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At least that’s what you’d think in relation to the latest height & genetics paper, Defining the role of common variation in the genomic and biological architecture of adult human height:

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies , we show that the most strongly associated ~2,000, ~3,700 and ~9,500 SNPs explained ~21%, ~24% and ~29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate–related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

download Three things to note. First, Peter M Visscher (control-f in the author list) predicted this result ahead of time (i.e., x number of loci will explain y % of variation). Nice empirical validation of the theory. Second, in 2009 an interesting paper was published which showed that classical methods way outperformed genomics when it came to height prediction. I’m not sure that we’ll say that in 2019 at current rates of accounting for heritability via genomics. A lot of work needs to be done to make these results robust for prediction in most cases. But we might get there. Third, it looks that the largest height loci have about one magnitude larger effect than intelligence. Visscher was on the recent IQ and genomics paper which presented only a few valid SNPs. So that domain is far behind height. In the early 2000s I read Behavioral Genetics in the Postgenomic Era. It looks to me that that book (and James Watson’s introduction) will be seen to be a generation ahead of its times. Though I’d still recommend the book, as there’s a lot of information in there that it would behoove you to know.

 
• Category: Science • Tags: Height, Science 
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Update: First, people coming to this weblog for the first time should know that I moderate comments. So if you leave an obnoxious one it’s basically like an email to me (no one will see it). Second, the correlation between height and intelligence is not that high. This association is probably not going to be intuitively visible to anyone, but rather only shows up in large data sets. So please stop offering yourself as a counter-example of the trend (also, the key is to look within families, because the signal here is going to be swamped by other factors when you compare across populations). Third, a friend has sent me another paper which does confirm that even within sibling cohorts there does seem to be a correlation between height and I.Q. The problem is that it is a very small one, so you need large data sets with a lot of power to see it.

End Update

One moderately interesting social science finding is that there is a positive correlation between height and measured intelligence (e.g., on an I.Q. test). Setting aside the possibility that I.Q. tests designs are culturally biased against shorter people, one wonders why this is so. Height is a highly heritable trait where most of the variation within the population is due to variation as numerous genes. In other words, there isn’t a “tall” or “short” gene, but thousands and thousands of variants which shape the variation of the trait across the population. When I say it is highly heritable, I mean to imply that most of the variation in height in developed societies is due to genes (80-90%). As it happens intelligence is somewhat similar in its genetic architecture, heritable due to small effects across many genes. In general estimates for the heritability of intelligence tend to be somewhat lower, on the order of ~50% rather than 80-90%.

It is due to the highly polygenic nature that both of these traits have been posited as candidates for a “good genes” model of sexual selection. Presumably individuals with a higher mutational load will have lower intelligence and be shorter, all things equal, because these traits have extensive genome-wide coverage and are big targets. Geoffrey Miller’s The Mating Mind: How Sexual Choice Shaped the Evolution of Human Nature, was predicated on this logic. If the mutational load argument holds then the reduced I.Q. of shorter individuals may simply be due to the same cause: “bad genes.”


Another scenario is that assortative mating between tall and intelligent people has generated a correlation between alleles which tend toward this end of the trait distribution. The phenomenon is simple enough to describe; height and intelligence are both attractive, and even if they are not due to the same genetic loci the pairing of tall and smart results in the correlation between the traits. My own assumption is that something like this, perhaps with a mutational effect at the bottom of the distribution (due to large effect deleterious alleles knocking people down in height and intelligence), generates most of the correlation. Part of this is due to my reading of The g Factor:

It is now well established that both height and weight are correlated with IQ. When age is controlled, the correlations in different studies range mostly between 0.10 and 0.30, and the average about 0.20. Studies based on siblings find no significant within-family correlation, and gifted children (who are taller than their age mates in the general population) are not taller than their non-gifted siblings.

Whenever people posit a pleiotropic relationship between traits I am always curious about the possibility that the traits may be correlated (or not) in siblings. Population structure of some sort can produce correlations, but patterns within families are often more informative of the genuine genetic basis of these correlations.

A new paper in PLOS GENETICS tackles this with more sophisticated techniques. They conclude:

Traits that are attractive to the opposite sex are often positively correlated when scaled such that scores increase with attractiveness, and this correlation typically has a genetic component. Such traits can be genetically correlated due to genes that affect both traits (“pleiotropy”) and/or because assortative mating causes statistical correlations to develop between selected alleles across the traits (“gametic phase disequilibrium”). In this study, we modeled the covariation between monozygotic and dizygotic twins, their siblings, and their parents (total N = 7,905) to elucidate the nature of the correlation between two potentially sexually selected traits in humans: height and IQ. Unlike previous designs used to investigate the nature of the height–IQ correlation, the present design accounts for the effects of assortative mating and provides much less biased estimates of additive genetic, non-additive genetic, and shared environmental influences. Both traits were highly heritable, although there was greater evidence for non-additive genetic effects in males. After accounting for assortative mating, the correlation between height and IQ was found to be almost entirely genetic in nature. Model fits indicate that both pleiotropy and assortative mating contribute significantly and about equally to this genetic correlation.

Pleiotropy here means that the same gene is impacting different traits (height and I.Q.). The additive genetic correlation between height and I.Q. was 0.08 and 0.17 in males and females respectively. These are small correlations obviously, but it’s what we’d expect.

After the statistical modeling using a twin design there’s a lot of talk about sexual selection and the long arc of evolutionary genetics (e.g., additive genetic variation being exhausted by selection). This is what you have to do in discussions, but I’m not sure it really adds any value. How strong is sexual selection for intelligence and height? The data show that taller men have more sexual partners, but the problem here is that taller men have taller daughters, and these daughters are not necessarily so reproductively fit. Whenever you are talking about sexual selection you need to take into account the antagonism that might entail because of the differential value of a trait between the sexes (e.g., masculine men may have masculine daughters). As for I.Q., I’m not sure about the long term distribution of fitness for this trait. I have a suspicion that the “sweet spot” for mating is to be only somewhat smarter than than the average, but not so clever so as to be obnoxious.

In the end I’d really like to see a massive number of siblings compared. I think that’s doable with this data set, but I didn’t see it in the paper (tell me if I’ve missed something). At some point we’ll have accurate high coverage whole genomes for many pairs, and we can ascertain whether it’s mutational load and pleiotropy more directly when it comes to correlations like this. Since pedophiles tend to be shorter and less intelligent I’m willing to accept deep biological connections across many traits. But I feel that the whole area is somewhat of a muddle right now. And talking a lot about sexual selection strikes me as excessive hand waving.

Citation: Keller MC, Garver-Apgar CE, Wright MJ, Martin NG, Corley RP, et al. (2013) The Genetic Correlation between Height and IQ: Shared Genes or Assortative Mating? PLoS Genet 9(4): e1003451. doi:10.1371/journal.pgen.1003451

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Anthropology, Genetics, Genomics, Height, Intelligence, Select 
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A quick mea culpa. Yesterday I put up a post on the difference in height between northern and southern Europe, following the lead of the heading of the paper which I blogged. But, in the text they do note that their sample is skewed toward northern Europe. Additionally, their geographic coverage is stated in the supplements. As noted by some commenters not only is it northern Europe skewed, but it’s really western Europe biased. There’s nothing wrong with that as such, but it leaves much of Europe outside of this west-central transect unsampled. Therefore, I’m a little more cautious of making pan-European latitudinal generalizations.


That being said, I still suspect there is going to be spatially structured differences in the concentration of alleles which predispose one to great height. I’d especially be curious to see if the people of the Dinaric region tend to cluster with northern Europeans, rather than their Balkan neighbors. Please note that one of the important aspects of this study is that they replicated their findings among siblings. When observing correlations between traits on a population-by-population basis and then extrapolating, it is of the essence that those patterns can be replicated in family-based studies. This applies to within population observations as well. For example, there is some correlation between height and intelligence. But that correlation disappears among siblings (i.e., tall siblings are no more intelligent than short siblings).

Finally, a friend brought to my attention some serious concerns about the evolutionary quantitative genetic model outlined in the paper. After reading their critique I would say that though they have convinced me of the likely importance of these alleles in generating inter-population differences, I am less than confident of their adaptive model.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Genetics, Genomics, Height 
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In part, genes. Luke Jostins reported this from a conference last year, so not too surprising. Evidence of widespread selection on standing variation in Europe at height-associated SNPs. Let me jump to the summary:

In summary, we have provided an empirical example of widespread weak selection on standing variation. We observed genetic differences using multiple populations from across Europe, thereby showing that the adult height differences across populations of European descent are not due entirely to environmental differences but rather are, at least partly, genetic differences arising from selection. Height differences across populations of non-European ancestries may also be genetic in origin, but potential nongenetic factors, such as differences in timing of secular trends, mean that this inference would need to be directly tested with genetic data in additional populations. By aggregating evidence of directionally consistent intra-European frequency differences over many individual height-increasing alleles, none of which has a clear signal of selection on its own, we observed a combined signature of widespread weak selection. However, we were not able to determine whether this differential weak selection (either positive or negative) favored increased height in Northern Europe, decreased height in Southern Europe or both. One possibility is that sexual selection or assortative mating (sexual selection for partners in similar height percentiles) fueled the selective process. It is also possible that selection is not acting on height per se but on a phenotype closely correlated with height or a combination of phenotypes that includes height.

