Over the past decade evolutionary geneticist Mike Lynch has been articulating a model of genome complexity which relies on stochastic factors as the primary motive force by which genome size increases. The argument is articulated in a 2003 paper, and further elaborated in his book The Origins of Genome Architecture. There are several moving parts in the thesis, some of which require a rather fine-grained understanding of the biophysical structural complexity of the genome, the nature of Mendelian inheritance as a process, and finally, population genetics. But the core of the model is simple: there is an inverse relationship between long term effective population size and genome complexity. Low individual numbers ~ large values in terms of base pairs and counts of genetic elements such as introns.

A quick reminder: effective population size denotes the proportion of the population which contributes genes to the next generation. So, in the case of insects with extremely high mortality in the larval stage the effective population size may be orders of magnitude smaller than the census size at any given generation evaluating over all stages of life history. In contrast, with humans a much larger proportion of children end up contributing to the genetic makeup of the subsequent generation. With large organisms I’ve heard you can sometimes use a rule of thumb that effective population size is ~1/3 of census size, though this probably overestimates the effective population size. One reason that reproductive variation reduces the effective population, because many individuals contribute far less to the next generation than other individuals. The greater the variance, the more evolutionary genetic variation is impacted by a few individuals within the population at a given generation, reducing effective population which contributes to the next (the reproductive variance is often assumed to be poisson, but that is likely an underestimate). Additionally, there is the issue of variation over time. Long term effective population is much more sensitive to low bound values than high bound values, so it is liable to be much smaller than the census size at any given period for a species which goes through cycles. Humans for example have a relatively small long term effective population size evaluated over the past 100,000 years because we seem to have expanded from a small initial population. Mathematically since long term effective population size is given by the harmonic mean it stands to reason that low bound values would be critical. If that doesn’t make sense to you, remember the outsized impact which population bottlenecks may have on the long term trajectory of a species, in particular by removing genetic variation.

How does this influence genome complexity? Basically Lynch’s thesis is that when you reduce effective population you dampen the power of natural selection, specifically purifying selection, from preventing the addition of non-adaptive complexity through random processes. It isn’t that selection is rendered moot, rather, its signal is overwhelmed by the noise. Here’s the abstract of his 2003 paper:

Complete genomic sequences from diverse phylogenetic lineages reveal notable increases in genome complexity from prokaryotes to multicellular eukaryotes. The changes include gradual increases in gene number, resulting from the retention of duplicate genes, and more abrupt increases in the abundance of spliceosomal introns and mobile genetic elements. We argue that many of these modifications emerged passively in response to the long-term population-size reductions that accompanied increases in organism size. According to this model, much of the restructuring of eukaryotic genomes was initiated by nonadaptive processes, and this in turn provided novel substrates for the secondary evolution of phenotypic complexity by natural selection. The enormous long-term effective population sizes of prokaryotes may impose a substantial barrier to the evolution of complex genomes and morphologies.

The implication here is that prokaryotes with massive population sizes are biased toward smaller genomes by the more efficacious application natural selection. In contrast, more complex organisms which have smaller population sizes, and so are more impacted by the random fluctuations generation to generation due to sample variance, are less streamlined genomically because selection can do only so much against the swelling sea of noise. One intriguing argument of Lynch is that the genomic complexity is then later useful downstream as the building block of phenotypic complexity, but let’s set that aside for now.

A new paper in PLoS Genetics challenges the statistical analysis of the original data which Lynch et al. used to make their case. Technically the argue was that there was an inverse relationship between N_eu and genome size. N_e is effective population size, and u is nucleotide mutation rate. Though argument is technical, and the basic objection should be easy to understand: there are other variables which may actually be responsible for the correlation which Lynch et al. discerned. To the paper, Did Genetic Drift Drive Increases in Genome Complexity?:

Genome size (the amount of nuclear DNA) varies tremendously across organisms but is not necessarily correlated with organismal complexity. For example, genome sizes just within the grasses vary nearly 20-fold, but large-genomed grass species are not obviously more complex in terms of morphology or physiology than are the small-genomed species. Recent explanations for genome size variation have instead been dominated by the idea that population size determines genome size: mutations that increase genome size are expected to drift to fixation in species with small populations, but such mutations would be eliminated in species with large populations where natural selection operates at higher efficiency. However, inferences from previous analyses are limited because they fail to recognize that species share evolutionary histories and thus are not necessarily statistically independent. Our analysis takes a phylogenetic perspective and, contrary to previous studies, finds no evidence that genome size or any of its components (e.g., transposon number, intron number) are related to population size. We suggest that genome size evolution is unlikely to be neatly explained by a single factor such as population size.

In the original analysis by Lynch et al. ~66% of the variation in genome size was explained by N_eu! That’s a pretty large effect. Figure 1 illustrates how phylogeny could be a confound in adducing a relationship. Here’s some of the text which explains the figure:

In this hypothetical example, eight species have been measured for two traits, x and y, as indicated by pairs of values at the tips of the phylogenetic tree (A). Ordinary least-squares linear regression (OLS) indicates a statistically significant positive relationship (B; r-squared = 0.62, P = 0.02), potentially leading to an inference of a positive evolutionary association between x and y. However, inspection of the scatterplot (B) in relation to the phylogenetic relationships of the species (A) indicates that the association between x and y is negative for the four species within each of the two major lineages. Regression through the origin with phylogenetically independent contrasts…which is equivalent to phylogenetic generalized least squares (PGLS) analysis, accounts for the nonindependence of species and indicates no overall evolutionary relationship between the traits…The apparent pattern across species was driven by positively correlated trait change only at the basal split of the phylogeny; throughout the rest of the phylogeny, the traits mostly changed in opposite directions (A; basal contrast in red)….

The argument then seems to be that the relationship in the original work by Lynch was an artifact due to the evolutionary history of the species which he surveyed to infer the relationship. Instead of a general principle or law then what you have is an outcome of contingent historical processes. Not very neat and clean. You can see the taxa-clustered nature of the relationship in figure 1 from the 2003 paper in Science:

OK, now let’s look at the visualization of the same data set from this paper, as a tree to illustrate the correlations:

The last figure shows the difference between a scatterplot using conventional OLS regression, and the phylogenetic least squares model (PGLS). You go from an obvious linear relationship, which translated into the high r-squared noted above, to basically nothing (r-squared near zero, no statistical significance).

The paper itself isn’t that long, the objection is pretty straightforward. They’re simply claiming that Lynch didn’t correct for an obvious alternative explanation/confound, and that we don’t know what we thought we knew. Additionally, there is the assertion that the idea that effective population size predicts genome size robustly is becoming conventional wisdom within the scientific community. I don’t know about that, this seems like such a young field in flux that I think they oversold how widespread this assumption is to make the force of their rebuttal more critical. Certainly the patterns in genome size can be quite perplexing, but my intuition is that an r-squared on the order of 2/3 of the variation in genome size being explained by one predictor variable is rather astounding. Obviously genome size is pretty easy to get in the “post-genomic era,” but N_e and u are harder to come by for many taxa, or even within a given taxon for a set of species of interest. It looks to me an opportunity for experimental evolutionalists, who can control the confounds, and observe changes within a lineage. And yet even if N_eu is predictive as an independent variable all things controlled, what if all things are not usually controlled, and random acts of phylogenetic history are more important? Mike Lynch is credited in the acknowledgements, so I assume we’ll be seeing a response from him in the near future.

Citation: Whitney KD, & Garland T Jr (2010). Did Genetic Drift Drive Increases in Genome Complexity? PLoS Genetics : 10.1371/journal.pgen.1001080

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There is a new paper in Nature which is a full frontal attack on the utility of William D. Hamilton’s inclusive fitness framework in explaining eusociality. Martin A. Nowak, Corina E. Tarnita, & Edward O. Wilson are the authors. Wilson is famous in large part for his authorship of Sociobiology: The New Synthesis, and is arguably the doyen of American organismic biology. He is both an active scientist, and, a premier public intellectual. So with that in mind, I notice that Dienekes Pontikos alludes to “E.O. Wilson’s change of mind about group selection.” This is conventional wisdom, but it is I think wrong (though from what I can tell Wilson has not done much to disabuse the press of the notion). In Defenders of the Truth Ullica Segerstrale notes that Wilson did not expunge group selection thinking even in Sociobiology. In Evolution for Everyone David Sloan Wilson recounts that it was in fact E. O. Wilson who pointed out a group selective interpretation of data he was presenting at a conference, helping to push him early on in a rather unfashionable direction. From what I have heard Wilson always believed that the empirical data was not adequately explained by a pure inclusive fitness model, and simply waited until things shook out before pushing back with more theoretically trained colleagues who had the same skepticism.

From page 30 of Sociobiology:

…….Nevertheless, Williams’ distaste for group-selection hypotheses wrongly lead him to urge the loading of the dice in favor of individual selection. As we shall see in chapter 5, group selection and higher levels of organization, however intuitively improbable they may seem, are at least theoretically possible under a wide range of conditions. The goal of the investigation should not be to advocate the simplest explanation, but rather to enumerate all of the possible explanations, improbable as well as likely, and then to devise tests to eliminate some of them.

And page 129, the last paragraph in the chapter on group selection (quoted in full so there’ll be no confusions as to whether I’m pulling it out of context):

In conclusion, although the theory of group selection is still rudimentary, it has already providd insights into some of the least understood and most disturbing qualities of social behavior. Like Arjuna faltering on the Field of Righteousness, the individual is forcd to make imperfect choices based on irreconcilable loyalties-between the “rights” and “duties” of self and those of family, tribe, and other units of selection, each of which evolves its own code of honor. No wonder the human spirit is in constant turmoil. Arjuna agonized, “Restless is the mind, O Krishna, turbulent, forceful, and stubbon. I think it is no more aesily to be controlled than is the wind.” And Krishna replied, “For one who is uncontrolled, I agree the Rule is hard to attain, but by the obedient spirits who will strive for it, it may be won by following the proper way.” In the opening chapter of this book, I suggested that the science of sociobiology, if coupled with neurophysiology, might transform the insights of ancient religions into a precise account of the evolutionary origin of ethics and hence explain the reasons why we make certain moral choices instead of others at particlar times. Whether such understanding will then produce the Rule remains to be seen. For the moment, perhaps it is enough to establish that a single strong thread does indeed run from the conduct of terminte colonies and turkey brotherhoods to the social behavior of man.

