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Evolutionary clocks out of sync
July 3, 2019 thegcatblogLeave a comment
Evolutionary time
It shouldn't come as a surprise to anyone with a basic understanding of evolution that it is a temporal (and also spatial concept). Time is a fundamental aspect of the process of evolution by natural selection, and without it evolution wouldn't exist. But time is also a fickle thing, and although it remains constant (let's not delve into that issue here) not all things experience it in the same way.
Adaptation, DNA, Evolution, Genetics, GenomicsEvolution, evolutionary history, evolutionary rate, generation time, life history, Molecular clock, Mutation, mutation rate, rate heterogeneity, rate variation
Genes in parallel
June 19, 2019 thegcatblogLeave a comment
Adaptation from genetic variation
One of the central themes of this blog, and indeed of evolutionary biology as a whole, is the notion that adaptation is often underpinned by genes. Genetic variation acts as the basis for natural selection to favour or disfavour traits: while this is directly through phenotypic traits (e.g. fur colour, morphology, behaviour), these traits are typically determined by a genetic component. In the early stages of adaptation, evolution can often be observed by changes in the frequency of genetic variants (alleles) within a species or population over time as natural selection acts, gradually leading to the observable (and sometimes dramatic) change in species over time.
Adaptation, DNA, Evolution, Genetics, Genomics, scienceecological speciation, Evolution, evolutionary history, freshwater, Genotype, marine, parallel evolution, Phenotype, Pleiotropy, Polygenic, Speciation, species, sticklebacks
UnConservation Genetics: tools for managing invasive species
May 8, 2019 thegcatblogLeave a comment
Conservation genetics
Naturally, all species play their role in the balancing and functioning of ecosystems across the globe (even the ones we might not like all that much, personally). Persistence or extinction of ecologically important species is a critical component of the overall health and stability of an ecosystem, and thus our aim as conservation scientists is to attempt to use whatever tools we have at our disposal to conserve species. One of the most central themes in conservation ecology (and to The G-CAT, of course) is the notion that genetic information can be used to better our conservation management approaches. This usually involves understanding the genetic history and identity of our target threatened species from which we can best plan for their future. This can take the form of genetic-informed relatedness estimates for breeding programs; identifying important populations and those at risk of local extinction; or identifying evolutionarily-important new species which might hold unique adaptations that could allow them to persist in an ever-changing future.
Just a few applications of genetic information in conservation management, such as in breeding programs and pedigrees (left), identifying new/cryptic species (centre) and identifying and maintaining populations and their structure (right).
Contrastingly, sometimes we might also use genetic information to do the exact opposite. While so many species on Earth are at risk (or have already passed over the precipice) of extinction, some have gone rogue with our intervention. These are, of course, invasive species; pests that have been introduced into new environments and, by their prolific nature, start to throw out the balance of the ecosystem. Australians will be familiar with no shortage of relevant invasive species; the most notable of which is the cane toad, Rhinella marina. However, there are a plethora of invasive species which range from notably prolific (such as the cane toad) to the seemingly mundane (such as the blackbird): so how can we possibly deal with the number and propensity of pests?
A table of some of the most prolific mammalian invasive species in Australia, including when they were first introduced and why, and their (relatively) recently estimated population sizes. Source: Wikipedia (and studies referenced therein). Some estimated numbers might not reflect current sizes as they were obtained from studies over the last 10 years.
Tools for invasive species management
There are a number of tools at our disposal for dealing with invasive species. These range from chemical controls (like pesticides), to biological controls and more recently to targeted genetic methods. Let's take a quick foray into some of these different methods and their applications to pest control.
Some of the broad categories of invasive species control. For any given pest species, such as the cane toad (top), we might choose to use a particular set of methods to reduce their numbers. These can include biological controls (such as the ladybird, for aphid populations (left)); chemical controls such as pesticides; or even genetic engineering technologies.
Biological controls
One of the most traditional methods of pest control are biological controls. A biological control is, in simple terms, a species that can be introduced to an afflicted area to control the population of an invasive species. Usually, this is based on some form of natural co-evolution or hierarchy: species which naturally predate upon, infect or otherwise displace the pest in question are preferred. The basis of this choice is that nature, and evolution by natural selection, often creates a near-perfect machine adapted for handling the exact problem.
Biological controls can have very mixed results. In some cases, they can be relatively effective, such as the introduction of the moth Cactoblastis cactorum into Australia to control the invasive prickly pear. The moth lays eggs exclusively within the tissue of the prickly pear, and the resultant caterpillars ravish the plant. There has been no association of secondary diet items for caterpillars, suggesting the control method has been very selective and precise.
The broad life cycle of the cactus moth and how it controls the invasive prickly pear in Australia. The ravenous caterpillar larvae of the moth is effective at decimating prickly pears, whilst the moth's specificity to this host means there is limited impact on other plant species.
On the contrary, bad biological controls can lead to ecological disasters. As mentioned above, the introduction of the cane toad into Australia has been widely regarded as the origin of one of the worst invasive pests in the nation's history. Initially, cane toads were brought over in the 1930s to predate on the (native) cane beetle, which was causing significant damage to sugar cane plantations in the tropical north. Not overly effective at actually dealing with the problem they were supposed to deal with, the cane toad rapidly spread across northern portion of the continent. Native species that attempt to predate on the cane toad often die to their defensive toxin, causing massive ecological damage to the system.
The potential secondary impact of biological controls, and the degree of unpredictability in how they will respond to a new environment (and how native species will also respond to their introduction) leads conservationists to develop new, more specific techniques. In similar ways, viral and bacterial-based controls have had limited success (although are still often proposed in conservation management, such as the planned carp herpesvirus release).
Genetic controls?
It is clear that more targeted and narrow techniques are required to effectively control pest species. At a more micro level, individual genes could be used to manage species: this is not the first way genetic modification has been proposed to deal with problem organisms. Genetic methods have been employed for years in crop farming through genetic engineering of genes to produce 'natural' pesticides or insecticides. In a similar vein, it has been proposed that genetic modification could be a useful tool for dealing with invasive pests and their native victims.
Gene drives
One promising targeted, genetic-based method that has shown great promise is the gene drive. Following some of the theory behind genetic engineering, gene drives are targeted suites of genes (or alleles) which, by their own selfish nature, propagate through a population at a much higher rate than other alternative genes. In conjunction with other DNA modification methods, which can create fatal or sterilising genetic variants, gene drives present the opportunity to allow the natural breeding of an invasive species to spread the detrimental modified gene.
An example of how gene drives are being proposed to tackle malaria. In this figure, the pink mosquito at the top has been genetically engineered using CRISPR to possess two important genetic elements: a genetic variant which causes the mosquito to be unable to produce eggs or bite (the pink gene), and a linked selfish genetic element (the gene drive itself; the plus) which makes this detrimental allele spread more rapidly than by standard inheritance. Sources: Nature and The Australian Academy of Science.
Although a relatively new, and untested, technique, gene drive technology has already been proposed as a method to address some of the prolific invasive mammals of New Zealand. Naturally, there are a number of limitations and reservations for the method; similar to biological control, there is concern for secondary impact on other species that interact with the invasive host. Hybridisation between invasive and native species would cause the gene drive to be spread to native species, counteracting the conservation efforts to save natives. For example, a gene drive could not reasonably be proposed to deal with feral wild dogs in Australia without massively impacting the 'native' dingo.
Genes for non-genetic methods
Genetic information, more broadly, can also be useful for pest species management without necessarily directly feeding into genetic engineering methods. The various population genetic methods that we've explored over a number of different posts can also be applied in informing management. For example, understanding how populations are structured, and the sizes and demographic histories of these populations, may help us to predict how they will respond in the future and best focus our efforts where they are most effective. By including analysis of their adaptive history and responses, we may start to unravel exactly what makes a species a good invader and how to best predict future susceptibility of an environment to invasion.
A comprehensive table of the different ways genetic information could be applied in broader invasive species management programs, from Rollins et al. (2006). This paper specifically relates to pest management within Western Australia but the concepts listed here apply broadly. Many of these concepts we have discussed previously in a conservation management context as well.
The better we understand invasive species and populations from a genetic perspective, the more informed our management efforts can be and the more likely we are to be able to adequately address the problem.
Managing invasive pest species
The impact of human settlement into new environments is exponentially beyond our direct influences. With our arrival, particularly in the last few hundred years, human migration has been an effective conduit for the spread of ecologically-disastrous species which undermine the health and stability of ecosystems around the globe. As such, it is our responsibility to Earth to attempt to address our problems: new genetic techniques is but one growing avenue by which we might be able to remove these invasive pests.
Biodiversity, Conservation, DNA, Genetics, Genomics, sciencebiological control, cactus moth, cane toad, chemical control, Conservation, conservation management, CRISPR, gene drive, invasive species, management, pest control, selfish genes
The human race(s)? Perspectives from genetics
April 10, 2019 thegcatblogLeave a comment
The genetic testing of race
In one form or another, you may have been (unfortunately) exposed to the notion of 'testing for someone's race using genetics.' In one sense, this is part of the motivation and platform of '23andMe', which maps the genetic variants across the human genome back to likely origin populations to determine the relative ancestry of a person. In a much darker sense, the connection between genetic identity and race is the basis of eugenics, by suggesting genetic "purity" (this concept is utter nonsense, for reference) of a population as justification for some racist hierarchy. Typically, this is associated with Hitler's Nazism, but more subversive versions of this association still exist in the world: for Australian readers, most notably when the far-right conservative minor party One Nation suggested that people claiming to be Indigenous should be subjected to genetic testing to verify their race.