Two points of note. First, simulations suggested that the genetic architecture is unlikely to be due to drift alone. In other words, natural selection. Selection on quantitative traits isn’t magic, there’s a whole agricultural industry based around this phenomenon. For the purposes of understanding human evolution the key is that we are now moving beyond looking for traits which emerged due to novel mutations (e.g., lactase persistence), and now trying to understand how selection and drift may work on standing variation. For example, humans have become smaller in overall size, and also in cranial capacity, over the past 10,000 years. Second, they validated their findings using a sibling cohort. This is something I always look for when people make inter-population inferences. A number of population wide correlations don’t pan out when you are looking within families. This matters in trying to understand causation.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Height, Quantitative Genetics 
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There’s a fair amount of social science and anecdata that tall males are more reproductively fit. More precisely, males one to two standard deviations above the norm in height seem to be at the “sweet spot” as an idealized partner (e.g., leading males). And, short men often have fewer children. Short women will pair up with tall men. Tall women will generally not pair up with shorter men. The question then has to be asked: why isn’t natural selection producing a situation where we’re all tall?

As it is, height is a highly heritable trait where there’s a lot of genetic variation present in the population. One hypothesis might be that short(er) people are simply individuals with a higher mutational load. In other words, there’s going to be variation in the load of deleterious alleles from person to person, and one’s value on quantitative traits (intelligence, height) is a reflection of one’s genetic fitness. There are problems with this model, starting with the fact that one you need to tease apart inter-population variation. Also, within families there doesn’t seem to be a correlation between height and intelligence, which you would expect to see if quantitative traits are reflections of variation in mutational load.

So naturally you have to move the possibility of balancing selection. I have suggested in the past that inter-population differences in height may be a function of expected levels of nutritional stress. Short people are smaller, and need to eat less. The same dynamic could produce variation in height within populations as well. But a new paper outlines what I think I think is the most elegant solution (though elegant does not mean right!), Intralocus sexual conflict over human height:

Intralocus sexual conflict (IASC) occurs when a trait under selection in one sex constrains the other sex from achieving its sex-specific fitness optimum. Selection pressures on body size often differ between the sexes across many species, including humans: among men individuals of average height enjoy the highest reproductive success, while shorter women have the highest reproductive success. Given its high heritability, IASC over human height is likely. Using data from sibling pairs from the Wisconsin Longitudinal Study, we present evidence for IASC over height: in shorter sibling pairs (relatively) more reproductive success (number of children) was obtained through the sister than through the brother of the sibling pair. By contrast, in average height sibling pairs most reproductive success was obtained through the brother relative to the sister. In conclusion, we show that IASC over a heritable, sexually dimorphic physical trait (human height) affects Darwinian fitness in a contemporary human population.

There isn’t much theoretical complexity in the paper. They’re looking at a huge data set of individuals from Wisconsin, and they observe that in families where siblings are short the sisters tend to be more fecund, and in families where the siblings are not short the brothers tend to be more fecund. The argument here is that antagonistic sexual selection maintains variation within the population. Some of the media reports suggest some sort of frequency dependent theory in the background; if the population gets too tall or short then males and females of the favored varieties may gain more fitness advantage.

As the authors note over time this sort of dimorphism should fix in a manner where the variation within the population diminishes as sex specific alleles emerge. But this takes a very long time, and may simply be impossible to attain toward equilibrium in the case of a trait with the genetic architecture of height. One would have to imagine modifier genes throwing out their net across the whole genome.

It’s easy to imagine why being tall might entail fitness gains for a male. What’s going on with females? I suspect that on the extreme margin very tall women probably have lower fertility for hormonal reasons. But that doesn’t explain to me why very short women seem to have such high fertility in relation to average height women. One explanation might be that they mature faster, and so enter their peak reproductive years rather early. This might extend their fertile period longer than average height or taller women. In contrast, this isn’t much of a gain for males, who have longer reproductive careers on the tail end.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Evolution, Height 
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Evidence of Inbreeding Depression on Human Height, a paper with over 1,000 authors! (I exaggerate) It’s interesting because it seems to establish that inbreeding does have a deleterious effect on traits whose genetic architecture is presumably polygenic and additive. Why is this theoretically important? Because inbreeding depression is often assumed to be driven by the exposure of rare recessive larger effect alleles, which recombine in near relations. Using tens of thousands of individuals from across a dozen European nations the authors found that there is a consistent relationship between inbreeding and reduction in height.

As the authors note height is a convenient trait to explore. First, it’s highly heritable. 80 to 90 percent of the variation in the population is explained by variation in genes. Second, it’s easy to measure. Also, implicit in the paper is the fact that in Europe today there is far less of a environmental effect on height (that’s why the heritability value is high). Even in poor European nations most people have enough to eat, so height is highly heritable, allowing for appropriate cross-national comparison.


The simplest way to state their results is that all things being equal the offspring of two first cousins will be ~3 cm shorter than those of unrelated individuals. But there are many caveats and qualifications here. First, there are different sources of the depression. Using the most sensitive measure of recent consanguinity, a statistic of run of homozygosity pruned of markers in linkage disequilibrium, the authors did not find a strong effect of magnitude of inbreeding increasing the depression. In other words, it looks like the “bang” sharply diminishes after the first “hit.” Second, even genomewide homozygosity has some independent relationship to depression in height. The distinction here is there are genetically homogeneous populations which are nevertheless not inbred. In contrast, there are populations where inbreeding is common, where homozygosity might be lower (an example here might be a Gulf Arab community with lots of African and Persian admixture, increasing the number of heterozygous loci, but where cousin marriage is ubiquitous). The result here suggests that near inbreeding can immediately bring together recessively expressed deleterious alleles and produce a reduction in trait value, but that there is some sort of hit to having a high fraction of homozygous loci as well (I suppose one could posit some sort of genomewide heteryzogote advantage, though I’m skeptical of that).

But perhaps the biggest caveat here is population heterogeneity. There is now fair evidence that height differences between European populations are in part genetic. So naturally the magnitude of the decrease due to inbreeding depression is going to vary by the nature of the genetics of height in a given population. Additionally, I’m not quite sure that they’ve totally accounted for issues of population structure here. Those populations which are naturally very tall may also tend toward greater homozygosity or inbreeding for independent reasons, reducing the effect size of the depression. This is where focusing on Europe is a weakness in this study. I would be very curious about inbreeding depression in Arab populations, for example.

The last sentence of their abstract is obvious and intriguing:

Although this exploratory work focuses on height alone, the methodology developed is generally applicable to heritable quantitative traits (QT), paving the way for an investigation into inbreeding effects, and therefore genetic architecture, on a range of QT of biomedical importance.

A major reason people study height is that it is a complex trait which seems to be tractable. You can measure it, and normal variation is relatively wide in realized trait value. The ultimate rationale is often to develop or test methods which can have biomedical application. The implication here is that if inbreeding between first cousins results in a ~3 cm depression in height, who knows what other ailments may be the product of these relationships? There seems a consistent result that offspring of first cousin marriage are less intelligent than similar outbred individuals (again, the magnitude seems to vary by study, but the direction of difference is consistent). But then there’s this, The Love That Dare Not Speak Its Surname:

Now a study by the National Society of Genetic Counselors says that having a child with your first cousin raises the risk of a significant birth defect from about 3-to-4 percent to about 4-to-7 percent.

I’ve seen this study being discussed on “rationalist” websites, illustrating the stupidity of religious taboos on incest. There are two issues which concern me here. First, the aggregate social cost to an increase in congenital defects from 3 to 4 percent is not trivial. Second, the focus on congenital defects ignores the impact on normal human variation. The offspring of cousin marriages may be less healthy, uglier, less intelligent, and perhaps shorter. This doesn’t mean that we should ban cousin marriages, anymore than we should ban marriages between stupid ugly people. Rather, it suggests we may need to add some extra parameters to the calculus of the wages of cousin marriage.

What about the flip side? In many species there is an equipoise of relatedness (e.g., philopatric frogs). Too close, and inbreeding depression. Too far, and outbreeding depression. Might that be an issue? In the case of height it seems unlikely. This paper indicates that the primary reason for the decrease in height are rare recessive alleles which have a large deleterious effect. The reason for outbreeding depression is most likely going to be some sort of antagonistic epistasis, deleterious gene-gene interactions. I’m skeptical that these are going to be very common (here is a rare example). But it will be an interesting question to address. Looking at long stabilized hybrid populations, such as the Uyghurs and Malagasy might be instructive.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Genetics, Genomics, Height 
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The Pith: Even traits where most of the variation you see around you is controlled by genes still exhibit a lot of variation within families. That’s why there are siblings of very different heights or intellectual aptitudes.

