Category Archives: Evolution

Ecology, Evolution, and Human Health

Yesterday, I spent most of the day collecting content for my upcoming classes this spring and getting the course web sites together.  For the first time in a while, I will (officially) be teaching two classes in one quarter (which effectively means teaching three or four when I add the other things like lab meetings in).  The first is our graduate class on statistics in the anthropological sciences.  I taught something like this back in the old department (i.e., Anthropological Sciences) but haven’t taught it in years (though a Google search for “department of anthropological sciences stanford” turns up the syllabus for this class).  It is technically a requirement for Ph.D. students in the Ecology and Environment focus within Anthropology, so it’s about time.  It will be fun to teach again and we’re looking to use the class as a platform to develop resources for anthropologists doing statistical work (more later).

The other class that I will be teaching starting next week is Ecology, Evolution, and Human Health, a class I first taught last year. This class is meant to be an introduction to the Ecology and Environment undergraduate focus in Anthropology.  I’m actually really looking forward to teaching it again.  The course material forms the core of a book I am writing on human population biology and my attempts at improving the lectures has done wonders for my writing output of late.  We’ll see what happens when the quarter actually starts. Hopefully, between trips to Rwanda and Tanzania and moving into Arroyo House this summer, I will find time to finish it!

Back in December, when the is-anthropology-science kerfuffle was going strong, I wrote a blog post in which I suggested that if you want to feel good about the future of scientific anthropology (which, I admit, can sometimes be difficult, even for an obstinate optimist), all you need to do is look at the great work coming from the new generation of trans-disciplinary anthropologists (and other biosocial scientists).  At the time, I put together a short list of people whose work I greatly admire.  These included:

  • Craig Hadley at Emory on food security and psychological well-being
  • Amber Wutich at ASU on vulnerability, water security, and common-pool resources
  • Lance Gravlee at UF on the embodiment of racial discrimination and its manifestations in health
  • Brooke Scelza at UCLA on parental investment and childhood outcomes
  • Dan Hrushka at ASU on how cultural beliefs, norms and values interact with economic constraints to produce health outcomes
  • Crickette Sanz at Washington University on multi-ape ecology of the Goualougo Triangle, Republic of Congo
  • Herman Pontzer at CUNY on measuring daily energy expenditures in hunter-gatherers
  • Rebecca and Douglas Bird on subsistence and signaling among Martu foragers

In preparing for Anthro 31, I started to put together a list of links to people doing the kind of work we will discuss.  In a pique of obsessiveness yesterday, I greatly expanded that list.  It occurred to me that this list is somewhat orphaned in an obscure directory for a particular class I occasionally teach and that it would make sense to share it more generally.  So, here we go, copied wholesale from my class links page (though that page still contains links to books, professional societies, and other resources for students interested in human ecology, demography, health, etc.):

There are a number of excellent practicing anthropologists who maintain science blogs. Among these are Kate Clancy‘s (UIUC) Context and Variation, Daniel Lende and Greg Downey‘s Neuroanthropology, Julienne Rutherford‘s AAPA BANDIT, and Patrick Clarkin’s blog dedicated to biological anthropology, war and health, growth nutrition. Along with Rebecca Stumpf, Kate Clancy is also the director of the Laboratory for Evolutionary Endocrinology (which has its own blog) at the University of Illinois.

Upon further reflection, I think that the University of Illinois has to be a major contender for best place to study biological anthropology. Wow, they’ve got an amazing group of biological anthropologists there. Stanley Ambrose, Kate Clancy, Paul Garber, Lyle Konigsberg, Steve Leigh, Ripan Malhi, John Polk, Charles Roseman, Laura Shackelford, Rebecca Stumpf. Too many to link to directly. I don’t know all of them, but the ones I know are outstanding. Yipes! I think they may be plotting to take over the field.

Back to the blog front, you can always count on gems of anthropological, evolutionary, and political wisdom from Greg Laden as well.

Susan C. Antón (NYU) and Josh Snodgrass (Oregon) organize the Bones and Behavior Working Group, the goal of which is to foster greater synthesis across the different sub-areas of biological anthropology. Of particular interest are their standardized protocols for anthropometry.

Mario Luis Small, at the University of Chicago, has done some really outstanding work measuring how social institutions affect social capital and the impact such differences in social capital actually have for people’s well-being.

Richard Bribiescas is the author of Men: Evolutionary and Life History and is director of the Reproductive Ecology Laboratory at Yale. Yale is also now the home to Catherine Panter-Brick who also happens to be the senior editor for medical anthropology at Social Science and Medicine.

