All the world in the palm of your hand: whole genome sequencing for evolution and conservation

Building an entire genome

If bigger is better, then biggest is best. Having the genome of a particular study species fully sequenced allows us to potentially look at all of the genetic variation in the entire gene pool: but how do we sequence the entirety of the genome? And what are the benefits of having a whole genome to refer to?

Whole genome assembly
A very, very simplified overview of whole genome sequencing. Similar to other genomic technologies, we start by fragmenting the genome into much smaller, easier to sequence parts (reads). We then use a computer algorithm which pieces these reads together into a consecutive sequence based on overlapping DNA sequence (like building a chain out of Lego blocks). From this assembled genome, we can then attach annotations using information from other species’ genomes or genetic studies, which can correlate a particular sequence to a gene, a function of that gene, and the resultant protein from these gene (although not always are all of these aspects included).

Well, assembling the whole genome of an organism for the first time is a very tricky process. It involves taking DNA sequence from only a few individuals, breaking them down into smaller fragments and multiplying these fragments into the billions (moreorless the same process used in other genomics technologies: the real difference is that we need the full breadth of the genome so that we don’t miss any spaces). From these fragments, we use a complex computer algorithm which builds up a consensus sequence like a Lego tower; by finding parts of sequences which overlap, the software figures out which pieces connect to one another. Hopefully, we eventually end up with one very long continuous sequence; the genome! Sometimes, we might end with a few very large blocks (called contigs), but this is also useful for analyses (correlated with how many/big blocks there are). With this full genome, we use information from other more completed genomes (such as those from model species like humans, mice or even worms) to figure out which sections of the genome relate to specific genes. We can then annotate these sections by labelling them as clear genes, complete with start and end point, and attach a particular physical function of that gene.

The benefits of whole genomes

Having an entire genome as a reference is an extremely helpful tool in conservation and evolutionary studies. The first, and perhaps most obvious benefit, is the sheer scale of the data we can use. By having the entirety of the genome available, we can use potentially billions of base pairs of sequence in our genetic analyses (for reference, the human genome is >3 billion base pairs long). Even if we don’t sequence the full genome for all of our samples, having a reference genome as basis for assembly our reduced datasets significantly improves the quantity and quality of sequences we can use.

Another very important benefit is the ability to prescribe function in our studies. Many of our processes for obtaining data, even for genomic technologies, use random and anonymous fragments of the genome. Although this is a cost-effective way to obtain a very large amount of data, it unfortunately means that we often have no idea which part of the genome our sequences came from. This means that we don’t know which sequences relate to specific genes, and even if we did we would have no idea what those genes are or do! But with an annotated genome, we can take even our fragmented sequence and check it against the genome and find out what genes are present.

Understanding adaptation

Based on that, it seems pretty obvious about exactly how having an annotated genome can help us in studies of adaptation. Knowing the functional aspect of our genetic data allows us to more directly determine how evolution is happening in nature; instead of only being able to say that two species are evolving differently from one another, for example, we can explicitly look at how they are evolving. Is one evolving tolerance to hotter temperatures? Are they evolving different genes to handle different diets? Are they evolving in response to an external influence, like a viral outbreak or changing climate? What are the physiological consequences of these changes? These questions are critical in understanding past and future evolution, and full genome analysis allows us to delve into them much deeper.

Manhattan plot example
A (slightly edited) figure of full genome comparisons between domestic dogs and wild wolves by Axelsson et al. (2013), with the aim of understanding the evolutionary changes associated with domestication. For avid readers, this figure probably looks familiar. This figure compares the genetic differentiation across the entire genome between dogs and wolves, with some sections of the genome (circled) showing clear differences. As there is an annotated dog genome, the authors then delved into these genes to understand the functional differences between the two. By comparing their genetic differences to functional genes, the authors can more explicitly suggest mechanisms or changes associated with the domestication process (such as adaptation to a starch-heavy and human-influenced diet).