In a post below I played fast and loose with the term correlation and caused some confusion. Correlation is obviously a set of precise statistical terms, but it also has a colloquial connotation. Additionally, I regularly talk about heritability. Heritability is in short the proportion of phenotypic variance which can be explained by genetic variance. In other words, if heritability is ~1 almost all the variation in the trait is due to variation in genes, while if heritability is ~0 almost none of it is. Correlation and heritability of traits across generations are obviously related, but they’re not the same.

This post is to clarify a few of these confusions, and sharpen some intuitions. Or perhaps more accurately, banish them.

 


The plot above shows relationship between heights of fathers and heights of sons in standard deviation units (yes, I removed some of the values!). You see that the slope is ~0.45, and that’s the correlation. At this point you probably know that heritability of height is on the order of 0.8-0.9. So why is the correlation so low? A simple biological reason is that you don’t know the value of the mothers. If the parents are not strongly correlated (assortative mating) obviously the values of the sons is going to diverge from that of the father. That being said, you probably notice that the correlation here is about 1/2 that of the heritability you know has been confirmed in the literature. That’s no coincidence. One way to estimate heritability is to take the slope of the plot of offspring vs. parents, and multiply that by 2. Therefore, the correlation (which equals the slope) is 1/2 × h2, where h2 represents heritability.

Correlation (parent to offspring) = 1/2 × h2

1/2 turns out to be the coefficient of relatedness of a parent to offspring. I’ll spare you the algebra, but suffice it to say that this is not a coincide. Where r = coefficient of relatedness the correlation between sets of relatives on a trait value is predicted to be:

Correlation (relative to relative) = r × h2

Where r is simply the coefficient of relatedness across the pair of relatives. Here are some values:

r relationship
0.5 (½) parent-offspring
0.25 (¼) grandparent-grandchild
1 identical twins; clones
0.5 (½) full siblings
0.25 (¼) half siblings
0.125 (⅛) first cousins

Here’s the kicker: the correlation coefficient of the midparent value and the offspring value does not equal the slope of the line of best fit. This is why I had second thoughts about using the term “correlation” so freely, and then switching to heritability. The formula is:

Correlation (midparent to offspring) = 1/√2 × h2

So the correlation of midparent to offspring is 0.71 × heritability.

Why is this something you might want to know? I think people are sometimes confused about how an extremely heritable trait, like height, where you’re given heritability values of 0.90, still yields families with such a wide range of heights. Well, recall that the coefficient of relatedness among siblings is 1/2. So their correlation is going to be the same as with parents. Therefore, the magnitude will be half that of the heritability. A correlation of 0.45 is not small, but neither is it extremely tight. The histogram below illustrates this with the above data set. The values are simply the real difference between fathers and sons:

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Correlation, Height, Quantitative Genetics 
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In the comments below a reader asks about the empirical difference in heights between siblings. I went looking…and I have to say that the data isn’t that easy to find, people are more interested in the deeper inferences on can make from the resemblances than the descriptive first-order data itself. But here’s one source I found:


Average difference Identical twins Identical twins raised apart Full siblings
Height, inches 0.67 0.71 1.8
Weight, pounds 4.2 9.9 10.4
IQ 5.9 8.2 9.8

These data indicate that IQ and height variation among sibling cohorts is on the order of ~2/3rd to 3/4th of the variation that one can find within the general population (my estimate of standard deviation of 2.5 inches for height below is about right, if a slight underestimate according to the latest data). But I also found a paper with more detailed statistics.


The aim of the paper was to find outliers from expectation. In other words, which siblings diverged a lot from what you’d expect in terms of normal variation within the cohort? In the process they do report some statistics on inter-sibling variation. The correlation of height between siblings after correcting for age and sex are 0.43. This is what I’ve seen in the literature. Next, the standard deviation is 6.7 centimeters. This is about ~2.7 inches. The average phenotypic difference between siblings was about 7.2 centimeters (D). Therefore, to a first approximation the recapitulation of population-wide variation in a continuous quantitative trait within sibling cohorts seems to hold. Though I’d be curious if readers can provide better and more diverse sources.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Height, Quantitative Genetics 
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Kobe Bryant is an exceptional professional basketball player. His father was a “journeyman”. Similarly, Barry Bonds and Ken Griffey Jr. both surpassed their fathers as baseball players. Both of Archie Manning’s sons are superior quarterbacks in relation to their father. This is not entirely surprising. Though there is a correlation between parent and offspring in their traits, that correlation is imperfect.

Note though that I put journeyman in quotes above because any success at the professional level in major league athletics indicates an extremely high level of talent and focus. Kobe Bryant’s father was among the top 500 best basketball players of his age. His son is among the top 10. This is a large realized difference in professional athletics, but across the whole distribution of people playing basketball at any given time it is not so great of a difference.

What is more curious is how this related to the reality of regression toward the mean. This is a very general statistical concept, but for our purposes we’re curious about its application in quantitative genetics. People often misunderstand the idea from what I can tell, and treat it as if there is an orthogenetic-like tendency of generations to regress back toward some idealized value.

Going back to the basketball example: Michael Jordan, the greatest basketball player in the history of the professional game, has two sons who are modest talents at best. The probability that either will make it to a professional league seems low, a reality acknowledged by one of them. In fact, from what I recall both received special attention and consideration because they were Michael Jordan’s sons. It is still noteworthy of course that both had the talent to make it onto a roster of a Division I NCAA team. This is not typical for any young man walking off the street. But the range in realized talent here is notable. Similarly, Joe Montana’s son has been bouncing around college football teams to find a roster spot. Again, it suggests a very high level of talent to be able to plausibly join a roster of a Division I football team. But for every Kobe Bryant there are many, many, Nate Montanas. There have been enough generations of professional athletes in the United States to illustrate regression toward the mean.


So how does it work? A few years ago a friend told me that the best way to think about it was a bivariate distribution, where the two random variables are additive genetic variation and environmental genetic variation. Clearer? For many, probably not. To make it concrete, let’s go back to the old standby: the quantitative genetics of height.

For height in developed societies we know that ~80% of the variation of the trait in the population can be explained by variation of genes in the population. That is, the heritability of the trait is 0.80. This means that the correspondence between parents and offspring on this trait is rather high. Having tall or short parents is a decent predictor of having tall or short offspring. But the heritability is imperfect. There is a random “environmental” component of variation. I put environmental in quotations because that really just means it’s a random noise effect which we can’t capture in the additive or dominance components (this sort of thing may be why homosexual orientation in individuals is mostly biologically rooted, even if its population-wide heritability is modest). It could be biological, such as developmental stochasticity, or gene-gene interactions. The point is that this is the component which adds an element of randomness to our ability to predict the outcomes of offspring from parents. It is the darkening of the mirror of our perceptions.

Going back to height, the plot to the left shows an idealized normal distribution of height for males. I set the mean as 70 inches, or 5 feet 10 inches. The standard deviation is 2.5, which means that if you randomly sampled any two males from the dataset the most likely value of the difference would be 2.5 inches which is just the average deviation from the mean (it’s a measure of dispersion). Obviously the height of a male is dependent upon the height of a father, but the mother matters as well (perhaps more due to maternal effects!). Here we have to note that there’s clearly a sex difference in height. How do you handle this problem? Actually, that’s easy. Just convert the heights of the parents to sex-controlled standard deviation units. For example, if you are 5 feet and 7.5 inches as a male you are 1 standard deviation unit below the mean. If you are a female at the same height you are 1.4 standard deviation units above the mean (assuming female mean height of 5 feet and 4 inches, and standard deviation of 2.5 inches). If height was nearly ~100% heritable you’d just average the two parental values in standard deviation units to get the expectation of the offspring in standard deviation units. In this case, the offspring should be 0.2 standard deviation units above the mean.

But height is not ~100% heritable. There is an environmental component of variation which isn’t accounted for by the parental genotypic values (at least the ones with effects of interest to us, the additive components). If height is ~80% heritable then you’d expect the offspring to regress 1/5th of the way back to the population mean. For the example above, the expectation of the offspring would be 0.16 standard deviation units, not 0.20.

Let’s make this more concrete. Imagine you sampled a large number of couples whose midparent phenotypic value is 0.20 standard deviation units above the mean in height. This means that if you convert the father and mother into standard deviation units, their average is 0.20. So one pair could be 0.20 and 0.20, and another could be of someone 2.0 and -1.6 standard deviation units. What’s the expected distribution of male offspring height?

