john hawks weblog

Here's an AP story about humor and the sexes:

Women seem more likely than men to enjoy a good joke, mainly because they don't always expect it to be funny.

...

Women were subjecting humor to more analysis with the aim of determining if it was indeed funny, Reiss said in a telephone interview.

Men are using the same network in the brain, but less so, he said, men are less discriminating.

"It doesn't take a lot of analytical machinery to think someone getting poked in the eye is funny," he commented when asked about humor like the Three Stooges.

Yes, stereotype is the basis of all science -- er, humor.

The MRI data show that this is another nucleus accumbens story, in this case with connection to the left prefrontal cortex. So humor connects the rational headquarters of the brain with the dopamine-rich reward center. Stooge that!

Arnie Cann, a psychology professor at the University of North Carolina, Charlotte, commented: "Given the findings in the current study, that women appear to use more executive functions, it could be that they are more engaged in scrutinizing the humor to decide if it fits their views on what is acceptable humor. Once they decide the humor is OK, they could be experiencing a relief-like response."

I'm usually very skeptical of claims that widespread DNA testing will result in bad effects -- "Big Brother" finding out your genotype and discriminating against you, for example. A lot of people are afraid of it, but there has been a lot of unnecessary scaremongering.

But today I read this New Scientist story by Alison Motluk:

Anonymous sperm donor traced on internet

LATE last year, a 15-year-old boy rubbed a swab along the inside of his cheek, popped it into a vial and sent it off to an online genealogy DNA-testing service. But unlike most people who contact the service, he was not interested in sketching the far reaches of his family tree. His mother had conceived using donor sperm and he wanted to track down his genetic father.

Now, that doesn't sound so bad, does it? The biological father must have also submitted his DNA to some genealogy database, which came up as a match, right?

Wrong!

The boy paid FamilyTreeDNA.com $289 for the service. His genetic father had never supplied his DNA to the site, but all that was needed was for someone in the same paternal line to be on file. After nine months of waiting and having agreed to have his contact details available to other clients, the boy was contacted by two men with Y chromosomes closely matching his own. The two did not know each other, but the similarity between their Y chromosomes suggested there was a 50 per cent chance that all three had the same father, grandfather or great-grandfather.

Importantly, the men both had the same last name, albeit with different spellings. This was the vital clue the boy needed to start his search in earnest. Though his donor had been anonymous, his mother had been told the man's date and place of birth and his college degree. Using another online service, Omnitrace.com, he purchased the names of everyone that had been born in the same place on the same day. Only one man had the surname he was looking for, and within 10 days he had made contact.

The implication is very simple: no one is anonymous. Suppose you let your DNA slip anywhere. Everyone does to the tune of 36 million cells per day, not to mention the occasional lucky sperm. Now if one of your relatives is careless enough to have his name and DNA sequence associated in a public database, then your entire genetic profile may be available to anyone who's interested.

This seems like a pretty simple way to find out the surname of any unknown DNA sample you might have. Most relatively common US surnames already have entries at one or more genealogy registry. If a motivated fifteen-year-old can do it once, the CIA can certainly do it indefinitely many times. All that is needed is for somebody to want to find you.

Better not give them a reason.

I've been noticing that Google Scholar searches are getting better at coming up with PDF versions of articles. Sometimes these are hosted from journal sites -- and pop right up if the university has a subscription. But more often they are hosted from the authors' websites -- as in, "Here is my lab website, here are PDF's of my papers."

Now, this is against most of those copyright agreements that authors have to sign to put their work in journals. Those boilerplate agreements almost always allow distribution of preprints (i.e. ugly double-spaced manuscripts) but not formatted proofs or journal PDF's. But most of the papers you will find in a search are original journal PDF's.

I just want to thank everyone who puts their papers online. It's incredibly useful. Personally, I'm moving more toward submitting to open access journals, at least where the papers are a good fit.

Can I also say that Google Scholar is incredibly easier for finding something at a journal site instead of using the search methods from the publishers themselves?

