutations (a and b) exist for two genes (A and B) acting at the end of the biosynthetic pathway responsible for a compound of the eye color. As gene B variation produces phenotypic variation only when a functional copy of gene A is present, gene A has an epistatic effect on gene B.

Recessive loss of function mutations exist for both genes A and B. While in the presence of a functional copy of Enzyme A, enzyme B variation produces phenotypic variation (dark eyes if a functional copy of B is present, red eyes otherwise), this is not the case when there are no functional copies of enzyme A (grey eyes regardless of locus B variation).

Pleiotropic effects: Pleiotropic effects are present when a single gene has multiple phenotypic effects. Natural selection on that gene will therefore pass through selection of multiple phenotypes. When one of the phenotypes is selected for, the other affected phenotypes will also change their occurrence in the next generations, even though they are not directly under selection.

Population bottleneck: A population bottleneck refers to a drastic reduction of population size due to environmental events (such as drought, flood, fires, pest attacks…). Such events drastically reduce the genetic variation of the population. Population bottleneck can result in genetic drift when the surviving individuals are selected randomly. Alternatively, a bottleneck can result in an extreme form of natural selection when the surviving individuals are the most fit to overcome the stress event.

Founder effect: Founder effect refers to the low genetic variation observed in new colonizing populations originated from few individuals that naturally poorly represent the gene-pool of the larger population from which they originate. Founder effects are closely related to bottlenecks but involve the colonization of new space for the species. Like in bottlenecks, mostly genetic drift and but also natural selection are the main evolutionary mechanisms at work.

Gene flow: Gene flow or Genetic migration refers to the transfer of genetic material from one population to another. When two populations have a really high gene flow and their genetic material is exchanged without limit, they may be considered a single population.

The discipline of population genetics describes, in mathematical terms, how the mechanisms in Box 1 cause changes in allele frequencies. To do so, the starting point is the null hypothesis in which no changes is allele frequencies are acting on a population and there is random mating and consequently random combination of alleles (individuals carrying allele A have the same chance to mate with individuals carrying allele A and B). In this theoretical null hypothesis, the population reaches the Hardy-Weinberg Equilibrium and there is no variation in allele frequencies between generations. So, in a locus with two alleles A and a, where we indicate the allele frequencies with f = p and f = q respectively, the corresponding genotype frequencies can be represented in a Punnett square (Figure 3) and they will be:

Freq(AA) = p2 Freq(aa) = q2 Freq(Aa) = 2pq

Figure 3: Punnett Square representing allele and genotype (AA, aa, Aa) frequencies in a population harbouring two alleles (A and a) at locus A. Allele frequencies (p and q) are represented by the length of the arrows, genotype frequencies (f(…)) by the area of the squares.

When the population is in HWE, the allele frequencies in the next generation will remain equal and can be calculated as:

p’ = (p2 + 2pq * 1⁄2) = p2 + pq = p(p+q) = p

q’ = (q2 + 2pq * 1⁄2) = q2 + pq = q(p+q) = q

In natural populations, it is possible to test for deviation from the HWE, i.e. to test whether a specific locus is under selection. To do so, it is necessary to measure genetic variation in two subsequent generations and compare genotype frequencies between these. Deviation from the HWE can be tested through a χ2 (chi-square) test.

Population genetics and genomics allow to draw a variety of information from the genetic variability measured in populations and species. In contrast, quantitative genetics focuses on the study of complex quantitative traits controlled by several genes. The name of this discipline refers to the sometimes-miniscule phenotypic differences among individuals that are quantified and utilized in order to unravel the nature of the trait’s genetic basis. More information and examples on the field of population genetics can be found on quantitative genetics in Falconer & Mackay (1996). A technical report of the most recent analysis available in quantitative genetics can be found on chapter “Differential Methylation”.

History of evolutionary theory

The evolutionary theory as we now know it, originated in the second half of 19th century, when Charles Darwin and Alfred Russel Wallace independently came up with the idea of evolution by natural selection (Darwin 1859; Wallace 1858). This revolutionary theory was opposing the Creationist theory, which considered different species as distinct entities created independently. Darwin and Wallace instead argued that species can produce diverging phenotypes which, given a large enough number of generations, can result in different species. This theory was providing a single explanation for the observation of micro- and macroevolution, justifying the entire diversity of life-forms present on Earth. Through his observations Darwin also understood that the force driving evolution is natural selection, acting on variation arising by chance. In other words, during the lifespan of a generation, slight differences arise stochastically between individuals and are transmitted to the progeny. These slight differences or “variation” make the individuals in the new generation diverge. The new generation is then selected by the environment as only individuals that are more fit for that specific environment will more likely survive and reproduce, propagating further only the “positive variation” arisen by chance. However, how this variation originated and was then passed on to the progeny was an important missing piece. It was not until the beginning of the 20th century that an answer emerged.

