Razib Khan One-stop-shopping for all of my content

December 29, 2018

Variation in general intelligence and our evolutionary history

Filed under: Evolution,Intelligence,IQ — Razib Khan @ 9:16 pm

In a bit of “TMI”, I’m far more intellectually promiscuous than I am in my personal life. My primary focus on this blog, if I have one, is probably historical population genetics of the sort highlighted in David Reich’s Who We Are and How We Got Here. But I have plenty of other interests, from economic history to cognitive psychology. Like religion, I have precise and clear opinions about a topic like “intelligence.” Unlike many people with an interest in evolutionary genetics I have read psychometric work, am familiar with some of the empirical results, as well as being personally acquainted with people in the field of psychometrics.

A few days ago Nassim Nicholas Taleb opined on intelligence, and I was silent. Today some individuals who I know from within the field of cultural evolution, another one of my interests, discussed intelligence, and I was silent. I’ve said all I really have to say over 15 years, and it isn’t as if I reanalyze psychometric data sets. But, a question that Taleb acolytes (and presumably Taleb) have brought up is if intelligence is such an important heritable trait, why isn’t everyone much smarter?

Think of this as the second Von Neumann paradox. What I’m alluding to is the fact that we know for a fact that human biology is capable of producing a god-made-flesh. With all due respect to another Jew who lived 2,000 years earlier than him, I speak here of John Von Neumann. We know that he is possible because he was. So why are the likes of Von Neumann bright comets amongst the dust of the stars of the common man, rather than the norm?

First, consider the case of Von Neumann himself. He had one daughter and two grandchildren. That is, within two generations genetically there was less “Von Neumann” than there had been. Though his abilities were clearly mentat-like, from the perspective of evolution Von Neumann was not a many sigma individual. He was within the normal range. Close to the median, a bit below in fecundity and fitness.

Taking a step back and focusing on aggregate populations, the fact that intelligence seems to be a quantitative trait that is at least moderately heritable and normally distributed due to polygenic variation tells us some things evolutionarily already. In Principles of Population Genetics is noted that heritable quantitative traits are often those where directional selection is not occurring due to huge consistent fitness differentials within the population.

Breaking it down, if being very smart was much, much, better than being of average smarts, then everyone would become very smart up to the physiological limit and heritable genetic variation would be removed from the population. Characteristics with huge implications for fitness tend not to be heritable because natural selection quickly expunges the deleterious alleles. The reason that fingerprints are highly heritable is that the variation genetically is not much impacted by natural selection.

The fact that being very intelligent is not evolutionarily clearly “good” seems ridiculousness to many people who think about these things. That’s because if you think about these things, you are probably very good at thinking, and no one wants to think that what they are good at is not evolutionarily very important. The thinking man cannot comprehend that thinking is not the apotheosis of what it is to be a man (similarly, the thinking religious man sometimes confuses theological rumination with the heights of spirituality; reality is that man does not know god through analysis, man experiences god).

So let’s talk about another quantitative trait which is even more heritable than intelligence, and easier to measure: height. In most societies males, in particular, seem to be more attractive to females if they are taller. As a male who is a bit shorter than the American average, it is obvious that there is some penalty to this in social and potentially reproductive contexts. And yet there is normal variation in height, and some populations seem to be genetically smaller than others, such as the Pygmy peoples of the Congo rainforest. Why?

Though being a tall male seems in most circumstances to be better in terms of physical attractiveness than being a short male, circumstances vary, and being too tall increases one’s mortality and morbidity. Being larger is calorically expensive. Large people need to eat more because they have larger muscles. Selection for smaller size in many marginalized rainforest populations is indicative of the fact that in such calorically challenging environments (humans in rainforests have to work hard to obtain enough calories in a hunter-gatherer context), the fitness gain due to intrasexual competition is balanced by reduced fitness during times of ecological stress as well as individual correlated responses (very large males die more often than smaller males).

Additionally, for height I mentioned the sexual component: there does not seem to be a necessary association with higher reproductive fitness with being a tall woman. Though this is subject to taste and fashion, there is likely some antagonistic selection across the two sexes at work, where tall men are the fathers of taller daughters, whose reproductive fitness may actually be lower than smaller women. And vice versa, as short men may produce more fit short daughters (though again, this depends on ecological context and cultural preconditions).