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Last week I took an intellectual road trip back nearly a century and explored the historical context and scientific logic by which R. A. Fisher definitively fused Mendelian genetics with quantitative evolutionary biology. In the process he helped birth the field of population genetics. While the genetics which we today are more familiar with begins at the biophysical substrate, the DNA molecule, and the phenomena which emerge from its concrete structure, population genetics starts with the abstract concept of the gene. This abstraction and its variants are construed as algebraic quantities from which one can infer a host of dynamics. These are the processes which are the foundations of evolutionary change, as population genetics flows into evolutionary genetics, and ultimately the raw material of natural history.

Fisher’s accomplishments were a function of both his abilities and his passions. He was a mathematical prodigy, with the ability to distill natural processes down to highly general abstractions. And like many English gentlemen of his age he had a passion for evolutionary biology, and cherished his copy of The Origin Of Species. His ultimate aim was to transform evolutionary biology into a discipline with the same analytical rigor as physical chemistry. But he wasn’t the only major figure on the scene in his era.

Sewall Wright was an American physiological geneticist with a background in animal breeding. While Fisher was a mathematician who sought to apply his skills to evolutionary biology, Wright was a biologist who taught himself mathematics to further his own understanding of evolutionary processes. The two were in many ways the Yin and the Yang of early population genetics, with their conflicts and disagreements being termed the Wright-Fisher controversies, and the common formal framework which they converged upon becoming the ubiquitous Wright-Fisher model. Wright’s life spanned 99 years, from 1889 to 1988. His biography, both personal and scientific, are explored in rich detail in Will Provine’s Sewall Wright and Evolutionary Biology. Because of the length and breadth of his influence in evolution it’s worth reading just to get a sense of how Wright shaped the Modern Neo-Darwinian Synthesis behind the scenes. Provine seems to indicate that Wright was the primary theoretical influence on Theodosius Dobzhansky,* who mentored a whole generation of evolutionary biologists to come (e.g., Dobzhansky → Lewontin → Coyne).

If I may make recourse to analogy, if R. A. Fisher was the Alfred Marshall of evolution, Sewall Wright’s mentality seems more characteristic of Thorstein Veblen’s work. Fisher’s aim was to formulate elegant and simple general principles which would explain evolutionary process top to bottom. His fundamental theorem of natural selection, “The rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time,” was perhaps the best example of Fisher’s grand general ambitions. Wright, by origin an experimental biologist, certainly aimed for grandeur, but I can not perceive in him the yearning for a clean concise elegance which discards the sloppiness he saw in evolution as it played out in the laboratory. This inability to ignore the detail was a “bug” which he in some ways turned into a feature when it came to his theorization of evolutionary process.

Many of the ideas which would be the focus of Wright’s career, and later shape the outlook of his acolytes, can be found in a 1932 paper The roles of mutation, inbreeding, crossbreeding and selection in evolution. In this paper Wright introduces concepts which are still with us today, and reviews the state of knowledge at the time. Some of his observations are almost amusing now 80 years later. He suggests that multicellular organisms likely have more than 1,000 genes. Wright also alludes to concepts such as allopatric speciation and postzygotic reproductive isolation which have spawned an enormous literature, and are the stuff of careers..

But the core of the paper seem to be the adaptive landscape and the shifting balance. What is the adaptive landscape? If you follow Will Provine’s reading no one really knows! OK, to be fair, the landscapes usually describe a topography where fitness is on the vertical y-axis, and x and z are frequencies of genes, or perhaps phenotypes. But are they frequencies within a whole population? Or do they represent genotypic combinations within individuals? Over the decades of the utilization of the metaphor Provine indicates that Wright and his students had different ideas of what the metaphor was in the specifics, suggesting its Rashomon-like aspects. The idea of landscapes across which evolution traverses over time is a very easy to visualize, but making use of the framework in a concrete sense is more difficult. This was especially so in the days before computer programs which could produce beautiful multi-dimensional visualizations.

To the right you see a primitive representation of a fitness landscape from Wright’s paper. The y & x axis are different genes, and at their intersection you have a gene-gene combination. There are several ideas at work in these evolutionary landscapes. The first of them are gene-gene interactions, epistasis. It is often asserted that Wright and Fisher disagreed on the importance of epistasis in evolution, with Wright arguing that these interactions were critical, and Fisher dismissing their long term importance. There are other interpretations, and much of the disagreement may actually have been more about fine weights than the basic thrust of their positions. But the general sketch is that biologists in the Fisherian tradition emphasize gradual continuous evolution through selection on additive genetic variance across genes of small effect through natural selection (I understand that this is somewhat a caricature of Fisher’s own views, and most of his intellectual descendants view this description as the creation of their critics, but that’s the perception). The Wrightian tradition is more pluralistic, and frankly somewhat confused because different thinkers have different spins (e.g., epistasis vs. drift). But in general it suggests that factors such as population substructure, gene-gene interaction, and random genetic drift, all play crucial roles in evolution. The partisans of contingency in evolutionary process and the importance of specific genetic architecture in constraining and shaping the arc of change would likely get more sympathy from Wright.

For Sewall Wright the specific nature of the fitness topography is critical in shaping how evolution plays out on the genetic level. If the topography is “rugged” so that there are many fitness peaks and valleys of disparate values along the y-axis, then populations may become “trapped” on a lower peak which is separated from the higher one by a valley. The movement of a population along the adaptive landscape clearly has a temporal interpretation, and so one can see how contingency and history are critical. Where you start out from may constrain where you can end up. At least, if you rely on conventional deterministic processes such as natural selection on a single locus. This is where Wright suggests that populations structured so that their effective sizes are smaller can evolve much faster, and leap across the valley’s so to speak, through the action of random genetic drift. It may be that to attain a given gene-gene combination (or gene-gene-gene-gene, etc.) is nearly an impossible proposition in a deterministic framework where one has to proceed on a step-by-step basis, but through the luck of random genetic drift one can envisage the odds being reduced by a few chance deviations in allele frequency.

Because Wright posits that much of evolution occurs by scaling a sequence of distinct and disparate adaptive peaks, he implicitly rejects gradualism and embraces discontinuity and rapid bursts of evolution. This sort of process occurs in a situation with moderate population substructure, so that effective population size is reduced within demes which then can “peak shift” more frequently, but, with enough population-to-population interaction that inbreeding does not drive mutational meltdown or pedigree collapse.  When a subpopulation reaches a particularly fortuitous adaptive peak, then it enters a phase of demographic expansion, and it can replace all the other demes of conspecifics (or at least genetically assimilate them to a great degree). This is where Sewall Wright introduces intergroup selection, or more colloquially group selection. Here Wright and Fisher part company again, naturally. Fisher believed that individual level selection was sufficient to explain evolutionary process, while Wright clearly did not. The debate between those who believe that group selection is a significant force in evolution, and those who do not, continues to this day (group selectionists now have a more general model, multilevel selection theory).

Epistasis. Drift. Moderate population structure and migration. Add to the mix mutation and selection, as well as the fact that the adaptive topography itself is in constant flux, and Wright already has the beginnings of a strong brew in The roles of mutation, inbreeding, crossbreeding and selection in evolution. I’ve only glanced over a few of the points. In other sections Wright touches upon what would one day become mutational meltdown, as well as the nature of speciation. There are many disparate threads here which would eventually lead into a range of disparate research programs.

So with that I want to get to my “human obsession.” Near the end of the paper Sewall Wright seems to offer that the emergence of our own species could be well characterized by a shifting balance model. I suspect that Wright may be right on this. The movement out of Africa was a great pulse, where one human lineage seems to have rapidly replaced or genetically assimilated all the others. Human populations do have substructure, but they also exchange genes, and leapfrog each other. Our cultures may be the perfect vehicles for intergroup selection on the memetic, if not genetic, level (between group variance in memes can be much greater than on genes). I suspect this is not going to be age where elegant one-size-fits-all theories are going to be of particular use, so we might want to dig back into Wright’s diverse set of ideas.

* More directly though he came out of the Morgan lab.

Citation: Sewall Wright (1932). The roles of mutation, inbreeding, crossbreeding and selection in evolution Proceedings of The Sixth International Congress of Genetics, 1

Related:

Notes on Sewall Wright: The Shifting Balance Theory – Part 1

Notes on Sewall Wright: The Shifting Balance Theory (Part 2)

R. A. Fisher and the Adaptive Landscape

R. A. Fisher and Epistasis

Notes on Sewall Wright: the Adaptive Landscape

Notes on Sewall Wright: Migration

Notes on Sewall Wright: Population Size

Notes on Sewall Wright: Wright’s F-statistics

Notes on Sewall Wright: Genetic Drift

Notes on Sewall Wright: the Measurement of Kinship

Notes on Sewall Wright: Path Analysis

Wright, Fisher, Haldane, and odds and ends

Image Credits: Evolutionary Systems Biology, Wikimedia, Scholarpedia, Science

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Dr. John Hawks points me to a review in BMC Biology, A question of scale: Human migrations writ large and small. It’s short, and of interest for the citations themselves. This a field in flux. One point which I think needs to be emphasized in relation to migration parameters is that there are going to be two primary modes which this might play out, short range deme-to-deme contact, and long range cultural/genetic revolution (there is going to be a range between the two, but let us suppose that these are two modes in the distribution).