When Gregor Mendel published his studies on the heredity of traits in plants (Mendel 1865) he described the heredity of discrete traits and for years his findings seemed to be incompatible with the gradual evolution by natural selection hypothesized by Darwin and Wallace. It was only at the beginning of the 1900s that Darwinian evolution by natural selection and Mendel’s laws of heredity were shown to be compatible and were joined in a common mathematical framework by the work of Ronald Fisher, Sewall Wright and J. B. S. Haldane, giving birth to the field of “Population genetics” (Provine 1978). This unification was initially made possible by showing that continuous traits result from the independent inheritance of several genetic loci with small additive effects (Fischer 1919), solving the apparent contrast between Darwin’s gradual evolution and Mendel’s discrete traits.

This unification, started in the 1920s with the work of population geneticists, continued until the 1940s, further developing the evolutionary theory in what was then called the “Modern synthesis” by Julian Huxley (Huxley 1943). The discovery of DNA in 1953 by James Watson and Francis Crick as the molecule responsible for the inheritance of traits answered the last major question left open. The MS was initially implemented with some numerical differences by its founders (Mayer, Stebbins and Dobzhansky 1950; Smocovitis 1997), but the basic principle was common: natural selection is acting on heritable genetic variation generated by random mutations.

Single locus epigenetic effects on phenotype

The ultimate requirement for DNA methylation variation to be adaptive, in addition to being stable and not under genetic control, is to have phenotypic relevance. There are several examples of phenotypic effects of epigenetic marks, from heterophylly (Herrera and Bazaga 2013) to stress responses (Kinoshita and Seki 2014) and others, but only few meet the requirements of being stable and “pure”, i.e. not under genetic control. Although rare, there are few examples of pure naturally occurring epimutations at a single locus which cause mutant phenotypes. The most famous example is the peloric mutant of Lynaria vulgaris, in which spontaneous DNA methylation of the Lcyc gene promoter drives a change in flower symmetry from bilateral to radial (Cubas, Vincent, and Coen 1999). Such a phenotype can be transmitted to the next generation and can sometimes revert back to WT during somatic development. This can sometimes result in branches of peloric and WT flowers originating from the same plant, confirming that the Lcyc promoter methylation is pure and not genetically controlled. A second striking example is the naturally occurring hypermethylation of the Colorless non-ripening (Cnr) gene promoter, inhibiting fruit ripening in Tomato (Manning et al. 2006). This epimutation is even more stable than the Lynaria case, as the fruit phenotype only very rarely reverts to WT. Other examples of epialleles were described in few more plant species, some naturally occurring and some resulting from induced mutagenesis. Some examples were listed by (Kalisz and Purugganan 2004) and few cases confidently exclude any chance of genetic control of the described epimutations.

Figure 4: Examples of single locus phenotypic effects of DNA methylation epialleles. WT phenotype is at the top while mutant phenotype at the bottom for A) tomato (Manning et al. 2006), B) Linaria vulgaris (Cubas et al. 1999), and C) oil palm (Ong-Abdulla et al. 2015).

Epigenetic effects on complex traits

Although single locus epialleles are undoubtedly rare, most plant traits are polygenic. Therefore, more insights into epigenetic effects on complex traits are required to understand the potential of epigenetic contribution to plant adaptation. Unfortunately, such studies are so technically difficult that they are basically nonexistent. Showing causation of a single epimutation with a clear phenotype is already a hard task, doing so for putative epigenetic variants controlling quantitative traits is even harder. The only studies that have tackled this challenge and successfully identified DNA methylation variants affecting quantitative traits, so called epiQTLs, made use of A. thaliana epigenetic recombinant inbred lines epiRILs (Cortijo et al. 2014; Furci et al. 2019). Because this population harbours random DNA methylation variation in a uniform genetic background, any heritable phenotypic effect can be directly attributed to the methylation variation. Using this system Cortijo et al. 2014 managed to identify Differentially Methylated Regions explaining 60% to 90% of the flowering time and primary root length variation observed. More recently, hypomethylated DNA loci controlling quantitative resistance to the fungal pathogen Hyaloperonospora arabidopsidis to were also identified in the epiRILs (Furci et al. 2019).

