Surviving the Real-World Apocalypse

The changing world

Climate change seems to be the centrefold of a large amount of scientific research and media attention, and rightly so: it has the capacity to affect every living organism on the planet. It’s our duty as curators and residents of Earth to be responsible for our influences on the global environmental stage. While a significant part of this involves determining causes and solutions to our contributions to climate change, we also need to know how extensive the effects will be: for example, how can we predict how well species will do in the future?

Predicting the effect of climate change on all of the world’s biodiversity is an immense task. Climate change itself is a complicated system, and causes diverse, interconnected and complex alterations to both global and local climate. Adding on top of this, though, is that climate affects different species in different ways; where some species might be sensitive to some climatic variables (such as rainfall, available sunlight, seasonality), others may be more tolerant to the same factors. But all living things share some requirements, so surely there must be some consistency in their responses to climate change, right?

Apocalypse 2
Lucky for Mr Fish here, he’s responding to a (very dramatic) climate change much, much better than his bird counterpart.

How predictable are species responses to climate change?

Well, evidence would surprisingly suggest not. Many species, even closely related ones, can show very different responses to the exact same climatic pressures or biogeographical events. There are a number of different traits that might affect a species’ ability to adapt, particularly their adaptive genetic diversity (which underpins ‘adaptive potential’). Thus, we need good information of a variety of genetic, physiological and life history traits to be able to make predictions about how likely a species is to adapt and respond to future (and current) climate changes.

Although this can be hard to study in species of high extinction risk (getting a good number of samples is always an issue…), traditional phylogeographic methods might help us to make some comparisons. See, although the modern Earth is rapidly changing (undoubtedly influenced by human society), the climate of the globe has always varied to some degree. There has always been some tumultuousness in the climate and specific Earth history events like volcano eruptions, sea-level changes, or glaciation periods (‘ice ages’) have had diverse effects on organisms globally.

Using comparative phylogeography to predict species responses

One tool for looking at how different species have, in the past, responded to the same biogeographical force is the domain of ‘comparative phylogeography’. Phylogeography itself is something we have discussed before: the ‘comparative’ aspect simply means comparing (with complex statistical methods) these patterns across different and often unrelated species to see how universal (‘congruent’) or unique (‘incongruent’) these patterns are among species. The more broadly we look at the species community in the region, the more we can observe widespread effects of any given environmental or geographical event: if we only look at fish, for example, we might not to be able to infer what response mammals, birds or invertebrates have had to our given event. Sometimes this still meets the scale we wish to focus; other times, we want to see how all the species of an area have been affected.

Actual island diagram
An (very busy) example of different species responses to a single environmental event. In this example, we have three species (a fish, a lizard, and a bird) all living on the same island. In the middle of the island, there is a small mountain range (A). At this point in time, all three species are connected across the whole island; fish can travel via lakes and wetlands (green arrows), lizards can travel across the land (blue arrow) and birds can fly anywhere. However, as the mountain range grows with tectonic movements, the waterways are altered and the north and south are disconnected (B). The fish species is now split into two evolutionarily separate groups (green and gold), while lizards and birds are not. As the range expands further, however, the dispersal route for lizards is cut off, causing them to eventually also become separated into blue and black groups (C). Birds, however, have no problems flying over the mountain range and remain one unified and connected orange group over time (D). Thus, each species has a different response to the formation of the mountain range.
Evol history of island diagram
The phylogenetic history of the three different species in the above example. As you can see, each lineage has a slightly different pattern; birds show no divergences at all, whereas the timing of the lizard and fish N/S splits are different (i.e. temporally incongruent).

Typically, comparative phylogeographic studies have looked at the neutral components of species’ evolution (as is the realm of traditional phylogeography). This includes studying the size of populations over time, how well connected they are and were, what their spatial patterns are and how these relate to the environment. Comparing all of these patterns across species can allow us to start painting a fuller picture of the history of biota in a region. In this way, we can start to see exactly which species have shown what responses and start to relate these to the characteristics that allowed them to respond in that certain way (and including adaptation in our studies). So, what kinds of traits are important?

