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).
Prompted by my post Ta-Nehisi Coates reached out to Neil Risch for clarification on the nature (or lack thereof) of human races. All for the good. The interview is wide ranging, and I recommend you check it out. Read the comments too! Very enlightening (take that however you want).
When it comes to this debate I have focused on the issue of population substructure, or race. The reason is simple. Due to Lewontin’s Fallacy it is widely understood among the “well informed general public” that “biology has disproved race.” Actually, this is a disputable assertion. For a non-crank evolutionary biologist who is willing to defend the race concept for humans, see Jerry Coyne. When you move away from the term “race,” then you obtain even more support from biologists for the proposition that population structure matters. For example, a paper in PLoS GENETICS which came out last week: Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration. In other words, it is useful to understand the genetic relationships of populations, and individual population identity, because traits correlate with population history. Barring total omniscience population history will always probably matter to some extent, because population history influences suites of traits. If nothing in evolutionary biology makes sense except in light of phylogeny, much of human biology is illuminated by phylogeny.
But that doesn’t speak to the real third rail, intelligence. Very few people are offended by the idea of the correlation between lactase persistence and particular populations. Neil Risch says in the interview with Coates:
In light of the previous post I was curious about the literature on inbreeding depression of IQ. A literature search led me to conclude two things:
– This is not a sexy field. A lot of the results are old.
– The range in depression for first cousin marriages seems to be on the order of 2.5 to 10 IQ points. In other words ~0.15 to ~0.65 standard deviation units of decline in intelligence.
The most extreme case was this paper from 1993, Inbreeding depression and intelligence quotient among north Indian children. The authors compared the children of first cousin marriages, and non-bred in individuals, from a sample of Muslims in Uttar Pradesh of comparable socioeconomic status (though the authors note that inbreeding has a positive correlation with socioeconomic status in this community). A table with results speaks for itself:
In light of my previous posts on GRE scores and educational interests (by the way, Education Realist points out that the low GRE verbal scores are only marginally affected by international students) I was amused to see this write-up at LiveScience, Low IQ & Conservative Beliefs Linked to Prejudice. Naturally over at Jezebel there is a respectful treatment of this research. This is rather like the fact that people who would otherwise be skeptical of the predictive power of I.Q. tests become convinced of their precision of measurement when it comes to assessing whether a criminal facing the death penalty is mentally retarded or not! (also see this thread over at DailyKos). You can see some of the conservative response too.
A questioner below was curious if vocabulary test differences by ethnic and region persist across income. There’s a problem with this. First, the INCOME variable isn’t very fine-grained (there is a catchall $30,000 or greater category). Second, it doesn’t seem to control for inflation. But, there is a variable, DEGREE, which asks the highest level of education attained. I used this to create a “college” and “non-college” category (i.e., do you have a bachelor’s degree or not). Because of sample size considerations I removed some of the ethnic groups, but replicated the earlier analysis.
Below are two tables. One shows the mean vocab score for region and ethnicity (for whites) for those without college educations, and another shows those with college educations. I decided to generate a correlation over the two rows, even though it sure isn’t useful as a quantitative statistical measure because of the small number of data points. Rather, I just wanted a summary of the qualitative result. The short answer is that the average vocabulary difference seems to persist across educational levels (the exception here is the “German” ethnicity).
The title says it all, and I yanked it from a paper that is now online (and free). It’s of interest because of its relevance to the future genetic understanding of complex cognitive and behavioral traits. Here’s the abstract:
General intelligence (g) and virtually all other behavioral traits are heritable. Associations between g and specific single-nucleotide polymorphisms (SNPs) in several candidate genes involved in brain function have been reported. We sought to replicate published associations between 12 specific genetic variants and g using three independent, well-characterized, longitudinal datasets of 5571, 1759, and 2441 individuals. Of 32 independent tests across all three datasets, only one was nominally significant at the p ~ .05 level. By contrast, power analyses showed that we should have expected 10–15 significant associations, given reasonable assumptions for genotype effect sizes. As positive controls, we confirmed accepted genetic associations for Alzheimer disease and body mass index, and we used SNP-based relatedness calculations to replicate estimates that about half of the variance in g is accounted for by common genetic variation among individuals. We conclude that different approaches than candidate genes are needed in the molecular genetics of psychology and social science.