A number of excellent human biologists find their home in the Laboratory for Human Biology Research at Northwestern. This includes Bill Leonard, Thom McDade, and Chris Kuzawa. Rumor has it that alumna Elizabeth Sweet is moving back to Northwestern as well. She is doing truly innovative work integrating the rigorous analysis of biomarkers of health (and a bicultural perspective favored by the Northwestern group) and the political economy of economic and social disparities — really getting at how inequality ‘gets under the skin.’  I really look forward to seeing what comes from her future research.

Karen Kramer, in the department formerly known as (Biological) Anthropology at Harvard, is a real leader in integrating evolutionary, demographic, and economic perspectives on human reproduction and the life histories.

Patrick Clarkin at UMass, Boston has a very interesting research program employing biocultural and evolutionary models to understand the effects of war on nutrition and growth among SE Asian diaspora. UMass, Boston is also home to Colleen Nyberg who does great work on acculturation and health, the psychobiology of stress and HPA function, and growth and development.

Julienne Rutherford at the University of Illinois, Chicago School of Dentistry works on the role of the intrauterine environment on health. Of particular interest for this class is her collaborative work on understanding the epigenetic regulation of placental systems of amino acid transport as part of the Cebu Longitudinal Study in the Philippines. UIC also has a number of excellent human biologists scattered about in anthropology, including Betsy Abrams and Crystal Patil, Epidemiology (Bob Bailey) and Community Health Sciences (Nadine Peacock).

Let’s not forget our friends across The Pond. Durham may have lost Catherine Panter-Brick to Yale, but they got a number of new folks who, when combined with the veterans, make it a very appealing place to study ecological/evolutionary anthropology. Among the faculty there are my colleagues Gillian Bentley, Rebecca Sear, and Frank Marlowe, and numerous others. Rebecca does very sophisticated work in anthropological demography, while Frank is one of the leading ethnographers of contemporary hunter-gatherers (and my collaborator on our Hadza demography project).

Ruth Mace, in my opinion, does some of the best work in human behavioral ecology right now and she keeps churning out top students at UCL.

I’m looking forward to working with Mhairi Gibson at Bristol on our new project on the transmission dynamics of primate retroviruses and human-wildlife contact in Uganda. She has done excellent work on the behavioral ecology of reproduction and parental investment in Ethiopia.

I will also mention a number of excellent researchers who teach classes that are relevant to Ecology, Evolution, and Human Health:

Mark Moritz at Ohio State University has established a Hunter-Gatherer Wiki is conjunction with his course on Hunter-Gatherers. Mark came and gave a terrific talk on livestock exchanges among FulBe pastoralists at the MAPSS colloquium this year.

Mike Gurven at UCSB teaches a course on the behavioral ecology of hunter-gatherers. Mike does some of the most interesting biodemographic work out there these days.

Bruce Winterhalder at UC Davis, a founding father of human behavioral ecology, has a very interesting course on classics in cultural ecology.

Claudia Valeggia, at Penn, does great work among the Toba people of Argentina teaches a class on reproductive ecology.

Lots of good people. Lots of good work.  Surely, there is reason for optimism…

On Systematic Nomenclature

OK, this may seem like a pretty serious geek-out, but a pet peeve of mine has just been tweaked by the New York Times.  Olivia Judson has written her usual stimulating and thought-provoking essay, this time on the recent decoding and publication of the Neanderthal genome by Svante Pääbo and colleagues at the Max Planck Institute for Evolutionary Anthropology in Leipzig.  As she notes, this is a remarkable accomplishment that raises so many questions — in the best of senses.

My peeve is the use of the Latin binomial for our species in the caption of the figure comparing an anatomically modern human skeleton with a reconstruction of a Neanderthal skeleton. The caption reads:

A reproduction of a Neanderthal skeleton, left, and the original modern homo sapien skeleton, right.

Egads! First, our genus is, of course, Homo, not homo (note the capitalization and some sort of typographic elaboration, either italics or underline to denote the special status of a Latin binomial).  Second, the species name is sapiens not sapien.

Systematic nomenclature is abused all the time.  I shudder every time I read the ingredients list on the Aveda shampoo in our shower (ingredient #3 of the clove shampoo is written “Prunus Amydalus Dulcis (Sweet Almond)”).  It’s nice that they are trying to be specific and precise about the composition of their product, but get the naming conventions right!  Even if it’s impractical to italicize the text for printing reasons, please remember, genera are capitalized; species (and sub-species) are lower case.