What traits matter? Who wins?

Often, we find that life history traits of an organism better dictates how they will respond to a certain pressure than other factors such as phylogeny (e.g. one group does not always do better than another). Instead, individual species with certain physical characteristics might handle the pressure better than others. For example, a fish, bird and snake that are all able to tolerate higher temperatures than other fish, birds or snakes in that region are more likely to survive a drought. In this case, none of the groups (fish, birds or snakes) inherently do better than the other two groups. Thus, it can be hard to predict how a large swathe of species will respond to any given environmental change, unless we understand the physical characteristics of every species.

Climate change risk flowchart
A generalised framework of various factors, and their interactions, on the vulnerability of species under current and future climate changes by Williams et al. 2018. The schematic includes genetic, ecological, physical and environmental factors and how these can interact with one another to alleviate or exacerbate the risk of extinction.

We can also see that other physiological or ecological traits, such as climatic preferences and tolerance thresholds, can be critical for adapting to climatic pressures. Naturally, the genetic diversity of species is also an important component underlying their ability to adapt to these new selective pressures and to survive into the future. Trying to incorporate all of these factors into a projected model can be difficult, but with more data of higher quality we can start to make more refined predictions. But by understanding how particular traits influence how well a species may adapt to a changing climate, as well as knowing the what traits different species have, might just be the key to predicting who wins and who dies in the real-world Game of Thrones.

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.

The direction of selection

The nature of adaptation

One of the most fundamental aspects of natural selection and evolution is, of course, the underlying genetic traits that shape the physical, selected traits. Most commonly, this involves trying to understand how changes in the distribution and frequencies of particular genetic variants (alleles) occur in nature and what forces of natural election are shaping them. Remember that natural selection acts directly on the physical characteristics of species; if these characteristics are genetically-determined (which many are), then we can observe the flow-on effects on the genetic diversity of the target species.

Although we might expect that natural selection is a fairly predictable force, there are a myriad of ways it can shape, reduce or maintain genetic diversity and identity of populations and species. In the following examples, we’re going to assume that the mentioned traits are coded for by a single gene with two different alleles for simplicity. Thus, one allele = one version of the trait (and can be used interchangeably). With that in mind, let’s take a look at the three main broad types of changes we observe in nature.

Directional selection

Arguably the most traditional perspective of natural selection is referred to as ‘directional selection’. In this example, nature selection causes one allele to be favoured more than another, which causes it to increase dramatically in frequency compared to the alternative allele. The reverse effect (natural selection pushing against a maladaptive allele) is still covered by directional selection, except that it functions in the opposite way (the allele under negative selection has reduced frequency, shifting towards the alternative allele).

Directional selection diagram
An example of directional selection. In this instance, we have one population of cats and a single phenotypic trait (colour) which ranges from 0 (yellow) to 1 (red). Red colour is selected for above all other colours; the original population has a pretty diverse mix of colours to start. Over time, we can see the average colour of the entire population moves towards more red colours whilst yellow colours start to disappear. Note that although the final population is predominantly red, there is still some (minor) variation in colours. These changes are reflected in the distribution of the colour-coding alleles (right), as it moves towards the red end of the spectrum.

Balancing selection

Natural selection doesn’t always push allele frequencies into different directions however, and sometimes maintains the diversity of alleles in the population. This is what happens in ‘balancing selection’ (sometimes also referred to as ‘stabilising selection’). In this example, natural selection favours non-extreme allele frequencies, and pushes the distribution of allele frequencies more to the centre. This may happen if deviations from the original gene, regardless of the specific change, can have strongly negative effects on the fitness of an organism, or in genes that are most fit when there is a decent amount of variation within them in the population (such as the MHC region, which contributes to immune response). There are a couple other reasons balancing selection may occur, though.

