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August 20, 2012

Inter-population difference in European height

Filed under: Genetics,Genomics,Height — Razib Khan @ 9:15 pm

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 ...

August 19, 2012

Why northern Europeans are taller than southern Europeans?

Filed under: Height,Quantitative Genetics — Razib Khan @ 10:24 pm

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 ...

August 9, 2012

Why aren’t we all tall?

Filed under: Evolution,Height — Razib Khan @ 3:05 am

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 ...

July 19, 2012

Inbred shorter people

Filed under: Genetics,Genomics,Height — Razib Khan @ 11:10 pm

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 ...

December 30, 2011

How do relatives correlate in traits?

Filed under: Correlation,Height,Quantitative Genetics — Razib Khan @ 1:24 am

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:

December 24, 2011

How much do siblings differ in height?

Filed under: Height,Quantitative Genetics,variation in siblings — Razib Khan @ 1:28 pm

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.

A mediocre man’s great son, a great man’s mediocre son

Filed under: Genetics of Height,Height,Kobe Bryant,Quantitative Genetics — Razib Khan @ 1:26 am

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

July 6, 2011

Marry far and breed tall strong sons

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 ...

May 14, 2011

Natural selection for height in Europeans

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 ...

May 9, 2011

Pygmies are short because nature made them so


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 ...

December 26, 2010

Patterns of human height & lifestyle

Filed under: Culture,Genetics,Height — Razib Khan @ 3:02 pm

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.

November 10, 2010

The future Indian Yao Ming

Filed under: Genetics,Height,Quantitative Genetics — Razib Khan @ 10:10 pm

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.

September 29, 2010

Every variant with an author!

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.

August 1, 2010

Was Yao Ming bred?

Filed under: Genetics,Height,Quantitative Genetics,Yao Ming — Razib Khan @ 7:43 am

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….

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