The first case would involve the exchanges of individuals (voluntary or involuntary) between nearby groups. This is where the disjunction of the nature of inheritance between genes and culture comes into play. There are cases, such as in Iraq, where Indo-European (Kurdish languages) and Semitic (Arabic) speakers come into contact. The between group difference in speech is enormous, but it turns out that the genes are not really that different. In fact, Persian speakers from Khorasan, may be a genetic outgroup to Arabic & Persian speakers from western Iran, Kurdistan and Mesopotamia. Though on a macroscale linguistic and genetic trees may be similar because of geography, on a finer resolution the nature of language means that there’s a much sharper linguistic than genetic distinction.

But the second dynamic are cultural revolutions which produce demographic explosions and transformations. We have plenty of them of recent vintage. The rise of Europeans stands out, but the spread of Bantu dialects was probably concomitant with a genetic change across much of Africa, while Mongol Empire seems to have left a residue of ethno-genetic anomalies across Eurasia. These events seem to be more common in the recent historical period, likely because of the greater rate of cultural innovation, but the rise and spread of anatomically modern humans outside of Africa is arguably the first cultural/genetic explosion.

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A comment below:

Razib, I don’t know much about genetics but is it true that these people of Melanesia are among the least related people (even more so than Europeans) to sub-saharan Africans genetically??

This is a common question. The typical scientifically curious intelligent person is generally aware that on the order of 100,000 years ago there was a movement of anatomically modern humans from Africa. They know that Africans have the most genetic variation of any human population, and that in fact Africa has more genetic variation than the rest of the world combined. It would stand to reason then that the further you are from Africa, the more genetically distant you are. Simply because of recent admixture of Sub-Saharan African ancestry in much of the Middle East there is some truth to this, but I think it misses the “big picture.”

To the best of my knowledge the current consensus on the origin and expansion of modern humans goes like so:

1) Anatomically modern humans emerge in Africa first ~200,000 years ago. This population is a sister lineage to the various Eurasian hominins, Neandertals, X-woman, etc.

2) Between 50,000 and 200,000 years ago a subset of the African population left Africa.

3) Sometime between the exit-from-Africa event and the present the anatomically modern humans replaced all other lineages (with some assimilation) and diversified.

My confidence in any specific aspect of the “orthodox census” is very high, though joint probability of the details is more modest. #3 for example had to be modified a bit recently because of the possible existence of Neandertal admixture in Eurasians. So back to my question, assuming this model, which population is most genetically distant from Africans? The answer is really none. Here are some figures from Xing et al., which gets at why the answer is “none of the above”:

Here’s the text for the figure:

Figure 3. Population relationships between the 40 populations. A) Neighbor-joining tree. Populations are color-coded based on their continental origins. The hypothetical ancestral population is shown. Bootstrap support values for most branches are larger than 95% (the bootstrap consensus tree is shown in Supp. Figure S1). B) Principal components analysis. First two principal components (PCs) are shown. Each individual is represented by one dot and the color label corresponding to their regional origin. The percentage of variance explained by each PC is shown on the axis. C) Individual grouping inferred by ADMIXTURE. Results from K = 4 and K = 12 are shown. Each individual’s genome is represented by a vertical bar composed of colored sections, where each section represents the proportion of an individual’s ancestry derived from one of the K ancestral populations. Individuals are arrayed horizontally and grouped by population as indicated.

The tree makes it clear: all non-Africans form their own independent branch from Africans. In the PCA you see that along the biggest component of variation in the genetic data the non-African groups are about the same distance from Africans. And in the ADMIXTURE analysis when you assume four ancestral populations, the Africans and non-Africans separate out cleanly excluding groups which a high likelihood of European or Arab admixture. Remember the part about how Africans have more genetic diversity than all non-Africans combined? That’s also part of the puzzle. In some ways all non-Africans can be thought of as a subset of the genetic variation of Africans. Those humans who reside outside of Africa are simply a diversified branch of Africans. From what I can tell the data is converging on the likelihood that there was only one migration out of Africa which resulted in the branches of non-African humanity. That means that those of us of non-African ancestry are all equally distant from the African root.

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In the early 20th century there was a rather strange (in hindsight) debate between two groups of biological scientists attempting to understand the basis of inheritance and its relationship to evolutionary processes. The two factions were the biometricians and Mendelians. As indicated by their appellation the Mendelians were partisans of the model of inheritance formulated by Gregor Mendel. Like Mendel many of these individuals were experimentalists, with a rough & ready qualitative understanding of biological processes. William Bateson was arguably the model’s most vociferous promoter. Set against the Mendelians were more mathematically minded thinkers who viewed themselves as the true inheritors of the mantle of Charles Darwin. Though the grand old patron of the biometricians was Francis Galton, the greatest expositor of the school was Karl Pearson.* Pearson, along with the zoologist W. F. R. Weldon, defended Charles Darwin’s conception of evolution by natural selection during the darkest days of what Peter J. Bowler terms “The Eclipse of Darwinism”.** One aspect of Darwin’s theory as laid out in The Origin of Species was gradual change through the operation of natural selection upon extant genetic variation. There was a major problem with the model which Darwin proposed: he could offer no plausible engine in regards to mode of inheritance. Like many of his peers Charles Darwin implicitly assumed a blending model of inheritance, so that the offspring would be an analog constructed about the mean of the parental values. But as any old school boy knows the act of blending diminishes variation! This, along with other concerns, resulted in a general tendency in the late 19th century to accept the brilliance of the idea of evolution as descent with modification, but dismiss the motive engine which Charles Darwin proposed, gradual adaptation via natural selection upon heritable variation.

Mendels theory of inheritance rescued Darwinism from the problem of gradual diminution of natural selection’s raw material through the process of sexual reproduction. Yet due to personal and professional rivalries many did not see in Mendelism the salvation of evolutionary theory. Pearson and the biometricians scoffed at Bateson and company’s innumeracy. They also argued that the qualitative distinctions in trait value generated by Mendel’s model could not account for the wide range of continuous traits which were the bread & butter of biometrics, and therefore natural selection itself. Some of the Mendelians also engaged in their own flights of fancy, seeing in large effect mutations which they were generating in the laboratory an opening for the possibility of saltation, and rendering Darwinian gradualism absolutely moot.

There were great passions on both sides. The details are impeccably recounted in Will Provine’s The Origins of Theoretical Population Genetics. Early on in the great debates the statistician G. U. Yule showed how Mendelism could be reconciled with biometrics. But his arguments seem to have fallen on deaf ears. Over time the controversy abated as biometricians gave way to the Mendelians through a process of attrition. Weldon’s death in 1906 was arguably the clearest turning point, but it took a young mathematician to finish the game and fuse Mendelism and biometrics together and lay the seeds for a hybrid theoretical evolutionary genetics.

That young mathematician was R. A. Fisher. Fisher’s magnum opus is The Genetical Theory of Natural Setlection, and his debates with the American physiologist and geneticist Sewall Wright laid the groundwork for much of evolutionary biology in the 20th century. Along with J. B. S. Haldane they formed the three-legged population genetic stool upon which the Modern Neo-Darwinian Synthesis would come to rest. Not only was R. A. Fisher a giant within the field of evolutionary biology, but he was also one of the founders of modern statistics. But those accomplishments were of the future, first he had to reconcile Mendelism with the evolutionary biology which came down from Charles Darwin. He did so with such finality that the last embers of the debate were finally doused, and the proponents of Mendelism no longer needed to be doubters of Darwin, and the devotees of Darwin no longer needed to see in the new genetics a threat to their own theory.

One of the major issues at work in the earlier controversies was one of methodological and cognitive incomprehension. William Bateson was a well known mathematical incompetent, and he could not follow the arguments of the biometricians because of their quantitative character. But no matter, he viewed it all as sophistry meant to obscure, not illuminate, and his knowledge of concrete variation in form and the patterns of inheritance suggested that Mendelism was correct. The coterie around Karl Pearson may have slowly been withering, but the powerful tools which the biometricians had pioneered were just waiting to be integrated into a Mendelian framework by the right person. By 1911 R. A. Fisher believed he had done so, though he did not write the paper until 1916, and it was published only in 1918. Titled The Correlation Between Relatives on the Supposition of Mendelian Inheritance, it was dense, and often cryptic in the details. But the title itself is a pointer as to its aim, correlation being a statistical concept pioneered by Francis Galton, and the supposition of Mendelian inheritance being the model he wished to reconcile with classical Darwinism in the biometric tradition. And in this project Fisher had a backer with an unimpeachable pedigree: a son of Charles Darwin himself, Leonard Darwin.

You can find this seminal paper online, at the R. A. Fisher digital archive. Here is the penultimate paragraph:

In general, the hypothesis of cumulative Mendelian factors seems to fit the facts very accurately. The only marked discrepancy from existing published work lies in the correlation for first cousins. Snow, owning apparently to an error, would make this as high as an avuncular correlation; in our opinion it should differ by little from that of the great-grandparent. The values found by Miss Elderton are certainly extremely high, but until we have a record of complete cousinships measured accurately and without selection, it will not be possible to obtain satisfactory numerical evidence on this question. As with cousins, so we may hope that more extensive measurements will gradually lead to values for the other relationship correlations with smaller standard errors. Especially would more accurate determinations of the fraternal correlation make our conclusions more exact.

I have to admit at the best of times that R. A. Fisher can be a difficult prose stylist to follow. One might wish to add from a contemporary vantage point that his language has a quaint and dated feel which compounds the confusion, but the historical record is clear that contemporaries had great difficulty in teasing apart distinct elements in his argument. Much of this was due to the mathematical aspect of his thinking, most biologists were simply not equipped to follow it (as late as the 1950s biologists at Oxford were dismissing Fisher’s work as that of a misguided mathematician according to W. D. Hamilton). In the the text of this paper there are the classic jumps and mysterious connections between equations along the chain of derivation which characterize much of mathematics. The problem was particularly acute with Fisher because his thoughts were rather deep and fundamental, and he could hold a great deal of complexity in his mind. Finally, there are extensive tables and computations of correlations of pedigrees from that period drawn from biometric research which seem extraneous to us today, especially if you have Mathematica handy.