I saw this link posted on twitter, IQ and Human Intelligence:
An interesting finding from genetic research, which Mackintosh mentions, only in passing, as posing a problem in the estimation of the heritability of g, is that there is greater assortative mating for g than for any other behavioral trait; that is, spouse correlations are only ∼.1 for personality and only ∼.2 for height or weight, but the correlation for assortative mating for g is ∼.4. In addition to indicating that people are able to make judgments about g in real life, this finding suggests that assortative mating may contribute to the substantial additive genetic variance for g, because positive assortative mating for a character can increase its additive genetic variance.
I’ve seen these sort of results before. The review is from 1999. In general I always wonder if quantitative values for personality are not to be trusted because of issues with the measurement of personality types. But this is clearly not an issue with height or weight. And in the case of height the overwhelming causal explanation for variation in the West is genetic variation. Overall I’m rather surprised by the rather low correlations for some of these traits, such as height and intelligence. I wonder if beauty, perhaps measured by an index of facial symmetry, might exhibit higher correlation values?
A new paper in Molecular Psychiatry has been reported on extensively in the media, and readers have mentioned it several times in the comments. I read it. It’s titled Genome-wide association studies establish that human intelligence is highly heritable and polygenic. But the fact is that I read this paper last year. Back then it was titled Common SNPs explain a large proportion of the heritability for human height. I kid, but you get the picture. The new paper establishes for intelligence what we already suspected: most of the genetic variation in this heritable trait is accounted for by numerous genes of small effect. You inherit variants of these numerous genes from your two parents, and your own trait value is to a large extent a combination of the parental values. The issue is not if intelligence is heritable, but the extent of that heritability.
Update: Stephen Dubner emailed me, and pointed me to this much longer segment which has a lot of Bryan Caplan. So it seems like the omission that I perceived was more of an issue with the production and editing process and constraints of the Marketplace segment than anything else.
I play a lot of podcasts during the day as I go about my business on my iPod shuffle. One of them is Marketplace, which has a regular Freakonomics Radio segment, where Stephen Dubner “freaks” you out with incredible facts and analysis, often with a helping hand from Steven Levitt. With all due respect to Dubner and Levitt, this still has very pre-Lehman feel. Economics has “solved” the workings of the explicit market, so why not move on to other areas which are ripe for conquest by the “logic of life?”
In any case this week’s episode kind of ticked me off just a little. It started off with the observation that college educated women apparently put 22 hours weekly into childcare today, vs. 13 hours in the 1980s. I guess fewer latchkey kids and more “helicopter parents?” Dubner basically indicates that the reasoning behind this is many parents are in a “red queen” arms race to polish the c.v.’s of their children for selective universities. This makes qualitative sense, but can we explain an increase of 9 hours on average for the ~25% of women who are college educated on striving to make sure that their kids have Wesleyan as the safety school?
Let’s put our quantitative “thinking-caps” on “freakonomics” style. ~25% of adults have university degrees. ~80% of these have public university degrees, which are usually not too selective. Some of the ~20% are from not particularly elite religious colleges. So the subset of Americans who graduated from elite universities is actually not too large a number. You can include these as natural aspirants for the best spots for their children. And a proportion of the large remainder, I’d estimate ~90%, who didn’t go to a university which required a great deal of stress and c.v. polishing would certainly strive and hope for better for their kids. But can this explain a 9 hour average rise among tens of millions of women? Doesn’t seem to pass the smell test for me. I suspect there’s a more general norm of shifting toward “high investment parenting” among the college educated cohorts.
In my experience most scientists are not too clear on the details of intelligence testing, perhaps because the whole area is somewhat in ill repute (except when you want to brag about your own SAT/GRE score!). This despite the fact that the profession of science is skewed toward the right end of the intelligence bell curve. Steve Hsu, a physicist at the University of Oregon (and someone I’ve known for a while in the interests of “full disclosure”) has a nice presentation up in PDF format which summarizes the major points of interest in this area. Worth a skim if you are unfamiliar. Additionally he alludes to future directions in the study of the genetic basis of intelligence using genomics. Here’s his abstract:
I begin with a brief review of psychometric results concerning intelligence (sometimes referred to as the g factor, or IQ). The main results concern the stability, validity (predictive power) and heritability of adult IQ. Next, I discuss ongoing Genome Wide Association Studies which investigate the genetic basis of intelligence. Due mainly to the rapidly decreasing cost of sequencing (currently below $5k per genome), it is likely that within the next 5-10 years we will identify genes which account for a significant fraction of total IQ variation. Finally, I end with an analysis of possible near term genetic engineering for intelligence.