A simplified overview of how DNA Ancestry methods work, by associating particular genetic variants within your genome to likely regions of origin. Note the geographic imprecision in the method on the map on the right, as well as the clear gaps. Source: Ancestry blog.
The biological concept of a 'race'
Beyond the apparent ethical and moral objections to the invasive nature of demanding genetic testing for Indigenous peoples, a crucial question is one of feasibility: even if you decided to genetically test for race, is this possible? It might come as a surprise to non-geneticists that actually, from a genetic perspective, race is not a particularly stable concept.
The notion of races based on genetics has been a highly controversial topic throughout the development of genetic theory and research. Even recently, James Watson (as in of Watson & Crick, who were credited with the discovery of the structure of DNA) was stripped of several titles (including Chancellor Emeritus) following some controversial (and scientifically invalid) comments on the nature of race, genetics and intelligence. Comfortingly, the vast majority of the scientific community opposed his viewpoints on the matter, and in fact it has long been held that a 'genetic race' is not a scientifically stable concept.
James Watson himself. I bet Rosalind Franklin never said anything like this… Source: Wikipedia.
You might ask: why is that? There are perceivable differences in the various peoples of the world, surely some of those could be related to both a 'race' and a 'genetic identity', right? Well, the issue is primarily due to the lack of identifiability of genetic variants that can be associated with a race. Decades of research in genetic variation across the global human population indicates that, due to the massive size of the human population and levels of genetic variation, it is functionally impossible to pinpoint down genetic variants that uniquely identify a 'race'. Human genetic variation is such a beautiful spectrum of alleles that it becomes impossible to reliably determine where one end of the spectrum ends or begins, or to identify a strict number of 'races' within the kaleidoscope of the human genome.
How does this relate to 23AndMe?
How does this relate to your '23AndMe' results? Well, chances are that some genetic variants might be able to be traced back to a particular region (e.g. Europe, somewhere). But naturally, there's a significant number of limitations to this kind of inference; notably, that we don't have reliable references from ancient history to draw upon very often. This, combined with the fact that humans have mixed among ourselves (and even with other species) for millennia, means that tracing back individual alleles is exceedingly difficult.
A diagram of exactly why identifying a genetic basis for race is impossible in humans. A) The 'idealised' version of race; people are easily classified by their genetic identity, with some variation within each classification (in this case, race) but still distinctiveness between them. B) The reality of human genetic variation, which makes it exceedingly difficult to make any robust or solid boundaries between groups of people due to the sheer amount of variation. Source: Harvard University blog.
This is exponentially difficult for people who might have fewer sequenced ancestors or relatives; without the reference for genetic variation, it can be even harder to trace their genetic ancestry. Such is the case for Indigenous Australians, for which there is a distinct lack of available genetic data (especially compared to European-descended Australians).
The non-genetic components
The genetic non-identifiability of race is but one aspect which contradicts the rationality of genetic race testing. As we discussed in the previous post on The G-CAT, the connection between genetic underpinning and physicality is not always clear or linear. The role of the environment on both the expression of genetic variation, as well as the general influence of environment on aspects such as behaviour, philosophy, and culture necessitate that more than the genome contributes to a person's identity. For any given person, how they express and identify themselves is often more strongly associated with their non-genetic traits such as beliefs and culture.
A comparison of genetic vs. cultural inheritance, which demonstrates (as an example) how other factors (in this case, other people) influence the passing on of cultural traits. Remember that this but one aspect of the factors that determine culture and identity, and equally (probably more) complex networks exist for other influences such as environment and development. Source: Creanza et al. (2017), PNAS.
These factors cannot reliably be tested under a genetic framework. While there may be some influence of genes on how a person's psychology develops, it is unlikely to be able to predict the lifestyle, culture and complete identity of said person. For Indigenous Australians, this has been confounded by the corruption and disruption of their identity through the Stolen Generation. As a result, many Indigenous descendants may not appear (from a genetic point of view) to be purely Indigenous but their identity and culture as an Indigenous person is valid. To suggest that their genetic ancestry more strongly determines their identity than anything else is not only naïve from a scientific perspective, but nothing short of a horrific simplification and degradation of those seeking to reclaim their identity and culture.
The non-identifiability of genetic race
The science of genetics overwhelmingly suggests that there is no fundamental genetic underpinning of 'race' that can be reliably used. Furthermore, the impact of non-genetic factors on determining the more important aspects of personal identity, such as culture, tradition and beliefs, demonstrates that attempts to delineate people into subcategories by genetic identity is an unreliable method. Instead, genetic research and biological history fully acknowledges and embraces the diversity of the global human population. As it stands, the phrase 'human race' might be the most biologically-sound classification of people: we are all the same.
DNA, Genetics, science23AndMe, ancestral, ancestry, culture, genetic diversity, genetic race, inheritance, race
Crossing the Wires: why 'genetic hardwiring' is not the whole story
March 27, 2019 thegcatblog2 Comments
The age-old folly of 'nature vs. nurture'
It should come as no surprise to any reader of The G-CAT that I'm a firm believer against the false dichotomy (and yes, I really do love that phrase) of "nature versus nurture." Primarily, this is because the phrase gives the impression of some kind of counteracting balance between intrinsic (i.e. usually genetic) and extrinsic (i.e. usually environmental) factors and how they play a role in behaviour, ecology and evolution. While both are undoubtedly critical for adaptation by natural selection, posing this as a black-and-white split removes the possibility of interactive traits.
We know readily that fitness, the measure by which adaptation or maladaptation can be quantified, is the product of both the adaptive value of a certain trait and the environmental conditions said trait occurs in. A trait that might confer strong fitness in white environment may be very, very unfit in another. A classic example is fur colour in mammals: in a snowy environment, a white coat provides camouflage for predators and prey alike; in a rainforest environment, it's like wearing one of those fluoro-coloured safety vests construction workers wear.
The real Circle of Life. Not only do genes and the environment interact with one another, but genes may interact with other genes and environments may be complex and multi-faceted.
Genetically-encoded traits
In the "nature versus nurture" context, the 'nature' traits are often inherently assumed to be genetic. This is because genetic traits are intrinsic as a fundamental aspect of life, inheritable (and thus can be passed on and undergo evolution by natural selection) and define the important physiological traits that provide (or prevent) adaptation. Of course, not all of the genome encodes phenotypic traits at all, and even less relate to diagnosable and relevant traits for natural selection to act upon. In addition, there is a bit of an assumption that many physiological or behavioural traits are 'hardwired': that is, despite any influence of environment, genes will always produce a certain phenotype.
A very simplified example of adaptation from genetic variation. In this example, we have two different alleles of a single gene (orange and blue). Natural selection favours the blue allele so over time it increases in frequency. The difference between these two alleles is at least one base pair of DNA sequence; this often arises by mutation processes.
Despite how important the underlying genes are for the formation of proteins and definition of physiology, they are not omnipotent in that regard. In fact, many other factors can influence how genetic traits relate to phenotypic traits: we've discussed a number of these in minor detail previously. An example includes interactions across different genes: these can be due to physiological traits encoded by the cumulative presence and nature of many loci (as in quantitative trait loci and polygenic adaptation). Alternatively, one gene may translate to multiple different physiological characters if it shows pleiotropy.
One non-direct way genetic information can impact on the phenotype of an organism is through something we've briefly discussed before known as differential expression. This is based on the notion that different environmental pressures may affect the expression (that is, how a gene is translated into a protein) in alternative ways. This is a fundamental underpinning of what we call phenotypic plasticity: the concept that despite having the exact same (or very similar) genes and alleles, two clonal individuals can vary in different traits. The is related to the example of genetically-identical twins which are not necessarily physically identical; this could be due to environmental constraints on growth, behaviour or personality.
An example of differential expression in wild populations of southern pygmy perch, courtesy of Brauer et al. (2017). In this figure, each column represents a single individual fish, with the phylogenetic tree and coloured boxes at the top indicating the different populations. Each row represents a different gene (this is a subset of 50 from a much larger dataset). The colour of each cell indicates whether the expression of that gene is expressed more (red) or less (blue) than average. As you can see, the different populations can clearly be seen within their expression profiles, with certain genes expressing more or less in certain populations.
From an evolutionary perspective, the ability to translate a single gene into multiple phenotypic traits has a strong advantage. It allows adaptation to new, novel environments without waiting for natural selection to favour adaptive mutations (or for new, adaptive alleles to become available from new mutation events). This might be a fundamental trait that determines which species can become invasive pests, for instance: the ability to establish and thrive in environments very different to their native habitat allows introduced species to quickly proliferate and spread. Even for species which we might not consider 'invasive' (i.e. they have naturally spread to new environments), phenotypic plasticity might allow them to very rapidly adapt and evolve into new ecological niches and could even underpin the early stages of the speciation process.