Why am I so uptight about such a seemingly trivial issue of typography and convention?  It’s because nomenclature matters in science. In particular, I think that systematic nomenclature (i.e., nomenclature that describes evolutionary relationships) does for biology what Bertrand Russell argued good formal notation does for mathematics and logic: provides a subtlety and suggestiveness that allows it to almost teach for itself.

Best Simile Ever?

Matt Ridley pens a hilarious simile in his great book, Nature Via Nurture (published as The Agile Gene in the United States) that I think you might actually need to be an evolutionary anthropologist to fully appreciate.  And I quote:

Just as sex enabled mammals to combine two great inventions — lactation and the placenta — so trade enabled early people to combine draft animals and wheels to better effect. (Ridley 2003: 228)

Just like it.  Awesome!

Most Cited Papers in Current Anthropology

A friend sent me a link the other day to the top 20 most cited articles in the journal, Current Anthropology. Much to my delight, I found that a paper that I co-authored is the #7 all-time citation leader and a paper co-authored by my Stanford colleague Rebecca Bird is the #19. As I walked over to Coupa café this morning to get coffee, I realized that I also made a small contribution to the #1 on this list, Leslie Aiello and Peter Wheeler’s paper on the Expensive Tissue Hypothesis.  At the time the manuscript was first circulated, I was a graduate student obsessed with brains, energetics, and scaling in human evolution. My advisor, Richard Wrangham, was asked to comment on the manuscript and he asked me if, given my obsessions, I might have something to say. Needless to say, I did. Having just read our comment, I think it stands pretty well (if I do say so): (1) basal metabolic rate (BMR) is not really a constraint and (2) what are the implications for allometric scaling of different organs with respect to body mass?  Most of the expensive organs scale isometrically (that is, with a scaling exponent of one) but the brain, of course, is a big exception. It scales with an exponent closer to 3/4. Because guts and brains scale differently with increasing body mass, perhaps larger brains could be maintained by dietary compensation?

My colleague Herman Pontzer has some very interesting things to say about energetics and constraints and I’m really looking forward to some forthcoming work of his on this topic.  In a paper in PNAS, he recently showed that, contrary to the expectations of a naïve trade-off model, mammals with larger home ranges actually have greater lifetime fertility and greater total offspring mass.  We have a lot to learn about trade-offs, both physiological and economic, and their role in shaping human behavior and life histories.

The Key to the Survival of the Human Species?

Perhaps it’s just me being a bit groggy from jet-lag, but I just read one of the most bizarre things I think I have ever seen in the New York Times.  There is a generally very interesting article by Sarah Kershaw on so-called “cougars,” older women who have sexual relationships with younger men. It was the first I had ever heard the term – shows what I know. As the article concludes, Kershaw makes the following statement:

The paradox, of course, is that the older-woman relationship makes perfect sense when it comes to life expectancy, with women outliving men by an average of five years. But with men’s fertility far outlasting women’s, biology makes the case for the older-man scenario, and recent research has even suggested that older men having children with younger women is a key to the survival of the human species.

Say what?! Survival of the species??

It’s a pretty strange statement that strangely lacks attribution, particularly given how well referenced all the other scholarly work discussed in the article is.  I wonder if it isn’t a vague allusion to the work of my colleague Shripad Tuljapurkar who has shown that systematic differences in mean age of childbearing would mitigate the so-called “wall of death” predicted by W.D. Hamilton’s famous paper on the evolution of senescence.

Stanford Workshop in Biodemography

On 29-31 October, we will be holding our next installment of the Stanford Workshops in Formal Demography and Biodemography, the result of an ongoing grant from NICHD to Shripad Tuljapurkar and myself.  This time around, we will venture onto the bleeding edge of biodemography.  Specific topics that we will cover include:

  • The use of genomic information on population samples
  • How demographers and biologists use longitudinal data
  • The use of quantitative genetic approaches to study demographic questions
  • How demographers and biologists model life histories

Information on the workshop, including information on how to apply for the workshop and a tentative schedule, can be found on the IRiSS website. We’ve got an incredible line-up of international scholars in demography, ecology, evolutionary biology, and genetics coming to give research presentations.

The workshop is intended for advanced graduate students (particularly students associated with NICHD-supported Population Centers), post-docs, and junior faculty who want to learn about the synergies between ecology, evolutionary biology, and demography. Get your applications in soon — these things fill up fast!