This talk is aimed at physicists and should be accessible even to those with no specialized background in psychology or biology.
Also, in case you are skeptical, Steve is quite aware of the difficulties with the enterprise which he outlines in the presentation assuming that the genetic architecture of intelligence is as he assumes. As sequencing gets cheaper and the sample size of full genomes hits the tens of thousands someone will tackle this, so he and his colleagues figured why not now?
WORDSUM is a variable in the General Social Survey. It is a 10 word vocabulary test. A score of 10 is perfect. A score of 0 means you didn’t know any of the vocabulary words. WORDSUM has a correlation of 0.71 with general intelligence. In other words, variation of WORDSUM can explain 50% of the variation of general intelligence. To the left is a distribution of WORDSUM results from the 2000s. As you can see, a score of 7 is modal. In the treatment below I will label 0-4 “Dumb,” 5-7 “Not Dumb,” and 8-10 “Smart.” Who says I’m not charitable? You also probably know that general intelligence has some correlation with income and wealth. But to what extent? One way you can look at this is inspecting the SEI variable in the GSS, which combines both monetary and non-monetary status and achievement, and see how it relates to WORDSUM. The correlation is 0.38. It’s there, but not that strong.
To further explore the issue I want to focus on two GSS variables, WEALTH and INCOME. WEALTH was asked in 2006, and it has a lot of categories of interest. INCOME has been asked a since 1974, but unfortunately its highest category is $25,000 and more, so there’s not much information at the non-low end of the scale (at least in current dollar values).
Below you see WEALTH crossed with WORDSUM. I’ve presented columns and rows adding up to 100%. Then you see INCOME crossed with WORDSUM. I’ve just created two categories, low, and non-low (less than $25,000 and more). Additionally, since the sample sizes were large I constrained to those 50 years and older for INCOME.
One of the interesting and robust nuggets from behavior genetics is that heritability of psychological traits increases as one ages. Imagine for example you have a cohort of individuals you follow over their lives. At the age of 1 the heritability of I.Q. may be ~20%. This means that ~20% of the variation in the population of I.Q. explained by variation in the genes of the population. More concretely, you would only expect a weak parent-offspring correlation in I.Q. in this sample. At the age of 10 the heritability of I.Q. in the same sample may be ~40%, and in mature adulthood it may rise to ~80% (those are real numbers which I’ve borrowed from Robert Plomin). Many people find this result rather counterintuitive. How can a trait like intelligence become “more genetic”?
Remember that I’m talking about heritability here, not an ineffable “more” or “less” quantum of “genetic” aspect of a trait. In other words: does variation in genes due to different parental backgrounds matter for a trait? Second, the nature of psychological traits is somewhat slippery and plastic. As I’ve noted before the correlation between a score on a 10-world vocabulary test and general intelligence is pretty good. You can expect people with high scores on the vocabulary test to have higher I.Q.’s than those who have low scores. But if you take an individual and lock them in a room without human contact for their first 15 years, they are unlikely to exhibit any such correspondence. You don’t have to be a rocket scientist to understand why. Quantitative behavior genetic traits are complex and are subject to a host of background conditions, and express themselves in an environmental context.
Jonah Lehrer has a post up, How Preschool Changes the Brain over at Frontal Cortex. He reports on a paper, Investing in our young people, which has been around for about 5 years. The top line of it is this, an investment in a $2,500/year (inflation adjusted) pre-school program in the early 1960s seems to have been effective in improving the life outcomes of at-risk low SES young black Americans tracked over their lives up to the age of 40. Their measured I.Q.s were not initially high, 85-75, 15th to the 5th percentile (though the median black American IQ is ~85, so not so low within ethnic group). They did gain an initial I.Q. boost, but like most of these programs that boost disappeared over time. But in terms of their non-cognitive skills there remained an appreciable effect which impact their life outcomes. What were these non-cognitive skills? To me they resemble classical bourgeois values rooted in low time preference. Willing to be a “grind,” work hard and forgo short-term pleasures and not cave in to impulses with short-term gains and long-term costs.
Here’s a figure from the paper which I’ve reedited with labels: