It’s more than genes, it’s networks and systems

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Most of you don’t understand evolution. I mean this in the most charitable way; there’s a common conceptual model of how evolution occurs that I find everywhere, and that I particularly find common among bright young students who are just getting enthusiastic about biology. Let me give you the Standard Story, the one that I get all the time from supporters of biology.

Evolution proceeds by mutation and selection. A novel mutation occurs in a gene that gives the individual inheriting it an advantage, and that person passes it on to their children who also gets the advantage and do better than their peers, and leave more offspring. Given time, the advantageous mutation spreads through the population so the entire species has it.

One example is the human brain. An ape man millions of years ago acquired a mutation that made his or her brain slightly larger, and since those individuals were slightly smarter than other ape men, it spread through the population. Then later, other mutations occured and were selected for and so human brains gradually got larger and larger.

You either know what’s wrong here or you’re feeling a little uneasy—I gave you enough hints that you know I’m going to complain about that story, but if your knowledge is at the Evolutionary Biology 101 level, you may not be sure what it is.

Just to make you even more queasy, the misunderstanding here is one that creationists have, too. If you’ve ever encountered the cryptic phrase “RM+NS” (“random mutation + natural selection”) used as a pejorative on a creationist site, you’ve found someone with this affliction. They’ve got it completely wrong.

Here’s the problem, and also a brief introduction to Evolutionary Biology 201.

First, it’s not exactly wrong — it’s more like taking one good explanation of certain kinds of evolution and making it a sweeping claim that that is how all evolution works. By reducing it to this one scheme, though, it makes evolution far too plodding and linear, and reduces it all to a sort of personal narrative. It isn’t any of those things. What’s left out in the 101 story, and in creationist tales, is that: evolution is about populations, so many changes go on in parallel; selectable traits are usually the product of networks of genes, so there are rarely single alleles that can be categorized as the effector of change; and genes and gene networks are plastic or responsive to the environment. All of these complications make the actual story more complicated and interesting, and also, perhaps to your surprise, make evolutionary change faster and more powerful.

Think populations

Mutations are the root of biological variation, of course, but we often have a naive view of their consequences. Most mutations are neutral. Even advantageous mutations are subject to laws of chance in their propagation, and a positive selection coefficient does not mean there will be an inexorable march to fixation, where every individual has the allele. This is also true of deleterious mutations: chance often dominates, and unless it is a strongly negative allele, like an embryonic lethal mutation, there’s also a chance it can spread through the population.

Stop thinking of mutations as unitary events that either get swiftly culled, because they’re deleterious, or get swiftly hauled into prominence by the uplifting crane of natural selection. Mutations are usually negligible changes that get tossed into the stewpot of the gene pool, where they simmer mostly unnoticed and invisible to selection. Look at human faces, for instance: they’re all different, and unless you’re looking at the extremes of beauty or ugliness, the variations simply don’t make much difference. Yet all those different faces really are the result of subtly different combinations of mutant forms of genes.

“Combinations” is the magic word. A single mutation rarely has a significant effect on a feature, but the combination of multiple mutations may have a detectable or even novel effect that can be seen by natural selection. And that’s what’s going on all the time: the population is a huge reservoir of genetic variation, and what we do when we reproduce is sort and mix and generate new combinations that are then tested in the environment.

Compare it to a game of poker. A two of hearts in itself seems to be a pathetic little card, but if it’s part of a flush or a straight or three of a kind, it can produce a winning hand. In the game, it’s not the card itself that has power, it’s its utility in a pattern or combination of other cards. A large population like ours is a great shuffler that is producing millions of new hands every day.

We know that this recombination is essential to the rapid acquisition of new phenotypes. Here are some results from a classic experiment by Waddington. Waddington noted that fruit flies expressed the odd trait of developing four wings (the bithorax phenotype) instead of two if they were exposed to ether early in development. This is not a mutation! This is called a phenocopy, where an environmental factor induces an effect similar to a genetic mutation.

What Waddington did next was to select for individuals that expressed the bithorax phenotype most robustly, or that were better at resisting the ether, and found that he could get a progressive strengthening of the response.

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The progress of selection for or against a bithorax-like response to ether treatment in two wild-type populations. Experiments 1 and 2 initially showed about 25 and 48% of the bithorax (He) phenotype.

This occurred over 10s of generations — far, far too fast for this to be a consequence of the generation of new mutations. What Waddington was doing was selecting for more potent combinations of alleles already extant in the gene pool.

This was confirmed in a cool way with a simple experiment: the results in the graph above were obtained from wild-caught populations. Using highly inbred laboratory strains that have greatly reduced genetic variation abolishes the outcome.

Jonathan Bard sees this as a powerful potential factor in evolution.

Waddington’s results have excited considerable controversy over the years, for example as to whether they reflect threshold effects or hidden variation. In my view, these arguments are irrelevant to the key point: within a population of organisms, there is enough intrinsic variability that, given strong selection pressures, minor but existing variants in a trait that are not normally noticeable can rapidly become the majority phenotype without new mutations. The implications for evolution are obvious: normally silent mutations in a population can lead to adaptation if selection pressures are high enough. This view provides a sensible explanation of the relatively rapid origins of the different beak morphologies of Darwin’s various finches and of species flocks.

Think networks

One question you might have at this point is that the model above suggests that mutations are constantly being thrown into the population’s gene pool and are steadily accumulating — it means that there must be a remarkable amount of genetic variation between individuals (and there is! It’s been measured), yet we generally don’t see most people as weird and obvious mutants. That variation is largely invisible, or represents mere minor variations that we don’t regard as at all remarkable. How can that be?

One important reason is that most traits are not the product of single genes, but of combinations of genes working together in complex ways. The unit producing the phenotype is most often a network of genes and gene products, such at this lovely example of the network supporting expression and regulation of the epidermal growth factor (EGF) pathway.

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The EGF pathway (from www.sabiosciences.com/pathwaycentral.php)

That is awesomely complex, and yes, if you’re a creationist you’re probably wrongly thinking there is no way that can evolve. The curious thing is, though, that the more elaborate the network, the more pieces tangled into the pathway, the smaller the effect of any individual component (in general, of course). What we find over and over again is that many mutations to any one component may have a completely indetectable effect on the output. The system is buffered to produce a reliable yield.

This is the way networks often work. Consider the internet, for example: a complex network with many components and many different routes to get a single from Point A to Point B. What happens if you take out a single node, or even a set of nodes? The system routes automatically around any damage, without any intelligent agency required to consciously reroute messages.

But further, consider the nature of most mutations in a biological network. Simple knockouts of a whole component are possible, but often what will happen are smaller effects. These gene products are typically enzymes; what happens is a shift in kinetics that will more subtly modify expression. The challenge is to measure and compute these effects.

Graph analysis is showing how networks can be partitioned and analysed, while work on the kinetics of networks has shown first that it is possible to simplify the mathematics of the differential equation models and, second, that the detailed output of a network is relatively insensitive to changes in most of the reaction parameters. What this latter work means is that most gene mutations will have relatively minor effects on the networks in which their proteins are involved, and some will have none, perhaps because they are part of secondary pathways and so redundant under normal circumstances. Indirect evidence for this comes from the surprising observation that many gene knockouts in mice result in an apparently normal phenotype. Within an evolutionary context, it would thus be expected that, across a population of organisms, most
mutations in a network would effectively be silent, in that they would give no selective advantage under normal conditions. It is one of the tasks of systems biologists to understand how and where mutations can lead to sufficient variation in networks properties for selection to have something on which to act.

Combine this with population effects. The population can accumulate many of these sneaky variants that have no significant effect on most individuals, but under conditions of strong selection, combinations of these variants, that together can have detectable effects, can be exposed to selection.

Think flexible genes

Another factor in this process (one that Bard does not touch on) is that the individual genes themselves are not invariant units. Mutations can affect how genes contribute to the network, but in addition, the same allele can have different consequences in different genetic backgrounds — it is affected by the other genes in the network — and also has different consquences in different external environments.

Everything is fluid. Biology isn’t about fixed and rigidly invariant processes — it’s about squishy, dynamic, and interactive stuff making do.

Now do you see what’s wrong with the simplistic caricature of evolution at the top of this article? It’s superficial; it ignores the richness of real biology; it limits and constrains the potential of evolution unrealistically. The concept of evolution as a change in allele frequencies over time is one small part of the whole of evolutionary processes. You’ve got to include network theory and gene and environmental interactions to really understand the phenomena. And the cool thing is that all of these perspectives make evolution an even more powerful force.

Bard J (2010) A systems biology view of evolutionary genetics. Bioessays 32: 559-563.

How not to evaluate a big science program

Nicholas Wade of the NY Times has written one of those stories that make biologists cringe — it just gets so much wrong. It’s a look back at the human genome project, and I was turned off at the first paragraph. The HGP was badly marketed from the very beginning in the sense that there was a misrepresentation of the scientific goals; it was well-marketed if your goal was wringing money out of congress. Unfortunately, now we’ve got to deal with science writers complaining that nobody has generated any miracle cures from all that work. Pay attention to what Harold Varmus said:

“Genomics is a way to do science, not medicine,” said Harold Varmus, president of the Memorial Sloan-Kettering Cancer Center in New York, who in July will become the director of the National Cancer Institute.

The genome is a basic research tool, not a recipe book for curing diseases. I can’t entirely blame Wade for complaining about this, though, since some prominent people like Francis Collins were selling the HGP as the first step in generating a panacea.

But Wade ought to be embarrassed at the rampant linear ladder thinking in his article. Both Jonathan Eisen and Larry Moran take him to task for that — he makes this error-filled statement:

The barely visible roundworm needs 20,000 genes that make proteins, the working parts of cells, whereas humans, apparently so much higher on the evolutionary scale, seem to have only 21,000 protein-coding genes.

Humans aren’t high on the evolutionary scale…there is no evolutionary scale. We aren’t the pinnacle of anything. It’s also weird to see people still expressing astonishment that we “only” have about 20,000 genes. Way, way back in the dim and distant past, when I was a lowly undergraduate in 1977 (AD, I think), my genetics professor, Larry Sandler, lectured to us about how Drosophila was thought to have about 10-15,000 genes and humans might have about twice that…but that when you looked at the C-value paradox (that the quantity of DNA in organisms doesn’t correlate at all well with our perceptions of complexity), it really didn’t mean much, especially since we didn’t (and still don’t) know what most of those genes do. In the early days of the HGP there was a mad flurry of speculation, mostly from people with economic interests in more genes, that there were 100-200,000 genes, but everyone who knew anything about genetics gave those a squinty cynical look.

Apparently, there’s going to be a second article in this series from Wade: “Next: Drug companies stick with genomics but struggle with information overload.” Please. If you want to do a retrospective on the impact of the human genome project, don’t go talking to the drug companies.

Autism and the search for simple, direct answers

I’ve gotten some email asking for a simplified executive summary of this paper, so here it is.

A large study of almost a thousand autistic individuals for genetic variations that make them different from control individuals has found that Autism Spectrum Disorder has many different genetic causes: there isn’t one single gene responsible for ASD, but a constellation of hundreds, each with the potential to affect the development of the brain and cause the symptoms of autism. They don’t know exactly how each of these genes contributes to the disorder, but they have found that many of them are involved in growth and cell communication and the formation of synapses in the brain.

The bottom line is that there are many different ways to cause the symptoms of autism, and it’s a mistake to try to pin it all on single, simple causes. Any hope for amelioration lies in understanding the general functional processes that are disrupted by mutations in various pathways.

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Coming up with simple, one-size-fits-all answers to serious problems is so tempting and so satisfying. Look at autism, for instance: a mysterious disease with a wide range of expression, so wide that it is more properly called Autism Spectrum Disorder (ASD), and the popular press and various celebrities all want it to be pegged to a simple cause: it’s vaccines, or it’s mercury, or it’s the dose of the vaccines, and all we have to do to fix it is not vaccinate, or reduce the number of vaccinations, or use chelation therapy to extract poisons, and presto, a cure! This is magical thinking, pure and simple, and it doesn’t work.

ASD isn’t simple, it’s not one disease, it doesn’t have one cause, and vaccines are definitely not the cause: if there’s one thing the research has done, it’s to thoroughly rule out the idea that giving kids shots at an early age causes autism. What we’re actually discovering more and more is that ASD can be traced to genetic variation.

Again, though, the causes aren’t simple. There is no single mutation to which ASD can be pinned.

For example, one hot spot for an association of genes with autism is the long arm of chromosome 22; cases of developmental delays and autistic behavior have been associated with partial deletions in chromosome 22, and the problems have even been narrowed down to one specific gene, SHANK3, which is expressed in neurons and localized to synapses. We know that if you’ve got a broken copy of this particular gene, you’re likely to have ASD.

How many ASD individuals have this specific genetic change? 0.75%. It is a cause in less than 1% of all affected individuals, but it cannot be the sole cause of ASD in all cases. We have to get out of this mindset that tries to find single causes for complex phenomena; ASD is a case where we have a complex range of disorders with multiple, complex causes.

So how do we get a handle on ASD? This is where the work gets interesting: just because something is multi-causal does not mean that science can’t get a grip on it and that we can’t learn anything interesting about it. We’ve got lots of new tools for analyzing broad properties of genomes now, and one promising line of attack are methods for measuring and identifying copy number variants in individuals and populations.

Copy number variants (CNVs) are surprisingly common. If you’ve had any biology instruction at all, you’re probably familiar with the Mendelian concept that we have two copies of each chromosome, and two copies of each gene. As it turns out, that is an oversimplification: sometimes, a piece of a chromosome is accidentally duplicated, and then you’ll carry two copies of the associated gene on one chromosome, and one copy on another chromosome, for a total of 3 copies. And in some cases, these duplications have occurred often enough that you’ll have many more than 3; the median number of copies of the amylase gene (an enzyme that breaks down starch) in European American populations is 7, with a range of 2 to 15 in different individuals. Get used to it, this kind of variation in copy number seems to happen fairly often.

Now in the case of amylase, the effect of this variation is mild — individuals with more copies of the gene produce more of the enzyme and break down starchy foods faster. It does have evolutionary effects, since cultures with diets rich in starch contain individuals who have, on average, more copies of the gene than individuals where starches are less common in the diet. But what if these chance variations in copy number affect genes involved in the function of the brain? We might see more profound effects on behavior or cognitive ability. The defect in SHANK3 mutations is an example of a reduction in copy number of that gene; what if we could screen populations of ASD individuals not for a specific gene variant, but for the more general occurrence of frequent variations in copy number of any genes…and then we could ask which genes are often affected?