Genetic Architecture of Maize Flowering Time

There is a really cool paper by Buckler and colleagues in the current issue of Science.  The basic gist of this paper is that flowering time in maize (Zea mays) is not controlled by any large-effect genes (actually quantitative trait loci or QTLs — these are positions in the genome that are associated with genes underlying a particular trait).  Instead, flowering time is controlled by lots of different QTLs, all with small effect.  I know what you’re saying: “this fool anthropologist has really gone off the deep end this time. Why would he ever care about such boring stuff?!” Ah, but this is anything but boring and it has a very clear relevance to the evolution of life histories, human included.

Genes are discrete entities.  Yet we know that they code for things that can essentially vary continuously like stature or age at first reproduction. Probably the simplest (and remarkably general, even though this isn’t how we learn about genetics in high school) way to think of genes is on-off switches.  At a particular locus, if you have allele A you get a +1 on your phenotype.  If you have allele a you get a +0.  For a locus that has just these two alleles, the possible phenotypes (assuming no environmental inputs, gene-gene interactions, or anything exotic) are 0,1, and 2 corresponding to aa, Aa/aA, and AA genotypes respectively.  You get twice as many individuals with phenotype=1 because there are two ways of making the heterozygote but only one way of making each of the homozygotes.  This is more or less high school biology.

Now, let’s say that our phenotype is controlled by not one locus but many.  If we can reasonably assume that the different loci are independent of each other (not a hard-and-fast assumption), then a famous result from probability theory has important bearing on the question at hand.  In particular, I’m referring to the normal approximation of the binomial distribution. The binomial distribution is the coin-flipping distribution: how many tails will you get if you toss a coin (with a probability of tails = p) n times.  It turns out that when the product np is greater than 5 (a very rough rule of thumb as there are other mild conditions) the binomial distribution — a discrete probability distribution — is very well approximated by a normal distribution — a continuous one.  This is essentially how we get continuous phenotypes from discrete genotypes. The normal distribution is the limiting distribution whenever we have lots of small things drawn independently from the same process acting in an additive way (this is the mechanistic explanation of why the binomial can be approximated so well by the normal when np gets large).   Lots of loci, each acting in an additive way with small effect, leads to a normal distribution of a phenotype. When we have a few loci acting with large effect and possibly with strong interactions between loci (i.e., epistasis), the distribution of phenotypes is more likely to be more discrete (as with the one-locus, two-allele model).

Maize lives in a broad range of environments and, like us, it reproduces sexually. Unlike us, some plants can “self.” That is, they don’t need two to tango — they reproduce sexually but a single individual provides both sets of gametes. The genetic architecture of flowering in two other genetically well-studied plants, rice and Arabidopsis (the ubiquitous mustard plant that is the favored model system of many developmental geneticists), does not show the same design as maize.  Both rice and Arabidopsis are selfing species.  It turns out that their genetic architecture resembles that of the simple additive system — one with major gene effects and a more discrete distribution of flowering times. In these species, there is more evidence for gene-gene interactions and environmental inputs in the flowering time phenotype as well.

The fact that flowering time in maize is controlled by many loci with small additive effects means that the distribution of flowering time in any sizable population of maize will be approximately normally distributed.  There will be approximately continuous variation across the possible range of phenotypes and there will be far more individuals with phenotypes near the mean of the population.  In fact, about 95% of the phenotypes will be within ± 2 standard deviations of this mean.  Variation is the stuff of natural selection.  In order for evolution to occur by natural selection you need three things: (1) variation in the phenotype, (2) a heritable basis for this variation, and (3) differential fitness as a result of the variation.  No variation in the phenotype, no response to selection.  A trait with a nice continuous distribution — as apparently maize flowering time is — can respond with exquisite precision to selection imposed by the environment (assuming the population is big enough so that random events don’t interfere). Maize shows an amazing degree of phenotypic diversity.  Flowering date is no exception with a range — the landraces of maize vary in their developmental time between an astounding 2 and 11 months.

So there is plenty of phenotypic variation in maize to respond to selection.  Another important implication of the normal distribution of flowering time also means that local variation will fall into a fairly restricted range (that 95% of variation falling within ± 2 standard deviations thing).  If the distribution were more discrete, a field might contain two completely different flowering phenotypes.  In a species that requires out-crossing, producing gametes at two completely different times doesn’t do you much good.  When you have a nice normal distribution of flowering times, there is variation, but it is not too extreme — particularly in any local area.  This trait architecture allows maize to thread the evolutionary needle: enough variation to allow response to selection but not so much as to make reproduction impossible.