But the logic behind The Correlation Between Relatives on the Supposition of Mendelian Inheritance is rather simple: in the patterns of correlations betweens relatives, and the nature of variance in trait value across those relatives, one could perceive the nature of Mendelian inheritance. It was Mendelian inheritance which could explain most easily the patterns of variation across continuous traits as they were passed down from parent to offspring, and as they manifested across a pedigree. Early on in the paper Fisher observes that a measured correlation between father and son in stature is 0.5. From this one can explain 1/4 of the variance in the height across the set of possible sons. This biological relationship is just a specific instance of the coefficient of determination, how much of the variance in a value, Y (sons’ heights), you can predict from the variance in X (fathers’ heights). Correcting for sex one can do the same for mothers and their sons (and inversely, fathers and their daughters).*** So combing the correlations of the parents to their offspring you can explain about half of the variance in the offspring height in this example (the correlation is higher in contemporary populations, probably because of much better nutrition in the lower orders). But you need not constraint yourself to parent-child correlations. Fisher shows that correlations across many sorts of relationships (e.g., grandparent-grandchild, sibling-sibling, uncle-niece/nephew) have predictive value, though the correlation will be a function of genetic distance.

What does correlation, a statistical value, have to do with Mendelism? Remember, Fisher argues that it is Mendelism which can explain in the details patterns of correlations on continuous traits. There were peculiarities in the data which biometricians explained with abstruse and ornate models which do not bear repeating, so implausible were the chain of conjectures. It turns out that Mendelism is not only the correct explanation for inheritance, but it is elegant and parsimonious when set next to the alternatives proposed which had equivalent explanatory power. A simple blending model could not explain the complexity of life’s variation, so more complex blending models emerged. But it turned out that a simple Mendelian model explained complexity just as well, and so the epicycles of the biometricians came crashing down. Mendelism was for evolutionary biology what the Copernican model was for planetary astronomy.

To a specific case where Mendelism is handy: in the data Fisher noted that the height of a sibling can explain 54% of the variance of height of other siblings, while the height of parents can explain only 40% of that of their offspring. Why the discrepancy? It is noted in the paper that the difference between identical twins is marginal, and other workers had suggested that the impact of environment could not explain the whole residual (what remains after the genetic component). Though later researchers observe that Fisher’s assumptions here were too strong (or at least the state of the data on human inheritance at the time misled him) the big picture is that siblings have a component of genetic correlation which they share with each other which they do not share with their parents, and that is the fraction accounted for by dominance. When dominance is included in the equation heritability is referred to as the “broad sense,” while when dominance is removed it is termed “narrow sense.”

A concept such as dominance can of course be easily explained by Mendelism, at least formally (the physiological basis of dominance was later a point of contention between Fisher and Sewall Wright). Most of you have seen a Punnet square, whereby heterozygous parents will produce offspring in ratios where 50% are heterozygous, and 25% one homozygote and 25% another. But consider a scenario where one parent is a heterozygote, and the other a homozygote for the dominant trait. Both parents will express the same trait value, as will their offsprings. But, there will be a decoupling of the correlation between trait-value and genotype here, as the offspring will be genotypically variant. Parent-offspring correlations along the regression line become distorted by a dominance parameter, and so reduce correlations. In contrast, full siblings share the same dominance effects because they share the same parents and can potentially receive the same identical by descent alleles twice. Consider a rare recessively expressed allele, one for cystic fibrosis. As it is rare in a population in almost all cases where the offspring are homozygotes for the disease causing allele, both parents will be heterozygotes. They will not express the disease because of its recessive character. But 25% of their offspring may because of the nature of Mendelian inheritance. So there’s a major possible disjunction between trait values from the parental to offspring cohorts. On the other hand, each sibling has a 25% chance of expressing the disease, and so the correlation is much higher than that with the parents (who do not express disease). In other words siblings can resemble each other much more than they may resemble either parent! This makes intuitive sense when you consider the inheritance constraints and features of Mendelism in diploid sexual species. But obviously a simple blending model can account for this. What it can not account for is the persistence of variation. It is through the segregation of independent Mendelian alleles, and their discrete and independent reassortment, that one can see how variation would not only persist from generation to generation, but manifest within families as alleles across loci shake out in different combinations. A simple model of inheritance can then explain two specific phenomena which are very different from each other.

There is much in Fisher’s paper which prefigures later work, and much which is rooted in somewhat shaky pedigrees and biometric research of his day. The take home is that Fisher starts from an a priori Mendelian model, and shows how it could cascade down the chain of inferences and produce the continuous quantitative characteristics we see all around us. From the Hardy-Weinberg principle he drills down through the inexorable layers of logic to generate the formalisms which we associate with heritability, thick with variance terms. The Correlation Between Relatives on the Supposition of Mendelian Inheritance was a marriage between what was biometrics and Mendelism which eventually gave rise to population genetics, and forced the truce between the seeds of that domain and what became quantitative genetics.

As I said, the paper itself is dense, often opaque, and characterized by a prose style that lends itself to exegesis. But I find that it is often useful to see the deep logics behind evolution and genetics laid bare. Some of the issues which we grapple with today in the “post-genomic era” have their intellectual roots in this period, and Fisher’s work which showed that quantitative continuous traits and discrete Mendelian characters were one in the same. The “missing heritability” hinges on the fact that classical statistical techniques tell us that Mendelian inheritance is responsible for the variation of many traits, but modern statistical biology which has recourse to the latest sequencing technology has still not be able to crack that particular nut with satisfaction. Perhaps decades from now biologists will look at the “missing heritability” debate and laugh at the blindness of current researchers, when the answer was right under their noses. Alas, I suspect that we live in the age of Big Science, and a lone genius is unlikely to solve the riddle on his lonesome.

Citation: Fisher, R. A. (1918). On the correlation between relatives on the supposition of Mendelian inheritance Transactions of the Royal Society of Edinburgh

Suggested Reading: The Origins of Theoretical Population Genetics, R.A. Fisher: The Life of a Scientist, and The Genetical Theory of Natural Selection.

* Though I will spare you the details, it may be that the Galtonians were by and large more Galtonian than Galton himself! It seems that Francis Galton was partial was William Bateson’s Mendelian model.

** To be fair, I believe the phrase was originally coined by Julian Huxely.

*** Just use standard deviation units.

Image Credit: Wikimedia

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One of the major issues which has loomed at the heart of biology since The Origin of Species is why species exist, as well as how species come about. Why isn’t there a perfect replicator which performs all the conversion of energy and matter into biomass on this planet? If there is a God the tree of life almost seems to be a testament to his riotous aesthetic sense, with numerous branches which lead to convergences, and a inordinate fascination with variants on the basic morph of beetles. From the outside the outcomes of evolutionary biology look a patent mess, a sprawling expanse of experiments and misfires.

A similar issue has vexed biologists in relation to sex. Why is it that the vast majority of complex organisms take upon themselves the costs of sex? The existence of a non-offspring bearing form within a species reduces the potential natural increase by a factor of two before the game has even begun. Not only that, but the existence of two sexes who must seek each other out expends crucial energy in a Malthusian world (selfing hermaphrodites obviously don’t have this problem, but for highly complex organisms they aren’t so common). Why bother? (I mean in an ultimate, not proximate, sense)

It seems likely that part of the answer to both these questions on the grande scale is that the perfect is the enemy of long term survival. Sexual reproduction confers upon a lineage a genetic variability which may reduce fitness by shifting populations away from the adaptive peak in the short term, but the fitness landscape itself is a constant bubbling flux, and perfectly engineered asexual lineages may all too often fall off the cliff of what was once their mountain top. The only inevitability seems to be that the times change. Similarly, the natural history of life on earth tells us that all greatness comes to an end, and extinction is the lot of life. The universe is an unpredictable place and the mighty invariably fall, as the branches of life’s tree are always pruned by the gardeners red in tooth and claw.

But it is one thing to describe reality in broad verbal brushes. How about a more rigorous empirical and theoretical understanding of how organisms and the genetic material through which they gain immortality play out in the universe? A new paper which uses plant models explores the costs and benefits of admixture between lineages, and how those two dynamics operate in a heterogeneous and homogeneous world. Population admixture, biological invasions and the balance between local adaptation and inbreeding depression:

When previously isolated populations meet and mix, the resulting admixed population can benefit from several genetic advantages, including increased genetic variation, the creation of novel genotypes and the masking of deleterious mutations. These admixture benefits are thought to play an important role in biological invasions. In contrast, populations in their native range often remain differentiated and frequently suffer from inbreeding depression owing to isolation. While the advantages of admixture are evident for introduced populations that experienced recent bottlenecks or that face novel selection pressures, it is less obvious why native range populations do not similarly benefit from admixture. Here we argue that a temporary loss of local adaptation in recent invaders fundamentally alters the fitness consequences of admixture. In native populations, selection against dilution of the locally adapted gene pool inhibits unconstrained admixture and reinforces population isolation, with some level of inbreeding depression as an expected consequence. We show that admixture is selected against despite significant inbreeding depression because the benefits of local adaptation are greater than the cost of inbreeding. In contrast, introduced populations that have not yet established a pattern of local adaptation can freely reap the benefits of admixture. There can be strong selection for admixture because it instantly lifts the inbreeding depression that had built up in isolated parental populations. Recent work in Silene suggests that reduced inbreeding depression associated with post-introduction admixture may contribute to enhanced fitness of invasive populations. We hypothesize that in locally adapted populations, the benefits of local adaptation are balanced against an inbreeding cost that could develop in part owing to the isolating effect of local adaptation itself. The inbreeding cost can be revealed in admixing populations during recent invasions.