When we look at the epigenetic machinery as a whole, with its complexity and its tight links to the genetic background, epigenetic mechanisms could contribute to or impact local adaptation and evolution in a few different ways. Excluding plasticity induced genetic accommodation (Table 1), which is not a direct mechanism and is not exclusively driven by epigenetics, we can envision at least four main direct mechanisms in which epigenetic marks could contribute to local adaptation and evolution:

1. Epimutations can arise stochastically and over evolutionary time they could undergo natural selection and, when functionally relevant, produce stable phenotypic variation (see phenotypic plasticity chapter). This would work in a similar manner as genetic mutations, with the difference that epimutations arise at a higher rate than genetic mutations but are also much more likely to revert (Becker et al. 2011; Johannes and Schmitz 2018).

2. Environmentally induced epigenetic variation could arise in response to changing environmental conditions and, if stably inherited over generations, could provide a means of rapid evolution (Richards 2006; Whitelaw and Whitelaw 2006).

3. DNA methylation variation may generate further genetic variation when transposable elements TEs are released (Dubin et al. 2015; Secco et al. 2015). This could result in different environmental cues releasing different TE families or in epigenetically distant accessions being more likely to generate different genetic variation.

4. In addition to these mechanisms, it is relevant to mention that methylated DNA has a higher mutation rate due to 5-methylcytosine being more prone to C to T transitions by deamination (Cambareri et al. 1989; Pfeifer 2006). This could increase the likelihood of epigenetically distant individuals to accumulate genetic mutations in different regions of the genome.

Nevertheless, in order to be taken into account, the main candidate mechanisms need to meet some basic requirements that are discussed in the next chapters. Most importantly, evolutionary relevant epigenetic variation has to be at least partially “stable”, meaning heritable through successive generations (see section “Stability of epigenetic marks”), have an effect on the phenotype (see section “Phenotypic effects”) and should be “pure” i.e. not rely on DNA sequence variation for its heritability (see section “Genetics – epigenetics”).

In order to determine whether epigenetic marks play a relevant role in evolution, it is crucial to understand to which extent they are stably inherited across generations. While in sexually reproducing species the concept of generation is very straightforward, it is not the same in asexually reproducing species. Therefore, when not specified, we will refer to “stability” as the ability of an epigenetic mark to be inherited through meiosis in sexually reproducing species. Alternatively, when dealing with asexually reproducing species, we will consider epigenetics marks transmittable through mitosis as “stable” as well.

Histone modifications

Histone modifications were shown to be associated with stress memory and to be, in some cases, stably inherited through mitosis in plants, yeast and animals (Shido et al. 2005; Audergon et al. 2015). Nevertheless, they seem to revert back upon meiosis and not to be transgenerationally inherited by the offspring (Pecinka and Mittelsten Scheid 2012). This is consistent with the observation that the parental H3 histone, carrying several modifications related to differential transcription, is replaced during zygote development in Arabidopsis thaliana (Ingouff et al. 2010). Although these findings do not completely rule out the possibility that locus specific histone modifications could be copy-pasted to the zygote, this was never observed so far.

Considering these observations, histone modifications do not seem to be good candidates in playing a role in the evolution of sexually reproducing species as they are not transmissible through meiosis. Nevertheless, this could be different for asexually reproducing species which do not undergo meiosis (Castonguay and Angers 2012). More on histone modifications can be found in Chapter 8.

DNA methylation

When looking at the “origin” of DNA methylation variation from this perspective, we have to consider that while stochastic epimutations appear randomly in the genome (and therefore have the same chance of appearing in different genomic elements) environmentally induced DNA methylation is “intentionally” targeting regions in which it has a function such as promoters and other regulatory regions … (Secco et al. 2015). For this reason, these two origins of DNA methylation variation should be considered separately. Several stress memory experiments were carried out to test for the heritability of environmentally induced DNA methylation and the general trend is that induced variation reverts back to its original state after one or few generations (Secco et al. 2015; Lämke and Bäuerle 2017). Stochastic epimutations, on the other hand, can be studied using Mutation Accumulation Lines with one study showing that stochastic epimutations revert back at a quicker rate than genetic mutations, but can remain stable over several generations (Becker et al. 2011; Johannes and Schmitz 2018).