 

 

This includes allowing us to better understand how adaptation actually works in nature. As we’ve discussed before, more traditional studies often assumed that single, or very few, genes were responsible for allowing a species to adapt and change, and that these genes had very strong effects on their physiology. But what we see far more often is polygenic adaptation; small changes in a very large number of genes which, combined together, allow the species to adapt and evolve. By having the entirety of the genome available, we are much more likely to capture all of the genes that are under natural selection in a particular population or species, painting a clearer picture of their evolutionary trajectory.

Understanding demography

The much larger dataset of full genomes is also important for understanding the non-adaptive parts of evolution; the demographic history. Given that selectively neutral impacts (e.g. reductions in population size) are likely to impact all of the genes in the gene pool somewhat equally, having a full genome allows us to more accurately infer the demographic state and historical patterns of species.

For both adaptive and non-adaptive variation, it is also important to consider what we call linkage disequilibrium. Genetic sequences that are physically close to each other in the genome will often be inherited together due to the imprecision of recombination (a fairly technical process, so I won’t delve into this): what this can mean is that if a gene is under very strong selection, then sequences around this gene will also look like they’re under selection too. This can give falsely positive adaptive genes (i.e. sequences that look like genes under selection but are just linked to a gene that is) or can interfere with demographic analyses (since they often assume no selection, or linkage to selection, on the sequences used). With a whole genome, we can actually estimate how far away a base pair has to be before it’s not linked anymore; we call these linkage blocks, and they’re very useful additions to analyses.

Linkage_example
An example of linkage as a process. We start with a particular sequence (top); during recombination, this sequence may randomly break and rearrange into different parts. In this example, I’ve simulated four different ‘breaks’ (dashed coloured lines) due to recombination. Each of these breaks leads to two separate blocks of fragments; for example, the break at the blue line results in the second two sequence blocks (middle). If we focus on one target base pair in the sequence (golden A), then we can see in some fragments it remains with certain bases, but sometimes it gets separated by the break. If we compare how often the golden A is in the same block (i.e. is co-inherited) as each of the other bases, across all 4 breaks, then we see that the bases that are closest to it (the golden A is represented by the golden bar) are almost always in the same block. This makes sense: the further away a base is from our target, the more likely that there will be a break between it. This is shown in the frequency distributions at the bottom: the left figure shows the actual frequencies of co-inheritance (i.e. linkage) using the top example and those 4 breaks. The right figure shows a more realistic depiction of how linkage looks in the genome; it rapidly decays as we move away from the target (although the width and rate of this can vary).

Improving conservation management

In a similar fashion to demography, full genome datasets can improve our estimates of relatedness and pedigrees in captive breeding programs. The massive scale of whole genomes allows us to more easily trace the genealogical history of individuals, allowing us to assign parents more accurately. This also helps with our estimations of genetic relatedness, arguably the most critical aspect of genetic-based breeding programs. This is particularly helpful for species with tricky mating patterns, such as polyamory, brood spawning or difficult to track organisms.

Pedigrees
An example of how whole genomes can improve our estimation of pedigrees. Say we have a random individual (star), and we want to know how they fit into a particular family tree (pedigree). With only a few genes, we might struggle to pick where in the family it fits based on limited genetic information. With a larger genetic dataset (such as reduced-representation genomics), we might be able to cross off a few potential candidate spots but still have some trouble with a few places (due to unknown parents, polygamy or issues with genetic analysis). With whole genomes, we should be able to much better clarify the whole pedigree and find exactly where our star individual fits in the tree (red circle). It is thanks to whole genomes, we can do those ancestry analyses that have gone viral lately!

The way forwards

While many non-model species are still lacking in the available genomic information, whole genomes are progressively being sequenced for more and more species. As this astronomical dataset grows, our ability to investigate, discover and test theories about evolution, natural selection and conservation will also improve. Many projects already exist which aim specifically to increase the number of whole genomes available for certain taxonomic groups such as birds and bats: these will no doubt prove to be invaluable resources for future studies.