The relevant points:

1) The midparent value naturally is constrained to have no variance (though as I indicate above since it’s an average the selected parents may have a wide variance)

2) The male offspring are somewhat above the average population in distribution of height

3) It remains a distribution. The expected value of the offspring is a specific value, but environmental and genetic variation remains to produce a range of outcomes (e.g., Mendelian segregation and recombination)

4) There has been some regression back to the population mean

I only displayed the males. There are obviously going to be females among the offspring generation. What would the outcome be if you mated the females with the males? Recall that the female heights would exhibit the same mean, 0.16 units above the original population mean. This is where many people get confused (frankly, those whose intelligence is somewhat closer to the mean!). They presume that a subsequent generation of mating would result in further regression back to the mean. No! Rather, the expected value of the offspring would be 0.16 units. Why?

Because through the process of selection you’ve created a new genetic population. The selection process is imperfect in ascertaining the exact causal underpinning of the trait value of a given individual. In other words, because height is imperfectly heritable some of the tall individuals you select are going to be tall for environmental reasons, and will not pass that trait to heir offspring. But height is ~80% heritable, which means that the filtering process of genes by using phenotype is going to be rather good, and the genetic makeup of the subsequent population will be somewhat deviated from the original parental population. In other words, the reference population to which individuals “regress” has now changed. The environmental variation remains, but the additive genetic component around which the regression is anchored is now no longer the same.

This is why I state that regression toward the mean is not magical in a biological sense. There is no population with fixed traits to which selected individuals naturally regress or revert to. Rather, populations are useful abstractions in making sense of the statistical correlations we see around us. The process of selection is informed by population-wide trends, so we need to bracket a set of individuals as a population. But what we really care about are the genetic variables which underpin the variation across the population. And those variables can change rather easily through selection. Obviously regression toward the mean would be exhibit the magical reversion-toward-ideal-type property that some imagine if the variables were static and unchanging. But if this was the matter of things, then evolution by natural selection would never occur!

Therefore, in quantitative genetics regression toward the mean is a useful dynamic, a heuristic which allows us to make general predictions. But we shouldn’t forget that it’s really driven by biological processes. Many of the confusions which I see people engage in when talking about the dynamic seem to be rooted in the fact that individuals forget the biology, and adhere to the principle as if it is an unthinking mantra.

And that is why there is a flip side: even though the offspring of exceptional individuals are likely to regress back toward the mean, they are also much more likely to be even more exceptional than the parents than any random individual off the street! Let’s go back to height to make it concrete. Kobe Bryant is 6 feet 6 inches tall. His father is 6 feet 9 inches. I don’t know his mother’s height, but her brother was a basketball player whose height is 6 feet 2 inches. Let’s use him as a proxy for her (they’re siblings, so not totally inappropriate), and convert everyone to standard deviation units.

Kobe’s father: 4.4 units above mean

Kobe: 3.2 units above mean

Kobe’s mother: 1.6 units above the mean

Using the values above the expected value for the offspring of Kobe’s father & mother is a child 2.4 units above the mean. Kobe is somewhat above the expected value (assuming that Kobe’s mother is a taller than average woman, which seems likely from photographs). But here’s the important point: his odds of being this height are much higher with the parents he has than with any random parents. Using a perfect normal distribution (this is somewhat distorted by “fat-tailing”) the odds of an individual being Kobe’s height are around 1 in 1,500. But with his parents the odds that he’d be his height are closer to 1 out of 5. In other words, Kobe’s parentage increased the odds of his being 6 feet 6 inches by a factor of 300! The odds were still against him, but the die was loaded in his direction in a relative sense. By analogy, in the near future we’ll see many more children of professional athletes become professional athletes both due to nature and nurture. But, we’ll continue to see that most of the children of professional athletes will not have the requisite talent to become professional athletes.

Image Credit: Wikipedia

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ResearchBlogging.orgThe Pith: When it comes to the final outcome of a largely biologically specified trait like human height it looks as if it isn’t just the genes your parents give you that matters. Rather, the relationship of their genes also counts. The more dissimilar they are genetically, the taller you are likely to be (all things equal).

Dienekes points me to an interesting new paper in the American Journal of Physical Anthropology, Isolation by distance between spouses and its effect on children’s growth in height. The results are rather straightforward: the greater the distance between the origin of one’s parents, the taller one is likely to be, especially in the case of males. These findings were robust even after controlling for confounds such as socioeconomic status. Their explanation? Heterosis, whether through heterozygote advantage or the masking of recessive deleterious alleles.

The paper is short and sweet, but first one has to keep in mind the long history of this sort of research in the murky domain of human quantitative genetics. This is not a straight-forward molecular genetic paper where there’s a laser-like focus on one locus, and the mechanistic issues are clear and distinct. We are talking about a quantitative continuous trait, height, and how it varies within the population. We are also using geographical distance as a proxy for genetic distance. Finally, when it comes to the parameters affecting these quantitative traits there are a host of confounds, some of which are addressed in this paper. In other words, there’s no simple solution to the fact that nature can be quite the tangle, more so in some cases than others.

Because of the necessity for subtlety in this sort of statistical genetic work one must always be careful about taking results at face value. From what I can gather the history of topics such as heterosis in human genetics is always fraught with normative import. The founder of Cold Spring Harbor Laboratory, Charles Davenport, studied the outcomes of individuals who were a product of varied matings in relation to genetic distance in the early 1920s. This was summed up in his book Race Crossing in Jamaica:

A quantitative study of 3 groups of agricultural Jamaican adults: Blacks, Whites, and hybrids between them; also of several hundred children at all developmental stages. The studies are morphological, physiological, psychological, developmental and eugenical. The variability of each race and sex in respect to each bodily dimension and many basis vary just as morphological traits do. In some sensory tests the Blacks are superior to Whites; in some intellectual tests the reverse is found. A portion of the hybrids are mentally inferior to the Blacks. The negro child has, apparently, from birth on, different physical proportions than the white child.


Because of the fears of miscegenation in the early 20th century scholars had a strong bias toward finding the data to confirm the assumption that admixture between divergent human kinds resulted in a breakdown and depression in trait value in relation to both parental lineages. Today this is not so. Rather, I would argue that the bias is now in the opposite direction, at least in the West. My friend Armand Leroi wrote Meet the world’s most perfect mutant seven years ago. Who is the most perfect human according to Armand? She is Saira Mohan, a model of Indian, Irish and French ancestry. Armand concludes:

If deleterious mutations rob us of it, they should do so with particular efficacy if we marry our relatives. Most novel mutations are at least partly recessive, and inbreeding should accentuate their negative effects. Many weird genetic disorders come from Pakistan and Saudi Arabia, where there is a strong tradition of first-cousin marriage.

Conversely, people of mixed ancestry should show the benefits of concealing recessive mutations. And this, I suspect, is the true meaning of Saira Mohan: half Punjabi, quarter Irish, quarter French and altogether delightful. She, too, is a mutant – but a little less so than most of us.


Thandie Newton masking recessive alleles

This is entirely in keeping with the dominant ethos of the global elite, which aims for a panmixia of genes in concert with an alignment of a particular set of cosmopolitan post-materialist memes. But, as I pointed out to Armand there are also cases where crosses between genetic backgrounds may have deleterious consequences. For example, a European specific allele in African Americans may have a negative fitness interaction with the predominant African genetic background of this population. I am not implying here that science is fiction, a construction of our biases and preconceptions. But the dominant cultural narrative framework does put pressure upon how we interpret science, and all the more so in domains which require a level of statistical subtlety and personal candor.

Of course now that we can see exactly how individuals are mutant at the level of the genome Armand’s supposition can actually be tested. That is, we can see how many deleterious recessive alleles are in fact masked in people of hybrid origin. That at least may plug one of the fuzzy spots in our picture of how genetic backgrounds interact in humans.

I prefaced the review of a paper on marital distance and height with some history of science and a reflection of how contemporary values influence the generation and interpretation of knowledge because there’s a lot of confusing material in the literature on correlations between genetic distance and trait value. There is the result that marriages between 3rd cousins seem the most fertile in Iceland. Is this because of a balance between genetic incompatibilities and expression of recessive diseases? Or perhaps the answer lies in social dynamics, insofar as people who come from related lineages are more likely to weather difficult times in their relationship? It’s one study from Iceland. But of course the minority who vociferously argue against racial amalgamation and admixture on moral/normative grounds will focus upon this specific positive empirical finding in the literature. Now, Iceland is ideal for many human genetic studies because it has excellent records and is culturally homogeneous. But at the end of the day Iceland is still Iceland.