It’s being done. A new paper in Nature describes a screen of control and ASD individuals to identify rare copy number variants associated with autism. It worked! In fact, it worked maybe a little too well, since we now have an embarrassment of riches, a great many genes that may be related to ASD.

The autism spectrum disorders (ASDs) are a group of conditions characterized by impairments in reciprocal social interaction and communication, and the presence of restricted and repetitive behaviours. Individuals with an ASD vary greatly in cognitive development, which can range from above average to intellectual disability. Although ASDs are known to be highly heritable (~90%), the underlying genetic determinants are still largely unknown. Here we analysed the genome-wide characteristics of rare (<1% frequency) copy number variation in ASD using dense genotyping arrays. When comparing 996 ASD individuals of European ancestry to 1,287 matched controls, cases were found to carry a higher global burden of rare, genic copy number variants (CNVs) (1.19 fold, P = 0.012), especially so for loci previously implicated in either ASD and/or intellectual disability (1.69 fold, P = 3.4 × 10-4). Among the CNVs there were numerous de novo and inherited events, sometimes in combination in a given family, implicating many novel ASD genes such as SHANK2, SYNGAP1, DLGAP2 and the X-linked DDX53-PTCHD1 locus. We also discovered an enrichment of CNVs disrupting functional gene sets involved in cellular proliferation, projection and motility, and GTPase/Ras signalling. Our results reveal many new genetic and functional targets in ASD that may lead to final connected pathways.

They analyzed both affected individuals and their parents, and found both familial transmission — that is, the child with ASD had received a copy number variant from a parent who was a carrier — and de novo events — that is, the child had a spontaneous, new mutation that was not present in either parent. There is no one single gene that can be tagged as the cause of autism: they identified 226 de novo and 219 inherited copy number variants in affected individuals. No one individual carries all of these variants, of course — the results tell us that there are many different paths to ASD.

Oh, no, you may be tempted to wail, autism is hundreds of diseases, with even more possible combinations of variants, and every individual is unique — this is no way to get a handle on what’s actually happening to autistic kids! Don’t despair, though, this is just the start. Although there are many genes involved, we can try to ask what all of them have in common functionally. There may be common consequences from all of these different genes, so maybe we can identify the common errors in the process of building a brain that lead to ASD.

Here’s a first stab at puzzling out what these genes do. The genes that have been identified as being deficient in ASD individuals are mapped out by known functions, and what jumps out at you is that the hundreds of specific genes fall into a smaller number of functional categories. Many of them cluster in a few functional roles: cell proliferation (genes that affect the number of cells in a tissues) and cell projection (particularly important in neurons, where cells will extend long processes that project into target regions), and a specific class of cell signaling molecules, RAS-GTPases, which are involved in how cells communicate with one another and are particularly important in synapses, or the linkages between neurons.

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(Click for larger image)
Enrichment results were mapped as a network of gene sets (nodes) related by mutual overlap (edges), where the colour (red, blue or yellow) indicates the class of gene set. Node size is proportional to the total number of genes in each set and edge thickness represents the number of overlapping genes between sets. a, Gene sets enriched for deletions are shown (red) with enrichment significance (FDR q-value) represented as a node colour gradient. Groups of functionally related gene sets are circled and labelled (groups, filled green circles; subgroups, dashed line). b, An expanded enrichment map shows the relationship between gene sets enriched in deletions (a) and sets of known ASD/intellectual disability genes. Node colour hue represents the class of gene set (that is, enriched in deletions, red; known disease genes (ASD and/or intellectual disability (ID) genes), blue; enriched only in disease genes, yellow). Edge colour represents the overlap between gene sets enriched in deletions (green), from disease genes to enriched sets (blue), and between sets enriched in deletions and in disease genes or between disease gene-sets only (orange). The major functional groups are highlighted by filled circles (enriched in deletions, green; enriched in ASD/intellectual disability, blue).

The second map above ties the various copy number variants to previously known disease genes involved in ASD, and what catches my eye is the dense cloud of variants associated with central nervous system development. That tells me right there that it is inappropriate to treat ASD as something that is switched on or off by simple causal factors: ASD is the product of long-developing, subtle changes in the growth of the nervous system in embryos and infants.

So the conclusion, as expected, is that ASD is a multi-factorial disorder with a strong genetic component — but definitely not single-locus inheritance, as many different genes are involved.

Our findings provide strong support for the involvement of multiple rare genic CNVs, both genome-wide and at specific loci, in ASD. These findings, similar to those recently described in schizophrenia, suggest that at least some of these ASD CNVs (and the genes that they affect) are under purifying selection. Genes previously implicated in ASD by rare variant findings have pointed to functional themes in ASD pathophysiology. Molecules such as NRXN1, NLGN3/4X and SHANK3, localized presynaptically or at the post-synaptic density (PSD), highlight maturation and function of glutamatergic synapses. Our data reveal that SHANK2, SYNGAP1 and DLGAP2 are new ASD loci that also encode proteins in the PSD. We also found intellectual disability genes to be important in ASD. Furthermore, our functional enrichment map identifies new groups such as GTPase/Ras, effectively expanding both the number and connectivity of modules that may be involved in ASD. The next step will be to relate defects or patterns of alterations in these groups to ASD endophenotypes. The combined identification of higher-penetrance rare variants and new biological pathways, including those identified in this study, may broaden the targets amenable to genetic testing and therapeutic intervention.

There aren’t any simple answers. There are some hints of hope for future treatment, though, in the recognition that there are a few functional modules that are being commonly impaired by these many different genes — it at least focuses the direction of future research in to some narrower domains.

One fact is so obvious that it’s unfortunate I have to mention it: no external agent, such as a vaccine, can generate a consistent pattern of duplication and deletions in an affected individual’s cells. These data say it’s an error to chase down transient environmental agents given relatively late in life to people.

Pinto D et al. (2010) Functional impact of global rare copy number variation in autism spectrum disorders Nature doi:10.1038/nature09146.

Neandertal!

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You don’t have to tell me, I know I’m late to the party: the news about the draft Neandertal genome sequence was announced last week, and here I am getting around to it just now. In my defense, I did hastily rewrite one of my presentation to include a long section on the new genome information, so at least I was talking about it to a few people. Besides, there is coverage from a genuine expert on Neandertals, John Hawks, and of course Carl Zimmer wrote an excellent summary. All I’m going to do now is fuss over a few things on the edge that interested me.

This was an impressive technical feat. The DNA was extracted from a few bone fragments, and it was grossly degraded: the average size of a piece of DNA was less than 200 base pairs, much of that was chemically degraded, and 95-99% of the DNA extracted was from bacteria, not Neandertal. An immense amount of work was required to filter noise from the signal, to reconstruct and reassemble, and to avoid contamination from modern human DNA. These poor Neandertals had died, had rotted thoroughly, and the bacteria had worked their way into almost every crevice of the bone to chew up the remains. All that was left were a few dead cells in isolated lacunae of the bone; their DNA had been chopped up by their own enzymes, and death and chemistry had come to slowly break them down further.