Related to this alternative expression of genes is another relatively recent concept: that of epigenetics. In epigenetics, the expression and function of genes is controlled by chemical additions to the DNA which can make gene expression easier or more difficult, effectively promoting or silencing genes. Generally, the specific chemicals that are attached to the DNA are relatively (but not always) predictable in their effects: for example, the addition of a methyl group to the sequence is generally associated with the repression of the gene underlying it. How and where these epigenetic markers may in turn be affected by environmental conditions, creating a direct conduit between environmental ('nurture') and intrinsic genetic ('nature') aspects of evolution.
A diagram of different epigenetic factors and the mechanisms by which they control gene expression. Source: Wikipedia.
Typically, these epigenetic 'marks' (chemical additions to the DNA) are erased and reset during fertilisation: the epigenetic marks on the parental gametes are removed, and new marks are made on the fertilised embryo. However, it has been shown that this removal process is not 100% effective, and in fact some marks are clearly passed down from parent to offspring. This means that these marks are heritable, and could allow them to evolve similarly to full DNA mutations.
The discovery of epigenetic markers and their influence on gene expression has opened up the possibility of understanding heritable traits which don't appear to be clearly determined by genetics alone. For example, research into epigenetics suggest that heritable major depressive disorder (MDD) may be controlled by the expression of genes, rather than from specific alleles or genetic variants themselves. This is likely true for a number of traits for which the association to genotype is not entirely clear.
Epigenetic adaptation?
From an evolutionary standpoint again, epigenetics can similarly influence the 'bang for a buck' of particular genes. Being able to translate a single gene into many different forms, and for this to be linked to environmental conditions, allows organisms to adapt to a variety of new circumstances without the need for specific adaptive genes to be available. Following this logic, epigenetic variation might be critically important for species with naturally (or unnaturally) low genetic diversity to adapt into the future and survive in an ever-changing world. Thus, epigenetic information might paint a more optimistic outlook for the future: although genetic variation is, without a doubt, one of the most fundamental aspects of adaptability, even horrendously genetically depleted populations and species might still be able to be saved with the right epigenetic diversity.
A relatively simplified example of adaptation from epigenetic variation. In this example, we have a species of cat; the 'default' cat has non-tufted ears and an orange coat. These two traits are controlled by the expression of Genes A and B, respectively: in the top cat, neither gene is expressed. However, when this cat is placed into different environments, the different genes are "switched on" by epigenetic factors (the green markers). In a rainforest environment, the dark foliage makes darker coat colour more adaptive; switching on Gene B allows this to happen. Conversely, in a desert environment switching on Gene A causes the cat to develop tufts on its ears, which makes it more effective at hunting prey hiding in the sands. Note that in both circumstances, the underlying genetic sequence (indicated by the colours in the DNA) is identical: only the expression of those genes change.
Epigenetic research, especially from an ecological/evolutionary perspective, is a very new field. Our understanding of how epigenetic factors translate into adaptability, the relative performance of epigenetic vs. genetic diversity in driving adaptability, and how limited heritability plays a role in adaptation is currently limited. As with many avenues of research, further studies in different contexts, experiments and scopes will reveal further this exciting new aspect of evolutionary and conservation genetics. In short: watch this space! And remember, 'nature is nurture' (and vice versa)!
Adaptation, DNA, Genetics, GenomicsAdaptation, adaptive potential, Differential expression, Epigenetics, fitness, natural selection, Phenotype, phenotypic plasticity
The 'other' allele frequency: applications of the site frequency spectrum
January 30, 2019 thegcatblog1 Comment
The site-frequency spectrum
In order to simplify our absolutely massive genomic datasets down to something more computationally feasible for modelling techniques, we often reduce it to some form of summary statistic. These are various aspects of the genomic data that can summarise the variation or distribution of alleles within the dataset without requiring the entire genetic sequence of all of our samples.
One very effective summary statistic that we might choose to use is the site-frequency spectrum (aka the allele frequency spectrum). Not to be confused with other measures of allele frequency which we've discussed before (like Fst), the site-frequency spectrum (abbreviated to SFS) is essentially a histogram of how frequent certain alleles are within our dataset. To do this, the SFS classifies each allele into a certain category based on how common it is, tallying up the number of alleles that occur at that frequency. The total number of categories would be the maximum number of possible alleles: for organisms with two copies of every chromosome ('diploids', including humans), this means that there are double the number of samples included. For example, a dataset comprised of genomic sequence for 5 people would have 10 different frequency bins.
For one population
The SFS for a single population – called the 1-dimensional SFS – this is very easy to visualise as a concept. In essence, it's just a frequency distribution of all the alleles within our dataset. Generally, the distribution follows an exponential shape, with many more rare (e.g. 'singletons') alleles than there are common ones. However, the exact shape of the SFS is determined by the history of the population, and like other analyses under coalescent theory we can use our understanding of the interaction between demographic history and current genetic variation to study past events.
An example of the 1DSFS for a single population, taken from a real dataset from my PhD. Left: the full site-frequency spectrum, counting how many alleles (y-axis) occur a certain number of times (categories of the x-axis) within the population. In this example, as in most species, the vast majority of our DNA sequence is non-variable (frequency = 0). Given the huge disparity in number of non-variable sites, we often select on the variable ones (and even then, often discard the 1 category to remove potential sequencing errors) and get a graph more like the right. Right: the 'realistic' 1DSFS for the population, showing a general exponential decline (the blue trendline) for the more frequent classes. This is pretty standard for an SFS. 'Singleton' and 'doubleton' are alternative names for 'alleles which occur once' and 'alleles which occur twice' in an SFS.
Expanding the SFS to multiple populations
Further to this, we can expand the site-frequency spectrum to compare across populations. Instead of having a simple 1-dimensional frequency distribution, for a pair of populations we can have a grid. This grid specifies how often a particular allele occurs at a certain frequency in Population A and at a different frequency in Population B. This can also be visualised quite easily, albeit as a heatmap instead. We refer to this as the 2-dimensional SFS (2DSFS).
An example of a 2DSFS, also taken from my PhD research. In this example, we are comparing Population A, containing 5 individuals (as diploid, 2 x 5 = max. of 10 occurrences of an allele) with Population B, containing 4 individuals. Each row denotes the frequency at which a certain allele occurs in Population B whilst the columns indicate the frequency a certain allele occurs in Population A. Each cell therefore indicates the number of alleles that occur at the exact frequency of the corresponding row and column. For example, the first cell (highlighted in green) indicates the number of alleles which are not found in either Population A or Population B (this dataset is a subsample from a larger one). The yellow cell indicates the number of alleles which occur 4 times in Population B and also 4 times in Population A. This could mean that in one of those Populations 4 individuals have one copy of that allele each, or two individuals have two copies of that allele, or that one has two copies and two have one copy. The exact composition of how the alleles are spread across samples within each population doesn't matter to the overall SFS.
The same concept can be expanded to even more populations, although this gets harder to represent visually. Essentially, we end up with a set of different matrices which describe the frequency of certain alleles across all of our populations, merging them together into the joint SFS. For example, a joint SFS of 4 populations would consist of 6 (4 x 4 total comparisons – 4 self-comparisons, then halved to remove duplicate comparisons) 2D SFSs all combined together. To make sense of this, check out the diagrammatic tables below.
A summary of the different combinations of 2DSFSs that make up a joint SFS matrix. In this example we have 4 different populations (as described in the above text). Red cells denote comparisons between a population and itself – which is effectively redundant. Green cells contain the actual 2D comparisons that would be used to build the joint SFS: the blue cells show the same comparisons but in mirrored order, and are thus redundant as well.
Expanding the above jSFS matrix to the actual data, this matrix demonstrates how the matrix is actually a collection of multiple 2DSFSs. In this matrix, one particular cell demonstrates the number of alleles which occur at frequency x in one population and frequency y in another. For example, if we took the cell in the third row from the top and the fourth column from the left, we would be looking at the number of alleles which occur twice in Population B and three times in Population A. The colour of this cell is moreorless orange, indicating that ~50 alleles occur at this combination of frequencies. As you may notice, many population pairs show similar patterns, except for the Population C vs Population D comparison.
The different forms of the SFS
Which alleles we choose to use within our SFS is particularly important. If we don't have a lot of information about the genomics or evolutionary history of our study species, we might choose to use the minor allele frequency (MAF). Given that SNPs tend to be biallelic, for any given locus we could have Allele A or Allele B. The MAF chooses the least frequent of these two within the dataset and uses that in the summary SFS: since the other allele's frequency would just be 2N – the frequency of the other allele, it's not included in the summary. An SFS made of the MAF is also referred to as the folded SFS.
Alternatively, if we know some things about the genetic history of our study species, we might be able to divide Allele A and Allele B into derived or ancestral alleles. Since SNPs often occur as mutations at a single site in the DNA, one allele at the given site is the new mutation (the derived allele) whilst the other is the 'original' (the ancestral allele). Typically, we would use the derived allele frequency to construct the SFS, since under coalescent theory we're trying to simulate that mutation event. An SFS made of the derived alleles only is also referred to as the unfolded SFS.