And today Poland is still Poland. I say that because this study tracks thousands of Polish youth over the years. Here’s the abstract:

Heterosis is thought to be an important contributor to human growth and development. Marital distance (distance between parental birthplaces) is commonly considered as a factor favoring the occurrence of heterosis and can be used as a proximate measure of its level. The aim of this study is to assess the net effect of expected heterosis resulting from marital migration on the height of offspring, controlling for midparental height and socioeconomic status (SES). Height measurements on 2,675 boys and 2,603 girls ages 6 to 18 years from Ostrowiec Świętokrzyski, Poland were analyzed along with sociodemographic data from their parents. Midparental height was calculated as the average of the reported heights of the parents. Analyses revealed that marital distance, midparental height, and SES had a significant effect on height in boys and girls. The net effect of marital distance was much more marked in boys than girls, whereas other factors showed comparable effects. Marital distance appears to be an independent and important factor influencing the height of offspring. According to the “isolation by distance” hypothesis, greater distance between parental birthplaces may increase heterozygosity, potentially promoting heterosis. We propose that these conditions may result in reduced metabolic costs of growth among the heterozygous individuals.

As you may know, height is substantially heritable. That means that ~80-90% of the variation in the trait within the population in developed nations is due to variation in genes. This has some validity even within families. Tall parents tend to given rise to tall offspring, though there is a variation around the expectation. In other words, siblings differ in height, in part because of environmental factors, but also in part because siblings differ in their genetic endowments from their parents. So naively one can model this like so:

Height ~ Genetic endowment + Environmental contingencies

The genetic endowment is a function of the mid-parent value in standard deviation units. That means you average the standard deviations of the parents from the sex-controlled mean. Let’s give a concrete example. Imagine a male who is 5’8 inches, and a female who is 5’7 inches. The standard deviation for height is ~3 inches, with the American male mean being 5’10 inches and female being 5’4 inches. That means that the male is -2/3 standard deviations below the mean, and the female is 1 standard deviation above the mean. The expectation for their offspring then will be 1/3 standard deviation above the mean (5’11 for males, 5’5 for females). But because of the variation in the nature of genetics and environment, there’s actually going to be a standard deviation of ~3 inches for the offspring (e.g., ~70% chance that the male will be between 5’8 and 6’2). There is also the reality that because environmental factors aren’t heritable the offspring should regress somewhat back to the population mean all things equal, though in the case of height not too much because it is so genetically influenced.

A few years ago I played this game with libertarian pundits Megan McArdle and Peter Suderman, who announced their engagement. Megan and Peter are both 6’2. I estimated that the expected value is that any son of theirs would be 6 feet 3.6 inches, and any daughter 5 feet 9.6 inches. How can it be that their sons should be taller than either of them? Remember that Megan is much taller than Peter in standard deviation units in relation to her sex.

Now how would expectation be altered if Megan McArdle and Peter Suderman were full-siblings? (they are not full-siblings, this is a thought experiment!) At this point even if you had never taken college genetics you might be wondering whether it makes sense to calculate an expectation for the height of the offspring of two full-siblings. You know very well that there are much more serious genetic issues at hand. Going back to the relation above, you might update it like so:

Height ~ Genetic endowment + Environmental contingencies – Incest decrement

Even stipulating viability of the offspring, any child of full-siblings would exhibit all the problems that Armand alludes to above. It seems likely that whatever potential their parents might impart to their offspring, the combination of their genotypes would be highly deleterious, because near kin carry the same recessives. The paper above posits the inverse effect, where outbreeding results in greater outcomes than are to be expected based on the mid-parent trait value. In this telling, height is a proxy for health and development. This seems biologically plausible in the case of humans. Individuals who marry those genetically dissimilar impart gains of fitness to their offspring by virtue of elevated heterozygosity. So now we create a new relation:

Height ~ Genetic endowment + Environmental contingencies + Magnitude of outbreeding

In pre-modern societies individuals tended to marry those close to them geographically. Even if cousin marriage was not normally practiced, over time clusters of villages would form networks of de facto consanguinity. In the 19th and especially 20th century much of this in the extreme cases abated in Europe because of better transport. L. L. Cavalli-Sforza documented this in Consanguinity, Inbreeding, and Genetic Drift in Italy. Modern roads resulted in a radical drop in inbreeding in mountainous regions of the country. Some researchers have argued that this shift resulted in an increased level of height, intelligence, and health, among European populations.

With that, here’s a nice map from Consang.net:

Going back to the paper, after controlling for socioeconomic status they found that:

1) The increased marital distance predicts taller height than expected, especially in boys.

2) This effect is most noticeable in boys who already have parents who are relatively tall.

3) Finally, greater marital distance seems to be correlated with greater height in the parents!

The last is actually a possible reason why there’s no reason to appeal to heterosis at all. This might simply be a function of assortative mating of tall individuals who are more mobile. In the paper the authors go at length about sexual selection, greater mobility of individuals who are taller, etc. But whatever the reason, this shows exactly the care which must be taken with these sorts of results. It is known for example that taller individuals seem to have higher I.Q.s, leading some to assert that the genes which control height and I.Q. variance must be the same (some of them almost certainly are if there are many loci of small effect). But, it turns out that this height-I.Q. correlation disappears within families (tall siblings are no smarter than short siblings), implying that the correlation might be a function of assortative mating.

As for why there may be a sex difference, the authors suggest that heterosis may manifest at different points in the developmental arc of children. Females mature somewhat faster than males. This may be so, the sexes differ and such. But my own preference is that the original results merit a deeper and expanded examination before we posit an evolutionary story (that’s not possible in a scientific paper which needs a discussion, but I’m proposing an ideal world of knowledge generation and refinement!). The empirics need to be firmed up before we scaffold it in theory. Poland is Poland, and if you troll through enough data sets there’ll be millions of correlations which are publishable. And yet we are living in the age of information, so we had better get going in sieving through it. At the end of the paper the authors go in a direction which I think might yield some interesting finds in the future:

One possible limitation of our study and explanation of the results may come from the fact that we used geographical distance between parental birthplaces as the only approximate measure of offspring heterozygosity. Further studies should focus on more direct examination of individuals’ allele diversity and its influence on physiological processes. Of particular interest would be investigation of a possible relationship between the level of basal metabolic rate and individual’s heterozygosity both in general term as well as heterozygosity of specific locus. Such suggestion seems to be supported by previous studies which indicate that the variation in energy expenditure at rest is determined by substantial genetic component (Bouchard et al., 1989; Bouchard and Tremblay, 1990) and heterogeneity of gene loci (Jacobson et al., 2006; Loos et al., 2007). More studies in this regard may be crucial for a better and profound understanding of the Homo sapiens metabolism and energy budget.

Because of the advances in genomics, as well as the proliferation of social science data sets (thanks to corporations and government) I hope that we can begin breaking out of the habit of being led about by the nose by our norms in more areas of human genetics than just the study of Mendelian diseases! That’s a hope. I’m not saying I’d bet money on it.

Citation: Sławomir Kozieł, Dariusz P. Danel, & Monika Zaręba (2011). Isolation by distance between spouses and its effect on children’s growth in height American journal of physical anthropology : 10.1002/ajpa.21482

Image Credit: Caroline Bonarde Ucci.

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It is known that Northern Europeans tend to be somewhat taller than Southern Europeans. This seems intuitively obvious if you spend a bit of time around representative populations. Growing up in the Pacific Northwest I’ve always been on the short side at 5 feet 8 inches, but when I was in Italy for 3 weeks one year back (between Milan and Rome, with disproportionate time spent in the Piedmont) I didn’t feel as small (I recall feeling similarly when I was in Cajun country in the early 2000s). Steve Hsu alerts me to the fact that Luke Jostins is back blogging at Genetic Inference, reporting from the Biology of Genomes meeting. Apparently Michael Turchin has found that:

1) Alleles known to be associated with greater height are found at higher frequencies in Northern Europeans

2) Alleles known to be associated with greater height also exhibit signatures of natural selection


He used the GIANT consortium data set. How big is it? 129 thousand individuals! Luke adds:

This is a textbook example of how an evolutionary study should be done; you show a phenotypic difference exists, that it is heritable, and that it is under selection. This opens the question as to why height has been selected in Northern Europe (or shortness in Southern Europe). Could the same data be used to test specific hypotheses there?

One thing we do know is that there isn’t much difference in heights between black Americans and white Americans, who are predominantly Northern European in ancestry. I wonder if perhaps the smaller sizes of Southern Europeans is due to the fact that these populations have lived for a longer period under a high density agricultural regime than either Northern Europeans or Africans (northern Sweden still was dominated by hunter-gatherers until ~5,000 years B.P.). My working hypothesis that for various reasons stable agricultural societies may reduce lifetime mortality rates but maintain higher levels of morbidity, making large body sizes less feasible. But that’s just speculation. At last European is a good testing ground for these sorts of explorations, as obviously obligate nutritional differences aren’t much of an issue anymore on that continent.

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Aka Pygmies

The Pith: There has been a long running argument whether Pygmies in Africa are short due to “nurture” or “nature.” It turns out that non-Pygmies with more Pygmy ancestry are shorter and Pygmies with more non-Pygmy ancestry are taller. That points to nature.