Don’t hold your breath waiting for the draft genome of Homo erectus. Time is unkind.

We have to appreciate the age of these people, too. The oldest Neandertal fossils are approximately 400,000 years old, and the species went extinct about 30,000 years ago. That’s a good run; as measured by species longevity, Homo sapiens neandertalensis is more successful than Homo sapiens sapiens. We’re going to have to hang in there for another 200,000 years to top them.

The samples taken were from bones found in a cave in Vindija, Croatia. Full sequences were derived from these three individuals, and in addition, some partial sequences were taken from other specimens, including the original type specimen found in the Neander Valley in 1856.

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Samples and sites from which DNA was retrieved. (A) The three bones from Vindija from which Neandertal DNA was sequenced. (B) Map showing the four archaeological sites from which bones were used and their approximate dates (years B.P.).

The three bones used for sequencing were directly dated to 38.1, 44.5, and 44.5 thousand years ago, which puts them on the near end of the Neandertal timeline, and after the likely time of contact between modern humans and Neandertals, which probably occurred about 80,000 years ago, in the Middle East.

Just for reference: these samples are 6-7 times older than the entire earth, as dated by young earth creationists. The span of time just between the youngest and oldest bones used is more than six thousand years old, again, about the same length of time as the YEC universe. Imagine that: we see these bone fragments now as part of a jumble of debris from one site, but they represent differences as great as those between a modern American and an ancient Sumerian. I repeat once again: the religious imagination is paltry and petty compared to the awesome reality.

A significant revelation from this work is the discovery of the signature of interbreeding between modern humans and Neandertals. When those humans first wandered out of the homeland of Africa into the Middle East, they encountered Neandertals already occupying the land…people they would eventually displace, but at least early on there was some sexual activity going on between the two groups, and a small number of human-Neandertal hybrids would have been incorporated into the expanding human population—at least, in that subset that was leaving Africa. Modern European, Asian, and South Pacific populations now contain 1-4% Neandertal DNA. This is really cool; I’m proud to think that I had as a many-times-great grandparent a muscular, beetle-browed big game hunter who trod Ice Age Europe, bringing down mighty mammoths with his spears.

However, it is a small contribution from the Neandertals to our lineage, and it’s not likely that these particular Neandertal genes made a particularly dramatic effect on our ancestors. They didn’t exactly sweep rapidly and decisively through the population; it’s most likely that they are neutral hitch-hikers that surfed the wave of human expansion. Any early matings between an expanding human subpopulation and a receding Neandertal population would have left a few traces in our gene pool that would have been passively hauled up into higher numbers by time and the mere growth of human populations. In a complementary fashion, any human genes injected into the Neandertal pool would have been placed into the bleeding edge of a receding population, and would not have persevered. No uniquely human genes were found in the Neandertals examined, but we can’t judge the preferred direction of the sexual exchanges in these encounters, though, because any hybrids in Neandertal tribes were facing early doom, while hybrids in human tribes were in for a long ride.

Here’s the interesting part of these gene exchanges, though. We can now estimate the ancestral gene sequence, that is, the sequences of genes in the last common ancestor of humans and Neandertals, and we can ask if there are any ‘primitive’ genes that have been completely replaced in modern human populations by a different variant, but Neandertal still retained the ancestral pattern (see the red star in the diagram below). These genes could be a hint to what innovations made us uniquely human and different from Neandertals.

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Selective sweep screen. Schematic illustration of the rationale for the selective sweep screen. For many regions of the genome, the variation within current humans 0 is old enough to include Neandertals (left). Thus, for SNPs in present-day humans, Neandertals often carry the derived -1 allele (blue). However, in genomic regions where an advantageous mutation arises (right, red star) and sweeps to high frequency or fixation in present-day humans, Neandertals will be devoid of derived alleles.

There’s good news and bad news. The bad news is that there aren’t very many of them: a grand total of 78 genes were identified that have a novel form and that have been fixed in the modern human population. That’s not very many, so if you’re an exceptionalist looking for justification of your superiority to our ancestors, you haven’t got much to go on. The good news, though, is that there are only 78 genes! This is a manageable number, and represent some useful hints to genes that would be worth studying in more detail.

One other qualification, though: these are 78 genes that have changes in their coding sequence. There are also several hundred other non-coding, presumably regulatory, sequences that are unique to humans and are fixed throughout our population. To the evo-devo mind, these might actually be the more interesting changes, eventually…but right now, there are some tantalizing prospects in the coding genes to look at.

Some of the genes with novel sequences in humans are DYRK1A, a gene that is present in three copies in Down syndrome individuals and is suspected of playing a role in their mental deficits; NRG3, a gene associated with schizophrenia, and CADPS2 and AUTS2, two genes associated with autism. These are exciting prospects for further study because they have alleles unique and universal to humans and not Neandertals, and also affect the functioning of the brain. However, let’s not get confused about what that means for Neandertals. These are genes that, when broken or modified in modern humans, have consequences on the brain. Neandertals had these same genes, but different forms or alleles of them, which are also different from the mutant forms that cause problems in modern humans. Neandertals did not necessarily have autism, schizophrenia, or the minds of people with Down syndrome! The diseases are just indications that these genes are involved in the nervous system, and the differences in the Neandertal forms almost certainly caused much more subtle effects.

Another gene that has some provocative potential is RUNX2. That’s short for Runt-related transcription factor 2, which should make all the developmental biologists sit up and pay attention. It’s a transcription factor, so it’s a regulator of many other genes, and it’s related to Runt, a well known gene in flies that is important in segmentation. In humans, RUNX2 is a regulator of bone growth, and is a master control switch for patterning bone. In modern humans, defects in this gene lead to a syndrome called cleidocranial dysplasia, in which bones of the skull fuse late, leading to anomalies in the shape of the head, and also causes characteristic defects in the shape of the collar bones and shoulder articulations. These, again, are places where Neandertal and modern humans differ significantly in morphology (and again, Neandertals did not have cleidocranial dysplasia — they had forms of the RUNX2 gene that would have contributed to the specific arrangements of their healthy, normal anatomy).

These are tantalizing hints to how human/Neandertal differences could have arisen—by small changes in a few genes that would have had a fairly extensive scope of effect. Don’t view the many subtle differences between the two as each a consequence of a specific genetic change; a variation in a gene like RUNX2 can lead to coordinated, integrated changes to multiple aspects of the phenotype, in this case, affecting the shape of the skull, the chest, and the shoulders.

This is a marvelous insight into our history, and represents some powerful knowledge we can bring to bear on our understanding of human evolution. The only frustrating thing is that this amazing work has been done in a species on which we can’t, for ethical reasons, do the obvious experiments of creating artificial revertants of sets of genes to the ancestral state — we don’t get to resurrect a Neandertal. With the tools that Pääbo and colleagues have developed, though, perhaps we can start considering some paleogenomics projects to get not just the genomic sequences of modern forms, but of their ancestors as well. I’d like to see the genomic differences between elephants and mastodons, and tigers and sabre-toothed cats…and maybe someday we can think about rebuilding a few extinct species.

Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, Patterson N, Li H, Zhai W, Fritz MH, Hansen NF, Durand EY, Malaspinas AS, Jensen JD, Marques-Bonet T, Alkan C, Prüfer K, Meyer M, Burbano HA, Good JM, Schultz R, Aximu-Petri A, Butthof A, Höber B, Höffner B, Siegemund M, Weihmann A, Nusbaum C, Lander ES, Russ C, Novod N, Affourtit J, Egholm M, Verna C, Rudan P, Brajkovic D, Kucan Z, Gusic I, Doronichev VB, Golovanova LV, Lalueza-Fox C, de la Rasilla M, Fortea J, Rosas A, Schmitz RW, Johnson PL, Eichler EE, Falush D, Birney E, Mullikin JC, Slatkin M, Nielsen R, Kelso J, Lachmann M, Reich D, Pääbo S. (2010) A draft sequence of the Neandertal genome. Science 328(5979):710-22.

How to make a snake

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First, you start with a lizard.

Really, I’m not joking. Snakes didn’t just appear out of nowhere, nor was there simply some massive cosmic zot of a mutation in some primordial legged ancestor that turned their progeny into slithery limbless serpents. One of the tougher lessons to get across to people is that evolution is not about abrupt transmutations of one form into another, but the gradual accumulation of many changes at the genetic level which are typically buffered and have minimal effects on the phenotype, only rarely expanding into a lineage with a marked difference in morphology.

What this means in a practical sense is that if you take a distinct form of a modern clade, such as the snakes, and you look at a distinctly different form in a related clade, such as the lizards, what you may find is that the differences are resting atop a common suite of genetic changes; that snakes, for instance, are extremes in a range of genetic possibilities that are defined by novel attributes shared by all squamates (squamates being the lizards and snakes together). Lizards are not snakes, but they will have inherited some of the shared genetic differences that enabled snakes to arise from the squamate last common ancestor.

So if you want to know where snakes came from, the right place to start is to look at their nearest cousins, the lizards, and ask what snakes and lizards have in common, that is at the same time different from more distant relatives, like mice, turtles, and people…and then you’ll have an idea of the shared genetic substrate that can make a snake out of a lizard-like early squamate.

Furthermore, one obvious place to look is at the pattern of the Hox genes. Hox genes are primary regulators of the body plan along the length of the animal; they are expressed in overlapping zones that specify morphological regions of the body, such as cervical, thoracic, lumbar, sacral/pelvic, and caudal mesodermal tissues, where, for instance, a thoracic vertebra would have one kind of shape with associated ribs, while lumbar vertebra would have a different shape and no ribs. These identities are set up by which Hox genes are active in the tissue forming the bone. And that’s what makes the Hox genes interesting in this case: where the lizard body plan has a little ribless interruption to form pelvis and hindlimbs, the snake has vertebra and ribs that just keep going and going. There must have been some change in the Hox genes (or their downstream targets) to turn a lizard into a snake.

There are four overlapping sets of Hox genes in tetrapods, named a, b, c, and d. Each set has up to 13 individual genes, where 1 is switched on at the front of the animal and 13 is active way back in the tail. This particular study looked at just the caudal members, 10-13, since those are the genes whose expression patterns straddle the pelvis and so are likely candidates for changes in the evolution of snakes.

Here’s a summary diagram of the morphology and patterns of Hox gene expression in the lizard (left) and snake (right). Let’s see what we can determine about the differences.

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Evolutionary modifications of the posterior Hox system in the whiptail lizard and corn snake. The positions of Hox expression domains along the paraxial mesoderm of whiptail lizard (32-40 somites, left) and corn snake (255-270 somites, right) are represented by black (Hox13), dark grey (Hox12), light grey (Hox11) and white (Hox10) bars, aligned with coloured schemes of the future vertebral column. Colours indicate the different vertebral regions: yellow, cervical; dark blue, thoracic; light blue, lumbar; green, sacral (in lizard) or cloacal (in snake); red, caudal. Hoxc11 and Hoxc12 were not analysed in the whiptail lizard. Note the absence of Hoxa13 and Hoxd13 from the corn snake mesoderm and the absence of Hoxd12 from the snake genome.

The morphology is revealing: snakes and lizards have the same regions, cervical (yellow), thoracic (blue), sacral (or cloacal in the snake, which lacks pelvic structures in most species) in green, and caudal or tail segments (red). The differences are in quantity — snakes make a lot of ribbed thoracic segments — and detail — snakes don’t make a pelvis, usually, but do have specializations in that corresponding area for excretion and reproduction.

Where it really gets interesting is in the expression patterns of the Hox genes, shown with the bars that illustrate the regions where each Hox gene listed is expressed. They are largely similar in snake and lizard, with boundaries of Hox expression that correspond to transitions in the morphology of vertebrae. But there are revealing exceptions.

Compare a10/c10 in the snake and lizard. In the snake, these two genes have broader expression patterns, reaching up into the thoracic region; in the lizard, they are cut off sharply at the sacral boundary. This is interesting because in other vertebrates, the Hox 10 group is known to have the function of suppressing rib formation. Yet there they are, turned on in the posterior portion of the thorax in the snake, where there are ribs all over the place.

In the snake, then, Hox a10 and c10 have lost a portion of their function — they no longer shut down ribs. What is the purpose of the extended domain of a10/c10 expression? It may not have one. A comparison of the sequences of these genes between various species reveals a detectable absence of signs of selection — the reason these genes happen to be active so far anteriorly is because selection has been relaxed, probably because they’ve lost that morphological effect of shutting down ribs. Those big bars are a consequence of simple sloppiness in a system that can afford a little slack.

The next group of Hox genes, the 11 group, are very similar in their expression patterns in the lizard and the snake, and that reflects their specific roles. The 10 group is largely involved in repression of rib formation, but the 11 group is involved in the development of sacrum-specific structures. In birds, for instance, the Hox 11 genes are known to be involved in the development of the cloaca, a structure shared between birds, snakes, and lizards, so perhaps it isn’t surprising that they aren’t subject to quite as much change.

The 13 group has some notable differences: Hox a13 and d13 are mostly shut off in the snake. This is suggestive. The 13 group of Hox genes are the last genes, at the very end of the animal, and one of their proposed functions is to act as a terminator of patterning — turning on the Hox 13 genes starts the process of shutting down the mesoderm, shrinking the pool of tissue available for making body parts, so removing a repressor of mesoderm may promote longer periods of growth, allowing the snake to extend its length further during embryonic development.

So we see a couple of clear correlates at the molecular level for differences in snake and lizard morphology: rib suppression has been lost in the snake Hox 10 group, and the activity of the snake Hox 13 group has been greatly curtailed, which may be part of the process of enabling greater elongation. What are the similarities between snakes and lizards that are also different from other animals?

This was an interesting surprise. There are some differences in Hox gene organization in the squamates as a whole, shared with both snakes and lizards.

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Genomic organization of the posterior HoxD cluster. Schematic representation of the posterior HoxD cluster (from Evx2 to Hoxd10) in various vertebrate species. A currently accepted phylogenetic tree is shown on the left. The correct relative sizes of predicted exons (black boxes), introns (white or coloured boxes) and intergenic regions (horizontal thick lines) permit direct comparisons (right). Gene names are shown above each box. Colours indicate either a 1.5-fold to 2.0-fold (blue) or a more than 2.0-fold (red) increase in the size of intronic (coloured boxes) or intergenic (coloured lines) regions, in comparison with the chicken reference. Major CNEs are represented by green vertical lines: light green, CNEs conserved in both mammals and sauropsids; dark green, CNEs lost in the corn snake. Gaps in the genomic sequences are indicated by dotted lines. Transposable elements are indicated with asterisks of different colours (blue for DNA transposons; red for retrotransposons).