By far my biggest complaint is the whole implementation of the digital object identifiers (DOI) by publishers. Remember how those were supposed to make everything easily searchable with a single number? Now, some journals require you to include DOI's in bibliographic entries, presumably so that they can do online hotlinks in their online fulltext. But have you every tried to search for something by DOI? Most journals give you no results at all. And there's no way to search for things across many different journals with the DOI. It's all a mess. And here most of us are busily entering DOI's in our bibliographic databases. What a waste of time!

Of course, the Google Scholar citation search is less complete than ISI. Heck, ISI is even tracking my weblog. But I am really beginning to lean on having the first line of text in the search results. It really cuts my time sifting out stuff I don't need or want.

A short review in Science by Greg Miller discusses genetic correlates of dyslexia.

In one new study, a collaboration of 20 researchers led by Haiying Meng and Jeffrey Gruen of Yale University School of Medicine homed in on a region of chromosome 6 that had been fingered previously. Using DNA from 536 people with a dyslexic in their families, the researchers tracked 147 single-nucleotide polymorphisms (SNPs), spots where the genetic code differs by one letter among individuals. Searching for SNPs that tend to have one "spelling" in people with reading impairments and another spelling in normal readers, the researchers found a disproportionate number of such SNPs in a gene called DCDC2. They also found that about 17% of dyslexics were missing a short stretch of DNA within DCDC2. Everyone who had this deletion had dyslexia, Gruen says.

Am I the only one who finds it particularly perverse to refer to SNPs as having "another spelling" in people with reading impairments?

In any event, two of the genes identified here -- DCDC2 and ROBO1 (OMIM) -- both have functions in neuron migration and connections in animal models.

ROBO1 is especially interesting, because it appears to be an inhibitor of neural connections across the midline:

Axonal growth cones that cross the nervous system midline change their responsiveness to midline guidance cues: they become repelled by the repellent Slit (603746) and simultaneously lose responsiveness to the attractant netrin (601614). These mutually reinforcing changes help to expel growth cones from the midline by making a once-attractive environment appear repulsive. Stein and Tessier-Lavigne (2001) provided evidence that these 2 changes are causally linked: in the growth cones of embryonic Xenopus spinal axons, activation of the Slit receptor Robo silences the attractive effect of netrin-1, but not its growth-stimulatory effect, through direct binding of the cytoplasmic domain of Robo to that of the netrin receptor DCC (120470). Biologically, this hierarchical silencing mechanism helps to prevent a tug-of-war between attractive and repulsive signals in the growth cone that might cause confusion. Molecularly, silencing is enabled by a modular and interlocking design of the cytoplasmic domains of these potentially antagonistic receptors that predetermines the outcome of their simultaneous activation.

Using a variety of mammalian, avian, and Drosophila constructs and cells, Rhee et al. (2002) showed that Robo, activated by Slit (see 603742), disrupted cell adhesion and neurite outgrowth mediated by N-cadherin (114020). Robo activation was accompanied by tyrosine phosphorylation of beta-catenin (116806) and the loss of beta-catenin from N-cadherin complexes. The result was the formation of a complex between Robo and N-cadherin, uncoupling N-cadherin from its association with the cytoskeleton. The C terminus of Robo mediated these effects. Rhee et al. (2002) concluded that a repulsive cue could be directly converted into decreased traction between the growth cone and the substrate (OMIM).

In other words, ROBO1 may help mediate the connection of the two hemispheres. The functional link to dyslexia is not yet clear, but one might speculate about a possible disruption (or even an undesirable increase) of across-brain communication and integration of sensory or cognitive information.

A third gene, KIAA0319, may also be involved in brain development.

One may imagine a system of toolkit genes, activated differently in different parts of the developing cortex during early embryonic development. Later, the gene products regulate the responsiveness of different neurons to growth activators and inhibitors. This system results in the shape of the brain -- where the neurons are, and where they aren't. It can draw the migration of some neurons across cortical layers, and sketch their axons across the brain.

It is therefore interesting that we are beginning to connect observable variation in brain function to some of these activators and inhibitors -- the last links in the chain of brain development. In contrast, toolkit genes are essential to tissue differentiation and the spatial determination of body parts. Elsewhere in the body, alterations to toolkit genes may result in segmental duplications, agenesis of body parts (such as limbs, fingers, or organs) and other gross anatomical abnormalities.