This type of architecture — lots of small effects from independent genes — is common in two other laboratory favorites, fruit flies and mice. It is also common in humans according to Flint & Mcckay (2009). Human stature, in particular, is a trait that has this kind of architecture. We know from the work of Rebecca Sear at the London School of Economics and her colleagues, that height predicts  reproductive success in humans (albeit in complex ways). Whether age at first reproduction (the human equivalent to flowering) is constructed the same way as maize flowering or human stature is an open question but I seriously doubt that we will find it to be controlled by a couple genes with large effect.  Such quantitative genetic architecture in complex life history traits bodes well for prediction.  We have a very well-developed theory that relates response to selection to the genetic variation in quantitative traits and the force of selection acting on those traits.  In order for this prediction to work well though, we need to also understand how life history traits are related to each other. That is, we need to not just know the means and standard deviations of one trait (like stature or age at sexual maturity).  We also need to know how traits are related to each other.  In other words, we need to measure the covariances between traits.  Fortunately, the normal approximation that we use for one trait can easily be extended to multiple traits, providing a nice framework for both estimation and prediction. I am working at this very moment on estimating the quantitative genetic relationships between human life history traits using a large genealogical database of a historical population.  More on this later…

On Intelligence

Nicholas Kristof has an interesting Op-Ed piece this week in the Times.  Reporting on University of Michigan Professor Richard Nisbett’s new book, Intelligence and How to Get It, Kristof argues for the general malleability of intelligence.  He writes,

If intelligence were deeply encoded in our genes, that would lead to the depressing conclusion that neither schooling nor antipoverty programs can accomplish much. Yet while this view of I.Q. as overwhelmingly inherited has been widely held, the evidence is growing that it is, at a practical level, profoundly wrong.

I think that this is an important point that is worth pursuing.  There is indeed a widely held view that intelligence is “genetically determined” (whatever that means — how you define it matters), perhaps most infamously articulated in Charles Murray and Richard Herrnstein’s book, The Bell Curve. This idea comes from numerous studies of the correlation of relatives’ scores in standardized intelligence tests, the most common design for which is the twin study.  The basic idea is that you compare the concordance in test scores of monozygotic (i.e., genetically identical) twins with dizygotic twins who share only 50% of their genes.  The assumption is that both monozygotic and dizygotic twins will share the same rearing environment.  Therefore, differences that appear in the observed concordance should be attributable to genes.

Twin studies show that IQ, like many other features of human behavior, is moderately “heritable.”  Now, a key to understanding this field and the debate that it has spawned is understanding what is meant by heritability.  Geneticists posit two different conceptions of heritability.  The first is the common parlance sense: heritability simply means that a trait is genetically determined and can therefore be inherited from one’s parents.  This is known as “broad-sense heritability.”  In contrast, “narrow sense heritability” has a fairly precise technical meaning. Narrow sense heritability is the fraction of total phenotypic variance attributable to additive genetic variance.  Based simply on this definition, laden with unfamiliar terms, you can see why most people think in terms of broad-sense heritability.

So let’s parse out the definition of narrow-sense heritability.  First, “total phenotypic variance” simply means the total observed variance in the trait in question (e.g., IQ) for some well-defined population (e.g., the sample of individuals in the study).  This variance arises from a variety of sources, some genetic, some environmental, some both.  It is very important to note that variance is central to both definitions of heritability.  A trait can be completely genetically determined (whatever that means) but have no variance in a population.  Think head-number among human beings.  This trait is so deeply developmentally canalized that there is no variance (everybody has one) and, thus, zero heritability.

As sexual beings, when we reproduce, our alleles (variants of genes) reshuffle whenever we generate our gametes, or reproductive cells (i.e., eggs and sperm), during the process of meiosis.  One of the principles that Mendel is known for is the principle of independent assortment.  This is the idea that when our alleles get reshuffled during meiosis, they appear in any given gamete independently of what the other alleles that show up in that gamete are.  It turns out that independent assortment is not in any way universal.  Some alleles assort independently while others are linked to other alleles, typically because they are near each other on a chromosome (but sometimes for more interesting reasons).  The additive genetic variance in the definition of heritability refers to the variance attributable to just the alleles that assort independently. These are the so-called additive effects.  Additivity arises from the independence of the different allelic effects.  We care so much about the additive effects because these are what let us make predictive models.  When an animal breeder wants to know the response to selection of some quantitative trait (e.g., body size, milk fat percentage, age at maturity), she uses an equation that multiplies the narrow-sense heritability and the selective advantage of the trait in question.  Now, our scientific interest in the heritability of intelligence ostensibly arises from the desire to create predictive and explanatory models like this breeder’s equation.  In the absence of explanatory or predictive power, I don’t see much scientific value.