That’s a diagram of the structure of the chromosome in the neighborhood of the Hox d10-13 genes in various vertebrates. For instance, look at the human and the turtle: the layout of our Hox d genes is vary similar, with 13-12-11-10 laid out with approximately the same distances between them, and furthermore, there are conserved non-coding elements, most likely important pieces of regulatory DNA, that are illustrated in light yellow-reen and dark green vertical bars, and they are the same, too.

In other words, the genes that stake out the locations of pelvic and tail structures in turtles and people are pretty much the same, using the same regulatory apparatus. It must be why they both have such pretty butts.

But now compare those same genes with the squamates, geckos, anoles, slow-worms, and corn snakes. The differences are huge: something happened in the ancestor of the squamates that released this region of the genome from some otherwise highly conserved constraints. We don’t know what, but in general regulation of the Hox genes is complex and tightly interknit, and this order of animals acquired some other as yet unidentified patterning mechanism that opened up this region of genome for wider experimentation.

When these regions are compared in animals like turtles and people and chickens, the genomes reveal signs of purifying selection — that is, mutations here tend to be unsuccessful, and lead to death, failure to propagate, etc., other horrible fates that mean tinkering here is largely unfavorable to fecundity (which makes sense: who wants a mutation expressed in their groinal bits?). In the squamates, the evidence in the genome does not witness to intense selection for their particular arrangement, but instead, of relaxed selection — they are generally more tolerant of variations in the Hox gene complex in this area. What was found in those enlarged intergenic regions is a greater invasion of degenerate DNA sequences: lots of additional retrotransposons, like LINES and SINES, which are all junk DNA.

So squamates have more junk in the genomic trunk, which is not necessarily expressed as an obvious phenotypic difference, but still means that they can more flexibly accommodate genetic variations in this particular area. Which means, in turn, that they have the potential to produce more radical experiments in morphology, like making a snake. The change in Hox gene regulation in the squamate ancestor did not immediately produce a limbless snake, instead it was an enabling mutation that opened the door to novel variations that did not compromise viability.

Di-Po N, Montoya-Burgos JI, Miller H, Pourquie O, Milinkovitch MC, Duboule D (2010) Changes in Hox genes’ structure and function during the evolution of the squamate body plan. Nature 464:99-103.

The presumption of Rick Warren

Rick Warren regularly scribbles up these cloying little messages he calls the Daily Hope — and rather than hope, they offer nothing but trite platitudes and unfounded certainty about a godly purpose that I find extremely discouraging. How can people find this lying tripe uplifting?

God deliberately shaped and formed you to serve him in a way that makes your ministry unique. He carefully mixed the DNA recipe that created you. David praised God for this incredible personal attention to detail God gave in designing each of us: “You made all the delicate, inner parts of my body and knit me together in my mother’s womb. Thank you for making me so wonderfully complex! Your workmanship is marvelous” (Psalm 139:13-14, NLT).

Not only did God shape you before your birth, he planned every day of your life to support his shaping process. David continues, “Every day of my life was recorded in your book. Every moment was laid out before a single day had passed” (Psalm 139:16, NLT).

This means nothing that happens in your life is insignificant. God uses all of it to mold you for your ministry to others, and shape you for your service to him.

This man needs to spend some time doing recombination experiments with fruit flies. They’re simple and revealing. For instance, genes for body and eye color (called yellow and white, respectively) are located close together on the X chromosome of Drosophila. If you cross a female carrier for the yellow body and white eye alleles to a wild type male, you will discover that the male progeny (which inherited a nearly empty Y chromosome from their fathers) reveal the rearrangement of alleles that occurred during the production of the female egg. Most will have inherited one of the non-recombinant X chromosomes from their mother, for example, either a chromosome with two wild-type alleles, so they look wild-type with grayish bodies and red eyes, and others will have inherited an X chromosome with the two mutant alleles, so they’ll have yellow bodies and white eyes. And some will have inherited a chromosome rearranged by recombination events, so they’ll have gray bodies and white eyes, or yellow bodies and red eyes. And of course, if you do lots of crosses, you will get occasional mutations in those genes that produce completely unexpected results.

The important point, though, is that you learn quickly that the distribution of progeny is dictated by chance, not purpose. There is no benign allele sorter who recognizes that white eyes, for instance, are deleterious, and therefore carefully arranges each meiotic division of the egg so that the white allele gets discarded in a polar body. No, it’s random — chance alone “mixes the DNA recipe” for each individual. I am the product of a random assortment of half my father’s genes and half my mother’s genes, as are my brothers and sisters, and we’ve each acquired some deleterious and some advantageous alleles, all by chance. We are all a throw of the dice, or a chance hand dealt from the deck.

What Darwin revealed, and has since been explained in greater detail with our understanding of genetics, is that there is a historical bias: individuals who had the most lucky throws of the dice are more likely to produce offspring with their fortunate distribution of alleles. Again, it’s not because a god shines down upon the lucky, it’s because the lucky acquired an advantage, and that advantage can be propagated into successive generations. Nothing more. No purpose, no intent, no plan required. We look at the distribution of traits in a population, and it fits a chance distribution, sometimes modified by natural selection.

And that’s the way I like it.

I have been dealt a hand by chance, and some of my cards are real stinkers — one side of my family, for instance, has a history of early heart disease. I don’t like the bad luck there, but that it is by chance alone is far more reassuring than the idea that a meddling deity chose to give my father a battery of risk factors that led to his early death, and that he also chose to stick me with some of those, too. If a loving god were actually paying “incredible personal attention to detail”, you’d think there would have been some quality control in spermatogenesis that might have weeded out some of the defective alleles, or more precise matching of sperm and egg to make sure all weaknesses in one were compensated by strengths in the other. This doesn’t happen.

While we have all the flaws concomitant with being children of chance, we also have an advantage: we’re free. There is no cosmic fiddler. There is no domineering father in the sky who has a mission for us, who decreed at our birth that there is something we must do with our lives, who has slotted you into one specific role without your consent. You are not driven by an arbitrary external purpose, and you should find the idea of such a daily dictator of every detail of your existence abhorrent to an extreme.

It’s a real mystery to me why anyone would find the deterministic slave-philosophy of Rick Warren at all appealing or consoling, especially since the evidence all says that it is wrong, as well. There must be something some people find pleasant in surrendering responsibility to an imaginary scapegoat.

Personally, I appreciate the fact that I’m a combination of traits, some lucky and some unlucky, that are mine and not the product of the whims of some puppetmaster. I’ll make of them what I can and what I will, and who I am is my responsibility and to my credit or blame.

α-actinin evolution in humans

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Perhaps your idea of the traditional holiday week involves lounging about with a full belly watching football — not me, though. I think if I did, I’d be eyeing those muscular fellows with thoughts of muscle biopsies and analyses of the frequency of α-actinin variants in their population vs. the population of national recliner inhabitants. I’m sure there’s an interesting story there.

