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elementarysofevolutionarygenetics In the early 1970s the eminent evolutionary geneticist Richard C. Lewontin wrote that population genetics “was like a complex and exquisite machine, designed to process a raw material that no one had succeeded in mining.” By this, Lewontin meant that in the 1930s when R. A. Fisher, Sewall Wright and J. B. S. Haldane established the theoretical foundations of the field, the techniques to discover the variation in populations to test their suppositions was rather thin (naturally, this resulted in many controversies, see The Origins of Theoretical Population Genetics). Geneticists were using classical methods, utilizing salient phenotypes which were proxies for underlying genetic markers, and tracing patterns of co-inheritance of traits with known locations in the genetic map with novel mutants. Researchers were not even clear at that point as to the underlying biochemical structure of the particle of Mendelian inheritance, what we term DNA. That arrived onto the scene in in the 1960s. But in the early 1970s when the above was written we’re not talking about DNA sequencing. Rather, this is the allozyme era, which Lewontin helped usher in with a paper in 1966. He expresses the excitement of the times later in the passage:

Quite suddenly the situation has changed. The mother-lode has been tapped and facts in profusion have been poured into the hoppers of this theory machine. And from the other end has issued–nothing. It is not that the machine does not work, for a great clashing of gears is clearly audible, if not deafening, but it somehow cannot transform into a finished product the great volume of raw material that has been provided.”

Despite the pessimism expressed above the emergence of molecular evolution stimulated the debates around neutral theory. Over a generation ago evolutionary geneticists were grappling with the swell of data which was confronting theoretical frameworks constructed in the early 20th century. Today we live in the “post-genomic” era, and now think in terms of whole genomes. The details may differ, but many of Lewontin’s observations in the 1970s still hold true, as novel results meet the paradigms of old. Last month in PNAS Brian Charlesworth published a paper which brought this to mind, Causes of natural variation in fitness: Evidence from studies of Drosophila populations. You may know Charlesworth as the coauthor of Elements of Evolutionary Genetics, an encyclopedia of a text which I highly recommend to all. In the paper, which is both review for those of us not steeped in Drosophila genetics, and a distillation of derivations to be found in the supplements, Charlesworth notes that there is a contradiction in terms of the typical selection coefficients inferred for deleterious alleles from population genomics in relation to those from quantitative genetics. Population genomics is a new field, and involves sequencing many markers (often whole genomes) to good accuracy across a reasonable number of individuals. Quantitative genetics is a more classical framework utilizing statistical methods which interpret variation in traits within laboratory populations.

220px-Drosophila_repleta_lateral The fruit fly has a storied role in Mendelian genetics. To a great extent the study of the fruit fly is the early history of Mendelian genetics (see Lords of the Fly: Drosophila Genetics and the Experimental Life). Therefore it is natural that a large body of research exists in this area, and one can’t accept novel results obtained through new methods such as genomics at face value without some degree of skepticism. Charlesworth notes that the extremely small fitness effects of the mutation discovered via genomic methods are biased toward single nucleotide variants (SNVs); point mutations. In contrast it seems likely that the larger effect mutations implied by quantitative genetic studies, which are rather rare, and so missed in population genomic sample sizes, are due to transposable elements (TEs) interspersing themselves across the genome, and presumably disrupting function. In line with older theoretical models, most of the variation in fitness is due to a small number of mutations. Presumably as genomic methods get better (e.g., longer read to catch repeat elements and larger sample sizes) they will converge upon the older established quantitative genetic methods. Two interesting other results in this paper is that much of the variation is due to balancing selection. For theoretical reasons balancing selection can not be pervasive across the genome (too much fitness variation would result in huge death rates per generation), but, of the variation within the population much of it is maintained by balancing selection according to Charlesworth. Another interesting dynamic is that the population genomic method seem to be better at capturing the distribution of fitness effects in humans, because of our smaller effective population size. You can read the paper for the technical reason why, but the key here is to remember that one has to be careful about extrapolating from model organisms. The models are imperfect, and we always need to never outrun our ability to generalize.