In terms of how one conceptualizes the relationship of variation in genes to variation in a trait one can frame it as a spectrum with two extremes. One the one hand you have monogenic traits where the variation is controlled by differences on just one locus. Many recessively expressed diseases fit this patter (e.g., cystic fibrosis). Because you have one gene with only a few variants of note it is easy to capture in one’s mind’s eye the pattern of Mendelian inheritance for these traits in a gestalt fashion. Monogenic traits are highly amenable to a priori logic because their atomic units are so simple and tractable. At the other extreme you have quantitative polygenic traits, where the variation of the trait is controlled by variation on many, many, genes. This may seem a simple formulation, but to try and understand how thousands of genes may act in concert to modulate variation on a trait is often a more difficult task to grokk (yes, you can appeal to the central limit theorem, but that means little to most intuitively). This is probably why heritability is such a knotty issue in terms of public understanding of science, as it concerns the component of variation in quantitative continuous traits which is dispersed across the genome. The traits where there is no “gene for X.” Additionally, quantitative traits are likely to have a substantial environmental component of variation, confounding a simple genotype to phenotype mapping.

ResearchBlogging.org Arguably the classic quantitative trait is height. It is clear and distinct (there aren’t arguments about the validity of measurement as occurs in psychometrics), and, it is substantially heritable. In Western societies with a surfeit of nutrition height is ~80-90% heritable. What this means is that ~80-90% of the variance of the trait value within the population is due to variance of the genes within the population. Concretely, there will be a very strong correspondence between the heights of offspring and the average height of the two parents (controlled for sex, so you’re thinking standard deviation units, not absolute units). And yet height is at the heart of the question of the “missing heriability” in genetics. By this, I mean the fact that so few genes have been associated with variation in height, despite the reality that who your parents are is the predominant determination of height in developed societies.


The issue gets even more thorny when you talk about variation across societies. This is a simple and yet complex issue. On the one hand we know that over time people across the world have gotten taller as nutrition has gotten better. What is less well known is that human populations have been shrinking until the past few centuries since the the Last Glacial Maximum ~20,000 years ago. Why? One can posit many reasons, both genetic and environmental, but it does point us to the reality that the story of height is not monotonic. That is, it doesn’t go in one direction, and has no simple one size fits all answer.

But that’s just the dimension of time. How about space? The question of whether different populations have final different genetic potentials for height is a disputed one. And yet it seems plausible that at the extremes there are genuine differences in the gene frequencies across populations which will speak to their different distributions in trait values. This is particularly interesting in the case of very populations characterized by low median adult heights, often termed “pygmies.” Of particular note are the Pygmies of Central Africa, who exist in a state of cultural symbiosis with their Bantu and Nilotic neighbors, adopting their languages, but remaining distinct.

These populations have very low median heights, but they are clearly not dwarfs (they are proportionate). Thankfully at least the population genetics of the Pygmies of Africa are now relatively well understood. It seems that the Western and Eastern Pygmy populations are very distinct clusters, with a common ancestry perhaps on the order of tens of thousands of years in the past. And not surprisingly the genetic distance between the Pygmy groups and their non-Pygmy neighbors is very large. The Western Pygmies tend to show more evidence of admixture with their Bantu neighbors than the Eastern ones (I suspect this is due to the longer residence of Bantus in this region). But for me the hardest issue to grapple with is the reality that the Pygmies of Central Africa seem to be genetically closer to the Khoisan people of Southern Africa than their Bantu or Nilotic neighbors! I believe this is evidence of an ancient hunter-gatherer continuum within Africa which has been marginalized and overlain by the recent expansion of Bantu farmers and Nilotic pastoralists.

In any case, what does all this have to do with the genetics of height? A new paper in the American Journal of Physical Anthropology synthesizes the inferences generated from population genetics with the basic logical assumptions of quantitative genetics to adduce that the difference between Pygmies and non-Pygmies in height is actually likely to be due to heritable differences. Indirect evidence for the genetic determination of short stature in African Pygmies:

Central African Pygmy populations are known to be the shortest human populations worldwide. Many evolutionary hypotheses have been proposed to explain this short stature: adaptation to food limitations, climate, forest density, or high mortality rates. However, such hypotheses are difficult to test given the lack of long-term surveys and demographic data. Whether the short stature observed nowadays in African Pygmy populations as compared to their Non-Pygmy neighbors is determined by genetic factors remains widely unknown. Here, we study a uniquely large new anthropometrical dataset comprising more than 1,000 individuals from 10 Central African Pygmy and neighboring Non-Pygmy populations, categorized as such based on cultural criteria rather than height. We show that climate, or forest density may not play a major role in the difference in adult stature between existing Pygmies and Non-Pygmies, without ruling out the hypothesis that such factors played an important evolutionary role in the past. Furthermore, we analyzed the relationship between stature and neutral genetic variation in a subset of 213 individuals and found that the Pygmy individuals’ stature was significantly positively correlated with levels of genetic similarity with the Non-Pygmy gene-pool for both men and women. Overall, we show that a Pygmy individual exhibiting a high level of genetic admixture with the neighboring Non-Pygmies is likely to be taller. These results show for the first time that the major morphological difference in stature found between Central African Pygmy and Non-Pygmy populations is likely determined by genetic factors.

First, is there a plausible physiological reason for the difference in adult height between Pygmies and non-Pygmies? The authors review the relevant evidence:

Endocrinologists have described the physiological determination of the African Pygmies’ short stature: serum levels of Insulin-Like Growth Factor 1 (IGF1) and of Growth Hormone Binding Protein (GHBP) are abnormally low, whereas the levels of Growth Hormone (GH) and IGF2 do not differ from Non-Pygmy controls…In this context, Merimee…proposed that the short stature of African Pygmies could be attributed to the absence of a growth spurt during puberty and that the genetic factor(s) implicated in the Pygmy stature were to be found in the GH-IGF1 axis…A recent gene-expression study further showed a slight (1.8-fold) under-expression of GH and a more dramatic (8-fold) under-expression of the GH receptor in adult African Pygmies, which was not found in Non-Pygmy Bantu speakers…However, the only genetic study focusing specifically on Pygmies’ stature, failed to find allele frequency differences in the promoter region of the gene encoding IGF1 between two African Pygmy populations and Non-Pygmy controls…In this context, whether the Pygmy populations’ short stature is solely due to environmental pressures experienced by individuals during growth (i.e., phenotypic plasticity), or to a complex genetic mechanism, remains to be demonstrated.

I believe that IGF can be found in meat and milk, so there are plausible dietary reasons that one could imagine this difference. As far as looking at differences between the genes which are known to impact height within populations across populations, there simply aren’t that many genes known which could account for the large between population differences. Not to mention that many of the current studies have used European populations, and so would likely have an ascertainment bias which might miss a lot of variance which is common within African populations.

The basic method in this paper is not too difficult to understand:

1) Use STRUCTURE, a program which assigns different ancestral quanta to individuals.

2) And compare the variation in a particular Pygmy-modal quantum across the population with variation in height.

If there are many genetic variants of small effect within the Pygmy genome which are resulting in their relatively low adult median height then dollops of Pygmy genome through admixture will reduce the height of non-Pygmies and dollops of non-Pygmy admixture in Pygmies will increase their height. The presumption is that if there are strong environmental impacts on height due to social differences then the disjunction between genetic identity and anthropological identity will be informative. For example, if Pygmies are put under particular stress or deprived specific nutritional intake because of their communal identity as marginalized Pygmies then different admixture levels with non-Pygmies should not matter much (and vice versa).

There’s a lot of statistics toward the aim of achieving significance in this paper (p-value > 0.05). And I really don’t understand the point of disaggregating males and females, for example. Just convert them to standard deviation units deviated from sex median! But in any case the major correlation is well illustrated by the two panels below. Pygmies are in red and non-Pygmies are in blue:

The y-axis is straightforward, height. You can see the Pygmies in their sample are shorter, on average. The x-axis is an ancestral component inferred from STRUCTURE which is generally found in non-Pygmies. You can see that as expected non-Pygmies have more of this than the Pygmies, but the descriptive statistic of a correlation between the non-Pygmy ancestry and height in Pygmies is evident even in this plot. Conversely, the Pygmy ancestry is correlated with lower adult height in non-Pygmies.

As a single result this particular finding isn’t too earth-shaking. If there was one population which was short due to genetic factors, I suspect that one would have to bet on the Pygmies of Central Africa. And as noted in the paper Pygmoid morphology is found among other hunter-gatherer tropical populations. This may not be a human ancestral type, but it is a type which has emerged repeatedly in our history, whether due to genetic or environmental factors. The big picture is that this same general procedure can be used to explore the differences in genetic dispositions across groups for many quantitative traits. With the coming era of cheap genotyping and sequencing I’m sure it will be done. A intrepid researcher has plenty of admixed populations in the New World to select from. There are in Brazil people who are socially identified and self-identify as white who have less European ancestry than those who are socially identified and self-identify as non-white. To compare the the social and genetic valences of African and European ancestral contributions for medical and psychological quantitative traits these sorts of populations will be of great future interest.