Why we should always pander to diversity

Diversity in the natural world

‘Diversity’ is a term that gets used a lot these days, albeit usually in reference to social changes and structures. However, diversity is not merely a human construct and reflects an extremely important aspect of the natural world at a variety of levels. From the smallest genes to the biggest ecosystems, diversity is a trait that confers a massive range of benefits to individuals, populations, species and even the entire globe. Let’s dissect this diversity down at different scales and see how beneficial it can be.

Hierarchy of diversity
The generalised hierarchy at life, with diversity being an important component of each tier. At the smallest tier, genes underpin all life. The collection of genetic diversity is often summarised into a population (as a single cohesive genetic unit). Several populations can be pooled together into a single (usually) cohesive speciesDifferent species are then components of a larger community (which in turn are components of a broader ecosystem).

Genetic diversity

At the smallest scale in the hierarchy of genetic differentiation, we have the genes themselves. It is a well-established concept that having a diversity of genetic variants (alleles) within a population or species is critical to their future adaptation, evolution and persistance. This is because different alleles will have different benefits (or costs) depending on the environmental pressure that influences them; natural selection might favour one allele over another at one time, but a different one as the pressure changes. Having a higher number of alleles within the population or species means that there is a greater chance at least a few individuals will possess an adaptive gene with the changing environment (which we know can be quite rapid and very, very strong). The diversity serves as a ‘buffer’ against extinction; evolution by natural selection functions best when there are many options to choose from.

Without this diversity, species run the risk of having no adaptive genes at the ready to deal with a selective pressure. Either a new adaptive gene must mutate (or come about in other ways, such as through gene flow from another population or species) or the population/species will suffer and potentially go extinct. As strong selection causes the species to dwindle, it enters what is referred to as the ‘extinction vortex’. Without genetic diversity, they can’t adapt: thus, more individuals die off, causing more genetic diversity to be lost from the population. This pattern is a vicious cycle which can inevitably destroy species (without serious intervention).

Extinction vortex
A very dramatic representation of the extinction vortex.

For this reason, captive breeding programs aim to maintain as much of the genetic diversity of the original population as possible. This reduces the probability of entering a downward extinction spiral from inbreeding depression and helps to maintain populations into the future (both the captive one and the wild population when we reintroduce individuals into the wild).

“Population”  diversity

Because genetic diversity is critically important for species survival, we must also try to preserve the diversity of the entire gene pool of a species. This means conserving highly genetically differentiated populations within a species as a priority, as they may be the only ones that possess the necessary adaptive genes to save the rest of the species. This adaptive genetic variation can then be introduced into other populations in genetic rescue programs and serve as a means to semi-naturally allow the species to evolve. Evolutionarily-significant units (ESUs) are one measure of the invaluable nature of genetically unique populations.

Although many more traditional conservationists strongly believe that ESUs should be managed entirely independently of one another (to preserve their evolutionary ‘pedigree’ and prevent the risk of outbreeding depression), it has been suggested that the benefit of genetic rescue in many cases significantly outweighs this risk of outbreeding depression. For some species, this really is an act of rescue: they are at the edge of extinction, and if we do nothing we condemn them to die out.

Introducing genetic material across populations (or even species!) can generate new functional genes that allow the recipient species to adapt to selective pressures. This might sound very strange, and could be extremely rare, but examples of adaptive genetic material in one species originating from another species through hybridisation do exist in nature. For example, the black coat of wolves is a highly adaptive trait in some populations and is encoded for by the Melanocortin 1 receptor (Mc1r) gene. However, the specific mutation in Mc1r gene that generates the black coat colour actually first originated in domestic dogs; when wild wolves and domestic dogs interbred, this mutation was transferred into the wolf gene pool. Natural selection strongly favoured this new variant, and it very rapidly underwent strong positive selection. Thus, the adaptiveness of black wolves is thanks to a domestic dog mutation!

Species diversity

At a higher level of the hierarchy, the diversity of species within a particular community or ecosystem has been shown to be important for the health and stability of said community. Every species, however small or seemingly unimpressive, plays a role in the greater ecosystem balance, through interactions with other species (e.g. as predator, as prey, as competitor) and the abiotic environment. While some species are known to have very strong impacts on the immediate ecosystem (often dubbed ‘keystone species’, such as apex predators), all species have some influence on the world around them (we’re especially good at it).