First, plants are good models to explore evolutionary genetics. They’re not as constrained as say mammals, or the typical tetrapod, when it comes to barriers to gene flow between distinct taxa. Hybridization is common, and plants can also self-fertilize as well as cross-fertilize, allowing researchers to push the genetic pool in different directions (”selfing” obviously reduces the effective population and is an extreme form of inbreeding, so it’s a good way to purge genetic variation really quickly). In a perfect abstract world of evolution one might imagine Richard Dawkins’ vehicles and replicators as fluid entities which float along a turbid sea of evolutionary genetic parameters, drift, migration, mutation and selection. But reality is constrained to DNA substrate, which have their own parameters such as recombination, modulators such as epigenetics, and numerous ways to express variation through gene regulation. It’s complicated, and stripping the issues down to their pith is easier said that done.

But the broader dynamics here being examined is the generalist-specialist trade-off, which I think is relevant to the two issues I introduced earlier in this post. Specialists are optimized for their own position in the adaptive landscape, but have difficulties when it is perturbed. Generalists always less than maximum fitness in all landscapes, but higher average fitness across them because they can adapt to changes. Specialization is local adaptation of particular lineages, while in the generalist case you can have invasive species in novel environments. They’re obviously facing an adaptive landscape which is at some remove from what any of the introduced genotypes were “optimized” for, so hybridization produces something new for something new.

In the first figure of the paper you see F3 wild barley descended from two parental lineages, ME and AQ. The left panels show seed output as a function of heterozygosity, and the right panels as a function of ME genome content. Remember that in subsequent generations the descendants of hybrids will vary quite a big in genetics and phenotype as the original alleles re-segregate.

The takeaway is that in novel environments genetic variation seems to result in increased fitness. Why? One concept which one has to introduce is heterosis, whereby crosses between homogeneous lineages produce more fitness offspring. One reason this may be is that there is overdominance, where heterozygotes have greater fitness than the homogyzotes. This is the case with sickle-cell malaria disease. Another reason may be that in the original parental lineages there was a higher fraction of alleles which were deleterious in homozygote genotypes. In plain English, inbreeding resulted in genetic drift which cranked up the proportion of alleles implicated in recessively express negative phenotypes. The authors argue though that in the context local adaptation is strong enough to be a barrier against too much gene flow between the parental wild barely lineages, so the deleterious alleles are less likely to be masked. Only in a novel environment when that benefit was removed from the equation could the negative consequences of inbreeding come to the fore in the total calculus.

Figure 2 shows the results of experiments which examine the fitness of white campion, a European species which has been introduced in North America. In the left panel are crosses between native European lineages, with distance between parental lineages on the x-axis. In the right panel you have the same experiment, but with North American variants, which are products of introductions from various regions of Europe. The plants were grown in a “common garden,” to show how all the genotypes performed when environment was controlled.

As you can see moderate levels of hybridization entailed a benefit in the European variants, but not the North American variants. Hybridization between variants which were too distant did produce outbreeding depression in the European case, suggesting perhaps that disruption of co-adapted gene complexes resulted in a greater fitness cost than the masking of deleterious alleles due to inbreeding. One can make the inference from these data that the introduced white campion lineages are already hybridized, the barriers to crossing being removed by a disruption of the adaptive landscapes which each native lineages was optimized for.

Here are the authors from the discussion talking about invasions of exotic species:

Provided that multiple introductions from different source populations have occurred, the benefits of admixture become freely available to introduced populations that do not yet show a pattern of local adaptation. Because the benefits are potentially large, admixture may play an important role during early invasions. Native populations often show evidence of inbreeding depression…and one instant reward of admixture in the introduced range is the release of this genetic burden. Such heterosis effects can contribute significantly to the establishment and early success of invasive species…When tested together in a common garden experiment, invaders can show enhanced fitness-related traits compared with populations from their native range…If there is evidence of admixture, the effects of heterosis might be a default explanation for such observations, perhaps providing a null expectation against which other explanations (such as trait evolution) need to be tested.

What have plants to do with life as a whole? I assume much. Plants differ in the details, but compared to other complex multicellular organisms in regards to evolutionary genetics they’re quite liberated. By this, I mean that their modes of reproduction and promiscuity in hybridization make them more of an ideal “frictionless” test case of evolutionary biology and the power of the classical parameters. Perhaps given enough time natural selection would produce the ideal replicator to rule them all, to drive all others to extinction. But that day is not this day. And that day may never come because the universe is far too protean and erratic. Life is varied, on the phenotypic and genotypic level, and the exogenous processes of climate and geology continue to warp and reshape the adaptive landscape. And more subtly, but just as critically, life is always in an endless race with itself, as pathogens co-evolve with their hosts, and predators figure out how to outfox their prey. Life warps its own adaptive landscapes, and the innovation of one branch may lead to extinction of others as well as the proliferation of new branches.

More prosaically and anthropocentrically what does this say about us? Humans are an expansive species, and over the past 500 years different lineages have been hybridizing promiscuously. New genotypes have arisen in altered landscapes, and our pathogens are also riding the high tide of globalization onward and upward. We are ourselves a “natural experiment.”

Image Credit: Olivia Munn by Gage Skidmore

Link hat tip: Dienekes.

Citation: Verhoeven KJ, Macel M, Wolfe LM, & Biere A (2010). Population admixture, biological invasions and the balance between local adaptation and inbreeding depression. Proceedings. Biological sciences / The Royal Society PMID: 20685700

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“…the occupation of Australia/New Guinea is momentous in that it demanded watercraft and provides by far the earliest evidence of their use in history. Not until about 30,000 years later (13,000 years ago) is there strong evidence of watercraft anyway else in the world, from the Mediterranean.

Initially, archaeologists considered the possibility that the colonization of Australia/New Guinea was achieved accidentally by just a few people swept to sea while fishing on a raft near an Indonesian island. In an extreme scenario the first settlers are picture as having consisted of a single pregnant young woman carrying a male fetus. But believers in the fluke-colonization theory have been surprised by recent discovers that still other islands, lying to the east of New Guinea, were colonized soon after New Guinea itself, by around 35,000 years ago….”

- page 42 of Guns, Germs and Steel

The settlement of Australia is a breakthrough in the “human story.” Very soon after anatomically modern humans began to replace (and to some extent assimilate) other lineages of our genus in Eurasia we pushed beyond the previous outer limits of the domains of humankind. The ancestors of Australian Aboriginals swept past the Wallace Line, and quickly settled the Ice Age continent of Sahul, consisting of Australia and Papua New Guinea. The biogeography of Australia is well known. Aside from bats and some endemic rodents the continent was free of placental mammals before modern humans arrived.

As for when these humans made landfall, there is some debate as to that particular issue. The oldest remains from Australia, Mungo Man, has been dated to anywhere between 70,000, and 30,000, years before the present. If we took the older date then Australia would have been settled almost immediately after the expansion of non-African modern humanity. If we accepted the younger date, then the settlement of Australia would have been concurrent with the final replacement of Neandertals by modern humans in Europe. The current consensus seems to be that Mungo Man dates to approximately 46,000 years before the present. As the first dating of a particular individual from a species in a region is liable to miss earlier individuals who were not fossilized it seems likely that Australia was settled by anatomically modern humans on the order of 46,000 years before the present, but somewhat earlier than that date. That would imply that Australia was populated by anatomically modern humans at least 10,000 years before Europe. One should probably not be too surprised by this. Out-of-Africa humans were probably initially tropically adapted so lateral migration would have been easier, but also, there were no hominin competitors in Australia.

But how do these archaeological insights relate to the current Aboriginal population of Australia? Such questions are fraught with politics, but let’s put that to the side. We know that Australia was not totally isolated from the rest of the world. The dingo arrived from Southeast Asia within the last 4,000 years. The Aboriginals of northern Australia were certainly familiar with the idea of agriculture, because they traded with the Torres Straits Islanders, who were farmers and seafarers, and who had contacts with New Guinea (see After the Ice). Some anthropologists, such as Joseph Birdsell, proposed that modern Aboriginals were a compound of multiple migration events, and had undergone a great deal of evolution in situ. Additionally, classically trained physical anthropologists in the early 20th century noted morphological parallels between Australian Aboriginals and the peoples of India, giving rise to the construct of the Australoid race (a term still used by Indian anthropologists). As I noted earlier the connection between South Asia and Australia genetically seems likely to be distant and tenuous at best, inferring from what we know of uniparental markers (genetic variants passed only through the mother or father, the mtDNA and Y). The genetic data tentatively seem to reject Birdsell’s model, and favor a more parsimonious one of a single original settlement on Sahul, and subsequent diversification and isolation (Australia, Tasmania and Papua New Guinea were separated only ~10,000 years ago with rising sea levels).

But there’s only so much that uniparental lineages can tell us. There are limits to the information one can glean from relatively short sequences of mtDNA and Y, and, these gene lineages are subject to their own particular dynamics. Not only do human mating patterns exhibit sex-specific biases, but the neutrality of these lineages from an evolutionary perspective has been questioned. And, the haploid nature of these loci also mean that the effective population size is small (i.e., only copy of each per person, instead of two as in the case of most genes) and stochastic fluctuations may be more extreme than in the rest of the genome. On the one hand more random variation could allow for the emergence of greater between population differences which might be informative, but on the other hand it can also swamp out the past history too quickly and result in convergences which tell us nothing about phylogenetic connections.