As genomics becomes pervasive in population genetics this sort of analysis will be more common. Rather than “genome-of-the-week” papers we’ll move to actually trying to grapple with what the sequence data is telling us specifically about the lineage in question, and, what we can generalize from the results about evolution writ large. Some organisms have a long history of scientific study, so population genomics will supplement and complement. In other cases though organisms do not have such a rich literature and scientific culture, and the pitfalls that are highlighted here might alert us to the deficiencies in genomic methods.

Citation: Charlesworth, Brian. “Causes of natural variation in fitness: Evidence from studies of Drosophila populations.” Proceedings of the National Academy of Sciences (2015): 201423275.

 
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Credit: Karl Magnacca

The Pith: In this post I review some findings of patterns of natural selection within the Drosophila fruit fly genome. I relate them to very similar findings, though in the opposite direction, in human genomics. Different forms of natural selection and their impact on the structure of the genome are also spotlighted on the course of the review. In particular how specific methods to detect adaptation on the genomic level may be biased by assumptions of classical evolutionary genetic models are explored. Finally, I try and place these details in the broader framework of how best to understand evolutionary process in the “big picture.”

A few days ago I titled a post “The evolution of man is no cartoon”. The reason I titled it such is that as the methods become more refined and our data sets more robust it seems that previously held models of how humans evolved, and evolution’s impact on our genomes, are being refined. Evolutionary genetics at its most elegantly spare can be reduced down to several general parameters. Drift, selection, migration, etc. Exogenous phenomena such as the flux in census size, or environmental variation, has a straightforward relationship to these parameters. But, to some extent the broadest truths are nearly trivial. Down to the brass tacks what are these general assertions telling us? We don’t know yet. We’re in a time of transitions, though not troubles.

ResearchBlogging.org Going back to cartoons, starting around 1970 there were a series of debates which hinged around the role of deterministic adaptive forces and random neutral ones in the domain of evolutionary process. You have probably heard terms like “adaptationist,” “ultra-Darwinian,” and “evolution by jerks” thrown around. All great fun, and certainly ripe “hooks” to draw the public in, but ultimately that phase in the scientific discourse seems to have been besides the point. A transient between the age of Theory when there was too little of the empirics, and now the age of Data, when there is too little theory. Biology is a very contingent discipline, and it may be that questions of the power of selection or the relevance of neutral forces will loom large or small dependent upon the particular tip of the tree of life to which the question is being addressed. Evolution may not be a unitary oracle, but rather a cacophony from which we have to construct a harmonious symphony for our own mental sanity. Nature is one, an the joints which we carve out of nature’s wholeness are for our own benefit.

The age of molecular evolution, ushered in by the work on allozymes in the 1960s, was just a preface to the age of genomics. If Stephen Jay Gould and Richard Dawkins were in their prime today I wonder if the complexities of the issues on hand would be too much even for their verbal fluency in terms of formulating a concise quip with which to skewer one’s intellectual antagonists. Complexity does not make fodder for honest quips and barbs. You’re just as liable to inflict a wound upon your own side through clumsiness of rhetoric in the thicket of the data, which fires in all directions.

In any case, on this weblog I may focus on human genomics, but obviously there are other organisms in the cosmos. Because of the nature of scientific funding for reasons of biomedical application humans have now come to the fore, but there is still utility in surveying the full taxonomic landscape. As it happens a paper in PLos Genetics, which I noticed last week, is a perfect complement to the recent work on human selective sweeps. Pervasive Adaptive Protein Evolution Apparent in Diversity Patterns around Amino Acid Substitutions in Drosophila simulans:

In Drosophila, multiple lines of evidence converge in suggesting that beneficial substitutions to the genome may be common. All suffer from confounding factors, however, such that the interpretation of the evidence—in particular, conclusions about the rate and strength of beneficial substitutions—remains tentative. Here, we use genome-wide polymorphism data in D. simulans and sequenced genomes of its close relatives to construct a readily interpretable characterization of the effects of positive selection: the shape of average neutral diversity around amino acid substitutions. As expected under recurrent selective sweeps, we find a trough in diversity levels around amino acid but not around synonymous substitutions, a distinctive pattern that is not expected under alternative models. This characterization is richer than previous approaches, which relied on limited summaries of the data (e.g., the slope of a scatter plot), and relates to underlying selection parameters in a straightforward way, allowing us to make more reliable inferences about the prevalence and strength of adaptation. Specifically, we develop a coalescent-based model for the shape of the entire curve and use it to infer adaptive parameters by maximum likelihood. Our inference suggests that ~13% of amino acid substitutions cause selective sweeps. Interestingly, it reveals two classes of beneficial fixations: a minority (approximately 3%) that appears to have had large selective effects and accounts for most of the reduction in diversity, and the remaining 10%, which seem to have had very weak selective effects. These estimates therefore help to reconcile the apparent conflict among previously published estimates of the strength of selection. More generally, our findings provide unequivocal evidence for strongly beneficial substitutions in Drosophila and illustrate how the rapidly accumulating genome-wide data can be leveraged to address enduring questions about the genetic basis of adaptation.