Species interactions flowchart

The overall health and stability of an ecosystem, as well as the benefits it can provide to all living things (including humans) is largely determined by the diversity of species. For example, ‘habitat engineers’ are types of species that, by altering the physical environment around them (such as to build a home), directly provide new habitat for other species. They are a fundamental underpinning of many incredibly vibrant ecosystems; think of what a reef system would look like if there were no corals in it. There’d be no anemones growing colourfully; no fish to live in them; no sharks to feed on these non-existent fish. This is just one example of a complex ecosystem that truly relies on its inhabiting species to function.

Ecosystem jenga
Much like Jenga, taking out one block (a species) could cause the entire stack (the ecosystem) to collapse in on itself. Even if it stands up, however, the system will still be weaker without the full diversity to support it.

Protecting our diversity

Diversity is not just a social construct and is an important phenomenon in nature, at a variety of different levels. Preserving the full diversity of life, from genetic diversity within populations and species to full species diversity within ecosystems, is critical to maintaining healthy and robust natural systems. The more diversity we have at each level of this hierarchy, the greater robustness and security we will have in the future.

The history of histories: philosophy in biogeography

Biogeography of the globe

The distribution of organisms across the Earth, both over time and across space, is a fundamental aspect of the field of biogeography. But our understanding of the mechanisms by which organisms are distributed across the globe, and how this affects their evolution, can be at times highly enigmatic. Why are Australia and the Americas the only two places that have marsupials? How did lemurs get all the way to Madagascar, and why are they the only primate that has made the trip? How did Darwin’s famous finches get over to the Galápagos, and why are there so many species of them there now?

All of these questions can be addressed with a combination of genetic, environmental and ecological information across a variety of timescales. However, the overall field of biogeography (and phylogeography as a derivative of it) has traditionally been largely rooted on a strong yet changing theoretical basis. The earliest discussions and discoveries related to biogeography as a field of science date back to the 18th Century, and to Carl Linnaeus (to whom we owe our binomial classification system) and Alexander von Humboldt. These scientists (and undoubtedly many others of that era) were among the first to notice how organisms in similar climates (e.g. Australia, South Africa and South America) showed similar physical characteristics despite being so distantly separated (both in their groups and geographic distance). The communities of these regions also appeared to be highly similar. So how could this be possible over such huge distances?

Arctic and fennec final
A pretty unreasonable mechanism (and example) of dispersal in foxes. And yes, all tourists wear sunglasses and Hawaiian shirts, even arctic fox ones.

 

Dispersal or vicariance?

Two main explanations for these patterns are possible; dispersal and vicariance. As one might expect, dispersal denotes that an ancestral species was distributed in one of these places (referred to as the ‘centre of origin’) before it migrated and inhabited the other places. Contrastingly, vicariance suggests that the ancestral species was distributed everywhere originally, covering all contemporary ranges within it. However, changes in geography, climate or the formation of other barriers caused the range of the ancestor to fragment, with each fragmented group evolving into its own distinct species (or group of species).

Dispersal vs vicariance islands
An example of dispersal vs. vicariance patterns of biogeography in an island bird (pale blue). In the top example, the sequential separation of parts of the island also cause parts of the distribution of the original bird species to become fragmented. These fragments each evolve independently of their ancestor and form new species (red, and then blue). In the bottom example, the island geography doesn’t change but in rare events a bird disperses from the main island onto a new island. The new selective pressures of that island cause the dispersed birds to evolve into new species (red and blue). In both examples, islands that were recently connected or are easy to disperse across do not generate new species (in the sandy island in the bottom right). You’ll notice that both processes result in the same biogeographic distribution of species.

In initial biogeographic science, dispersal was the most heavily favoured explanation. At the time, there was no clear mechanism by which organisms could be present all over the globe without some form of dispersal: it was generally believed that the world was a static, unmoving system. Dispersal was well supported by some biological evidence such as the diversification of Darwin’s finches across the Galápagos archipelago. Thus, this concept was supported through the proposals of a number of prominent scientists such as Charles Darwin and A.R. Wallace. For others, however, the distance required for dispersal (such as across entire oceans) seemed implausible and biologically unrealistic.