In case you’re wondering what α-actinin is, it’s a cytoskeletal protein that’s important in anchoring and coordinating the thin filaments of actin that criss-cross throughout your cells. It’s very important in muscle, where it’s localized in the Z-disk at the boundaries of sarcomeres, the repeated contractile units of the muscle. This diagram might help you visualize it:

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Actin (green), myosin (red). Rod-like tropomyosin molecules (black lines). Thin filaments in muscle sarcomeres are anchored at the Z-disk by the cross-linking protein α-actinin (gold) and are capped by CapZ (pink squares). The thin-filament pointed ends terminate within the A band, are capped by tropomodulin (bright red). Myosin-binding-protein C (MyBP-C; yellow transverse lines).

The most prominent elements in the picture are the thin filaments (made of actin) and thick filaments (made of myosin) which slide past each other, driven by motor proteins, to cause contraction and relaxation of the muscle. The α-actinin proteins are the subtle orange lines in the Z disks on the left and right.

The α-actinin proteins are evolutionarily interesting. In vertebrates, there are usually four different kinds: α-actinin 1, 2, 3, and 4. 1 and 4 are ubiquitous in all cells, since all cells have a cytoskeleton, and the α-actinins are important in anchoring the cytoskeleton. α-actinin-2 and -3 are the ones of interest here, because they are specifically muscle actinins. α-actinin-2 is found in all skeletal muscle fibers, cardiac muscle, and also in the brain (no, not muscle in the brain, there isn’t any: in the cytoskeleton of neurons). Just to complicate matters a bit, α-actinin-2 is also differently spliced in different tissues, producing a couple of isoforms from a single gene. α-actinin-3 is not found in the brain or heart, but only in skeletal muscle and specifically in type II fast glycolytic muscle fibers.

Muscle fibers are specialized. Some are small diameter, well vascularized, relatively slow fibers that are optimized for endurance; they can keep contracting over and over again for long periods of time. These are the fibers that make up the dark meat in your Christmas turkey or duck. Other fibers are large diameter, operate effectively anaerobically, and are optimized for generating lots of force rapidly, but they tend to fatigue quickly — and there are more of these in the white meat of your Christmas bird. (There are also intermediate fiber types that we won’t consider here.) Just keep these straight in your head to follow along: the fast type II muscle fibers are the ones that you use to generate explosive bursts of force, and may be enriched in α-actinin-3; the slower fibers are the ones you use to keep going when you run marathons, and contain α-actinin-2. (There are other even more important differences between fast and slow fibers, especially in myosin variants, so differences in α-actinins are not major determinants of muscle type.)

Wait, what about evolution? It turns out that invertebrates only have one kind of α-actinin, and vertebrates made their suite of four in the process of a pair of whole genome duplications. We made α-actinin-2 and -3 in a duplication event roughly 250-300 million years ago, at which time they would have been simple duplicates of each other, but they have diverged since then, producing subtle (and not entirely understood) functional differences from one another, in addition to acquiring different sites of expression. α-actinin-2 and -3 in humans are now about 80% identical in amino acid sequence. What has happened in these two genes is consistent with what we know about patterns of duplication and divergence.

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Using sarcomeric α-actinin as an example, after duplication of a gene capable of multiple interactions/functions, there are two possible distinct scenarios besides gene loss. A: Sub-functionalisation, where one interaction site is optimised in each of the copies. B: Neo-functionalisation, where one copy retains the ancestral inter- action sites while the other is free to evolve new interaction sites.

So what we’re seeing in the vertebrate lineage is a conserved pattern of specialization of α-actinin-3 to work with fast muscle fibers — it’s a factor in enhancing performance in the specific task of generating force. The α-actinin-3 gene is an example of a duplicated gene becoming increasingly specialized for a particular role, with both changes in the amino acid sequence that promoted a more specialized activity, and changes in the regulatory region of the gene so that it was only switched on in appropriate muscle fibers.

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Duplication and divergence model proposed by this paper. Before duplication the ancestral sarcomeric α-actinin had the functions of both ACTN2 and ACTN3 in terms of tissue expression and functional isoforms. After duplication, ACTN2 has conserved most of the functions of the preduplicated gene, while ACTN3 has lost many of these functions, which may have allowed it to optimise function in fast fibres.

That’s cool, but what we need is an experiment: we need to knock out the gene and see what happens. Mutations in α-actinin-2 are bad—they cause a cardiomyopathy. Losing α-actinin-4 leads to serious kidney defects (that gene is expressed in kidney tissue). What happens if we lose α-actinin-3?

It turns out you may be a guinea pig in that great experiment. Humans acquired a mutation in the α-actinin-3 gene, called R577X, approximately 40-60,000 years ago, and this mutation is incredibly common: about 50% of individuals of European and Asian descent carry it, and about 10% of individuals from African populations. Furthermore, an analysis of the flanking DNA shows relatively little recombination or polymorphism — which implies that the allele has reached this high frequency relatively recently and rapidly, which in turn implies that there has been positive selection for a nonsense mutation that destroys α-actinin-3 in us. The data suggests that a selective sweep for this variant began in Asia about 33,000 years ago, and in Europe about 15,000 years ago.

There is no disease associated with the loss of α-actinin-3. It seems that α-actinin-2 steps up to the plate and fills the role in type II fast muscle fibers, so everything functions just fine. Except…well, there is an interesting statistical effect.

The presence of a functional α-actinin-3 gene is correlated with athletic performance. A study of the frequency of the R577X mutation in athletes and controls found that there is a significant reduction in the frequency of the mutation among sprinters and power-lifters. At the Olympic level, none of the sprinters in the sample (32 individuals) carried the α-actinin-3 deficiency. Among Olympic power lifters, all had at least one functional copy of α-actinin-3.

Awesome. Now I’m wondering about my α-actinin-3 genotype, and whether I have a good biological excuse for why I always got picked last for team sports in high school gym class. This is also why I’m interested in taking biopsies of football players…both for satisfying a scientific curiosity, and for revenge.

You may be wondering at this point about something: α-actinin-3 has a clear beneficial effect in enhancing athletic performance, and its conservation in other animal species suggests that it’s almost certainly a good and useful protein. So why has there been positive selection (probably) for a knock-out mutation in the human lineage?

There is a weak correlation in that study of athletic performance that high-ranking athletes in endurance sports have an increased frequency of the R577X genotype; it was only seen in female long-distance runners, though. More persuasive is the observation that α-actinin-3 knockouts in mice also produced a shift in metabolic enzyme markers that are indicative of increased endurance capacity. The positive advantage of losing α-actinin-3 may be more efficient aerobic metabolism in muscles, at the expense of sacrificing some strength at the high end of athletic performance.

This is yet another example of human evolution in progress—we’re seeing a shift in human muscle function over the course of a few tens of thousands of years.

Lek M, Quinlan KG, North KN (2009) The evolution of skeletal muscle performance: gene duplication and divergence of human sarcomeric alpha-actinins. Bioessays 32(1):17-25. [Epub ahead of print]

MacArthur DG, Seto JT, Raftery JM, Quinlan KG, Huttley GA, Hook JW, Lemckert FA, Kee AJ, Edwards MR, Berman Y, Hardeman EC, Gunning PW, Easteal S, Yang N, North KN (2007) Loss of ACTN3 gene function alters mouse muscle metabolism and shows evidence of positive selection in humans. Nat Genet.39(10):1261-5.