Figure 1 C shows the top line. As you can see, there’s a “trough” around non-synonymous substitutions. Non-synonymous simply means that a base pair substitution at that position within the codon changes the amino acid encoded. In contrast, a synonymous change does not. A substitution is not just a mutant variant though. It is rather an assessment of a population level shift from one allele to another. Neutral theory posited that most substitutions were not driven by natural selection, but rather random walk processes. Ergo, most evolutionary change was not adaptive. A simple way to check the power of selection against this background of stochastic variation is to measure the ratio of substitution between non-synonymous and synonymous bases. But this sort of thing is more appropriate when comparing closely related species. In the paper on selective sweeps in humans obviously that’s not going on, they were looking within one species. Instead the authors looked at reduction of variation across regions which may have been targets of natural selection. The reduction occurs because when one particular allele becomes the target of strong positive selection it pulls along adjacent linked regions in a “hitchhiking” process. Recombination works against this, resulting in decay over time of linkage disequilibrium which spikes in th wake of selection.

But these conceptions are predicated on a simple model of the emergence of variants, and the way selection does, or doesn’t, target these variants. One imagines a new mutant which arises against the ancestral genetic background. In a single-gene model the probability of fixation, that is, going to ~100% and substitution in the population, is 1/N (or 2N for diploid). In plain English the fixation probability for a mutant is inversely proportional to the effective population size. In contrast, the probability of fixation of a mutant which is selectively favored is proportional to its selection coefficient, which simply measures its fitness as a ratio to that of the population mean. The fixation of neutral variants is random walk, and the time until fixation is directly proportional to population size. In contrast, selectively favored variants can sweep to fixation rather quickly. Being very conservative one can infer that the fixation of lactose tolerance in Northern Europeans due to a mutation on the LCT gene took about ~7,000 years, or a little less than 300 generations. Because of this rapidity recombination has far less leisure with which to “chop” apart the physical associations of variants on the ancestral mutant genetic background. No wonder the LCT locus has one of the longest “haplotype blocks” in the European genome; a sequence of associate markers.

But let’s modify our mental model a bit. Imaging that a genetic variant has been floating around at a low frequency for a long time. There may be many copies of the mutant, associated with different genetic variants due to the impact of recombination. We can for example imagine a recessively deleterious allele which persists in low frequencies because of the lack of efficacy of selection (most alleles are found in heterozygote individuals with normal fitness). Many variants have multiple effects. Imagine that this allele has a dominant phenotypic effect which goes from being neutral to being very selectively favored. Now you have a situation where the genomic region will be dragged upward in frequency during adaptation, but, there will be many region s, not just one. Concretely, if the selective event occurred only a few generations after the original mutant the impact on the local genome would be much stronger in terms of generating homogenization than if the event occurred dozens of generations after the original mutant, as the original genetic background would have been recombined and so lost its distinctive coherency.

This is a form of natural selection from “standing variation.” Old mutants floating around in the background noise, rather than new mutants. In the paper above the authors find a fair amount of conventional selective sweeps, but, they suggest that the higher ratios of the proportion of the genome under natural selection found by some researchers in Drosophila may be due to the fact that some methods catch the whole basket of selection, while others focus on more tractable “cartoon” models.

Of the selection which can be modeled as a classic selective weep the authors also found a “power law” effect. There was a combination of a few hits of powerful selection, and more numerous bouts of weak selection. This is not totally unexpected according to theory. Some of the human traits which have been amenable to genome-wide association, such as pigmentation, probably fall under this category. Most of the trait variance is due to a few genes of large effect, but there are a larger number of loci which account for the minority balance of variance. The same no doubt can hold across evolutionary time with the dynamics of natural selection.