When willing to study the role of epigenetics in plant adaptation and evolution, a fundamental step is to study epigenetic patterns in natural populations. Different environments offer different selective pressures and may induce the presence of different DNA methylation patterns. Moreover, isolation by distance may induce the accumulation of different epimutations in geographically distant populations. The only way to study the occurrence of these phenomena (or whether they occur at all) is to study them in real natural populations. Furthermore, it is important to bear in mind that what happens in one species may differ in another, especially for species with very different life-history traits (see the Chapter “Life history traits”). Several studies have tried to answer this kind of questions but many struggle to overcome the large-scale vs high-accuracy problem (see section “Genetics-Epigenetics”) focusing on either one of the two aspects (Richards et al. 2017). Nevertheless, each approach was able to address some fundamental questions. Studies based on large-scale surveys were able to show that DNA methylation correlates with habitat of origin and environmental variables (Gugger et al. 2016; Lira-Medeirose et al. 2010; Gáspár, Bossdorf, and Durka 2018), with some studies having found correlations with phenotypic traits (see section “Phenotypic effects”). Some experiments analysing subsequent generations also showed that DNA methylation is at least partially heritable (Gáspár, Bossdorf, and Durka 2018), but most of these studies lack the genomic resolution to fully test whether the observed epigenetic variation is independent of genetic polymorphisms. At present, the only plant species where the large-scale vs high-accuracy problem could be effectively overcome is Arabidopsis thaliana. Yet in this species most of the DNA methylation variation seems to be under genetic control (Dubin et al. 2015; Kawakatsu et al. 2016). Nevertheless, Arabidopsis was shown to be an outlier in terms of DNA methylation studies, harbouring a very low global genome methylation and TE content compared to other plant species (Alonso et al. 2015).

In addition to all these approaches, as previously discussed in the section “Phenotypic effects”, few naturally occurring epialleles were discovered in plant populations. These discoveries were made “by chance”, meaning that the studies that found them did not intentionally look for such epigenetic variation. Nevertheless, the presence of naturally occurring pure epialleles constitutes the most evident proof that epigenetic variation could potentially be adaptive.

Tight link between genetics and epigenetics

An important complicating factor, hindering advances in determining the importance of epigenetics in evolution, is posed by the tight link between the genome and the epigenome. In simple words, when a genetic polymorphism is controlling an epigenetic pattern, the latter will seem to be heritable and under selection, while in reality this is only true for the causal genetic polymorphism. This can be caused by trans-acting elements at the genome level, for example when a mutation affecting a methyltransferase is responsible for the induction of different DNA methylation patterns and different global DNA methylation (Dubin et al. 2015). Alternatively, this can also happen at the level of a specific locus, driven by cis-acting elements such as TE insertions causing DNA methylation of the neighbouring region. For example, in melon, a TE insertion next to the promoter of CmWIP1 causes silencing of this gene, resulting in a switch from male to female flowers (Martin et al. 2009).

Looking at this the other way around, epigenetic variation can also be in control of genetic variation, as for example TE activity is controlled by DNA methylation (Noshay et al. 2019). This link poses difficulties when studying the phenotypic effects of DNA methylation variation, as an induced change in the DNA methylation patterns (see chemical demethylation), often used to prove its contribution to phenotypic changes, may release TEs. It is therefore difficult to determine whether the phenotypic effect following a DNA methylation change is truly due to this change or due to an unseen TE insertion.

Controlling or excluding genetic variation

To overcome these difficulties, different approaches have been used and studies have found that epigenetic variation can, at least in some cases, be independent from DNA sequence variation (Cubas, Vincent, and Coen 1999; Riddle and Richards 2002; Shindo et al. 2006; Vaughn et al. 2007). These approaches are either based on excluding genetic variation, by looking at epigenetic variation in populations with the same genetic background or accounting for genetic variation. Some common approaches are listed below:

1. Excluding genetic variation: these are the most common approaches because they are simpler and normally less expensive. The downside of these methods is that they do not allow to look at epigenetic variation in natural populations of genetically variable species.

1. Using asexually reproducing species as these have nearly identical genetic background (Vanden Broeck et al. 2018; Heer 2018; Shi et al. 2019).

2. Chemical demethylation: Demethylating agents such as Zebularine and 5-azacytidine can be used to extensively, but randomly, erase DNA methylation from genomes without modifying the genetic background, except for the above-mentioned possibility of TE activation (Latzel 2016; Münzbergova et al. 2018).

3. Methylation mutants: Introducing epigenetic variation through a mutation in the epigenetic machinery and then removing the mutation (Johannes et al. 2009, Cortijo et al. 2014, Kooke el al. 2015, Zhang et al. 2019).