 

A paradigm shift in biogeography

Two particular developments in theory are credited with a paradigm shift in the field; cladistics and plate tectonics. Cladistics simply involved using shared biological characteristics to reconstruct the evolutionary relationships of species (think like phylogenetics, but using physical traits instead of genetic sequence). Just as importantly, however, was plate tectonic theory, which provided a clear way for organisms to spread across the planet. By understanding that, deep in the past, all continents had been directly connected to one another provides a convenient explanation for how species groups spread. Instead of requiring for species to travel across entire oceans, continental drift meant that one widespread and ancient ancestor on the historic supercontinent (Pangaea; or subsequently Gondwana and Laurasia) could become fragmented. It only required that groups were very old, but not necessarily very dispersive.

Lemur dispersal
Surf’s up, dudes! Although continental drift was no doubt an important factor in the distribution and dispersal of many organisms on Earth, it actually probably wasn’t the reason lemurs got to Madagascar. Sorry for the mislead.

From these advances in theory, cladistic vicariance biogeography was born. The field rapidly overtook dispersal as the most likely explanation for biogeographic patterns across the globe by not only providing a clear mechanism to explain these but also an analytical framework to test questions relating to these patterns. Further developments into the analytical backbone of cladistic vicariance allowed for more nuanced questions of biogeography to be asked, although still fundamentally ignored the role of potential dispersals in explaining species’ distributions.

Modern philosophy of biogeography

So, what is the current state of the field? Well, the more we research biogeographic patterns with better data (such as with genomics) the more we realise just how complicated the history of life on Earth can be. Complex modelling (such as Bayesian methods) allow us to more explicitly test the impact of Earth history events on our study species, and can provide more detailed overview of the evolutionary history of the species (such as by directly estimating times of divergence, amount of dispersal, extent of range shifts).

From a theoretical perspective, the consistency of patterns of groups is always in question and exactly what determines what species occurs where is still somewhat debatable. However, the greater number of types of data we can now include (such as geological, paleontological, climatic, hydrological, genetic…the list goes on!) allows us to paint a better picture of life on Earth. By combining information about what we know happened on Earth, with what we know has happened to species, we can start to make links between Earth history and species history to better understand how (or if) these events have shaped evolution.

Age and dating with phylogenetics

Timing the phylogeny

Understanding the evolutionary history of species can be a complicated matter, both from theoretical and analytical perspectives. Although phylogenetics addresses many questions about evolutionary history, there are a number of limitations we need to consider in our interpretations.

One of these limitations we often want to explore in better detail is the estimation of the divergence times within the phylogeny; we want to know exactly when two evolutionary lineages (be they genera, species or populations) separated from one another. This is particularly important if we want to relate these divergences to Earth history and environmental factors to better understand the driving forces behind evolution and speciation. A traditional phylogenetic tree, however, won’t show this: the tree is scaled in terms of the genetic differences between the different samples in the tree. The rate of genetic differentiation is not always a linear relationship with time and definitely doesn’t appear to be universal.

 

Anatomy of phylogenies.jpg
The general anatomy of a phylogenetic tree. A phylogeny describes the relationships of tips (i.e. which are more closely related than others; referred to as the topology), how different these tips are (the length of the branches) and the order they separated in time (separations shown by the nodes). Different trees can share some traits but not others: the red box shows two phylogenetic trees with similar branch lengths (all of the branches are roughly the same) but different topology (the tips connect differently: A and B are together on the left but not on the right, for example). Conversely, two trees can have the same topology, but show differing lengths in the branches of the same tree (blue box). Note that the tips are all in the same positions in these two trees. Typically, it’s easier to read a tree from right to left: the two tips who have branches that meet first are most similar genetically; the longer it takes for two tips to meet along the branches, the less similar they are genetically.

How do we do it?