But we also shouldn’t get lost in the genomic trees and lose sight of the forest. Not only are evolutionary processes subject to molecular scale parameters such as recombination and mutation rates, but they are also impacted by organism and population scale parameters. One presumes that fruit flies are subject to a different pressures and have had a different history from human beings, just as both have from philopatric amphibians. Humans have an enormous census size, huge populations, and, we’ve undergone a massive change in lifestyle over the last 10,000 years. But as land bound mammals we may exhibit more population substructure than some species, for example birds with a wide range. Additionally, because of a low long term effective population we have only so much genic variation to work with. Such a welter of details distorts attempts at elegance, but they need to be kept in mind.

The authors conclude:

In summary, our findings establish a distinctive, genome-wide signature of adaptation in D. simulans, suggesting that many amino acid substitutions are beneficial and are driven by two classes of selective effects. Enabled by a richer summary of diversity patterns that avoids an a priori choice of scale, these conclusions offer a coherent interpretation of the results of previous inferences. It will now be interesting to see whether similar findings emerge in other Drosophila species, which vary in their recombination rates, effective population sizes, and ecology.

I wouldn’t limit this just to Drosophila. Because the different fruit fly species have different distributions, natural histories, as well as common ancestral traits and genes, they’re an excellent laboratory of evolution. But eventually we’ll start sweeping our gazes across all the multitudinous branches of the tree of life. Soon.

Citation: Sattath S, Elyashiv E, Kolodny O, Rinott Y, & Sella G (2011). Pervasive Adaptive Protein Evolution Apparent in Diversity Patterns around Amino Acid Substitutions in Drosophila simulans PLoS Genetics : 10.1371/journal.pgen.100130

(Republished from Discover/GNXP by permission of author or representative)
 
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509px-Drosophila_residua_heNatural selection happens. It was hypothesized in copious detail by Charles Darwin, and has been confirmed in the laboratory, through observation, and also by inference via the methods of modern genomics. But science is more than broad brushes. We need to drill-down to a more fine-grained level to understand the dynamics with precision and detail, and so generate novel inferences which may then be tested. For example, there are various flavors of natural selection: stabilizing selection, negative selection, and positive directional selection. In the first case natural selection buffets the phenotype about an ideal mean, in the second case deleterious phenotypes and their associated alleles are purged from the genome, and finally, natural selection can also drive a novel trait toward greater prominence, and concomitantly the allelic variants which are associated with the fitter phenotype.

The last case is of particular interest to many because it is often with positive natural selection by which evolution as descent with modification occurs. Over time trait values and the nature of traits themselves shift such that a lineage changes its character beyond recognition. This phyletic gradualism and the scale independence of evolutionary process has been challenged, in particular from the domain of developmental biology (albeit, not all ,or even most, developmental biologists). But ultimately no one doubts that a classical understanding of evolution as change in allele frequency, often driven by natural selection, is part of the larger puzzle of how the tree of life came to be.

ResearchBlogging.org One of the phenomena associated with positive directional evolution is the selective sweep. How a selective sweep occurs, and its consequences, are rather straightforward. A genome consists of a sequence of base pairs (e.g., we have 3 billion base pairs). If a new mutation emerges at a particular base pair, a novel single nucelotide polymorphism (SNP), and, that allelic variant is ~10% fitter than the ancestral variant, natural selection could drive up its frequency (the conditionality is due to the fact that in all likelihood it would still go extinct because of the power of stochastic forces when a mutant is at low frequency). So the variant could in theory shift from ~0% (1 out of N, N being the number of individuals in a population, 2N if diploid, and so forth) to ~100%. This would be the fixation of the novel variant, driven by selective dynamics. So what’s the sweep aspect? The sweep in this case refers to the effect of the very rapid rise in frequency of the SNP in question on the adjacent genomic region. What is termed a genetic hitchiking dynamic results if the sweep occurs rapidly, so that nearby regions of the genome also move to fixation along with the favored SNP. But in a diploid organism with sexual reproduction genetic recombination persistently breaks apart associations across the physical genome. Therefore the span of the sequence of genetic markers nearby a favored SNP which form a haplotype is dependent on the rate of recombination as well as the rate of the rise in frequency of the allele, which is contingent on the strength of selection. A powerful selective sweep has the effect of homogenizing wide regions of the genome flanking the favored mutant; in other words the sweep “cleans” the gene pool of variation as one very long haplotype replaces many shorter haplotypes. As an example, in the genomes of Northern Europeans the locus LCT is characterized by a very long haplotype, which itself seems to correlate well with the trait of lactase persistence. The implication here is that the lactase persistence conferring variant arose relatively recently, and was swept up to near fixation by positive directional natural selection.