Genes can express their effects in ways other than through their additive effects.  For example, there is that familiar concept from Mendelian genetics, dominance.  Dominance is a type of allele-allele interaction, just limited to the special case of occurring within a single locus.  A more general case is allelic interactions is epistasis.  An epistatic gene is one that affects the expression of one or more other genes.  The epistatic gene is a regulator which can either increase or decrease (possibly turn off) the effect of other genes.  These interactions are harder to predict and typically go into the error term in the breeder’s equation.

The real gotcha in heritability analysis though is the existence of genotype-environment (GxE) interactions. These are generally not measured and can be quite large.  Lewontin, in his classic (1974) paper, first suggested that GxE interactions (in addition to other types of difficult-to-measure interactions like those arising from epistasis) might actually be large.  Much of the thought that followed has supported this idea (see, e.g., Pigliucci 2001). In twin study designs, GxE interactions are non-identifiable, meaning that we don’t have enough information to simultaneously estimate the interaction, genetic, and environmental effects, so they are generally assumed to be zero. I think that it is fair to say that the consensus among population geneticists is that heritability analyses, as done though twin studies, for example, are misleading at best because of this fundamental flaw.

In my mind, the fundamental problem with twin studies of the heritability of intelligence is that they can’t begin to measure GxE interactions and therefore their estimates of heritability are hopelessly suspect.

Where is heritability of intelligence likely to be large and not quite as fraught with the problems of unmeasurable and potentially large GxE interactions? One possibility is in homogenous, affluent communities, not entirely unlike Palo Alto.  Kristof notes in his Op-Ed piece that “Intelligence does seem to be highly inherited in middle-class households.” In such communities, external (“environmental”) sources of variation are relatively small.  Most kids have stable homes with (two) college educated parents who place high value of achievement in school, go to safe, well-funded schools with motivated and highly trained teachers, eat nutritious food and live fairly enriched lives.  When the total variance is low, whatever variance is explained by additive genetic effects is likely to be a higher fraction of the total variance. Hence, high heritability. This is a quite general point: the more environmentally homogenous a population is, the higher we should expect heritability to be.

It is very, very important, however, to note that this is generally not the case.  When we move out of relatively homogenous and affluent communities, the sources of environmental variance increase and compound.  The fact that a trait with such high measured heritability can be modified so extensively as discussed in Nisbett’s book suggests that intelligence is a trait with an enormous environmental effect and, I’m betting, a huge GxE interaction effect. It seems to me that the Flynn effect, the observation that IQ increases with time, provides further suggestive evidence for a massive environmental interaction. While the genomic evidence for recent strong selection on humans is mounting (in contrast to the bizarre idea that somehow selection came to a screeching halt with the advent of the Holocene), I doubt that there have been significant selective changes in the genes for intelligence (whatever that means) in the past century.  Now, the environment certainly has changed in the last 100 years.  This is what makes me thing big GxE interactions.

So, in a phrase, sure, genes help determine intelligence.  But the action of these genes is so fundamentally tied up in environmental interactions that it seems that the explanatory power of simple genetic models for intelligence and other complex social traits such as political and economic behavior or social network measures is very low indeed. Moreover, the predictive power of these models in changing environments is low.  Without explanatory or predictive potential, we are left with something that isn’t really science. I applaud efforts to more deeply understand how productive environments, good schools, and healthy decisions can maximize human potential. Heritability studies of IQ (and I worry about these other fashionable areas) seem to provide an excuse for the inexcusable failure to deal with the fundamental social inequalities that continue to mar our country — and the larger world.


Lewontin, R. 1974. The analysis of variance and the analysis of causes. American Journal of Human Genetics 26: 400-411.

Pigliucci, M. 2001. Phenotypic Plasticity: Beyond Nature and Nurture. Baltimore, Johns Hopkins University Press.

Always a Bridesmaid, Never a Bride

Well, it’s happened again.  My work has been written up in Science but I am not mentioned.  I’m actually not that concerned this time — we’re going to submit the paper for publication soon. I’ve been telling myself (and other people) that this thing we’ve ben working on (all the while being very cryptic about what this thing exactly is) is important.  Every once in a while, I wonder if I’ve just been fooling myself.  The fact that this work has been written up in Science the day after the paper was presented at the Montreal Conference on Retroviruses and Opportunistic Infections suggests to me that it is, indeed, important.