All this is why a new paper looking at the broader genomic patterns of variation in Australian Aboriginals is important for clarifying and adding more precision to our evolutionary historical assumptions, which would frame more specific inferences about this population. There are, and were, difficulties in obtaining the data for historical and political reasons. But now that the barrier has been breached, I assume that we’ll be seeing more in the near future. Whole-Genome Genetic Diversity in a Sample of Australians with Deep Aboriginal Ancestry:

Australia was probably settled soon after modern humans left Africa, but details of this ancient migration are not well understood. Debate centers on whether the Pleistocene Sahul continent (composed of New Guinea, Australia, and Tasmania) was first settled by a single wave followed by regional divergence into Aboriginal Australian and New Guinean populations (common origin) or whether different parts of the continent were initially populated independently. Australia has been the subject of relatively few DNA studies even though understanding regional variation in genomic structure and diversity will be important if disease-association mapping methods are to be successfully evaluated and applied across populations. We report on a genome-wide investigation of Australian Aboriginal SNP diversity in a sample of participants from the Riverine region. The phylogenetic relationship of these Aboriginal Australians to a range of other global populations demonstrates a deep common origin with Papuan New Guineans and Melanesians, with little evidence of substantial later migration until the very recent arrival of European colonists. The study provides valuable and robust insights into an early and important phase of human colonization of the globe. A broader survey of Australia, including diverse geographic sample populations, will be required to fully appreciate the continent’s unique population history and consequent genetic heritage, as well as the importance of both to the understanding of health issues.

The sample consisted of 38 individuals, 30 females and 8 males, from the Riverina region of New South Wales. The sample size may be small, but for the broad-brush and relatively coarse questions being asked in this paper they’re sufficient. Consider that genomic sequencing of one Native American and one Bushman could tell you that the latter is likely to come from a far more genetically diverse population than the former. If you constructed a phylogenetic tree with half a dozen individuals of each of the populations you’d see that the Native Americans are a subset of the Bushman genetically, so to speak. If you’re trying to distinguish between questions such as, “did the last common ancestor of Australian Aboriginals and Javanese live 5,000, or 50,000, years before the present”, then this is a sufficient sample. A bigger issue is that the sample has substantial European admixture through the paternal lineages. From what I have heard attempts were made to get a more “pure” Aboriginal group, but the logistics were too difficult in the end. Science is the art of the possible.

They used an Affymetrix chip with nearly 1 million SNPs (out of 3 billion base pairs), but filtered it down even further for this analysis. Most of the work used a data set of ~160,000 SNPs, arrived via quality controls, as well as the intersection with HapMap3 and HGDP SNP sets. Again, in light of the coarse questions asked 160,000, let along 16,000, should probably suffice. Remember they’re trying to move beyond what we can infer from classical autosomal markers and uniparental lineages. This is a big step in that direction.

The figure to the left shows two phylogenetic trees (note: I may reedit these figures for ease of display or clarity). The utility of the trees is obvious: they’re showing you how populations relate to each other. So you throw all the individuals in each given population into a pot, average out their genetic character, and perform pairwise calculations on them. The other groups are from the HGDP data set. The statistic they’re using is F_st; basically a measure of between population genetic variation. Alleles, genetic variants, vary in frequency from population to population, as well as the fact that different individuals within populations have different genotypes, and this is just capturing the component which is varying across populations. So as an example, if the allele x in two populations is at frequency 0.5 for both, then the F_st is 0. There’s no difference. If x is at 1 in one population and 0 in the other, then F_st = 1. All the variation is between the populations, since there is none within the populations.

The trees illustrate visually the relationships in an F_st matrix of pairwise population comparisons. Populations which are genetically close are not very distant from each other along the length of the tree, while those which are genetically very different are farther from each other terminus-to-terminus. But remember that these visualizations don’t tell us anything necessarily in a concrete manner before we interpret them through the filter of what we already know. For example, the Mozabites, “MOZ”, are outside of the main clusters. Why? Without knowing anything about their history we might assume that they were isolated from the original African population at an early point in time (though observe the minimal distance from the trunk, peculiar). But we know their history, and the topology in that region of the network is an outcome of admixture. The Mozabites have a substantial amount of recent Sub-Saharan African ancestry. Similarly, two of the groups near the root of the East Eurasian cluster are actually relatively recent admixtures between West and East Eurasian populations, the Uyghurs and Hazaras.

The Australian Aboriginals are similar to the Mozabites, Uyghurs and Hazaras. Their position in the first panel is near the root of the Oceanian cluster. This is due to their substantial European admixture, which we know is present through their oral history, recorded history, and, the physically composite nature of many modern Australian Aboriginals. To generate the second tree the authors reconstructed the allele frequencies of the Australian Aboriginals by subtracting the European component of admixture. They did this by noting that they had Western European populations in HapMap3, and the offspring population between these groups which they knew, and the unknown Aboriginal parent population.  Using the Structure program they simply performed the algebra, whereby Aboriginal = Admixed Aboriginal – European (OK, not “simply”). And as you can see, by using the reconstructed Aboriginal allele frequencies the tree now places this group, AuR*, firmly within the Oceanian cluster.

The clustering of the Oceanian groups itself alone gives us strong evidence that the settlement of Sahul was by one population which later diversified, rather than separate independent groups. But let’s back up a bit, and look at the admixture aspect again. To the left you see the PCA plots of the HGDP data set which you should be familiar with. Each axis represents and independent dimension of genetic variation.  For the first panel the x-axis, PC1, is the separation between Africans and non-Africans. This is the biggest dimension of variation, and points to the Out-of-Africa event. The second dimension seems to map well onto the east-west axis, more or less. Remember that each PC is rank ordered in terms of the proportion of the total genetic variation which it can explain independently. Interestingly PC3 and PC4 allow for the separation of Oceanians and Amerindians from other groups. In isolation-by-distance and serial bottleneck models it shouldn’t be too surprising that these two groups on the geographic margins of the traditional human range would exhibit some genetic peculiarities due to their history after separation from Eurasian groups. This is why the Kalash of Pakistan are also outliers, this non-Muslim tribe remained isolated in their mountain valley and so accumulated their own genetic distinctiveness.

And yet note the position of Amerindians and Oceanians in the first panel, they’re somewhat closer to West Eurasians than East Asians. In the case of Amerindians there has long been the model whereby the ancient Beringian population which expanded into the New World had a component of ancestry which was closer to West Eurasia. This is true today among Siberian groups such as Yakuts, but differentiating the more recent introgression of Russian ancestry with an ancient West Eurasian substratum is difficult.

But admixture is surely part of the puzzle too. Compare the linear topology of Aboriginals and Amerindians with African Americans. The PCA plot is putting the focus on between population differences, so these sorts of distributions, so cleanly linear, are indicative of possible recent admixture between two distinct populations. The populations of the New World and Australia were relatively small and thin in terms of distribution, so it should not be too surprising that a substantial uptake of European ancestry has occurred in both cases. Isolated cases of individuals “going native” probably illustrate a bigger trend. The 2002 film Rabbit-Proof Fench was a dramatization of the reality that quite often children of mixed heritage will identify with one culture and parent. This is not an atypical disjunction between genes and culture in terms of their mode of inheritance. Both parents contribute equally genetics to the autosome, but cultural contribution is more of a contingent matter.

To further explore the admixture within Aboriginals the authors performed a frappe and Structure analysis. These two methods differ in the details but perform basically the same operation; they take individuals and assign components of their genome to K putative ancestral groups. So K = 2 would indicate 2 ancestral groups, while K = 10 would indicate 10. Here’s we’re looking at K = 5. Again, proper caution is warranted with these methods because without context we may not be able to interpret the results. But in this case the ends are clear and distinct: what is the extent and range of European ancestry in this Australian Aboriginal sample? Both the frappe and Structure programs paralleled each other in outcome; the Aboriginal sample varied quite a bit in ancestral quanta. The Papuans and Melanesians serve as appropriate Oceanian references. The Melanesians have a residual component (shaded orange and yellow top to bottom) which is similar to East Asians. This is a marker of the Austronesian expansion into the Pacific. The Papuans and Aboriginals generally lack this, which stands to reason considering their greater cultural isolation from the Austronesians.

The frappe and Structure results dovetail perfectly with the PCA plots. Both suggest that the Aboriginal population is admixed, with the parental populations being West Eurasian (European) and Oceanian, and, that that admixture varies from individual to individual.  The F_st also suggested this, though at a coarser population wide scale, and only with prior knowledge of the possibility of admixture. Not only that, but the mtDNA and Y chromosomal results on these individuals also comes out to the same inferred proportion. Recall that there were only 8 males, so the Y sample is small. But they calculate that ~40% of the Y lineages are not Aboriginal, while nearly ~100% of the female ones are. This sort of disjunction is common in the New World among Mestizo and African American populations, as well as the mixed Cape Coloured population of South Africa. The proportional of autosomal ancestry inferred from these uniparental markers is about what was calculated with their SNP-chip, suggesting the persistence of this sex-biased admixture pattern over the past two centuries. Remember that if something more complex demographically had occurred we may not have been able to infer admixture from uniparental lineages. Imagine if the Aboriginal tribes in New South Wales which were admixed were decimated by an unadmixed group, so that only the females from the admixed group survived. After that event both mtDNA and Y chromosomal lineages would have been Aboriginal, but the European ancestry would persist in the autosome.

To the left is an estimate of individual-to-individual ancestral quanta. The mode, the most frequent value, is near the total genome estimate of the whole population, around ~2/3 Aboriginal. There is clearly a wide range of variation in admixture. It looks like that within this Australian Aboriginal community 20% of this sample are 50% or more European in ancestry. Interestingly one man is ~100% Aboriginal. The authors do remark that their estimates are probably low balling the Aboriginal ancestral quantum; the SNP-chip was constructed with European genetic variation as a baseline, so it is missing Australian variation on loci where Europeans are monomorphic. But even with that taken into account the  Aboriginal group here is substantially admixed. This prompts me to ask: is it possible that there is more distinctive indigenous genetic material in the ~20 million white citizens of Australia than within the indigenous groups themselves? I’ve already suggested that this dynamic is exactly what is operative in Brazil, but the analogy is only rough a best. Many Australian whites derive from recent waves of migration and may not have any ancestors with roots back to the 19th century (i.e., all their grandparents and/or parents may have been born in Europe or the British Isles). But that must be balanced against the fact that Australian Aboriginals are much more European in ancestry than Brazilian Amerindians likely are. The rapid growth of indigenous Australians can’t be a function purely of high fertility. Rather, many people of mixed heritage are identifying as indigenous. The outmarriage rates for urban Aboriginals in some of the literature is estimated to be in the 70-90% range.

So far we’ve covered aspects of Australian Aboriginal genetics relevant to paleoanthropology and historical population genetics. But as I have observed many a time, one of the primary reasons for this sort of population analysis is to clarify background parameters for medical genetics. The life expectancy gap between indigenous and non-indigenous Australians is on the order of 10 years. It also seems plausible that the same disease-driven population crash which occurred in the New World after contact with Europeans was also driving population changes in Australia around the time of European settlement. Since Aboriginals were generally hunter-gatherer groups many infectious diseases which required higher densities could only be incubated among Europeans. Aboriginals near European settlements, or those who settled within them, would naturally be exposed to these infections and suffer greater morbidity and mortality. One wonders if some of the diseases which Aboriginals suffer from are due to genetic differences between the populations in regards to immunity, as well as the “diseases of civilization” (e.g., type 2 diabetes).

As noted in the paper admixed populations present both pitfalls and opportunities when it comes to elucidating risk alleles:

Whereas the admixture present in the AuR [the Riverina Aboriginals -Razib] sample presents a potential challenge in conducting traditional association methods for disease gene discovery…it opens the possibility of using admixture mapping…Admixture mapping is most suitable for traits, like CKD, that differ in frequency between the two parental populations of an admixture group. The approach essentially looks for genomic regions with an excess of higher-risk population ancestry relative to other regions or controls..A set of markers, spread across the genome, that are highly informative as to ancestry (ancestry informative markers or AIMs) is an essential requirement for admixture mapping.

CKD above refers to chronic kidney disease, which Aboriginals suffer at ~10 times greater rates than non-Aboriginals. To the left is a figure which shows the distribution of SNP rs12458349, a derived allele which has the highest F_st value between AuR* and HapMap3 populations. The genomic region which rs12458349 is embedded within has been implicated in diabetic nephropathy, a major cause of CKD. Derived here means that the SNP is evolutionarily novel in relation to the ancestral state, which all other human populations exhibit.

So why is this derived variant at a high frequency in Aboriginals (and other Oceanians), but not other human populations? It could be random genetic drift. As populations migrated out of Africa they may have gone through bottlenecks and isolations in a step-wise fashion and each group down the spatial and temporal sequence would accumulate their own unique variants. Or, it may have been adaptation, which drive up frequencies through positive selection around that genomic region. But this is where limitations of sample size and representativeness crop up, as these are not quite the coarse questions which we were focusing on earlier. From the the text:

Genetic drift, or random changes in allele frequencies, is expected to be a major force in a population, like Aboriginal Australia, that has been relatively small and/or isolated for a long period of time. Differentiation could also be explained by natural selection. The presence of several highly differentiated SNPs in the region, spanning nearly 0.5 Mb in length, hints at the presence of a long common haplotype that might be indicative of genetic hitch-hiking and recent positive selection. However, it is difficult to distinguish between possible explanations because the sample is small, with extensive admixture hampering phasing and direct investigation of linkage-disequilibrium-based selection signals.

Note however the presence of the derived allele in Papuans and Aboriginals, but not Melanesians. In the phylogenetic network the Aboriginals are the outgroup, and yet on this character the Melanesians are. This is a locus which will no doubt be explored in the future, because the patterns here will be fascinating to tease apart, and, of possible medical relevance.

Overall this paper has confirmed much of what we know, or at least solidified our background assumptions. The contemporary peoples of Melanesia, New Guinea and Australia have a common ancestral heritage. Coalescence times back to the last common ancestor between these populations and non-Oceanian groups suggest that their residence in their current locations is antique, and possibly back to the first settlement. One should be duly cautious about extrapolating from contemporary patterns to variation to the past, but I think on this scale we’re on more solid ground. Parts of southern Australia and Tasmania may have an equitable climate where ancient DNA samples may have been preserved, so that could resolve the issues with more certainty in the future.

But aside from phylogeny, a closer study of Australian Aboriginal genetics may also give us insights into the impact which agriculture and higher population densities had upon our species’ genomes. The Australian Aboriginals were aware of agriculture because of contacts with the peoples of the Torres Straits, but they never seem to have adopted it. In contrast to Australia the highlands of New Guinea developed a relatively high population density with the spread of a gardening mode of production. So here you have two populations which were in contact ~10,000 years ago, and have diverged in mode of production subsequent to that period. A comparison in allele frequencies between these two populations would then be instructive as to the power of drift and selection to drive evolutionary change over 10,000 years.

Obviously there needs to be more work done, and extrapolating from one sample will not do. The north coast Aboriginals were certainly in contact with sailors from Southeast Asia before Europeans arrived, and they speak a different group of languages from those in the rest of the continent. The existence of tribes with non-trivial numbers of blonde individuals in the western deserts despite no other apparent European admixture also demands to be explored. From what Joseph Birdsell documented about the Mendelian inheritance patterns of blondism among these tribes it seems likely that the genetic architecture is very different from that in Europeans.

Over the past generation we’ve begun to really understand how the human tree of life branched out and flourished. Now it’s time to fill in the gaps, and with whole genome sequencing around the horizon many of the technical limitations will be removed. But what about the social and political ones? The consent given by this Aboriginal group has now opened a window into the evolutionary genetic history of all Australian Aboriginals, imperfect as that is. But what will happen when many more people in developed nations get sequenced, and so know their own genetic history with great detail? If a non-trivial proportion of Aboriginal ancestry is found across the old stock white population of Australia could a collaborative project just “reconstruct” the Aboriginal genome from these individuals, and so do an end-around the socio-political minefields? I suppose we’ll see soon enough.

Image credit: Aboriginal cricket team, 1868, Wikimedia Commons

Citation: McEvoy, Brian P., Lind, Joanne M., Wang, Eric T., Moyzis, Robert K., Visscher, Peter M., van Holst Pellekaan, Sheila M., & Wilton, Alan N. (2010). Whole-Genome Genetic Diversity in a Sample of Australians with Deep Aboriginal Ancestry The American Journal of Human Genetics : 10.1016/j.ajhg.2010.07.008

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Though I don’t blog about the topic with the breadth and depth of individuals such as Brian Switek or Darren Naish I do take some interest in natural history. This is the domain which was my original focus as a child when it came to science, and I continue to observe it from afar with great fondness. General questions, such as the role of contingency and necessity in the arc of evolution, are obviously the sort of issue which natural history can be brought to bear upon. But I also have a fascination with specific, often anomalous details. For example, the Monito del Monte of Chile is generally held to be more closely related to the marsupials of Australia than those of the New World. It is the only extant member of the order Microbiotheria, and its connection to Australian marsupials is one of those surprises which go to show you why science is done in the field, and not just theorized from your a priori beliefs. It’s why you play the game, and don’t simply allow the handicapping professionals to decide wins and losses.

A new paper in PLoS Biology explores the phylogenetic relationship of Australian and New World marsupials through a more robust genomically focused technique. Though the method has a “in silico” spin, the basics seem to be grounded in cladistics. Look for derived characters which can indicate monophyly. Monophyly simply means that all of a set of organisms descend from one common ancestor. So, famously, the class of reptiles is not monophyletic. Some of the descendants of the common ancestors of all reptiles are not included within the class, birds. Earlier generations of taxonomists tended to classify organisms based on their characters, and the set of characters which they chose for reptiles included groups, such as crocodiles and tortoises, which were genetically very distant (when compared to crocodiles and birds). Though anatomically informative, these sorts of taxonomic classifications misled one as to evolutionary history. Not a minor matter. Ergo, the rise of cladistic techniques which replaced intuition with a more formal hypothetico-deductive framework. Because of its generality as a method naturally you can substitute genetic loci for morphological character traits, and so you get papers such as the one below.

Tracking Marsupial Evolution Using Archaic Genomic Retroposon Insertions:

Ever since the first Europeans reached the Australian shores and were fascinated by the curious marsupials they found, the evolutionary relationships between the living Australian and South American marsupial orders have been intensively investigated. However, neither the morphological nor the more recent molecular methods produced an evolutionary consensus. Most problematic of the seven marsupial groups is the South American species Dromiciops gliroides, the only survivor of the order Microbiotheria. Several studies suggest that Dromiciops, although living in South America, is more closely related to Australian than to South American marsupials. This relationship would have required a complex migration scenario whereby several groups of ancestral South American marsupials migrated across Antarctica to Australia. We screened the genomes of the South American opossum and the Australian tammar wallaby for retroposons, unambiguous phylogenetic markers that occupy more than half of the marsupial genome. From analyses of nearly 217,000 retroposon-containing loci, we identified 53 retroposons that resolve most branches of the marsupial evolutionary tree. Dromiciops is clearly only distantly related to Australian marsupials, supporting a single Gondwanan migration of marsupials from South America to Australia. The new phylogeny offers a novel perspective in understanding the morphological and molecular transitions between the South American and Australian marsupials.