The parameters

There are a number of parameters that are required for estimating divergence times from a phylogenetic tree. These can be summarised into two distinct categories: the tree model and the substitution model.

The first one of these is relatively easy to explain; it describes the exact relationship of the different samples in our dataset (i.e. the phylogenetic tree). Naturally, this includes the topology of the tree (which determines which divergences times can be estimated for in the first place). However, there is another very important factor in the process: the lengths of the branches within the phylogenetic tree. Branch lengths are related to the amount of genetic differentiation between the different tips of the tree. The longer the branch, the more genetic differentiation that must have accumulated (and usually also meaning that longer time has occurred from one end of the branch to the other). Even two phylogenetic trees with identical topology can give very different results if they vary in their branch lengths (see the above Figure).

The second category determines how likely mutations are between one particular type of nucleotide and another. While the details of this can get very convoluted, it essentially determines how quickly we expect certain mutations to accumulate over time, which will inevitably alter our predictions of how much time has passed along any given branch of the tree.

Calibrating the tree

However, at least one another important component is necessary to turn divergence time estimates into absolute, objective times. An external factor with an attached date is needed to calibrate the relative branch divergences; this can be in the form of the determined mutation rate for all of the branches of the tree or by dating at least one node in the tree using additional information. These help to anchor either the mutation rate along the branches or the absolute date of at least one node in the tree (with the rest estimated relative to this point). The second method often involves placing a time constraint on a particular node of the tree based on prior information about the biogeography of the species (for example, we might know one species likely diverged from another after a mountain range formed: the age of the mountain range would be our constraints). Alternatively, we might include a fossil in the phylogeny which has been radiocarbon dated and place an absolute age on that instead.

Ammonite comic.jpg
Don’t you know it’s rude to ask an ammomite her age?

In regards to the former method, mutation rates describe how fast genetic differentiation accumulates as evolution occurs along the branch. Although mutations gradually accumulate over time, the rate at which they occur can depend on a variety of factors (even including the environment of the organism). Even within the genome of a single organism, there can be variation in the mutation rate: genes, for example, often gain mutations slower than non-coding region.

Although mutation rates (generally in the form of a ‘molecular clock’) have been traditionally used in smaller datasets (e.g. for mitochondrial DNA), there are inherent issues with its assumptions. One is that this rate will apply to all branches in a tree equally, when different branches may have different rates between them. Second, different parts of the genome (even within the same individual) will have different evolutionary rates (like genes vs. non-coding regions). Thus, we tend to prefer using calibrations from fossil data or based on biogeographic patterns (such as the time a barrier likely split two branches based on geological or climatic data).

The analytical framework

All of these components are combined into various analytical frameworks or programs, each of which handle the data in different ways. Many of these are Bayesian model-based analysis, which in short generates hypothetical models of evolutionary history and divergence times for the phylogeny and tests how well it fits the data provided (i.e. the phylogenetic tree). The algorithm then alters some aspect(s) of the model and tests whether this fits the data better than the previous model and repeats this for potentially millions of simulations to get the best model. Although models are typically a simplification of reality, they are a much more tractable approach to estimating divergence times (as well as a number of other types of evolutionary genetics analyses which incorporating modelling).

Molecular dating pipeline
A (believe it or not, simplified) pipeline for estimating divergence times from a phylogeny. 1) We obtain our DNA sequences for our samples: in this example, we’ll see each Sample (A-E) is a representative of a single species. We align these together to make sure we’re comparing the same part of the genome across all of them. 2) We estimate the phylogenetic tree for our samples/species. In a Bayesian framework, this means creating simulation models containing a certain substitution model and a given tree model (containing certain topology and branch lengths). Together, these two models form the likelihood model: we then test how well this model explains our data (i.e. the likelihood of getting the patterns in our data if this model was true). We repeat these simulations potentially hundreds of thousands of times until we pinpoint the most likely model we can get. 3) Using our resulting phylogeny, we then calibrate some parts of it based on external information. This could either be by including a carbon-dated fossil (F) within the phylogeny, or constraining the age of one node based on biogeographic information (the red circle and cross). 4) Using these calibrations as a reference, we then estimated the most likely ages of all the splits in the tree, getting our final dated phylogeny.

Despite the developments in the analytical basis of estimating divergence times in the last few decades, there are still a number of limitations inherent in the process. Many of these relate to the assumptions of the underlying model (such as the correct and accurate phylogenetic tree and the correct estimations of evolutionary rate) used to build the analysis and generate simulations. In the case of calibrations, it is also critical that they are correctly dated based on independent methods: inaccurate radiocarbon dating of a fossil, for example, could throw out all of the estimations in the entire tree. That said, these factors are intrinsic to any phylogenetic analysis and regularly considered by evolutionary biologists in the interpretations and discussions of results (such as by including confidence intervals of estimations to demonstrate accuracy).

Understanding the temporal aspects of evolution and being able to relate them to a real estimate of age is a difficult affair, but an important component of many evolutionary studies. Obtaining good estimates of the timing of divergence of populations and species through molecular dating is but one aspect in building the picture of the history of all organisms, including (and especially) humans.

The many genetic faces of adaptation

The transition from genotype to phenotype

While evolutionary genetics studies often focus on the underlying genetic architecture of species and populations to understand their evolution, we know that natural selection acts directly on physical characteristics. We call these the phenotype; by studying changes in the genes that determine these traits (the genotype), we can take a nuanced approach at studying adaptation. However, our ability to look at genetic changes and relate these to a clear phenotypic trait, and how and why that trait is under natural selection, can be a difficult task.

One gene for one trait

The simplest (and most widely used) models of understanding the genetic basis of adaptation assume that a single genotype codes for a single phenotypic trait. This means that changes in a single gene (such as outliers that we have identified in our analyses) create changes in a particular physical trait that is under a selective pressure in the environment. This is a useful model because it is statistically tractable to be able to identify few specific genes of very large effect within our genomic datasets and directly relate these to a trait: adding more complexity exponentially increases the difficulty in detecting patterns (at both the genotypic and phenotypic level).

Single locus figure
An example of a single gene coding for a single phenotypic trait. In this example, the different combination of alleles of the one gene determines the colour of the cat.

Many genes for one trait: polygenic adaptation

Unfortunately, nature is not always convenient and recent findings suggest that the overwhelming majority of the genetics of adaptation operate under what is called ‘polygenic adaptation’. As the name suggestions, under this scenario changes (even very small ones) in many different genes combine together to have a large effect on a particular phenotypic trait. Given the often very small magnitude of the genetic changes, it can be extremely difficult to separate adaptive changes in genes from neutral changes due to genetic drift. Likewise, trying to understand how these different genes all combine into a single functional trait is almost impossible, especially for non-model species.

Polygenic adaptation is often seen for traits which are clearly heritable, but don’t show a single underlying gene responsible. Previously, we’ve covered this with the heritability of height: this is one of many examples of ‘quantitative trait loci’ (QTLs). Changes in one QTL (a single gene) causes a small quantitative change in a particular trait; the combined effect of different QTLs together can ‘add up’ (or counteract one another) to result in the final phenotype value.

Height QTL
An example of polygenic quantitative trait loci. In this example, height is partially coded for by a total of ten different genes: the dominant form of each gene (Capitals, green) provides more height whereas the recessive form (lowercase, red) doesn’t. The cumulative total of these components determines how tall the person is: the person on the far right was very unlucky and got 0/10 height bonuses and so is the shortest. Progressively from left to right, some genes are contributing to the taller height of the people, with the far right person standing tall with the ultimate 10/10 pro-height genes. For reference, height is actually likely to be coded for by thousands of genes, not 10.

The mechanisms which underlie polygenic adaptation can be more complex than simple addition, too. Individual genes might cause phenotypic changes which interact with other phenotypes (and their underlying genotypes) to create a network of changes. We call these interactions ‘epistasis’, where changes in one gene can cause a flow-on effect of changes in other genes based on how their resultant phenotypes interact. We can see this in metabolic pathways: given that a series of proteins are often used in succession within pathways, a change in any single protein in the process could affect every other protein in the pathway. Of course, knowing the exact proteins coded for every gene, including their physical structure, and how each of those proteins could interact with other proteins is an immense task. Similar to QTLs, this is usually limited to model species which have a large history of research on these specific areas to back up the study. However, some molecular ecology studies are starting to dive into this area by identifying pathways that are under selection instead of individual genes, to give a broader picture of the overall traits that are underlying adaptation.