Heterozygote advantage

One example is known as ‘heterozygote advantage’. This is when an organism with two different alleles of a particular gene has greater fitness than an organism with two identical copies of either allele. A seemingly bizarre example of heterozygote advantage is related to sickle cell anaemia in African people. Sickle cell anaemia is a serious genetic disorder which is encoded for by recessive alleles of a haemoglobin gene; thus, a person has to carry two copies of the disease allele to show damaging symptoms. While this trait would ordinarily be strongly selected against in many population, it is maintained in some African populations by the presence of malaria. This seems counterintuitive; why does the presence of one disease maintain another?

Well, it turns out that malaria is not very good at infecting sickle cells; there are a few suggested mechanisms for why but no clear single answer. Naturally, suffering from either sickle cell anaemia or malaria is unlikely to convey fitness benefits. In this circumstance, natural selection actually favours having one sickle cell anaemia allele; while being a carrier isn’t ordinarily as healthy as having no sickle cell alleles, it does actually make the person somewhat resistant to malaria. Thus, in populations where there is a selective pressure from malaria, there is a heterozygote advantage for sickle cell anaemia. For those African populations without likely exposure to malaria, sickle cell anaemia is strongly selected against and less prevalent.

Malaria and sickle diagram
A diagram of how heterozygote advantage works in sickle cell anaemia and malaria resistance. On the top we have our two main traits: the blood cell shape (which has two different alleles; normal and sickle celled) and malaria infection by mosquitoes. Blue circles indicate that the trait has good fitness, whilst red crosses indicate the trait has bad fitness. For the left hand person, having two sickle cell alleles (ss) means they are symptomatic of sickle cell anaemia and is unlikely to have a good quality of life. On the right, having two normal blood cell alleles (SS) means that he is susceptible to malaria infection. The middle person, however, having only one sickle cell allele (Ss) means they are asymptomatic but still resistant to malaria. Thus, being heterozygous for sickle cell is actually beneficial over being homozygous in either direction: this is reflected in the distribution of alleles (bottom). The left side is pushed down by sickle cell anaemia whilst the right side is pushed down by malaria, thus causing both blood cell alleles (s and S) to be maintained at an intermediate frequency (i.e. balanced). 

Frequency-dependent selection

Another form of balancing selection is called ‘frequency-dependent selection’, where the fitness of an allele is inversely proportional to its frequency. Thus, once the allele has become common due to selection, the fitness of that allele is reduced and selection will start to favour the alternative allele (which is at much lower frequency). The constant back-and-forth tipping of the selective scales results in both alleles being maintained at an equilibrium.

This can happen in a number of different ways, but often the rarer trait/allele is fundamentally more fit because of its rarity. For example, if one allele allows an individual to use a new food source, it will be very selectively fit due to the lack of competition with others. However, as that allele accumulates within the population and more individuals start to feed on that food source, the lack of ‘uniqueness’ will mean that it’s not particularly better than the original food source. A balance between the two food sources (and thus alleles) will be maintained over time as shifts towards one will make the other more fit, and natural selection will compensate.

Frequency dependent selection diagram
An example of frequency-dependent selection. The colour of the cat indicates both their genotype and their food sources: black cats eat red apples whilst green cats eat green apples (this species has apparently developed herbivory, okay?) To start with, the incredibly low frequency of green cats mean that the one green cat can exploit a huge food source compared to black cats. Because of this, natural selection favours green cats. However, in the next generation evolution overcompensates and produces way too many green cats, and now black cats are getting much more food. Natural selection bounces back to favour black cats. Eventually, this causes and equilibrium balance of the two cat types (as shifts one way will cause a shift back the other way immediately after). These changes are reflected in the overall frequency of the two types over time (top right), which eventually evens out. The bottom right figure demonstrates that for both cat types, the frequency of that colour is inversely proportional to the overall fitness (measured as a proxy by amount of food per cat).

Disruptive selection

A third category of selection (although not as frequently mentioned) is known as ‘disruptive selection’, which is essentially the direct opposite of balancing selection. In this case, both extremes of allele frequencies are favoured (e.g. 1 for one allele or 1 for the other) but intermediate frequencies are not. This can be difficult to untangle in natural populations since it could technically be attributed to two different cases of directional selection. Each allele of the same gene is directionally selected for, but in opposite populations and directions so that overall pattern shows very little intermediates.