Link credit: Dienekes

Citation: Becker NS, Verdu P, Froment A, Le Bomin S, Pagezy H, Bahuchet S, & Heyer E (2011). Indirect evidence for the genetic determination of short stature in African Pygmies. American journal of physical anthropology PMID: 21541921

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Steve Hsu, The mystery of height:

I was looking at The Formosan Encounter: Notes on Formosa’s Aboriginal Society, A Selection of Documents from Dutch Archival Sources. The Dutch came to Taiwan (then called Formosa) in the early 17th century and these translated documents record their impressions of the Austronesian natives. (Both the Dutch and Chinese settlers traded with the natives during this period.)

One report states that the aboriginal men were taller by a head and neck, on average, than the Dutch. (The average Dutchman came only to the shoulder of the average native?) Another report describes the aborigines as tall and sturdily built, like semi-giants. This paper on historical Dutch height suggests that 17th century Dutchmen were about 170 cm or so on average. Holland was the richest country in Europe at the time, but nutritional conditions for average people were still not good by modern standards. So how tall were the aborigines? Presumably well above 180cm since “a head and neck” would be at least 20cm! (Some Native Americans were also very tall when the Europeans first encountered them.)

But, strangely, the descendants of these aborigines are not known for being particularly tall. This paper reports that modern day aboriginal children in Taiwan are shorter than their Han counterparts. On the other hand, the Dutch are now the tallest people in the world, with average male height exceeding 6 feet (183 cm). This kind of reversal makes one wonder whether, indeed, most groups of humans have similar potential for height under ideal conditions, as claimed here. (Note the epigenetic effects — several generations of good nutrition might be required for a group to reach its full height.)

And now from the The Economist:

About 12,000 years ago people embarked on an experiment called agriculture and some say that they, and their planet, have never recovered. Farming brought a population explosion, protein and vitamin deficiency, new diseases and deforestation. Human height actually shrank by nearly six inches after the first adoption of crops in the Near East….

Here is one model which I am in some sympathy with:

Constant warfare was necessary to keep population density down to one person per square mile. Farmers can live at 100 times that density. Hunter-gatherers may have been so lithe and healthy because the weak were dead. The invention of agriculture and the advent of settled society merely swapped high mortality for high morbidity, allowing people some relief from chronic warfare so they could at least grind out an existence, rather than being ground out of existence altogether.

The indigenous people of the Andaman Islanders in the Bay of Bengal are often classed as “Negritos.” This is in part a reference to their small size. But here is a description of the only population which has refused outside contact, the Sentinelese:

From the boat one could not made out their facial features but they appeared to be of a fairly good height. As our landing parties approached the beach the Sentinelese disappeared into the forest….

Here’s a video of a later contact with the Sentinelese. They seem to be a trim and normally proportioned people, though I can’t judge their heights too well.

In developed nations height is about ~80-90% heritable. That means that most of the population variance in height can be attributed to variance in genes. The distribution of heights of children are well predicted by the heights of parents. But what about between population differences? A great deal of this is obviously due to different environmental inputs. And yet some differences do seem to remain on the margin. Then there is the strange fact that American whites are now shorter than population-controlled Europeans, an inversion of the 19th century pattern.

A combination of epigenetics, genetic variation, and the balance of nutritional inputs, explain much of the world wide variation. Ten years ago I probably would have weighted #2 more than I do now. I suspect that the balance of nutrients, and not just the amount of calories, matters more than we might have thought. This may explain much, though not all, of the decrease in height with the shift from hunter-gatherer lifestyle to farming. Farming can extract an order of magnitude more calories per unit of land, but at the cost of nutritional diversity. Also, in regards to short hunter-gatherer populations such as the Bushmen, Pygmies, and many of the Negritos, their social and cultural marginalization has a lot of complex downstream effects (though please note black Americans have about the same mean height as white Americans, so we need to be careful here). Australian Aborigines are not particularly short, suggesting that long term co-existence with agriculturalists may have had an impact on other hunter-gatherer groups, as they were pushed into marginal lands, and exposed to the density dependent diseases of farmers. It is the last element which I believe explains some of the size difference of the Sentinelese from other Andaman Islanders. It may be that the common microbial flora of Eurasians has a deleterious impact on isolated populations, and results in low grade morbidity which shifts the development of these groups. When a group of Andamanese were separated from Indians in the 1960s they recovered much of their health, and the population began to grow again.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Culture, Genetics, Height 
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In a nation of ~1 billion, even one where a large minority are positively malnourished, you’d expect some really tall people. So not that surprising: NBA Awaits Satnam From India, So Big and Athletic at 14:

In a country of 1.3 billion people, 7-foot, 250-pound Satnam Singh Bhamar has become a beacon for basketball hope.

At age 14.

That potential starts with his size, which is incredible itself. At age 14, he is expected to grow for another couple of years. For now, he wears a size-22 basketball shoe. His hands swallow the ball. His father, Balbir Singh Bhamara, is 7-2. His grandmother on his father’s side is 6-9.

Punjab is one of India’s more prosperous states. Interestingly this kid’s paternal grandmother is as tall in standard deviation units as her son or grandson. In Western developed societies height is 80-90% heritable. That means that there’s very little expected regression back to the population mean for any given child. The article doesn’t mention the mother’s height though. If she is of more normal size then Satnam is either a fluke, or, there are dominant large effect rare alleles being passed down by the father, perhaps from the paternal grandmother.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Genetics, Height, Quantitative Genetics 
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I recall projections in the early 2000s that 25% of the American population would be employed as systems administrators circa 2020 if rates of employment growth at that time were extrapolated. Obviously the projections weren’t taken too seriously, and the pieces were generally making fun of the idea that IT would reduce labor inputs and increase productivity. I thought back to those earlier articles when I saw a new letter in Nature in my RSS feed this morning, Hundreds of variants clustered in genomic loci and biological pathways affect human height:

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2, 3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

The supplements run to nearly 100 pages, and the author list is enormous. But at least the supplements are free to all, so you should check them out. There are a few sections of the paper proper that are worth passing on though if you can’t get beyond the paywall.


fig1bIn this study they pooled together several studies into a meta-analysis. One thing not mentioned in the abstract: they checked their GWAS SNPs against a family based study. This was important because in the latter population stratification isn’t an issue. Family members naturally overlap a great deal in their genetic background. Also, if I read it correctly they’re focusing on populations of European origin, so this might not capture larger effect alleles which impact between population variance in height but don’t vary within a given population (note that if you explored pigmentation genetics just through Europeans you would miss the most important variable on the world wide scale, SLC24A5, because it’s fixed in Europeans). In any case, as you can see what they did was extrapolate out the number of loci which their methods could capture to explain variation with the predictor being the sample size. At 500,000 individuals they’re at ~700 loci, and around 20% of the heritable variation. My initial thought is that I’m not seeing diminishing returns here, but since I haven’t read the supplements I’ll let that pass since I don’t know the guts of this anyhow. They do assert that they are likely underestimating the power of these methods because there may be be smaller effect common variants which can top off the fraction.

But even they admit that they can go only so far. Here are some sections from the conclusion that lays it out pretty clearly:

By increasing our sample size to more than 100,000 individuals, we identified common variants that account for approximately 10% of phenotypic variation. Although larger than predicted by some models26, this figure suggests that GWA studies, as currently implemented, will not explain most of the estimated 80% contribution of genetic factors to variation in height. This conclusion supports the idea that biological insights, rather than predictive power, will be the main outcome of this initial wave of GWA studies, and that new approaches, which could include sequencing studies or GWA studies targeting variants of lower frequency, will be needed to account for more of the ‘missing’ heritability. Our finding that many loci exhibit allelic heterogeneity suggests that many as yet unidentified causal variants, including common variants, will map to the loci already identified in GWA studies, and that the fraction of causal loci that have been identified could be substantially greater than the fraction of causal variants that have been identified.

In our study, many associated variants are tightly correlated with common nsSNPs, which would not be expected if these associated common variants were proxies for collections of rare causal variants, as has been proposed27. Although a substantial contribution to heritability by less common and/or quite rare variants may be more plausible, our data are not inconsistent with the recent suggestion28 that many common variants of very small effect mostly explain the regulation of height.

In summary, our findings indicate that additional approaches, including those aimed at less common variants, will likely be needed to dissect more completely the genetic component of complex human traits. Our results also strongly demonstrate that GWA studies can identify many loci that together implicate biologically relevant pathways and mechanisms. We envisage that thorough exploration of the genes at associated loci through additional genetic, functional and computational studies will lead to novel insights into human height and other polygenic traits and diseases.