Labrador epistasis figure
My favourite example of epistasis on coat colour in labradors. Two genes together determine the colour of the coat, with strong interactions between them. The first gene (E/e) determines whether or not the underlying coat gene (B/b) is masked or not: two recessive alleles of the first gene (ee) completely blocks Gene 2 and causes the coat to become golden regardless of the second gene genotype (much like my beloved late childhood pet pictured, Sunny). If the first gene has at least one dominant allele, then the second gene is allowed to express itself. Possessing a dominant allele (BB or Bb) leads to a black lab; possessing two recessive alleles (bb) makes a choc lab!
Labrador epistasis table
The possible combinations of genotypes for the two above genes and the resultant coat colour (indicated by the box colour).

One gene for many traits: pleiotropy and differential gene expression

In contrast to polygenic traits, changes in a single gene can also potentially alter multiple phenotypic traits simultaneously. This is referred to as ‘pleiotropy’ and can happen if a gene has multiple different functions within an organism; one particular protein might be a component of several different systems depending on where it is found or how it is arranged. A clear example of pleiotropy is in albino animals: the most common form of albinism is the result of possessing two recessive alleles of a single gene (TYR). The result of this is the absence of the enzyme tyrosinase in the organism, a critical component in the production of melanin. The flow-on phenotypic effects from the recessive gene most obviously cause a lack of pigmentation of the skin (whitening) and eyes (which appear pink), but also other physiological changes such as light sensitivity or total blindness (due to changes in the iris). Albinism has even been attributed to behavioural changes in wild field mice.

Albinism pleiotropy
A very simplified diagram of how one genotype (the albino version of the TYR gene) can lead to a large number of phenotypic changes via pleiotropy (although many are naturally physiologically connected).

Because pleiotropic genes code for several different phenotypic traits, natural selection can be a little more complicated. If some resultant traits are selected against, but others are selected for, it can be difficult for evolution to ‘resolve’ the balance between the two. The overall fitness of the gene is thus dependent on the balance of positive and negative fitness of the different traits, which will determine whether the gene is positively or negatively selected (much like a cost-benefit scenario). Alternatively, some traits which are selectively neutral (i.e. don’t directly provide fitness benefits) may be indirectly selected for if another phenotype of the same underlying gene is selected for.

Multiple phenotypes from a single ‘gene’ can also arise by alternate splicing: when a gene is transcribed from the DNA sequence into the protein, the non-coding intron sections within the gene are removed. However, exactly which introns are removed and how the different coding exons are arranged in the final protein sequence can give rise to multiple different protein structures, each with potentially different functions. Thus, a single overarching gene can lead to many different functional proteins. The role of alternate splicing in adaptation and evolution is a rarely explored area of research and its importance is relatively unknown.

Non-genes for traits: epigenetics

This gets more complicated if we consider ‘non-genetic’ aspects underlying the phenotype in what we call ‘epigenetics’. The phrase literally translates as ‘on top of genes’ and refers to chemical attachments to the DNA which control the expression of genes by allowing or resisting the transcription process. Epigenetics is a relatively new area of research, although studies have started to delve into the role of epigenetic changes in facilitating adaptation and evolution. Although epigenetics is still a relatively new research topic, future research into the relationship between epigenetic changes and adaptive potential might provide more detailed insight into how adaptation occurs in the wild (and might provide a mechanism for adaptation for species with low genetic diversity)!

 

The different interactions between genotypes, phenotypes and fitness, as well as their complex potential outcomes, inevitably complicates any study of evolution. However, these are important aspects of the adaptation process and to discard them as irrelevant will not doubt reduce our ability to examine and determine evolutionary processes in the wild.