In direct contrast to balancing selection, disruptive selection can often be a case of heterozygote disadvantage (although it’s rarely called that). In these examples, it may be that individuals which are not genetically committed to one end or the other of the frequency spectrum are maladapted since they don’t fit in anywhere. An example would be a species that occupies both the desert and a forested area, with little grassland-type habitat in the middle. For the relevant traits, strongly desert-adapted genes would be selected for in the desert and strongly forest-adapted genes would be selected for in the forest. However, the lack of gradient between the two habitats means that individuals that are half-and-half are less adaptive in both the desert and the forest. A case of jack-of-all-trades, master of none.

Disruptive selection diagram
The above example of disruptive selection. Bird colour is coded for by a single gene; green birds have a HH genotype, orange birds have a hh genotype, and yellow birds are heterozygotes (Hh). Habitats where the two homozygote colours are most adaptive are found; green birds do well in the forest whereas orange birds do well in the desert. However, there’s no intermediate habitat between the two and so yellow birds don’t really fit well anywhere; they’re outcompeted in the forest and desert by the respective other colours. This means selection favours either extreme (homozygotes), shown in the top right. If we split up the two alleles of the genotype though, we can see that this disruptive selection is really the product of two directionally selective traits working in inverse directions: H is favoured at one end and h at the other.

Direction of selection

Although it would be convenient if natural selection was entirely predictable, it often catches up by surprise in how it acts and changes species and populations in the wild. Careful analysis and understanding of the different processes and outcomes of adaptation can feed our overall understanding of evolution, and aid in at least pointing in the right direction for our predictions.

Fantastic Genes and Where to Find Them

The genetics of adaptation

Adaptation and evolution by natural selection remains one of the most significant research questions in many disciplines of biology, and this is undoubtedly true for molecular ecology. While traditional evolutionary studies have been based on the physiological aspects of organisms and how this relates to their evolution, such as how these traits improve their fitness, the genetic component of adaptation is still somewhat elusive for many species and traits.

Hunting for adaptive genes in the genome

We’ve previously looked at the two main categories of genetic variation: neutral and adaptive. Although we’ve focused predominantly on the neutral components of the genome, and the types of questions about demographic history, geographic influences and the effect of genetic drift, they cannot tell us (directly) about the process of adaptation and natural selective changes in species. To look at this area, we’d have to focus on adaptive variation instead; that is, genes (or other related genetic markers) which directly influence the ability of a species to adapt and evolve. These are directly under natural selection, either positively (‘selected for’) or negatively (‘selected against’).

Given how complex organisms, the environment and genomes can be, it can be difficult to determine exactly what is a real (i.e. strong) selective pressure, how this is influenced by the physical characteristics of the organism (the ‘phenotype’) and which genes are fundamental to the process (the ‘genotype’). Even determining the relevant genes can be difficult; how do we find the needle-like adaptive genes in a genomic haystack?

Magnifying glass figure
If only it were this easy.

There’s a variety of different methods we can use to find adaptive genetic variation, each with particular drawbacks and strengths. Many of these are based on tests of the frequency of alleles, rather than on the exact genetic changes themselves; adaptation works more often by favouring one variant over another rather than completely removing the less-adaptive variant (this would be called ‘fixation’). So measuring the frequency of different alleles is a central component of many analyses.

FST outlier tests

One of the most classical examples is called an ‘FST outlier test’. This can be a bit complicated without understanding what FST is actually measures: in short terms, it’s a statistical measure of ‘population differentiation due to genetic structure’. The FST value of one particular population can determine how genetically similar it is to another. An FST value of 1 implies that the two populations are as genetically different as they could possibly be, whilst an FST value of 0 implies that they are genetically identical populations.

Generally, FST reflects neutral genetic structure: it gives a background of how, on average, different are two populations. However, if we know what the average amount of genetic differentiation should be for a neutral DNA marker, then we would predict that adaptive markers are significantly different. This is because a gene under selection should be more directly pushed towards or away from one variant (allele) than another, and much more strongly than the neutral variation would predict. Thus, the alleles that are way more or less frequent than the average pattern we might assume are under selection. This is the basis of the FST outlier test; by comparing two or more populations (using FST), and looking at the distribution of allele frequencies, we can pick out a few alleles that vary from the average pattern and suggest that they are under selection (i.e. are adaptive).

There are a few significant drawbacks for FST outlier tests. One of the most major ones is that genetic drift can also produce a large number of outliers; in a small population, for example, one allele might be fixed (has a frequency of 1, with no alternative allele in the population) simply because there is not enough diversity or population size to sustain more alleles. Even if this particular allele was extremely detrimental, it’d still appear to be favoured by natural selection just because of drift.

Drift leading to outliers diagram
An example of genetic drift leading to outliers, featuring our friends the cat population. Top row: Two cat populations, one small (left; n = 5) and one large (middle, n = 12) show little genetic differentiation between them (right; each triangle represents a single gene or locus; the ‘colour’ gene is marked in green). The average (‘neutral’) pattern of differentiation is shown by the dashed line. Much like in our original example, one cat in the small population is horrifically struck by lightning and dies (RIP again). Now when we compare the frequency of the alleles of the two populations (bottom), we see that (because a green cat died), the ‘colour’ locus has shifted away from the general trend (right) and is now an outlier. Thus, genetic drift in the ‘colour’ gene gives the illusion of a selective loci (even though natural selection didn’t cause the change, since colour does not relate to how likely a cat is to be struck by lightning).

Secondly, the cut-off for a ‘significant’ vs. ‘relatively different but possibly not under selection’ can be a bit arbitrary; some genes that are under weak selection can go undetected. Furthermore, recent studies have shown a growing appreciation for polygenic adaptation, where tiny changes in allele frequencies of many different genes combine together to cause strong evolutionary changes. For example, despite the clear heritable nature of height (tall people often have tall children), there is no clear ‘height’ gene: instead, it appears that hundreds of genes are potentially very minor height contributors.

Polygenic height figure final
In this example, we have one tall parent (top) who produces two offspring; one who is tall (left) and one who isn’t (right). In order to understand what genetic factors are contributing to their height differences, we compare their genetics (right; each dot represents a single locus). Although there aren’t any particular loci that look massively different between the two, the cumulative effect of tiny differences (the green triangles) together make one person taller than the other. There are no clear outliers, but many (poly) different genes (genic) acting together.

Genotype-environment associations

To overcome these biases, sometimes we might take a more methodological approach called ‘genotype-environment association’. This analysis differs in that we select what we think our selective pressures are: often environmental characteristics such as rainfall, temperature, habitat type or altitude. We then take two types of measures per individual organism: the genotype, through DNA sequencing, and the relevant environmental values for that organisms’ location. We repeat this over the full distribution of the species, taking a good number of samples per population and making sure we capture the full variation in the environment. Then we perform a correlation-type analysis, which seeks to see if there’s a connection or trend between any particular alleles and any environmental variables. The most relevant variables are often pulled out of the environmental dataset and focused on to reduce noise in the data.

The main benefit of GEA over FST outlier tests is that it’s unlikely to be as strongly influenced by genetic drift. Unless (coincidentally) populations are drifting at the same genes in the same pattern as the environment, the analysis is unlikely to falsely pick it up. However, it can still be confounded by neutral population structure; if one population randomly has a lot of unique alleles or variation, and also occurs in a somewhat unique environment, it can bias the correlation. Furthermore, GEA is limited by the accuracy and relevance of the environmental variables chosen; if we pick only a few, or miss the most important ones for the species, we won’t be able to detect a large number of very relevant (and likely very selective) genes. This is a universal problem in model-based approaches and not just limited to GEA analysis.

New spells to find adaptive genes?

It seems likely that with increasing datasets and better analytical platforms, many more types of analysis will be developed to delve deeper into the adaptive aspects of the genome. With whole-genome sequencing starting to become a reality for non-model species, better annotation of current genomes and a steadily increasing database of functional genes, the ability of researchers to investigate evolution and adaptation at the genomic level is also increasing.