Applications of the SFS
How can we use the SFS? Well, it can moreorless be used as a summary of genetic variation for many types of coalescent-based analyses. This means we can make inferences of demographic history (see here for more detailed explanation of that) without simulating large and complex genetic sequences and instead use the SFS. Comparing our observed SFS to a simulated scenario of a bottleneck and comparing the expected SFS allows us to estimate the likelihood of that scenario.
For example, we would predict that under a scenario of a recent genetic bottleneck in a population that alleles which are rare in the population will be disproportionately lost due to genetic drift. Because of this, the overall shape of the SFS will shift to the right dramatically, leaving a clear genetic signal of the bottleneck. This works under the same theoretical background as coalescent tests for bottlenecks.
A representative example of how a bottleneck causes a shift in the SFS, based on a figure from a previous post on the coalescent. Centre: the diagram of alleles through time, with rarer variants (yellow and navy) being lost during the bottleneck but more common variants surviving (red). Left: this trend is reflected in the coalescent trees for these alleles, with red crosses indicating the complete loss of that allele. Right: the SFS from before (in red) and after (in blue) the bottleneck event for the alleles depicted. Before the bottleneck, variants are spread in the usual exponential shape: afterwards, however, a disproportionate loss of the rarer variants causes the distribution to flatten. Typically, the SFS would be built from more alleles than shown here, and extend much further.
Contrastingly, a large or growing population will have a larger number of rare (i.e. unique) alleles from the sudden growth and increase in genetic variation. Thus, opposite to the bottleneck the SFS distribution will be biased towards the left end of the spectrum, with an excess of low-frequency variants.
A similar diagram as above, but this time with an expansion event rather than a bottleneck. The expansion of the population, and subsequent increase in Ne, facilitates the mutation of new alleles from genetic drift (or reduced loss of alleles from drift), causing more new (and thus rare) alleles to appear. This is shown by both the coalescent tree (left) and a shift in the SFS (right).
The SFS can even be used to detect alleles under natural selection. For strongly selected parts of the genome, alleles should occur at either high (if positively selected) or low (if negatively selected) frequency, with a deficit of more intermediate frequencies.
Adding to the analytical toolbox
The SFS is just one of many tools we can use to investigate the demographic history of populations and species. Using a combination of genomic technologies, coalescent theory and more robust analytical methods, the SFS appears to be poised to tackle more nuanced and complex questions of the evolutionary history of life on Earth.
DNA, Evolution, Genetics, Genomics, Uncategorized1DSFS, 2DSFS, allele frequency, allele frequency spectrum, Coalescent, folded SFS, genetic bottleneck, joint SFS, site frequency spectrum, unfolded SFS
The reality of neutrality
December 5, 2018 thegcatblogLeave a comment
The neutral theory
Many, many times within The G-CAT we've discussed the difference between neutral and selective processes, DNA markers and their applications in our studies of evolution, conservation and ecology. The idea that many parts of the genome evolve under a seemingly random pattern – largely dictated by genome-wide genetic drift rather than the specific force of natural selection – underpins many demographic and adaptive (in outlier tests) analyses.
This is based on the idea that for genes that are not related to traits under selection (either positively or negatively), new mutations should be acquired and lost under predominantly random patterns. Although this accumulation of mutations is influenced to some degree by alternate factors such as population size, the overall average of a genome should give a picture that largely discounts natural selection. But is this true? Is the genome truly neutral if averaged?
Non-neutrality
First, let's take a look at what we mean by neutral or not. For genes that are not under selection, alleles should be maintained at approximately balanced frequencies and all non-adaptive genes across the genome should have relatively similar distribution of frequencies. While natural selection is one obvious way allele frequencies can be altered (either favourably or detrimentally), other factors can play a role.
As stated above, population sizes have a strong impact on allele frequencies. This is because smaller populations are more at risk of losing rarer alleles due to random deaths (see previous posts for a more thorough discussion of this). Additionally, genes which are physically close to other genes which are under selection may themselves appear to be under selection due to linkage disequilibrium (often shortened to 'LD'). This is because physically close genes are more likely to be inherited together, thus selective genes can 'pull' neighbours with them to alter their allele frequencies.
An example of how linkage disequilibrium can alter allele frequency of 'neutral' parts of the genome as well. In this example, only one part of this section of the genome is selected for: the green gene. Because of this positive selection, the frequency of a particular allele at this gene increases (the blue graph): however, nearby parts of the genome also increase in frequency due to their proximity to this selected gene, which decreases with distance. The extent of this effect determines the size of the 'linkage block' (see below).
Why might 'neutral' models not be neutral?
The assumption that the vast majority of the genome evolves under neutral patterns has long underpinned many concepts of population and evolutionary genetics. But it's never been all that clear exactly how much of the genome is actually evolving neutrally or adaptively. How far natural selection reaches beyond a single gene under selection depends on a few different factors: let's take a look at a few of them.
Linked selection
As described above, physically close genes (i.e. located near one another on a chromosome) often share some impacts of selection due to reduced recombination that occurs at that part of the genome. In this case, even alleles that are not adaptive (or maladaptive) may have altered frequencies simply due to their proximity to a gene that is under selection (either positive or negative).
A (perhaps familiar) example of the interaction between recombination (the breaking and mixing of different genes across chromosomes) and linkage disequilibrium. In this example, we have 5 different copies of a part of the genome (different coloured sequences), which we randomly 'break' into separate fragments (breaks indicated by the dashed lines). If we focus on a particular base in the sequence (the yellow A) and count the number of times a particular base pair is on the same fragment, we can see how physically close bases are more likely to be coinherited than further ones (bottom column graph). This makes mathematical sense: if two bases are further apart, you're more likely to have a break that separates them. This is the very basic underpinning of linkage and recombination, and the size of the region where bases are likely to be coinherited is called the 'linkage block'.
Under these circumstances, for a region of a certain distance (dubbed the 'linkage block') around a gene under selection, the genome will not truly evolve neutrally. Although this is simplest to visualise as physically linked sections of the genome (i.e. adjacent), linked genes do not necessarily have to be next to one another, just linked somehow. For example, they may be different parts of a single protein pathway.
The extent of this linkage effect depends on a number of other factors such as ploidy (the number of copies of a chromosome a species has), the size of the population and the strength of selection around the central locus. The presence of linkage and its impact on the distribution of genetic diversity (LD) has been well documented within evolutionary and ecological genetic literature. The more pressing question is one of extent: how much of the genome has been impacted by linkage? Is any of the genome unaffected by the process?
Background selection
One example of linked selection commonly used to explain the proliferation of non-neutral evolution within the genome is 'background selection'. Put simply, background selection is the purging of alleles due to negative selection on a linked gene. Sometimes, background selection is expanded to include any forms of linked selection.
A cartoonish example of how background selection affects neighbouring sections of the genome. In this example, we have 4 genes (A, B, C and D) with interspersing neutral 'non-gene' sections. The allele for Gene B is strongly selected against by natural selection (depicted here as the Banhammer of Selection). However, the Banhammer is not very precise, and when decreasing the frequency of this maladaptive Gene B allele it also knocks down the neighbouring non-gene sections. Despite themselves not being maladaptive, their allele frequencies are decreased due to physical linkage to Gene B.
Under the first etymology of background selection, the process can be divided into two categories based on the impact of the linkage. As above, one scenario is the purging of neutral alleles (and therefore reduction in genetic diversity) as it is associated with a deleterious maladaptive gene nearby. Contrastingly, some neutral alleles may be preserved by association with a positively selected adaptive gene: this is often referred to as 'genetic hitchhiking' (which I've always thought was kind of an amusing phrase…).
Definitely not how genetic hitchhiking works.
The presence of background selection – particularly under the 'maladaptive' scenario – is often used as a counter-argument to the 'paradox in variation'. This paradox was determined by evolutionary biologist Richard Lewontin, who noted that despite massive differences in population sizes across the many different species on Earth, the total amount of 'neutral' genetic variation does not change significantly. In fact, he observed no clear relationship (directly) between population size and neutral variation. Many years after this observation, the influence of background selection and genetic hitchhiking on the distribution of genomic diversity helps to explain how the amount of neutral genomic variation is 'managed', and why it doesn't vary excessively across biota.
What does it mean if neutrality is dead?
This findings have significant implications for our understanding of the process of evolution, and how we can detect adaptation within the genome. In light of this research, there has been heated discussion about whether or not neutral theory is 'dead', or a useful concept.
A vague summary of how a large portion of the genome might not actually be neutral. In this section of the genome, we have neutral (blue), maladaptive (red) and adaptive (green) elements. Natural selection either favours, disfavours, or is ambivalent about each of this sections alone. However, there is significant 'spill-over' around regions of positively or negatively selected sections, which causes the allele frequency of even the neutral sections to fluctuate widely. The blue dotted line represents this: when the line is above the genome, allele frequency is increased; when it is below it is decreased. As we travel along this section of the genome, you may notice it is rarely ever in the middle (the so-called 'neutral' allele frequency, in line with the genome).