The second to last paragraph takes a shot at David Goldstein’s idea of synthetic associations.

We’re still where we were a a few years back though, old fashioned Galtonian quantitative genetics, a branch of statistics, is the best bet to predict the heights of your offspring. As with intelligence, “height genes”, are not improvements upon common sense. But if you’re going into the 10-20% range of variation explained it’s certainly not trivial, and the biological details are going to be of interest.

(Republished from Discover/GNXP by permission of author or representative)
 
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I knew that Yao Ming’s parents are very tall. Though his father, at 6’7, arguably contributed less than his mother, at 6’3, which is farther above the female mean in standard deviation units. But check this out from Superfusion: How China and America Became One Economy and Why the World’s Prosperity Depends on It:

Yao had essentially been bred. Both his parents played basketball. His 6’2 [different height from Wikipedia -Razib] mother, Fang Fengdi, perhaps the tallest woman in China, had been married to an even taller man. She had served as a Red Guard during the height of the Cultural Revolution and had been an ardent Maoist. She enthusiastically participated in the glorious plan of the local government to use her and her husband to produce a sports superstar. The Shanghai authorities who encouraged the match had gone back several generations to ensure that size was embedded in the bloodline. The result was Yao, a baby behemoth who just kept getting bigger.


What’s the chance of Yao? Let’s start with his mother being 6’3, his father being 6’7. Let’s assume that the genetic potentiality of Chinese women leaves a median height of 5’2, and men at 5’8. I suspect I’m low-balling this because there’s likely a fair amount of variability within China, with northerners being taller. Additionally, if Yao’s mother lived through the Cultural Revolution I’m wondering if she and her husband are even at their full height assuming normal nutrition. But let’s go with that. With 2 inches per standard deviation, ~85% heritability, you’d expect any of their children to be 6 standard deviations above the population norm in height (sex corrected). For a male that’s 6’8 (using the 5’8 figure as the median). Yao’s taller than that. In fact, at 7’6, he’s 5 standard deviations above the expected value. A freak if you will.

I think that that indicates that I’m being too conservative about the genetic potential of Yao’s parents, the full median height of the source population from which they derive assuming modern nutrition, and the heritability constraining to Yao’s family. In other words, I assume that the Chinese officials knew that neither of Yao’s parents were quite total freaks within their lineages, which indicates that there’ll be less regression back to the mean because their height is less likely attributable to non-replicable environmental variables. Though Yao is still freakishly tall in relation to both his parents, so I don’t think he was inevitable. Though of course the odds of someone of Yao’s height being born to his particular set of parents was orders of magnitude higher than for two random Chinese.

Note: To do the back-of-the-envelope I just used the breeder’s equation. Probably so far above the norm there are more non-linearities at work so that deviations from the expected values are probably higher. I guess only the Chinese officials who did the genealogical inquiries will know….

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Genetics, Height, Quantitative Genetics 
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Growth and Puberty in German Children: Is There Still a Positive Secular Trend?:

In Germany, as elsewhere in northern Europe, the upward secular trend in height is slowing (ca. 2 cm/decade up to the mid-20th century, currently less than 1 cm/decade), and the age at menarche has stabilized at just under 13 years. It remains an open question whether the observed slowing will merely be temporary, or whether it indeed represents the near-attainment of an endpoint owing to relatively stable environmental conditions.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Height 
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Steve has a modestly titled post up, Height and Weight, where he analyzes data from Anthropometric Reference Data for Children and Adults: 2003-2005 (PDF). This is government data on American men, women and children who are Non-Hispanic White, Non-Hispanic Black and Mexican American. I invite readers to peruse the raw data themselves. Steve did a little comparison of various parameters for males and females of the three populations. I thought it would be illustrative to plot the distributions of some the metrics so as to illustrate more intuitively the variation within the populations (the X axis are percentiles). Half Sigma pointed to Steve’s post, and the discussion is unsurprisingly vibrant. I think it’s safe to assume there is “structure” in something like weight within these populations due to geography and SES. You can see this even in New York City, just start from Bergdorf Goodman (especially around the Holidays) and walk north and east into the Upper East Side. Mean BMI starts dropping. In any case, like Steve I thought focusing on the 20-39 demographic was convenient, in part due to the nature of the readership of this weblog. Here’s the CDC’s BMI Calculator.

The data I used is here.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Health, Height, Human Biodiversity 
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From Barbarians to Angels: The Dark Ages Reconsidered:

Measurements taken on skeletal remains in cemeteries in southwestern Germany indicate that the average height for men was about five feet eight inches, for women about five feet four inches, statures well above those of late medieval and early modern times. Measurements taken on skeletons in other regions are comparable. In Denmark, for example, the average height for men was about five feet nine inches-just above those for southwestern Germany-and for women about five feet four inches. These average heights were not achieved again until the twentieth century. Compared with earlier and later populations in the same regions, these average measurements show that most people had adequate nutrition during most of their lives and their living conditions were generally good

This is in line with the charts I posted below. With the introduction of the three-field system, mouldboard plow and horse collar northwest Europe, in particular the regions of northern France, the Low Countries and the Rhineland, surpassed the Mediterranean as the population center of the continent (at least its western half). During the expansionary phase, i.e., 500-800, the span covered by Barbarians to Angels, the Malthusian pressures would have been relatively modest. The screws would have been tightened up to the medieval demographic peak before 1300.

In any case, remember my focus on morbidity vs. mortality? It might be apropos in this case. The uncertainty and political instabilities due to the collapse of the Pax Romana could plausibly have increased mortality as peasants were exposed to the erratic depredations of barbarian warrior bands. But as depopulation occurred, in part because of withdrawal from the frontiers in places like Gaul (France) an western Germany of most farmers, those who opted to remain and take on the risks would be relieved of some Malthusian pressures. I think the chart of European heights does point to this as well, you can discern a slight upward trend after the Black Death due to a radical population reduction. I’ve reedited one of the charts for clarity:

As for Barbarians to Angels, the author doesn’t really make me reconsider. I’ve talked about my skepticism of the idea of revisionism in regards to the decline of Rome. The author argues that technological advances occurred during the Dark Ages, and that many cities remained active nodes in trade networks. But the author’s treatment is highly qualitative where he had concrete examples of how complex society persisted after the collapse of the Pax Romana, and he repeatedly scolds the readers to not judge Dark Age societies by modern standards which would tend to align more with the priorities of Roman civilization (e.g., reading, writing, arithmetic, public architecture, basically what we might term civilization). If the author wants to strip the term “civilization” of any normative biases brought to bear due to the prejudice of moderns, the argument is won, amassing a large collection of ornate weapons with which one might be buried is just as Cultured as writing letters to your friends laced with literary references. A good cup of mead is at the same level as a Falernian.

(Republished from GNXP.com by permission of author or representative)
 
• Category: History, Science • Tags: Anthropology, Height, History 
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In the posts below I wanted to make clear my assumption that morbidity was likely more prevalent during the Neolithic than the Paleolithic. This does not mean of course that the Neolithic people were necessarily poorer than the Paleolithic peoples; Greg Cochran recently told me that people got healthier for obvious reasons during the Great Depression. I would not be surprised if the rate of mortality was somewhat higher than during the Paleolithic simply because the hunter-gatherer lifestyle had less buffering against disasters because trade and social networks were poorly developed so that environmental variation took a greater toll.

For me the biggest point to favor the idea of increased morbidity is that heights seem to have decreased after the Neolithic revolution. It seems plausible that nutritional shifts are the main reason that humans would shrink in size. Below the fold I have reproduced some charts from various papers for your reference.

The cite is Long Bone Dimensions as an Index of Socioeconomic Change in Ancient Asian Populations. Of course, height is not the only thing that changed. From Stature in Early Europeans:

…The sexual dimorphism creased in the more recent populations. Upper Palaeolithic humans not only were taller and had more robust bones in comaprison with the LInear Band Pottery Culture Neolithic people; they also had longer lims, a shorter trunk, and similar to modern African people, very long forearms and crural segments. The low brachial index is a very recently acquired characteristic of white Europeans.

…it is interesting to note that, though moern humans have returned to the body structure of their Early Palaolithic ancestors, they retain the modern proportions with short forearms and short crural segments

Agricultural populations as a whole have shifted toward a less robust physique. The increase in height due to better nutrition doesn’t seem to have resulted in a more robusticity or a Palaeolithic dentition. So there may be some biological evolutionary parameters at work here as well. The first paper notes that between region differences in height seem to persist over long periods of time (East Asians are smaller). Phenomena such as Bergmann’s rule point to changes in body form and size correlating with climatic shifts, and certainly the rise of agriculture is coincident with our current Interglacial.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Anthropology, Height 
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Razib Khan
About Razib Khan

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