That’s the broad theory. But as you know, evolution and its subcomponents are more than “just a theory,” they’re a set of models which are amenable to testing, whether through observation, or via controlled laboratory experiments. A new letter to Nature elaborates how exactly selective sweeps play out in Drosophila melanogaster, a classic “model organism.” Interestingly, this is a case of experimental evolution, something we are more familiar with Richard Lenski’s E. coli. Genome-wide analysis of a long-term evolution experiment with Drosophila:

Experimental evolution systems allow the genomic study of adaptation, and so far this has been done primarily in asexual systems with small genomes, such as bacteria and yeast…Here we present whole-genome resequencing data from Drosophila melanogaster populations that have experienced over 600 generations of laboratory selection for accelerated development. Flies in these selected populations develop from egg to adult ~20% faster than flies of ancestral control populations, and have evolved a number of other correlated phenotypes. On the basis of 688,520 intermediate-frequency, high-quality single nucleotide polymorphisms, we identify several dozen genomic regions that show strong allele frequency differentiation between a pooled sample of five replicate populations selected for accelerated development and pooled controls. On the basis of resequencing data from a single replicate population with accelerated development, as well as single nucleotide polymorphism data from individual flies from each replicate population, we infer little allele frequency differentiation between replicate populations within a selection treatment. Signatures of selection are qualitatively different than what has been observed in asexual species; in our sexual populations, adaptation is not associated with ‘classic’ sweeps whereby newly arising, unconditionally advantageous mutations become fixed. More parsimonious explanations include ‘incomplete’ sweep models, in which mutations have not had enough time to fix, and ‘soft’ sweep models, in which selection acts on pre-existing, common genetic variants. We conclude that, at least for life history characters such as development time, unconditionally advantageous alleles rarely arise, are associated with small net fitness gains or cannot fix because selection coefficients change over time

Critical to understanding what’s going on here is the distinction they make between ‘classic’ ‘hard sweeps’ and ‘soft sweeps.’ Hard sweeps follow the spare description I outlined above:

1) A new mutant arises in the genetic background

2) Selection favors the mutant

3) The mutant rises in frequency and sweeps to fixation, 0% → 100%, replacing the ancestral variants

In contrast, for a soft sweep:

1) Selection favors a set of minor polymorphisms already segregating in the gene pool

2) These polymorphisms rise in frequency

3) But they may not sweep to fixation

In the first case the signature of natural selection will be clear, distinct, and indubitable. A novel haplotype which has replaced the ancestral variants and produced a wide region of genetic homogeneity as all other allele states are expunged by the sweep will have resulted. That isn’t what they saw at the genomic level.

phendiffBut first, what did they do? The flies used in this experiment derive from a 30 year old lineage, and they selected them for 600 generations in the case of the treatments which were being driven to new phenotype values. 600 generations for humans would be about 15,000 years assuming 25 years per generation. If a trait is heritable, and you select offspring deviated away from the mean, over time you will see a shift in the trait value. This is classic quantitative genetics, and that’s what they saw. They had five lineages which exhibited accelerated development (ACO), and five which were controls which exhibited the ancestral phenotypes (CO). “Eclosion” refers to the fly’s emergence from the pupae. The lineages which were subject to natural had very different life histories from the control groups. The cluster of traits here shouldn’t be too surprising, we know from other taxa that short-lived fast-developing species tend to be smaller and metabolically more under-the-gun than the inverse.