We have developed a lot of information about toolkit genes in the body (the Hox clusters are the best-known example). But so far we know less about the genes that influence the early development and differentiation of the brain. Perhaps the microcephaly-related genes are part of this neurodevelopmental toolkit. Perhaps most of the toolkit effects are yet earlier in development, and mutations result in nonviability. Or perhaps there is a large set of low-incidence brain-region-specific developmental disorders that have yet to be systematized.

References:

Miller G. 2005. Genes that guide brain development linked to dyslexia. Science 310:759. Full text (subscription)

Rhee, J.; Mahfooz, N. S.; Arregui, C.; Lilien, J.; Balsamo, J.; VanBerkum, M. F. A. 2002. Activation of the repulsive receptor roundabout inhibits N-cadherin-mediated cell adhesion. Nature Cell Biol 4: 798-805. PubMed

Stein, E.; Tessier-Lavigne, M. 2001. Hierarchical organization of guidance receptors: silencing of netrin attraction by Slit through a Robo/DCC receptor complex. Science 291: 1928-1938. PubMed

I got about halfway through this Wired article by Mark Baard about the "resurgence" of cryptozoology, when I found this:

The media's renewed interest is partly due to the recent discoveries of the "hobbit" remains on Flores Island in Indonesia and the giant squid photographed by Japanese scientists, Coleman said.

And here is a loaded reference:

Cryptozoology has been taking its knocks since the discovery of Neanderthal man in the 19th century.

Many mainstream scientists at the time insisted the remains of Neanderthal were actually those of a sick or deformed human, said Coleman.

On one side, we have the creationists, who would like you to believe that every evolutionary biologist was suckered by Piltdown. "If they were wrong about that, they can't tell their butts from their elbows!"

On the other, we have the cryptozoologists, who would like you to remind you of all those bad, bad scientists who thought that Neandertals were sick or deformed. Why, "If they were wrong about that, they can't tell their butts from their elbows!"

Can't you just hear them? "They doubted us, but the ebu gogo is real! The giant squid is on tape! The truth is out there!

Can I just ask that future qualifying exams include a butt vs. elbow discrimination quiz?

From tonight's CSI:

KATHERINE: He manipulates evidence.

GRISSOM: He manipulates people. The public assumes that scientists are ethical, but many of us are no better than politicians, evidently.

Yesterday's post on mice mating songs left with a final question: Do FoxP2 knockout mice sing?

Thanks to a kind reader, I have a probable answer: no.

Although nobody has looked specifically at ultrasonic vocalizations in response to mating (since they didn't know about the singing before), they have studied ultrasonic vocalizations in pups when they are separated from their mothers. This paper by Weiguo Shu and colleagues (2005) studied such vocalizations in both heterozygous and homozygous knockouts.

Because FOXP2 has been directly implicated in speech and articulation, we examined the incidence of ultrasonic vocalizations in pups removed from their mothers. Ultrasonic calls are important for motherÐinfant social interaction (16) and represent important markers for neurobehavioral development (17). At postnatal day 6, the incidence of vocalization over time was dramatically reduced in both heterozygous and knockout animals as measured by automated vocalization monitoring (Fig. 4a). Repeated measures analysis demonstrated differences in mean number of vocalizations (P

Based on these results, we performed a spectrographic analysis of an independent group of animals at postnatal day 10 (e.g., Fig. 4b). There was a profound decrease in the number of ultrasonic vocalizations in heterozygous and homozygous knockout animals (Fig. 4c). The duration, peak frequency, and bandwidth of these vocalizations in the heterozygous animals were indistinguishable from wild-type animals (data not shown). In the course of these analyses, we also examined broad-spectrum clicks made by the mice. These clicks are of unknown function (18), and the information content of them has not been studied. Heterozygous and homozygous knockout animals were able to produce clicks, but the homozygous knockout animals produced clicks at a reduced incidence (Fig. 4d). The duration, peak frequency, and bandwidth of these vocalizations in the heterozygous and homozygous knockout animals were indistinguishable from wild-type animals (emphasis added).