4. Targeted DNA methylation: Using molecular biology techniques, such as transformation with inverted repeat (IR) sequences (Mette et al. 2000; Zicola et al. 2019) or Cas9-methyltransferase complexes, It is possible to methylate specific genomic regions. While Cas9 complexes can be used for any kind of sequence, IR insertions should only be used, when targeting non-transcribed regions or they will also degrade mRNAs through post transcriptional gene silencing.

2. Accounting for genetic variation: this strategy is used to study epigenetic variation in natural populations of species harbouring genetic variation. It is based on statistical methods allowing to determine whether the observed epigenetic variation is controlled by underlying genetic polymorphisms. This approach faces what we will call the large-scale vs high-accuracy problem, meaning it requires both large-scale collections and high-accuracy genomic tools. Large-scale surveys are fundamental to capture enough variation to provide a representative sample of the adaptive ability of a certain species. High-accuracy genomic tools are required at the DNA methylation level in order to capture the majority of the variation and to draw information about a variety of factors. Moreover, high-accuracy at the DNA sequence level, and therefore a reference genome, are also required due to the tight link between genetics and epigenetics. Some papers that adopted this approach in Arabidopsis are (Dubin et al. 2015, Sakani et al. 2019, and Kawakatsu et al. 2016).

Despite the efficacy of the Modern synthesis in describing the genetic implications of evolution, further advances were made in the last 20 years and exceptions to the basic assumptions of the MS were identified. These exceptions raised the question of whether these basic assumptions should be revisited or whether additional drivers should be simply added (Wilkins 2008; Laland et al. 2015). Following these findings and this line of reasoning Massimo Pigliucci and Gerd B. Müller postulated a new “Extended Evolutionary Synthesis” (EES; Müller 2007; Pigliucci 2009). This consists of revisiting the basic assumptions of the ES by giving more importance to the role of phenotypes and including additional evolutionary processes, as described in Table 1. The basic assumptions of the EES embrace and extend the ones of the ES. The three main EES assumptions are listed below (for mor details see Laland et al. 2015):

1. Inclusive inheritance: Genetic mutations but also other forms of inheritance generate variation.

2. Reciprocal causation: Evolutionary processes cause phenotypic variation in the new generations but vice versa the phenotypes influence the occurrence of evolutionary processes.

3. Non-random variation: There is evidence that variation is not generated fully randomly. One controversial example is “developmental bias” , referring to the observation that some phenotypes are more likely than others to arise due to developmental processes. Moreover, it was shown that, responding to specific stimuli, extra chromosomal DNA molecules can be replicated to create redundancy of beneficial genes. This mechanism was shown to induce glyphosate resistance in Amaranthus palmeri and constitutes another striking example of non-random genetic variation . Again, some families of transposable elements seem to be preferentially inserted in the proximity of genes and not fully randomly in the genome . Environmentally induced epigenetic variation, if stable through at least few generations, would constitute another case of non-random variation.

Table 1: Evolutionary processes considered by the Extended Evolutionary Synthesis as described in Laland et al. 2015.

Despite the evident effect of some of the EES additional drivers (especially when thinking about human evolution), some authors argue that these additional drivers are only affecting evolution indirectly and they introduce variation that can be already captured by the original drivers (Futuyma 2017). To give real life examples, it is possible to explain humans loss of body hairs as a result of niche construction, removing the selective pressure (cold protection) or to explain it directly with the loss of the selective pressure for that trait. In the same manner, genetic divergence between populations can be explained as the result genetic accommodation following colonization events only provided by plasticity, or directly as the result of different populations being exposed to different selective pressures.

Without getting into the details of such discussion, it is important to point out that transgenerational epigenetics, referring to epigenetic marks stably inherited through generations, when having a phenotypic effect, would be directly influencing evolution and not in an indirect way like other EES mechanisms. For this reason, in this chapter we will primarily focus on stable epigenetic variation (see “Stability of Epigentic marks”). The concept of “stable” strictly depends on the mode of reproduction of the species, hence in asexually reproducing species mitosis-heritable epigenetic marks should be considered stable, while stability through meiosis would be necessary for sexually reproducing species.

This considered, transgenerational epigenetics is one of the most prominent candidates to be included in new evolutionary models. Nevertheless, according to Occam’s razor, a good model should be simple, and the question of whether or not transgenerational epigenetics would significantly increase the accuracy and power of the evolutionary theory is still under debate (Dickins and Barton 2013; Haig 2007; Pigliucci and Finkelman 2014; Mesoudi et al. 2013). It is therefore crucial to determine the relative contribution of epigenetics to evolution, in order to implement it correctly in future evolutionary models.

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