But the real interesting aspects of this study are not the phenotypes. Who hasn’t seen weird things among the Drosophila? That’s one of the reasons they were chosen as model organisms in the first place! Rather, they explored the patterns of genomic variation within and across the lineages, and integrated the results into a broader theoretical framework of how evolutionary processes occur, and their implications for the genome-wide structure one should see. Below I’ve stitched together figure 2 & 3, which illustrate particular patterns of genomic variation.

compfig

The left figure shows differences in allele frequencies between the ACO and CO pooled lineages. The spikes indicate large differences, with the dotted line representing the threshold where there’s a 0.1% random chance of such a between population frequency difference. The vertical axis is log-scaled. The grey line at the bottom indicate the differences in one particular ACO lineage with the pooled ACO sample. In the right panel you see heterozygosities, with blue denoting the CO lineages, and red the selected ACO lineages which have shortened life histories. The grey again is a particular ACO lineage. Each vertical panel corresponds to a chromosomal arm of the the Drosophila melanogaster genome.

First, note the widespread distribution of allele frequency differences between ACO and CO. Additionally, there’s little difference between the specific ACO lineage, and the pooled sample. Despite their independent histories they seem to exhibit the same allelic configuration. Second, note that the heterozygosities in the case of the ACO pooled sample is lower than in the CO ancestral phenotype lineages. Why? Remember that selective sweeps should expunge genomic variation. But, the sweeps do not seem to have gone to fixation, otherwise we’d see many more inverted peaks converging to heterozygosity of ~0, as the selected variant replaces all others in the population.

What’s going on in the regions which exhibit differences between the controls and selected linages? They looked at the ~650 non-synonymous SNPs on ~500 genes which were most differentiated between ACO and CO (L 10FET score > 4) and found the following categories of genes enriched: imaginal disc development, smoothened signalling pathway, larval development, wing disc development, larval development (sensu Amphibia), metamorphosis, organ morphogenesis, imaginal disc morphogenesis, organ development and regionalization. Life history is complex. Combine the wide class of genes with the dispersed genomic impact of selection as evident in figures 2 and 3, you get a good sense of the sort of consequences on the substrate level which quantitative genetic evolutionary dynamics have. Also of interest, they found that the X chromosome seemed enriched for signatures of selection and evolution. Why? They note that this chromosome would be more subject to selection for recessive or partially recessive expressing SNPs.

Clearly this study did not find the clean hard sweeps which theory may have predicted. Rather, the researchers found a lot of partially completed sweeps distributed all across the genome. Sound familiar? Before we move on to broader considerations, here are their explanations:

- The sweeps are hard, but haven’t reached fixation. So the selection coefficients have be rather small for them to still be in transient

- Selection is operating on “standing variation.” That is, the genetic variation extant naturally within a given population, and which may be operated upon by natural selection to change the population trait value mean through classical breeding techniques

- And finally, selection coefficients (the greater fitness of positively selected variants against the population mean) may not be static parameters, but change over time as a function of allele frequency. This shouldn’t be that surprising. Frequency dependence and epistasis can impact on linear assumptions within a statistical genetic model. The authors refer to deleterious alleles or antagonistic pleiotropy as possible genetic level forces which also prevent fixation

I personally lean against the first option, because it seems like we see a similar pattern in human evolutionary genomics, lots of partial sweeps and incomplete fixation. How much time does a brother need? In the long run we’re dead, and heat death swallows the universe. In the short run evolutionary pressures are always shifting. Fix now, or forget it say I! The wide distribution of allelic differences as well as moderate heterozygosities seems to be an indication that a quantitative trait, life history, is being modified through mass action on genetic variation. Interestingly, there’s also the parallel to humans insofar as the X chromosome seems to have more signatures of selection and variation in this evolutionary experiment. Next question: who’s working on experimental evolution of 600 generations in mice?

Citation: Burke, Molly K., Dunham, Joseph P., Shahrestani, Parvin, Thornton, Kevin R., Rose, Michael R., & Long, Anthony D. (2010). Genome-wide analysis of a long-term evolution experiment with Drosophila Nature : 10.1038/nature09352

Image Credit: Karl Magnacca

(Republished from Discover/GNXP by permission of author or representative)
 
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Razib Khan
About Razib Khan

"I have degrees in biology and biochemistry, a passion for genetics, history, and philosophy, and shrimp is my favorite food. If you want to know more, see the links at http://www.razib.com"