Although I avoid having a strong stance here (if you're an evolutionary geneticist yourself, I will allow you to draw your own conclusions), it is my belief that the model of neutral theory – and the methods that rely upon it – are still fundamental to our understanding of evolution. Although it may present itself as a more conservative way to identify adaptation within the genome, and cannot account for the effect of the above processes, neutral theory undoubtedly presents itself as a direct and well-implemented strategy to understand adaptation and demography.
DNA, Evolution, Genetics, Genomicsbackground selection, Demography, genetic drift, genetic hitchhiking, Lewontins paradox, linkage block, linkage disequilibrium, Mutation, neutral theory, recombination
Bringing alleles back together: applications of coalescent theory
November 9, 2018 thegcatblog3 Comments
Coalescent theory
A recurring analytical method, both within The G-CAT and the broader ecological genetic literature, is based on coalescent theory. This is based on the mathematical notion that mutations within genes (leading to new alleles) can be traced backwards in time, to the point where the mutation initially occurred. Given that this is a retrospective, instead of describing these mutation moments as 'divergence' events (as would be typical for phylogenetics), these appear as moments where mutations come back together i.e. coalesce.
There are a number of applications of coalescent theory, and it is particularly fitting process for understanding the demographic (neutral) history of populations and species.
Mathematics of the coalescent
Before we can explore the multitude of applications of the coalescent, we need to understand the fundamental underlying model. The initial coalescent model was described in the 1980s, built upon by a number of different ecologists, geneticists and mathematicians. However, John Kingman is often attributed with the formation of the original coalescent model, and the Kingman's coalescent is considered the most basic, primal form of the coalescent model.
From a mathematical perspective, the coalescent model is actually (relatively) simple. If we sampled a single gene from two different individuals (for simplicity's sake, we'll say they are haploid and only have one copy per gene), we can statistically measure the probability of these alleles merging back in time (coalescing) at any given generation. This is the same probability that the two samples share an ancestor (think of a much, much shorter version of sharing an evolutionary ancestor with a chimpanzee).
Normally, if we were trying to pick the parents of our two samples, the number of potential parents would be the size of the ancestral population (since any individual in the previous generation has equal probability of being their parent). But from a genetic perspective, this is based on the genetic (effective) population size (Ne), multiplied by 2 as each individual carries two copies per gene (one paternal and one maternal). Therefore, the number of potential parents is 2Ne.
A graph of the probability of a coalescent event (i.e. two alleles sharing an ancestor) in the immediately preceding generation (i.e. parents) relatively to the size of the population. As one might expect, with larger population sizes there is low chance of sharing an ancestor in the immediately prior generation, as the pool of 'potential parents' increases.
If we have an idealistic population, with large Ne, random mating and no natural selection on our alleles, the probability that their ancestor is in this immediate generation prior (i.e. share a parent) is 1/(2Ne). Inversely, the probability they don't share a parent is 1 − 1/(2Ne). If we add a temporal component (i.e. number of generations), we can expand this to include the probability of how many generations it would take for our alleles to coalesce as (1 – (1/2Ne))t-1 x 1/2Ne.
The probability of two alleles sharing a coalescent event back in time under different population sizes. Similar to above, there is a higher probability of an earlier coalescent event in smaller populations as the reduced number of ancestors means that alleles are more likely to 'share' an ancestor. However, over time this pattern consistently decreases under all population size scenarios.
Although this might seem mathematically complicated, the coalescent model provides us with a scenario of how we would expect different mutations to coalesce back in time if those idealistic scenarios are true. However, biology is rarely convenient and it's unlikely that our study populations follow these patterns perfectly. By studying how our empirical data varies from the expectations, however, allows us to infer some interesting things about the history of populations and species.
Testing changes in Ne and bottlenecks
One of the more common applications of the coalescent is in determining historical changes in the effective population size of species, particularly in trying to detect genetic bottleneck events. This is based on the idea that alleles are likely to coalesce at different rates under scenarios of genetic bottlenecks, as the reduced number of individuals (and also genetic diversity) associated with bottlenecks changes the frequency of alleles and coalescence rates.
For a set of k different alleles, the rate of coalescence is determined as k(k – 1)/4Ne. Thus, the coalescence rate is intrinsically linked to the number of genetic variants available: Ne. During genetic bottlenecks, the severely reduced Ne gives the appearance of coalescence rate speeding up. This is because alleles which are culled during the bottleneck event by genetic drift causes only a few (usually common) alleles to make it through the bottleneck, with the mutation and spread of these alleles after the bottleneck. This can be a little hard to think of, so the diagram below demonstrates how this appears.
A diagram of how the coalescent can be used to detect bottlenecks in a single population (centre). In this example, we have contemporary population in which we are tracing the coalescence of two main alleles (red and green, respectively). Each circle represents a single individual (we are assuming only one allele per individual for simplicity, but for most animals there are up to two). Looking forward in time, you'll notice that some red alleles go extinct just before the bottleneck: they are lost during the reduction in Ne. Because of this, if we measure the rate of coalescence (right), it is much higher during the bottleneck than before or after it. Another way this could be visualised is to generate gene trees for the alleles (left): populations that underwent a bottleneck will typically have many shorter branches and a long root, as many branches will be 'lost' by extinction (the dashed lines, which are not normally seen in a tree).
This makes sense from theoretical perspective as well, since strong genetic bottlenecks means that most alleles are lost. Thus, the alleles that we do have are much more likely to coalesce shortly after the bottleneck, with very few alleles that coalesce before the bottleneck event. These alleles are ones that have managed to survive the purge of the bottleneck, and are often few compared to the overarching patterns across the genome.
Testing migration (gene flow) across lineages
Another demographic factor we may wish to test is whether gene flow has occurred across our populations historically. Although there are plenty of allele frequency methods that can estimate contemporary gene flow (i.e. within a few generations), coalescent analyses can detect patterns of gene flow reaching further back in time.
In simple terms, this is based on the idea that if gene flow has occurred across populations, then some alleles will have been transferred from one population to another. Because of this, we would expect that transferred alleles coalesce with alleles of the source population more recently than the divergence time of the two populations. Thus, models that include a migration rate often add it as a parameter specifying the probability than any given allele coalesces with an allele in another population or species (the backwards version of a migration or introgression event). Again, this might be difficult to conceptualise so there's a handy diagram below.
A similar model of coalescence as above, but testing for migration rate (gene flow) in two recently diverged populations (right). In this example, when we trace two alleles (red and green) back in time, we notice that some individuals in Population 1 coalesce more recently with individuals of Population 2 than other individuals of Population 1 (e.g. for the red allele), and vice versa for the green allele. This can also be represented with gene trees (left), with dashed lines representing individuals from Population 2 and whole lines representing individuals from Population 1. This incomplete split between the two populations is the result of migration transferring genes from one population to the other after their initial divergence (also called 'introgression' or 'horizontal gene transfer').
Testing divergence time
In a similar vein, the coalescent can also be used to test how long ago the two contemporary populations diverged. Similar to gene flow, this is often included as an additional parameter on top of the coalescent model in terms of the number of generations ago. To convert this to a meaningful time estimate (e.g. in terms of thousands or millions of years ago), we need to include a mutation rate (the number of mutations per base pair of sequence per generation) and a generation time for the study species (how many years apart different generations are: for humans, we would typically say ~20-30 years).
An example of using the coalescent to test the divergence time between two populations, this time using three different alleles (red, green and yellow). Tracing back the coalescence of each alleles reveals different times (in terms of which generation the coalescence occurs in) depending on the allele (right). As above, we can look at this through gene trees (left), showing variation how far back the two populations (again indicated with bold and dashed lines respectively) split. The blue box indicates the range of times (i.e. a confidence interval) around which divergence occurred: with many more alleles, this can be more refined by using an 'average' and later related to time in years with a generation time.
The basic model of testing divergence time with the coalescent is relatively simple, and not all that different to phylogenetic methods. Where in phylogenetics we relate the length of the different branches in the tree to the amount of time that has occurred since the divergence of those branches, with the coalescent we base these on coalescent events, with more coalescent events occurring around the time of divergence. One important difference in the two methods is that coalescent events might not directly coincide with divergence time (in fact, we expect many do not) as some alleles will separate prior to divergence, and some will lag behind and start to diverge after the divergence event.
The complex nature of the coalescent
While each of these individual concepts may seem (depending on how well you handle maths!) relatively simple, one critical issue is the interactive nature of the different factors. Gene flow, divergence time and population size changes will all simultaneously impact the distribution and frequency of alleles and thus the coalescent method. Because of this, we often use complex programs to employ the coalescent which tests and balances the relative contributions of each of these factors to some extent. Although the coalescent is a complex beast, improvements in the methodology and the programs that use it will continue to improve our ability to infer evolutionary history with coalescent theory.
DNA, Evolution, Genetics, Genomicsallele frequency, alleles, Coalescent, Demography, Divergence estimates, divergence time, Gene flow, Gene tree, genetic bottleneck, genetic drift, Migration, Ne, population size
What's the (allele) frequency, Kenneth?
October 24, 2018 October 24, 2018 thegcatblog3 Comments
Allele frequency
A number of times before on The G-CAT, we've discussed the idea of using the frequency of different genetic variants (alleles) within a particular population or species to test a number of different questions about evolution, ecology and conservation. These are all based on the central notion that certain forces of nature will alter the distribution and frequency of alleles within and across populations, and that these patterns are somewhat predictable in how they change.
One particular distinction we need to make early here is the difference between allele frequency and allele identity. In these analyses, often we are working with the same alleles (i.e. particular variants) across our populations, it's just that each of these populations may possess these particular alleles in different frequencies. For example, one population may have an allele (let's call it Allele A) very rarely – maybe only 10% of individuals in that population possess it – but in another population it's very common and perhaps 80% of individuals have it. This is a different level of differentiation than comparing how different alleles mutate (as in the coalescent) or how these mutations accumulate over time (like in many phylogenetic-based analyses).
An example of the difference between allele frequency and identity. In this example (and many of the figures that follow in this post), the circle denote different populations, within which there are individuals which possess either an A gene (blue) or a B gene. Left: If we compared Populations 1 and 2, we can see that they both have A and B alleles. However, these alleles vary in their frequency within each population, with an equal balance of A and B in Pop 1 and a much higher frequency of B in Pop 2. Right: However, when we compared Pop 3 and 4, we can see that not only do they vary in frequencies, they vary in the presence of alleles, with one allele in each population but not the other.
Non-adaptive (neutral) uses
Testing neutral structure
Arguably one of the most standard uses of allele frequency data is the determination of population structure, one which more avid The G-CAT readers will be familiar with. This is based on the idea that populations that are isolated from one another are less likely to share alleles (and thus have similar frequencies of those alleles) than populations that are connected. This is because gene flow across two populations helps to homogenise the frequency of alleles within those populations, by either diluting common alleles or spreading rarer ones (in general). There are a number of programs that use allele frequency data to assess population structure, but one of the most common ones is STRUCTURE.
An example of how gene flow across populations homogenises allele frequencies. We start with two initial populations (1 and 2 from above), which have very different allele frequencies. Hybridising individuals across the two populations means some alleles move from Pop 1 and Pop 2 into the hybrid population: which alleles moves is random (the smaller circles). Because of this, the resultant hybrid population has an allele frequency somewhere in between the two source populations: think of like mixing red and blue cordial and getting a purple drink.
An example of a Structure plot which long-term The G-CAT readers may be familiar with. This is taken from Brauer et al. (2013), where the authors studied the population structure of the Yarra pygmy perch. Each small column represents a single individual, with the colours representing how well the alleles of that individual fit a particular genetic population (each population has one colour). The numbers and broader columns refer to different 'localities' (different from populations) where individuals were sourced. This shows clear strong population structure across the 4 main groups, except for in Locality 6 where there is a mixture of Eastern and Merri/Curdies alleles.
Determining genetic bottlenecks and demographic change
Other neutral aspects of population identity and history can be studied using allele frequency data. One big component of understanding population history in particular is determining how the population size has changed over time, and relating this to bottleneck events or expansion periods. Although there are a number of different approaches to this, which span many types of analyses (e.g. also coalescent methods), allele frequency data is particularly suited to determining changes in the recent past (hundreds of generations, as opposed to thousands of generations ago). This is because we expect that, during a bottleneck event, it is statistically more likely for rare alleles (i.e. those with low frequency) in the population to be lost due to strong genetic drift: because of this, the population coming out of the bottleneck event should have an excess of more frequent alleles compared to a non-bottlenecked population. We can determine if this is the case with tests such as the heterozygosity excess, M-ratio or mode shift tests.
A diagram of how allele frequencies change in genetic bottlenecks due to genetic drift. Left: Large circles again denote a population (although across different sequential times), with smaller circle denoting which alleles survive into the next generation (indicated by the coloured arrows). We start with an initial 'large' population of 8, which is reduced down to 4 and 2 in respective future times. Each time the population contracts, only a select number of alleles (or individuals) 'survive': assuming no natural selection is in process, this is totally random from the available gene pool. Right: We can see that over time, the frequencies of alleles A and B shift dramatically, leading to the 'extinction' of Allele B due to genetic drift. This is because it is the less frequent allele of the two, and in the smaller population size has much less chance of randomly 'surviving' the purge of the genetic bottleneck.
Adaptive (selective) uses
Testing different types of selection
We've also discussed previously about how different types of natural selection can alter the distribution of allele frequency within a population. There are a number of different predictions we can make based on the selective force and the overall population. For understanding particular alleles that are under strong selective pressure (i.e. are either strongly adaptive or maladaptive), we often test for alleles which have a frequency that strongly deviates from the 'neutral' background pattern of the population. These are called 'outlier loci', and the fact that their frequency is much more different from the average across the genome is attributed to natural selection placing strong pressure on either maintaining or removing that allele.
Other selective tests are based on the idea of correlating the frequency of alleles with a particular selective environmental pressure, such as temperature or precipitation. In this case, we expect that alleles under selection will vary in relation to the environmental variable. For example, if a particular allele confers a selective benefit under hotter temperatures, we would expect that allele to be more common in populations that occur in hotter climates and rarer in populations that occur in colder climates. This is referred to as a 'genotype-environment association test' and is a good way to detect polymorphic selection (i.e. when multiple alleles contribute to a change in a single phenotypic trait).
An example of how the frequency of alleles might vary under natural selection in correlation to the environment. In this example, the blue allele A is adaptive and under positive selection in the more intense environment, and thus increases in frequency at higher values. Contrastingly, the red allele B is maladaptive in these environments and decreases in frequency. For comparison, the black allele shows how the frequency of a neutral (non-adaptive or maladaptive) allele doesn't vary with the environment, as it plays no role in natural selection.
Taxonomic (species identity) uses
At one end of the spectrum of allele frequencies, we can also test for what we call 'fixed differences' between populations. An allele is considered 'fixed' it is the only allele for that locus in the population (i.e. has a frequency of 1), whilst the alternative allele (which may exist in other populations) has a frequency of 0. Expanding on this, 'fixed differences' occur when one population has Allele A fixed and another population has Allele B fixed: thus, the two populations have as different allele frequencies (for that one locus, anyway) as possible.
Fixed differences are sometimes used as a type of diagnostic trait for species. This means that each 'species' has genetic variants that are not shared at all with its closest relative species, and that these variants are so strongly under selection that there is no diversity at those loci. Often, fixed differences are considered a level above populations that differ by allelic frequency only as these alleles are considered 'diagnostic' for each species.
An example of the difference between fixed differences and allelic frequency differences. In this example, we have 5 cats from 3 different species, sequencing a particular target gene. Within this gene, there are three possible alleles: T, A or G respectively. You'll quickly notice that the T allele is both unique to Species A and is present in all cats of that species (i.e. is fixed). This is a fixed difference between Species A and the other two. Alleles A and G, however, are present in both Species B and C, and thus are not fixed differences even if they have different frequencies.
Intrapopulation (relatedness) uses
Allele frequency-based methods are even used in determining relatedness between individuals. While it might seem intuitive to just check whether individuals share the same alleles (and are thus related), it can be hard to distinguish between whether they are genetically similar due to direct inheritance or whether the entire population is just 'naturally' similar, especially at a particular locus. This is the distinction between 'identical-by-descent', where alleles that are similar across individuals have recently been inherited from a similar ancestor (e.g. a parent or grandparent) or 'identical-by-state', where alleles are similar just by chance. The latter doesn't contribute or determine relatedness as all individuals (whether they are directly related or not) within a population may be similar.
To distinguish between the two, we often use the overall frequency of alleles in a population as a basis for determining how likely two individuals share an allele by random chance. If alleles which are relatively rare in the overall population are shared by two individuals, we expect that this similarity is due to family structure rather than population history. By factoring this into our relatedness estimates we can get a more accurate overview of how likely two individuals are to be related using genetic information.
The wild world of allele frequency
Despite appearances, this is just a brief foray into the many applications of allele frequency data in evolution, ecology and conservation studies. There are a plethora of different programs and methods that can utilise this information to address a variety of scientific questions and refine our investigations.
Adaptation, Conservation, DNA, Evolution, Geneticsallele frequency, Conservation, fixed differences, Gene flow, genetic bottleneck, genetic diversity, genetic drift, genotype-environment association, natural selection, population genetics, Population structure, relatedness, Structure


text:
Yes, one of the best series of 2021 is already here
John DoyleTelevision critic
Published January 14, 2021 Updated January 19, 2021
Lupin stars Omar Sy as Assane Diop.Netflix
More than one reviewer has called Lupin (new on Netflix), "the first great series of 2021." It's the second week of the year, for heaven's sake. But there's some merit in the assertion. It's a magnificent concoction and, with an uncanny timing, it is captivating in a way that connects to the time we live in.
In the wonderfully intricate and propulsive five-episode series (in French with English subtitles), nobody called "Arsène Lupin" actually appears. That figure, a "gentleman thief", created in a 1905 novel by the French writer Maurice Leblanc, is an ideal and an inspiration. Mainly he's an inspiration for central figure Assane Diop (Omar Sy), a Frenchman of Senegalese background who pulls off a spectacular heist that's a big, thrilling caper and is just the start of his revenge against a rich white family that caused his father's death.
One reason for those ecstatic reviews is the sheer charm of Assane. When we first meet him, we're led to believe he's a janitor who happens to have a cleaning job at the Louvre. Hassled by thugs to whom he owes money, he suggests a plan to steal a necklace once owned by Marie Antoinette. The thugs think it's a plausible heist and the action starts. But Assane Diop isn't who he seems. As we watch – mostly transfixed; this is a beautifully paced series – we learn that he's both a master of disguise and an extremely cunning provocateur.
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Power and race are the twin engines of the underlying plot beneath the heist story. As a Black man in France, Assane knows how to use racist presumptions for his own benefit. He is always underestimated and often barely seen, in a white society that assumes all Black men are the same. He simply tricks others by playing on their false assumptions. Also, he's very tall, and can be a tad intimidating when he wants to be. This is all handled with exquisite cleverness, but never becomes a dogmatic plot point.
What we get here is a delicious retaliation story. In the deftly done time-switching scenes we learn that Assane's father was an employee of the super-rich Pellegrini family. They treated the boy well but with extreme condescension. His dad was allowed to take books from the family library for the boy and his choice of that first book was the one about the thief Arsène Lupin. Things went awry when Assane's dad was accused of stealing a necklace. Two decades later, Assane has the intention of putting things right.
The series was created by English writer George Kay in collaboration with French writer François Uzan. Kay wrote several episodes of Killing Eve and Lupin has some of that show's buoyancy and wit. It's also a thrilling crime drama. And yes, likely one of the best of the year.
One personal footnote to it. I covered the opening game of the 2002 World Cup in Seoul. It was between World Cup and European champion France, and World Cup debutantes Senegal. And it was a historic upset. Senegal thoroughly outplayed France and won 1-0, the goal scored by the tall and immensely skilled Papa Bouba Diop, a man who died recently, and fit the description, "gentleman thief."
Also airing this weekend
Kate Phillips as Eliza Scarlet and Stuart Martin as William Wellington in Miss Scarlet and The Duke.Bernard Walsh/Courtesy of MASTERPIECE
Miss Scarlet and The Duke (Sunday, PBS, 8 p.m. on Mystery) is another new take on the Victorian London crime drama. This time it's done as a lightweight feminist take. Kate Phillips plays Eliza Scarlet, who inherits her father's private-detective agency and attempts to continue his work, with the reluctant assistance of a Scotland Yard chap called The Duke (Stuart Martin). Sumptuous and funny it is meant to be, this confection. And there's indeed some wit as a small army of bearded older men are fooled by Eliza's politeness and feigned delicacy. She's as tough as nails. There is a curious kind of sexual tension being cooked up constantly, and as entertainment the series – based on seeing one episode – is droll, not deep, and good fun for fans of English period-piece mysteries.
Five Bedrooms (Sunday, W, 10 p.m.) is new and has acquired a following on the U.S. streaming service Peacock. An Australian production, it might be best described as a new spin on the Friends format. Here, the group of friends who live together start out as strangers, more or less. They are a group of thirtysomethings and older, all fed up with something in their lives as singles. So they buy a sprawling house and form a family of sorts. At its centre is real estate agent Ainsley (Katie Robertson), who also is the show's narrator, and is secretly pining for a married work colleague. More bittersweet drama than outright comedy, it's no masterpiece but has considerable offbeat bounce to it.
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Switch gears. Give your brain a workout and do today's Daily Cryptic Crossword.
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redpajama_set_name: RedPajamaCommonCrawl


If you thought that buying a flat in London is a piece of cake, then think again. Devoting all your time to the house hunt is what it takes to find your dream apartment. From your budget to the choice of location and from the number of rooms to the size of the closet, everything is an important consideration. It can get tedious at times, especially if you are doing it for the first time and that is when you look for the experts to ease you through the process. Given that there could be a hundred considerations, here is a list of top 5 which will help you take the right pick. Read on!
1. What kind of flat do you want?
What kind of flat do you want to buy is the first funnel in the whole system. Whether it's an old dated flat or a brand new one, once you are sure of what you need, narrowing down the options will be much easier. Considering that you are on a budget, think outside the box and opt for what the other buyers are trying to escape. Will a flat on the main road with noise work for you, or a flat just above a shop or a mini restaurant won't be a botheration are a few questions which need to be answered to find the right property.
Next come the restrictions and the alterations. It is very rare to find a property which is exactly how you want it, no alterations at all. However, if you are not lucky enough, then ask the broker or the concerned authority about the restrictions on the property. While you will have a free hand in making the internal structural changes, there might be other restrictions such as having a pet or playing music after a particular time. As for the alterations, take a thorough look around the house to see what needs to be added or changed. From closet restructuring to the hardware change, every little alteration matters.
3. Service charges and repairs?
Before you settle in, get clarity on the service charges. What amount will you have to pay and what will be covered under it are two significant questions that you need to ask before you seal the deal. Service charges need to be paid regularly so get an idea on how many times is it reviewed in a year. Moving on the repairs, your service charges should be covering most of it, but what is the criterion that needs to be followed in case of extensive repairs? If it's a roof repair in your block, then inquire about who will be responsible for taking care of it and how will the charge be distributed. This will eliminate the surprise element of paying any last minute money about which you had no idea.
4. Who will you be dealing with?
In case of any doubts or any help required, who will be the one able to help you out? In London, it's a common concept that the buildings are owned by the landlords but are managed by professional management companies like Canonbury Management where you will be assisted with all that you need, including help with the right to manage act. Whoever the concerned person is, it's important that you know about them from day one.
So, what's the difference between the two and what should you choose? We'll tell you. With a freehold, you will be the owner of the property and the land, and you will have all the rights of the place alone to yourself. In case of a leasehold, you will have the right to occupy the property, but the rest of the rights will be sharable with someone else, the landlord most probably. Flats are leaseholds considering that there will be other people living in the building too so make sure that you get all the documents sorted by a trusted solicitor before you move in.
While these considerations will help you make the right choice, don't forget to look for grocery stores, hospitals, schools and eating joints in your locality keeping your family's requirements in mind. Use these tips to your advantage and find the ideal flat for you. Good luck!


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Q: Measuring performance for controls I want to analyze my own controls in my wpf app.
What the correct way to do that? I want to measuring from the control created level till rendering on the screen, which events can I use for start the stopwatch and stop?

A: The Stopwatch class can be of help in very specific cases, but I recommend using a profiler of some sort, which is much more reliable and easier to use.
You could use a regular .NET profiler, which does a pretty good job analyzing your entire application. One I have used and liked is Redgate's ANTS Performance Profiler.
For WPF you have a Performance Suite from Microsoft taylored to analyze WPF applications. That might be of good help too.

redpajama_set_name: RedPajamaStackExchange


The Technical Centre for Agricultural and Rural Cooperation ACP-EU (CTA), in collaboration with FARA, Yam-Pukri, CAFAN, AYF, ANAFE, SPC/PAFPNET and e-Agriculture is pleased to launch the 2nd Edition of the Youth in Agriculture Blog Competition (YoBloCo Awards).
This contest is organised in the framework of the ARDYIS project, which aims to raise youth awareness and improve their capacity on agricultural and rural development issues in ACP countries using Information and Communication Technologies (ICTs).
Individuals or organisations who wish to submit a blog and take part in the competition will have about 4 months to prepare or update their blogs with content related to agriculture. Blogs and all required information/documents will be submitted via an online form between December and January 2014.
Subscribe now, to get the latest news on the YoBloCo competition in your mailbox.


Serviam Career Coaching provides advanced career development and innovative job search strategy coaching for individual business leaders seeking new roles.
Our team has decades of experience in recruiting, executive search, human resources and operational leadership roles. We have interviewed thousands of candidates and worked with hundreds of companies. Based on these experiences as well as the principles and successful strategies laid out in the popular book LANDED! Proven Job Search Strategies for Today's Professional by founder Randy Hain, our team invests quality time in each coaching client to learn about who they are, what they are seeking and what might be preventing them from attaining their career and job search goals. We then develop a "road map" for achieving goals which is uniquely suited to the needs of each client.
What distinguishes Serviam Career Coaching from other career coaches?
Asking informed questions and customizing solutions for our clients. We never provide an "off the shelf" career solution.
About ways to improve and know how to share our direct and sometimes challenging feedback in a professional manner.
For coaching clients into our large national network of senior business leaders.
We have many years of experience in interviewing candidates and working with hiring companies. This has helped us develop a "best practices" approach to career development and job search strategy which is rooted in the most effective approaches being used by others.
We excel at guiding our clients through what we describe as the "pursuit of a more meaningful life." Many of our coaching clients have experienced significant career success, but reach a point in their lives where they seek to spend more time with family, make a difference in the community, change professions to pursue work they love or possibly other noble goals. Based on the practical guidance found in the book, Something More: The Professionals Pursuit of a Meaningful Life by founder Randy Hain, we always look forward to helping our clients map out the next exciting chapter in their lives.
Who is a Serviam Career Coaching client?
We are often engaged by individual business leaders for advanced career development or executive coaching to help them attain the next step in their careers. We are engaged by senior leaders in career transition who need our customized job search expertise and extensive network to land a new position. Regardless of how we are approached, we always adapt our expertise to the specific needs of the individual and work diligently to help them attain their goals.
How does Serviam Partners work with clients?
As stated above, we work with business leaders seeking advanced career development and often a more "meaningful life." We also work with leaders in career transition. Companies can engage our services in providing LinkedIn strategy workshops and workshops on other career related topics. All of our engagements involve a specified number of monthly in-person meetings for Atlanta based clients and phone/email access to our team.
Randy Hain is the most effective and engaging executive coach I have ever worked with, and I consider it a privilege to know him and call him my friend. His unique approach to "authentic relationship building" and his insightful approach to coaching resulted in me changing the way I "show up" at work and ultimately finding a role that was the perfect fit for me! I highly recommend him as a coach, mentor or speaker.
While acting as my professional coach, I have witnessed how he practices his mission statement, 'serve others' in all interactions. I always appreciate his openness and candor in our communications. His feedback and suggestions have added significant value for me. I highly recommend Randy and his firm, Serviam Partners.
I have had the pleasure of working with Randy at Serviam Partners. He has provided extensive consulting in the areas of business leadership, personal development and overall life coaching. He has provided a calm and thoughtful voice in the midst of constant change.
Randy's extensive professional network and experience in creating meaningful business relationships make him an invaluable resource for senior level professionals contemplating or engaged in career transition. Randy brings a high-energy, integrity-based, no-nonsense approach to his work – creating terrific value for his clients and business associates. He was a terrific resource for me as I contemplated my career aspirations and helped me build solid plans for achieving my goals well into the future.
Randy Hain is one of those individuals who has this uncanny ability to connect people and connect to people. His passion for helping is inherent in everything he does. Whether it is a coaching engagement with a senior leader, a coffee meeting with a colleague or with his charity work, Randy builds authentic relationships and is genuinely interested in making a positive impact on the lives of many. Over the last year and a half, I've had the opportunity to get to know Randy and work with Serviam Partners through a number of career coaching engagements with my current organization. With each engagement, he builds a stronger following in part due to his thoughtful approach to coaching and in part due to his personal investment in the success of each client.
Randy brings an invaluable process and perspective to senior leaders looking to plan their next career move, expand their network and build positive working relationships. He will draw on experiences, ask hard questions, push you towards self-awareness and provide a proven template for success. And he'll do it with the ear and voice of a friend that you truly value. I highly recommend him.
I have had the honor of knowing Randy for several years through various personal and professional endeavors. Without question, he epitomizes what it is to be a servant leader. He has a passion for making individuals and society better. He is able to apply his many years of experience in human resources towards coaching, speaking, and writing, and he has positively and notably impacted some of Atlanta's most respected and successful leaders and teams. Randy provides open, honest, candid, and productive feedback, but always in a respectful manner. I would highly recommend Randy for any individual or team looking to change for the better.
Randy is one of those few exceptional individuals you come across too rarely in your life. On top of being a very astute and successful business leader, he is a wonderful person who deeply cares about his business partners and those who cross his path. His unique combination of deep passion for what he does and deeply anchored personal values that drive his behavior make him such a wonderful person to know. Every minute spent with Randy is a unique occasion to learn something new and put things in perspective. His advice is priceless and transformational. It is a real honor to know Randy and I am looking forward to our paths crossing again and again.
Randy is a thought leader who understands deeply how organizations and individuals interface in the areas of executive search, career advancement and leadership development. Randy is an authentic person, with a commitment to the highest standards of integrity, who genuinely wants to help others through his writing, presenting and coaching. I highly recommend Randy to any organization or individual who is seeking to accelerate their development.
It's been a real pleasure working with Randy and Serviam Coaching to strengthen my personal and professional network. Randy is highly effective because of his unique mix of professional experience and personal guidance. Not only does Randy draw on his extensive background as an executive recruiter but he also takes time to understand who you are as a person and what your goals and challenges are. I'm looking forward to continue working with him in the years to come.
I have known Randy for about eight years. My very first conversation with him involved a polite invasion into my corporate persona and his observations of the behaviors that were limiting my effectiveness. I have been initiating conversations with him ever since. When Randy launched Serviam Partners I jumped at the opportunity to become one of his first clients and I have grown from the partnership. Randy's coaching style is open, insightful, and generous. As a result of my coaching relationship with Randy, I am able to clearly communicate the services of my business, leverage LinkedIn to highlight business accomplishments, and I've meet influential leaders willing to facilitate introductions within their networks. Randy willingly shares his own best practices and ideas for broadening and accelerating visibility and exposure. I highly recommend the services of Serviam Partners.
Randy has been an absolute joy to get to work with. He goes out of his way to help people and to connect them with the resources, knowledge, or tools they need to succeed or grow in their lives or their professional careers. Randy has also graciously shared his time with professionals, students, and the community that are in need of his outstanding subject matter expertise by helping them with career searches during this challenging economy. Lastly, Randy's unselfish efforts to share the knowledge he has acquired throughout his career through regularly published articles truly makes him a leader that inspires us all to give back to the world that has given us so much. Randy, you are a class act.


Europe do actually have the third highest average income out of the global sample at US$2,073 a month, behind those in Oceania and North America. Where they seem to fare the worst is education. According to the survey, therapists in the region have the least amount of training – only 33 hours a year on average – out of the global sample.
lifestyle measurements, was used to provide a cost of living comparison against New York City (NYC) which is valued at 100. If a city has a rank of 80, it's 20 per cent less expensive than NYC, for example. Numbeo fi gures are based on major cities in the relative countries.


Quite late post for the weekly photo challenge with a friend. Yesterday was NinaRicci Day. Nina&Ricci turned 6 years and 5 months together. Yey! Hahahaha!
Week 2 of my dear friend Zhaylah's World themed Keepsakes with a twist of texture blending.
I'm still adapting to the changes, and yeah, I feel tired on the quite longer travel, but I'll get use to it.
Anyway, I wanted to compile my #photoadaymay challenge by Fat Mum Slim. (I missed 3 though.. :< ) I found this challenge in Pinterest and decided to try it out and I loved it. I even joined again, this time with the #photoadayjune.
Anyway, I have been thinking this for quite sometime if I will be able to give something in return for the people who supported me and believed in my skills through these years. I asked a friend on how I would be able to give something back to all of you, who anonymously read and followed every single one of my blog posts; and we came up with a simple contest. Since I turned 2, I'll be giving away 2 special gifts. It's special because it's one of the things I loved to collect.
Please continue reading for further instructions….


This section features several reports. The main report provides data on the gilts in issue on a particular date including their first issue dates, redemption dates and the nominal amounts outstanding.
The issuance history report provides all the dates on which each individual gilt has been issued, along with the method of issuance and the issue price.
The redemption details report is retrospective and shows the final amount outstanding of each redeemed gilt along with its redemption date. In contrast, the report on the profile of gilt redemptions in future years shows the total amount of gilts in market hands that is expected to redeem in future financial years.
The report on the use of the standing repo and special repo facilities provides details on all the times that these facilities have been used, along with details of which gilt was lent, the amount lent and the rate at which the gilt was lent out.


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Pope Francis may not meet abuse survivors in Ireland, says archbishop
Archbishop Diarmuid Martin of Dublin speaks during a launch for the World Meeting of Families (Photo: CNS)
Archbishop Diarmuid Martin of Dublin has said it is still uncertain whether or not Pope Francis will meet abuse survivors during his two-day visit to Ireland later this month.
Archbishop Martin said: "He will obviously speak about various forms of abuse not just clerical abuse, the abuse in the institutions, the laundries and the mother and baby homes. It would be great to meet some of the victims or survivors from that group, but the time is very tight."
According to Irish media, the archbishop said the Vatican had not yet confirmed whether a meeting could take place.
Pope Francis will be in Ireland for 36 hours on the weekend of August 25-26. It will be the first time a pope has visited Ireland since 1979.
Among his stops will be Knock shrine and a Capuchin day centre for the homeless. On the Sunday he will celebrate the closing Mass of the World Meeting of Families in Phoenix Park, Dublin, with half a million people expected to attend.
Marie Collins, an abuse survivor and former member of the Pontifical Commission for the Protection of Minors, said on Twitter that if the Pope did not meet survivors it was "unlikely due to lack of time".
"More like an effort to avoid the issue of abuse as the crisis grows worldwide," she wrote. "No amount of ignoring will make it go away!"
David Quinn, director of the Iona Institute, which promotes marriage and religion, said it would be "very wrong" if the Pope did not meet survivors.
Pope Francis will meet his own family during trip to Turin to venerate Shroud
The Pope will spend two days in Turin in June
Pope Francis: I feel like my papacy will last only four or five years
Francis made the remark in his most candid interview to date
Pope Francis meets relatives of ISIS victims David Haines and Alan Henning
Barbara Henning and Mike Haines spoke to the Pope after the general audience in the Vatican today