Homozygote knockouts died prematurely, by 21 days after birth. Heterozygotes lived, but exhibited developmental delays.

The paper included histological study of the brains of the knockout mice, with this interesting finding:

No overt abnormalities were detected in the histologic appearance of the cerebral hemispheres and the subcortical structures, including the midbrain and pons. However, the knockout mice demonstrated the presence of a 3- to 4-cell thick external granular layer (EGL) at postnatal days 15Ð17, well after the normal resolution of the EGL (Fig. 3 aÐf). The heterozygous animals retained a one-cell-thick EGL at this age, whereas the wild-type mice were free of this early developmental feature. By adulthood, the EGL was absent in heterozygous animals (data not shown).

...

Granule cell progenitors in the EGL migrate to their final position in the granule cell layer along the radial fibers of the Bergmann glia (14). To determine whether the persistence of an EGL in the knockout animal might be at least, in part, explained by a failure of radial glial development, we stained radial glial fibers in cerebellar sections from postnatal day 17 animals with GFAP immunostaining (Fig. 3 jÐl). Radial glial fibers were visible in all genotypes. However, in contrast to the regularly aligned radial glial fibers in the wild-type animals, fibers in the heterozygous animals were in some areas thinner and less well aligned. In the knockout animals, there were often gaps in the radial glial network as well as areas where fibers appeared to be clumped into aggregates.

It's interesting to me that neural cell migration appears to be (at least one) important effect of the gene. This is the developmental step that forms the circuits in the brain.

References:

Shu W, Cho JY, Jiang Y, Zhang M, Weisz D, Elder GA, Schmeidler J, De Gasperi R, Sosa MA, Rabidou D, Santucci AC, Perl D, Morrisey E, Buxbaum JD. 2005. Altered ultrasonic vocalization in mice with a disruption in the Foxp2 gene. Proc Natl Acad Sci U S A 102:9643-9648. Full text (free)

I've been sifting through some HapMap-related stuff. It's a tremendous resource for looking at human variation, but it also presents some tremendous problems. An interesting review of some of the information coming out of HapMap by Gil McVean and colleagues is running in PLoS Genetics.

These guys are statisticians working to analyze some of the data, and they put into words very well some of the issues I've been butting against.

There is great heterogeneity across the genome in terms of patterns of genetic variation. Some of this heterogeneity is due to variation in factors such as mutation rate and recombination rate. Some of this heterogeneity arises because of the stochastic properties of mutation and genealogical history. But there are also other forces such as natural selection and genomic features such as inversions that may influence local patterns of variation. How can we look for the effects of such factors? There are two approaches. Either we can try to predict what we would expect to observe under models with and without such effects [23,24], or we can simply look at the empirical distribution of statistics of genetic variation and take as candidate regions those showing extreme or unusual patterns. The difficulty of the first approach is that accurately modelling human variation (and SNP ascertainment) is probably impossible. The difficulty of the latter approach is that there is no guarantee that empirically unusual patterns point to biologically interesting features (McVean et al. 2005:e54, emphasis added).

I would add that there is an additional difficulty with the second approach -- namely, that it assumes that selection or other factors cause heterogeneity. More about that later.

This point about ascertainment and demography is an important one. Predictions about the effects of evolution (including drift) upon genetic variation are simplest under random sampling. Since the beginning of human genetics, almost no one has attempted to sample people at random. Some nonrandomness may be desirable -- for example, recent demographic changes make a random sample of today's humans very different in composition from a random sample of humans in 1491, or 6000 B.C., or almost any time in the past. Which of these "random" samples would represent the population whose history we care about? If we are interested in prehistoric events, we may find it desirable to represent peoples in proportion to their prehistoric distributions. This has precisely been the approach of some genetic surveys.

But even from this simple example, the problems with human demography are clear. We can try to shift samples to represent prehistoric "distributions" of populations, but geography is not the only aspect of demography that has changed. There have been massive population mixtures that would never have had the opportunity to occur in prehistoric times. There have been diseases and demographic crashes that we know about, and probably many that we don't know about.

The question is whether it is possible to arrive at a "rough draft" of human demographic history that would be precise enough to generate theoretical distributions of human variation. As it happens, that is precisely the goal of this paper by Stephen Schaffner and colleagues in Genome Research. From the abstract:

With the advent of large empirical data sets, it is now possible to calibrate population genetic models with genome-wide data, permitting for the first time the generation of data that are consistent with empirical data across a wide range of characteristics. We present here the first such calibrated model and show that, while still arbitrary, it successfully generates simulated data (for three populations) that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation. No assertion is made about the accuracy of the proposed historical and recombination model, but its ability to generate realistic data meets a long-standing need among geneticists (Schaffner et al. 2005:1576, emphasis added).

The problem with the approach is precisely in the boldface sentence. It is possible to use empirical data to calibrate a model that generates simulated data that is similar to the empirical data. The point of using such a calibrated model is to be able to show how strange certain regions are if they don't fit the simulated distribution, which is based on the empirical distribution.

But it's all circular.

Suppose we wanted to use a detailed topographic survey of a road to find the potholes. But for everyday roads, there is a problem -- there are lots of bumps and grooves that aren't potholes. And different parts of the road are more or less bumpy. It would help a lot if we could use the empirical distribution of bumps to simulate a section of road -- then we could figure out whether anomalies in the real road were likely to be potholes or not.

Now suppose that the road isn't just pocked with the occasional pothole -- it has a pothole every three or four feet. Remember why we're using simulations -- not only do we not know where the potholes are, we don't know how common they are. So our simulations based on the pothole-rich road will find that pothole-sized bumps are normal. If pothole-sized bumps are not unusual, then our simulation can have only one result: a pothole is not a pothole.

Consider this current paper on the CCR5Δ32 allele, generally thought to be recently selected in Europeans as a disease defense against smallpox (my earlier entry). Sabeti et al. (2005) revise the date of the mutation from only around 700 years ago to around 5000. But more important, they deny that the allele was necessarily selected. Why? Because its pattern of linkage disequilibrium is relatively common across the genome.

Our reanalysis of CCR5 shows that CCR5-Δ32 does not clearly stand out from the rest of the genome in terms of allele frequency distribution, population differentiation, or long-range LD (Figure S8). The high population differentiation and long-range LD found for CCR5-Δ32 are, in fact, far more common in the genome than previously believed, and therefore do not provide support for the hypothesis of strong selection for CCR5-Δ32. Using methods described both in the previous study [8] and in the current study, and by examining currently available data, there is no detectable evidence for recent selection for CCR5-Δ32 (Sabeti et al. 2005:e378).

Ceci n'est pas un pothole.

Why is it that simulations like these are not attempts to make accurate historical models? Quite simply, because they can't. After years of attempts at reconstruction human evolution based on "neutral" genetic loci, the HapMap at last has thrown out the possibility entirely. If we want to use the broadest source of information, we have to take with it some significant lumps. And one of the biggest is that we simply don't know the selective dynamics of most of the genome.

For the anthropologist interested in history, it is not critical that we be able to develop a model of demographic history accurate enough to serve as a theoretical distribution for testing selection. Our goals are often much less ambitious, and there is much information about demographic history to be had from the HapMap and like projects. But neither should we minimize the problems. Attempting to use simulations to match the variation of the genome as a whole simply isn't going to work, if any substantial proportion of the genome has been under recent selection. And as we move to higher parameter models of demography (Schaffner et al. 2005 attempt a 21-parameter model) selection on relatively few sites becomes more and more capable of distorting demographic estimates.

By far the worse problem is finding selection.

For example, McVean et al. (2005) embark upon an examination of the "tails" of the genome -- the parts that show highly unusual patterns of variation compared to most regions. The idea is that if these very unusual loci had areas of biological interest (i.e., particular genes), their very strangeness might lead to a hypothesis of historical change (such as selection).

But they run into problems:

Of the 19 genes with previous evidence for historical selection, 12 show an unusual pattern of genetic variation in at least one population (defined as having a value lying in either the bottom 5% or top 5% of empirical values). Superficially, this result suggests that statistical tests based on rejecting a simple population genetics model are effective at detecting genes of interest. However, for 114 tests, we might expect 11 to lie in either the top or bottom 5% of observations, compared to the 17 observed. Another concern is that genes of known functional and selective importance, such as Duffy and CD40 ligand, do not fall in the tails of the empirical distribution of Tajima's D and Fay and Wu's H statistics and others, such as MMP3, hemochromatosis (HFE), and aldehyde dehydrogenase 2 (ALDH2) show patterns that are unusual, but not indicative of the action of recent selective sweeps.

Multiple comparisons are pretty tricky across the entire genome and in multiple populations. Considering this, we might see the HapMap and similar surveys as hotbeds of type II error: if we go looking for strange things, we are going to miss the forest for the trees.

There are two main conclusions from these analyses. First, that biologically interesting loci often do have unusual patterns of genetic variation, but that there is no single way of measuring "unusual" that is uniformly powerful for detecting the action of natural selection. Second, that rejection of neutral evolutionary models is no guarantee that the locus is unusual when compared to the rest of the genome (McVean et al. 2005:e54).

"Unusual compared to the rest of the genome" is a phrase you should expect to hear a lot of in the next few years.

References:

McVean G, Spencer CCA, Chaix R. 2005. Perspectives on human genetic variation from the HapMap project. PLoS Genetics 1:e54. Full text (free)

Sabeti PC et al. 2005. The case for selection at CCR5-Δ32. PLoS Biol 3:e378. Full text (free)

Schaffner SF, Foo C, Gabriel S, Reich D, Daly MJ, Altshuler D. 2005. Calibrating a coalescent simulation of human genome sequence variation. Genome Res 15:1576--1583. Full text (free)

Carl Zimmer has an article in Forbes covering recent experiments in chimpanzee vocal communication.

But don't write off those grunts and hoots just yet, at least according to a new study that appears in the Oct. 15 issue of the journal Current Biology. Katie Slocombe and Kaus Zuberbuhler, two primatologists at the University of St. Andrews in Scotland, investigated a particular noise chimpanzees make when they find food, called a "rough grunt." At the Edinburgh Zoo, the scientists fed the chimpanzees two different foods--apples and bread--and recorded the sounds they made. Chimpanzees prefer bread to apples, and Slocombe and Zuberbuhler discovered a corresponding difference in the rough grunts they made for each food. They hit a distinctively high note when they came across the bread, and but made lower and noisier grunts for apples.

It's a short article, supplemented by an entry on the Loom.

This online issue of Forbes

There are a number of short interview excerpts in the issue. One has Noam Chomsky discussing spontaneous language innovation in deaf communities. Another from Jane Goodall on the perils of e-mail communication:

I remember when I worked for Lewis [sic] Leakey, first as his secretary. He was very impulsive. He'd get a letter in the mail, and he would open it, and it would be perhaps something from a scientist he thought was quite ridiculous. You could hear him muttering "Bosh! Rubbish!" The poor bit of paper would be scored with his marks, and he'd turn to me and say "Get so and so on the phone!" I got very wise to his moods, so I would pretend the number was engaged, or the man wasn't there, and then an hour or two later, he was rational again.

And other interviews and articles, with Arthur C. Clarke, Wil Wheaton, Desmond Morris, Steven Pinker and many others. Many thanks to the reader who pointed me to the site.

From the "any publicity is good publicity" department: Popular Science's list of the worst jobs in science includes "Orangutan-pee collector".

"Have I been pissed on? Yes," says anthropologist Cheryl Knott of Harvard University. Knott is a pioneer of "noninvasive monitoring of steroids through urine sampling." Translation: Look out below! For the past 11 years, Knott and her colleagues have trekked into Gunung Palung National Park in Borneo, Indonesia, in search of the endangered primates. Once a subject is spotted, they deploy plastic sheets like a firemen's rescue trampoline and wait for the tree-swinging apes to go see a man about a mule. For more pee-catching precision, they attach bags to poles and follow beneath the animals. "It's kind of gross when you get hit, but this is the best way to figure out what's going on in their bodies," Knott says.

The short article does point out the great value of the work in wild primate conservation and biology. And it doesn't call them "whiz kids"!

And it is sure seeming easier than job number 3: "Kansas Biology Teacher".