Yang N, MacArthur DG, Gulbin JP, Hahn AG, Beggs AH, Easteal S, North K (2003) ACTN3 genotype is associated with human elite athletic performance. Am J Hum Genet 73(3):627-31.

A contemptible pseudoscientific scam

Grrr. I was sent a link to these lying, sleazebag scammers at mygeneprofile.com, and it’s the kind of thing that pisses me off. What you’ll find there is a long video where the lowlife in a suit talks about how your children have in-built genetic biases (“from God”, no less), and how if you want them to be truly happy and successful, you should tailor their upbringing to maximize their genetic potential. And he blathers on about how they will do a genetic test to determine whether your child has genes for science or art.

It’s a complete lie. There is no such test. There can be no such test. That’s not how genomes work, that they translate DNA in a comprehensible, measurable way into discrete traits for such abstract abilities as playing the piano.

They claim that “The Industry is Featured by CNN, CBS News.” I wonder what that means? That there were news reports about the human genome project? That they bought commercial time?

Anyway, it’s fraud. Don’t fall for it.

A creationist at the Chicago meeting

Last week, I described the lectures I attended at the Chicago 2009 Darwin meetings (Science Life also blogged the event). Two of the talks that were highlights of the meeting for me were the discussions of stickleback evolution by David Kingsley and oldfield mouse evolution by Hopi Hoekstra — seriously, if I were half my age right now, I’d be knocking on their doors, asking if they had room for a grad student or post-doc or bottle-washer. They are using modern techniques in genetics and molecular biology to look at variation in natural populations in the wild, and working out the precise genetic changes that led to the evolution of differences in development and morphology. They are doing stuff that, back when I actually was a graduate student, would have been regarded as technically impossible; you needed model systems in the laboratory to have the depth of molecular information required to track down the molecular basis of novel morphs, and you couldn’t possibly just grab some interesting but otherwise unknown species out on a beach or a pond and work out a map and localize genetic differences between individuals. They’re doing it now, though, and making it look easy.

Then there were all the other talks in population genetics and paleontology (and the talks on history and philosophy, which I almost entirely neglected)…this was a meeting that everywhere demonstrated major advances in our understanding of evolution. Every talk was about the successes of evolutionary theory and directions to take to overcome incomplete areas of understanding; this was a wonderfully positive and promising event that should have impressed all the attendees with the quality of the work that has been done and the excitement of the potential for future research. Like I said, there were a whole bunch of people here that I want to be when I grow up.

Well, normal people would feel that way. Paul Nelson, that creationist, was also there. Nelson is a weird guy; he’s always hanging around the edges of these scientific meetings, and you’d think that after all these years of lurking, he’d actually learn something, but no…the only skill he has mastered is the art of ignoring what he doesn’t like and incorporating fragments of sentences into his armor of ignorance. It’s very sad.

I talked with Nelson briefly at a reception at the meetings, and we both agreed on the quality of Kingsley’s work — but that’s about all. Nelson thought it supported ID better than neo-Darwinian evolutionary theory. His argument was that a) all anybody ever described was loss of features, and b) a large parent population was the source of all the allelic variation in the sub-populations studied, which is what ID predicts. He didn’t mention their favorite magic word of “front-loading”, but I could see what he was thinking.

How Nelson can hang about on the fringes of the evo-devo world and not notice that what was described by modern empirical research is exactly what the evo-devo theoreticians expected is a mystery — these were results that fit beautifully what science, not the wishful voodoo of intelligent design creationism, predicts.

Both Kingsley and Hoekstra are looking at recent species, subpopulations that separated from parent populations within the last ten thousand years, and have adapted relatively rapidly to new environmental conditions. The sticklebacks are fragments of marine species that were isolated in freshwater streams and lakes, while the beach mice are parts of a widespread population of oldfield mice that are adapting to gulf coast islands. They are also working with populations that can be bred back to the root stock, that retain the ability to do genetic crosses, so of course the variation is not on the magnitude of turning fins into limbs (we need large amounts of geological time to do that; it’s the kind of work Neil Shubin would do, and unfortunately, he can’t cross Tiktaalik with Acanthostega). Complaining that the variants the real scientists are looking at aren’t the kind that the creationists want is a particularly clueless kind of whine, since the scientists are intentionally focusing on the variants that are amenable to dissection by their techniques.

The other aspect of their work that confirms evo-devo expectations is that what they’re discovering is that the genetic mechanisms behind morphological variants are changes in regulatory DNA — that what’s happening is that regulatory genes like Pitx1 or Mc1r are being switched off or on. We anticipate that a lot of morphological novelty is going to be generated by switching genes off and on, and by recombination of patterns of gene expression. Nelson and Behe are reduced to carping on the sidelines that observed variants are just the product of getting large effects by trivially flipping switches, while all the real biologists are out there in the middle of the work happily announcing that we can get large-scale morphological effects by simply flipping switches, and hey, isn’t that cool, and doesn’t that tell us a lot about the origins of evolutionary novelties? It’s not just a to-may-to/to-mah-to difference in interpretation, this is a case of the creationists wilfully and ignorantly missing the whole point of an exciting line of research.

There’s also a fundamental failure of comprehension. Creationists see loss of a feature like pelvic spines, or a reduction in pigmentation, and declare that the evolutionary evidence is “all breaking things and losing things”. Wrong. What we have here is a complete lack of understanding of developmental genetics. What we typically find are changes in the pattern of expression of developmental genes, not wholesale losses. In the stickleback, Pitx1 is still there; what’s different is that the places in the embryo where it is turned on have changed, the map of the pattern of gene expression has shifted. You cannot describe that as simply a broken gene. Similarly, in the mouse, Hoekstra showed that the expression of genes that reduce pigmentation has expanded. We’ve seen the same thing in the blind cavefish; a creationist looks at it and says it’s just broken and has lost its eyes, but the scientists look closer and see that no, the fish have actually increased gene expression and expanded the domain of a midline gene.

Just wait for the detailed analysis of jaw morphology in cichlid fishes. These animals have radically different variants in feeding structures, which is thought to be the root of their adaptability and the radiation of different forms, and I guarantee you that the creationists will ignore the morphological novelties and focus on the fact that to achieve that, some genes will be downregulated (I also guarantee you that there will be such shifts in expression). It’s “all breaking things and losing things”, after all; just like baking a cake involves breaking eggs.

I don’t know how the creationists fit known variations in the coding sequences of genes (how do you translate a single-nucleotide polymorphism into their vision of all change being a matter of losses?) into their idea that all evolution is a matter of breaking DNA, or how they can claim all novelty requires a designer when people can track the progression of morphological shifts in the tetrapod transition, for instance, across tens of millions of years. It seems to be their desperate 21st century excuse in the face of the overwhelming progression of information from 21st century biological science.

Nelson ends his skewed summary of the meeting with the comment that “It’s a heck of a lot of fun to attend a conference like this, if you don’t mind being the butt of jokes.” I’m sure. I suppose Nelson could have even more fun if he put on a dunce cap and drooled a lot, because that’s basically his role at these meetings anyway — he’s the butt of jokes because he shows up and then happily demonstrates his ignorance about what’s going on. It’s not a role I’d enjoy, but the gang at the clown college called the Discovery Institute have a slightly different perspective, I suppose.