Retroposons are genetic elements which insert randomly throughout the genome, and rarely in the same location in across lineages. This avoids “false positives” where you observe genetic features across taxa which you incorrectly infer to indicate a phylogenetic relationship. The pattern of variation of randomly distributed distinctive retroposons can theoretically be used to map the sequence of relatedness of the same genes (orthologous) across species. Retroposon insertions copious within the marsupial genome, so naturally they’re a good candidate for markers which might exhibit the distinctiveness necessary to explore deep time evolutionary relationships. Additionally retroposons can nest within each other, within newer insertion events overlain over older ones, so that they create a sort of genetic palimpsest. These researchers filtered the loci harboring retroposons down to 53 which were particularly informative for relationships across the marsupial species for which they had genomic data, two species per order excluding orders without more than one species. The two species within each order were selected from lineages which were presumed to exhibit the deepest evolutionary split within the clade.

Granted, it isn’t as if taxonomists haven’t been interested in the relationships of marsupial mammals. As noted in the paper the nature of the phylogenetic tree frames plausible hypotheses which explain the current biogeographic pattern we see. Where there are two sets of marsupial mammals separated by the Pacific, but where the spatial pattern does not perfectly correspondent to the phylogenetic relationship. Here is a figure from a 2004 paper:

Australian and South American marsupials are color coded. As you can see, Dromiciops, Monito del Monte, is nested within the monophyletic clade which includes all the Australian mammals. But, the aforementioned paper was based on mitochondrial DNA. The DNA passed along the maternal lineage, easy to extract and amplify, as well as analyze (because of the lack of recombination). But for the purposes of exposing such deep time relationships mtDNA may not be optimal, and should not be the last word.

Much of the “guts” of the paper was obviously computational, and wasn’t explored in detail within the text. So let’s jump to the outcome, the new branch of the tree of life for marsupials:

Ah, now you see that Australian marsupials are a monophyletic clade! The Monito del Monte is no longer nested within their own lineage, but is now an outgroup. It would be peculiar if it was not the closest of the outgroups, so its positioning is reasonable in terms of what we’d expect. From the discussion:

Given the limitations just mentioned, the retroposon marker system identified a clear separation between the South American and Australasian marsupials. Thus, the current findings support a simple paleobiogeographic hypothesis, indicating only a single effective migration from South America to Australia, which is remarkable given that South America, Antarctica, and Australia were connected in the South Gondwanan continent for a considerable time.

The search for diagnostic South American or Australidelphian marsupial morphological characters has been so far confounded by the lack of a resolved marsupial phylogeny…The newly established marsupial tree can now be applied not only to morphological and paleontological studies but also to clearly distinguish genomic changes.

Life is not always parsimonious, but when more powerful techniques which can resolve issues to a greater degree of precision produce more parsimony, then the world is as it should be in science. The main curiosity I have is to wonder if the outcome isn’t a little too convenient for the generation of more elegant paleontological models. I’m not casting doubt on the integrity of the researchers, but with methods which require such heavy cognitive lifting, and operationally are a touch opaque because of the technical component, one would be assuaged by replication. I believe we will be in the future. If we have $1,000 genomes for human beings in a few years, NSF grants for taxonomists who lean on genomics may go a lot further in 2020.

Image Credit: José Luis Bartheld from Valdivia, Chile

Citation: Nilsson MA, Churakov G, Sommer M, Tran NV, Zemann A, Brosius J, & Schmitz J (2010). Tracking marsupial evolution using archaic genomic retroposon insertions. PLoS biology, 8 (7) PMID: 20668664

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One of the main criticisms of the population genetic pillar of the modern evolutionary synthesis was that too often it was a game of “beanbag genetics”. In other words population geneticists treated genes as discrete independent individual elements within a static sea. R.A. Fisher and his acolytes believed that the average effect of fluctuations of  genetic background canceled out as there was no systematic bias, and could be ignored in the analysis of long term evolutionary change. Classical population genetics focused on genetic variation as abstract elementary algebras of the arc of particular alleles (or several alleles). So the whole system was constructed from a few spare atomic elements in a classic bottom-up fashion, clean inference by clean inference. Naturally this sort of abstraction did not sit well with many biologists, who were trained in the field or in the laboratory. By and large the conflict was between the theoretical evolutionists, such as R. A. Fisher and J. B. S. Haldane, and the experimental and observational biologists, such as Theodosius Dobzhansky and Ernst Mayr (see Sewall Wright and Evolutionary Biology for a record of the life and ideas of a man who arguably navigated between these two extremes in 20th century evolution because of his eclectic training). With the discovery that DNA was the specific substrate through which Mendelian genetics and evolutionary biology unfolded physically from generation to generation a third set of players, the molecular biologists, entered the fray.

The details of genetics, the abstract models of theorists, the messy instrumentalism of the naturalists, and the physical focus of the molecular researchers, all matter. Through the conflicts between geneticists, some arising from genuine deep substantive disagreement, and some from different methodological foci,  the discipline can enrich our understanding of biological phenomena in all its dimensions. Genomics, which canvasses the broad swaths of the substrate of inheritance, DNA, is obviously of particular fascination to me, but we can also still learn something from old fashioned genetics which narrows in on a few genes and their particular dynamics.

A new paper in PLoS Biology, Cryptic Variation between Species and the Basis of Hybrid Performance, uses several different perspectives to explore the outcomes of crossing different species, in particular the impact on morphological and gene expression variation. You’ve likely heard of hybrid vigor, but too often in our society such terms are almost like black-boxes which magically describe processes which are beyond our comprehension (hybrid vigor and inbreeding depression freely move between scientific and folk genetic domains). This paper attempts to take a stab at peeling pack the veil and gaining a more fundamental understanding of the phenomenon. First, the author summary:

A major conundrum in biology is why hybrids between species display two opposing features. On the one hand, hybrids are often more vigorous or productive than their parents, a phenomenon called hybrid vigor or hybrid superiority. On the other hand they often show reduced vigour and fertility, known as hybrid inferiority. Various theories have been proposed to account for these two aspects of hybrid performance, yet we still lack a coherent account of how these conflicting characteristics arise. To address this issue, we looked at the role that variation in gene expression between parental species may play. By measuring this variation and its effect on phenotype, we show that expression for specific genes may be free to vary during evolution within particular bounds. Although such variation may have little phenotypic effect when each locus is considered individually, the collective effect of variation across multiple genes may become highly significant. Using arguments from theoretical population genetics we show how these effects might lead to both hybrid superiority and inferiority, providing fresh insights into the age-old problem of hybrid performance.

There are various ways one presumes that hybrid vigor could emerge. One the one hand the parental lines may be a bit too inbred and therefore have a heavier than ideal load of deleterious alleles which express recessively. Since two lineages will likely have different deleterious alleles, crossing them will result in immediate complementation and masking of the deleterious alleles in heterozygote state. Another model is that two different alleles when combined in heterozygote state have a synergistic fitness effect. We generally know of heterozygote advantage in cases where there’s balancing selection, so that one of the homozygotes is actually far less fit than the other, but the fitness of the heterozygote is superior to both homozygotes. But that is not a necessity, and presumably there could be cases where both homozygotes are of equal fitness, but the heterozygote is of marginally greater fitness.

As for hybrid inferiority, a simple model for that is that lineages have co-adapted complexes of genes which are enmeshed in gene-gene networks. These networks are finely tuned by evolution and introduction of novel alleles from alien lineages may lead in destabilization of the sensitive web of interconnections. This model taken to an extreme is a scenario whereby speciation could occur if two lineages become mutually exclusive on a particular genetic complex which is “mission critical” to biological machinery (imagine that the gene involved in spermatogenesis is effected).

These stories are fine as it goes, but they do have something of an excessively ad hoc aspect. A little light on formalization and heavy on exposition. In this paper the authors aim to fix that problem. To explore genetic interactions in hybrids, and how they effect gene expression, they selected the genus Antirrhinum as their model. These are also known as “snapdragons.” Like many plants Antirrhinum species can hybridize rather easily across species barriers. They observe the effect of taking genes from a set of species and placing them in the genetic background of another. In particular they are focusing on A. majus, hybridizing it with a variety of other Antirrhinum species, as well as introgressing alleles from the other species onto a A. majus genetic background (so an allele on a specific gene is placed within the genome of A. majus).

Just as they focus on a specific genus of organism, so they also focus on a specific set of genes and the molecular and developmental genetic phenomenon associated with those genes. The genes are CYC and RAD, which are located near each other genomically, with CYC being a cis-acting regulator of RAD. In other words, CYC modulates the expression of RAD which is on the same chromosome.  Variance in gene expression simply defines the concrete difference in levels of protein product. Mutant variants of CYC and RAD, cyc and rad, are created by insertion of transposons. Insertion of transposons can abolish gene expression, resulting in removal or alteration of function. What is that function? I’m rather weak on botanical morphology, so I’m going to be cursory on this particular issue lest a reader correct me strenuously for misapplication of terminology. So I’ll show you a figure:

I added the labels. C is basically what majus should look like, while G is a totally “ventralized” mutant. B and F approach wild type, but the other outcomes are more mixed. Note the genotypes in the small print. Table 1 measures the expression levels of the gene product for the various genotype:

Look at the first row; mutant variants of CYC which are nonfunctional reduce normal copies of RAD down to 20% levels of gene expression. That’s because CYC is a transcriptional regulator of RAD. The process is not reversed. RAD lacking functionality does not impact CYC (last row). Finally, the heterozygote states does result in reduced dosage of the gene product. Though the phenotypes might be closer to wild type than the mutant, the molecular expression of the gene is substantially changed. This is one of the issues which is always important to remember: the extent of dominance exhibited by a sequence of phenotypes consequent from a particular genotype may vary dependent on which phenotype you are a highlighting. On a molecular level there is incomplete dominance. Additive effects. On the level of exterior morphology there is more perceived dominance. This is not even addressing the issue of pleiotropy, where the same gene may have dominant and recessive expression on two different traits simultaneously in inverted directions (i.e., the recessively expressed allele in trait A may be dominant in B, and vice versa).

Figure 1 shows the different allelic expression levels in hybrids of Antirrhinum species. But what about the impact of the combinations on phenotype? I’ve reedited figure 4 so it fits better on this page:

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