Modern evolutionary genetics owes its origins to a series of intellectual debates around the turn of the 20th century. Much of this is outlined in Will Provines’ The Origins of Theoretical Population Genetics, though a biography of Francis Galton will do just as well. In short what happened is that during this period there were conflicts between the heirs of Charles Darwin as to the nature of inheritance (an issue Darwin left muddled from what I can tell). On the one side you had a young coterie around William Bateson, the champion of Gregor Mendel’s ideas about discrete and particulate inheritance via the abstraction of genes. Arrayed against them were the acolytes of Charles Darwin’s cousin Francis Galton, led by the mathematician Karl Pearson, and the biologist Walter Weldon. This school of “biometricians” focused on continuous characteristics and Darwinian gradualism, and are arguably the forerunners of quantitative genetics. There is some irony in their espousal of a “Galtonian” view, because Galton was himself not without sympathy for a discrete model of inheritance!
In the end science and truth won out. Young scholars trained in the biometric tradition repeatedly defected to the Mendelian camp (e.g. Charles Davenport). Eventually, R. A. Fisher, one of the founders of modern statistics and evolutionary biology, merged both traditions in his seminal paper The Correlation between Relatives on the Supposition of Mendelian Inheritance. The intuition for why Mendelism does not undermine classical Darwinian theory is simple (granted, some of the original Mendelians did seem to believe that it was a violation!). Many discrete genes of moderate to small effect upon a trait can produce a continuous distribution via the central limit theorem. In fact classical genetic methods often had difficulty perceiving traits with more than half dozen significant loci as anything but quantitative and continuous (consider pigmentation, which we know through genomic methods to vary across populations mostly due to half a dozen segregating genes or so).
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.
According to the reader survey 88 percent said they understood what heritability was. But only 34 percent understood the concept of additive genetic variance. For the purposes of this weblog it highlights that most people don’t understand heritability, but rather heritability. The former is the technical definition of heritability which I use on this weblog, the latter is heritability in the colloquial sense of a synonym for inheritance, biological and cultural. Almost everyone who understands the technical definition of heritability will know what heritability in the ‘narrow sense’ is, often just informally termed heritability itself. It is the proportion of phenotype variability that can be attributed to additive genetic variation. Those who understand additive genetic variance and heritability in the survey were 32 percent of readers. If you understand heritability in the technical manner you have to understand additive genetic variance. This sets the floor for the number who truly understand the concept in the way I use on this weblog (I suspect some people who were exceedingly modest who basically understand the concept for ‘government purposes’ put themselves in the ‘maybe’ category’). After nearly 10 years of blogging (the first year or so of which I myself wasn’t totally clear on the issue!) that’s actually a pretty impressive proportion. You take what you can get.
That’s the question a commenter poses, albeit with skepticism. First, the background here. New England was a peculiar society for various demographic reasons. In the early 17th century there was a mass migration of Puritan Protestants from England to the colonies which later became New England because of their religious dissent from the manner in which the Stuart kings were changing the nature of the British Protestant church.* Famously, these colonies were themselves not aiming to allow for the flourishing of religious pluralism, with the exception of Rhode Island. New England maintained established state churches longer than other regions of the nation, down into the early decades of the 19th century.
Between 1630 and 1640 about ~20,000 English arrived on the northeastern fringe of British settlement in North America. With the rise of co-religionists to power in the mid-17th century a minority of these emigres engaged in reverse-migration. After the mid-17th century migration by and large ceased. Unlike the Southern colonies these settlements did not have the same opportunities for frontiersmen across a broad and ecological diverse hinterland, and its cultural mores were decidedly more constrained than the cosmopolitan Middle Atlantic. The growth in population in New England from the low tends of thousands to close to 1 million in the late 18th century was one of endogenous natural increase from the founding stock.
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.
In earlier discussions I’ve been skeptical of the idea of “designer babies” for many traits which we may find of interest in terms of selection. For example, intelligence and height. Why? Because variation on these traits seems highly polygenic and widely distributed across the genome. Unlike cystic fibrosis (Mendelian recessive) or blue eye color (quasi-Mendelian recessive) you can’t just focus on one genomic region and then make a prediction about phenotype with a high degree of certainty. Rather, you need to know thousands and thousands of genetic variants, and we just don’t know them.
But I just realized one way that genomics might make it a little easier even without this specific information.
In response to comments and queries below I’ve been poking around for more experimental material on quantitative genetics, and in particular the breeder’s equation. That’s how I stumbled upon this very interesting and informative obituary of D. S. Falconer in Genetics. It reviews not only the biographical details of Falconer’s life, but much of his science. It’s free to all now, so I highly recommend it! (as well as Introduction to Quantitative Genetics, which is quite pricey right now, but just keep watching, I recall getting a relatively cheap copy of the 1996 edition) Curiously, quantitative genetics is rather unknown to the general public in comparison to the biophysical sexiness of molecular genetics, but in most ways it’s the much better complement to the “folk genetics” which often crops up in our day to day life (e.g., “why is so-and-so’s son so short when so-and-so is so tall”). DNA illuminates the discontinuities of Mendelian inheritance, often in the gloomy realm of disease, but quantitative genetics sheds light on the continuities and variations we see across the generations.
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|
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.
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.