Being very large impacts fitness through the genetic correlation of size with other characteristics. Very large males are subject to higher risk for sudden tears in their lungs, or suboptimal cardiac function. Humans select chickens to be very large in the breast for food, but these chickens can barely walk, and may not be able to reproduce without assistance. Evolution in a quantitative genetic sense may then be all about trade-offs.

So let’s go back to intelligence. What could be the trade-offs? First, there are now results presented at conferences that very high general intelligence may exhibit a correlation with some mental pathologies. Though unpublished, this aligns with some prior intuitions. Additionally, there is the issue where on some characteristics being “species-typical” increases reproductive fitness (an average size nose), while in other characteristics being at an extreme is more attractive (very curvy women with large eyes and small chins; secondary sexual characteristics). Within intelligence, one could argue that being too deviated from the norm might make socialization and pair-bonding difficult. Here is an anecdote about the genius Von Neumann:

Neumann married twice. He married Mariette Kövesi in 1930. When he proposed to her, he was incapable of expressing anything beyond “You and I might be able to have some fun together, seeing as how we both like to drink.”

Apparently having a very fast analytic mind which can engage in abstraction and conceptual manipulation does not mean that one can come up with anything better than that when it came to procuring a mate. And procuring a mate is one of the only “good” things from an evolutionary perspective.

The human mind is neither universally plastic, nor it is a prefabricated set of specialized modules. It is a mix of both. We clearly have some “pre-loaded” code, such as the ability to recognize faces intuitively and rapidly (which a small proportion of the population lacks). But other competencies develop over time, co-opting neurological architecture that grew organically for other purposes. In Reading in the Brain Stanislas Dehaene recounts how the region which specializes in the ability to recognize letter shapes is a preexistent visual-spatial module, probably developed for ecological adaptation to environments where recognition of various organic and inorganic objects was of fitness relevance (obviously now tied in to regions of the brain geared toward verbal comprehension). Dehaene even seems to suggest there may be a trade-off between various cognitive capacities when comparing individuals from urban developed societies and individuals from non-literate small-scale societies.

As human societies have specialized over the last 10,000 years a small number of people who naturally were on the end of a particular distribution in abstraction-and-analysis ability began to preferentially fill exotic niches that had previously not existed. From all we can tell the ancient polymath Archimedes was a Von Neumann for his age. Archimedes seems likely to have been of aristocratic background, and part of the class of leisured intellectuals. The fact that he had such innate talent and disposition, combined with his life circumstances, was simple happenstance.

Today we live in a different age. Specialization, and the post-industrial economy, put a premium on competencies associated only with individuals on the “right tail” of the IQ distribution. Similarly, our genetic background predisposes many of us to obesity because the modern environment is “obesogenic.” The reality is that obesity was not an issue for almost all of human history, so genetic variation (often behavioral/cognitive) that is associated with obesity today was not so associated with it in the past. There could be no selection against obesity when it wasn’t a trait within the population.

Just as the modern environment is potentially “obesogenic,” it is also potentially “intelligenic.” Here’s what I’m talking about, The Science Behind Making Your Child Smarter:

The research also lends insight into why many apps and training programs aimed at raising IQ fail to produce lasting effects, says Elliot Tucker-Drob, an associate professor of psychology at the University of Texas at Austin, and co-author of the study.

Raising IQ may require the kind of sustained involvement that comes with attending school, with all the practice and challenges it entails. “It’s not like you just go in for an hour of treatment a week. It’s a real lifestyle change,” he says

.

To be a “nerd” is a lifestyle only possible in the modern information-rich environment. The Flynn effect is evidence that changing environments can shift the whole distribution. But just as with obesity or adult-onset diabetes risk, there is also heritable variation latent across the genome that seems to affect one’s response to the intelligenic environment.

Humans have large brains for our size. We are smarter than other primates. But evolutionary genetics today seems to be coming to the conclusion that it wasn’t a quantum jump, but gradual selection and change. Having a very low intellectual capacity was probably correlated with low fitness in the past (though small brains are calorically less greedy). But, having a very high general intelligence does not seem to have resulted in that great of a gain in social or cultural status in comparison to being of normal intelligence. In fact, if the genetic correlation is such that it’s associated with some higher risk for mental instability, it could simply be that a form of stabilization selection over time kept humans within the “normal range” because that was evolutionarily optimal. Be smart enough. But not too smart that you are weird.

And, as theorists from cultural evolution have observed, we are a “hive-mind” which leverages collective wisdom. Most of us don’t have to derive mathematical equations, we can use the formula provided to us. Though it’s useful to have a few people around who can invent statistics that the rest of us use…

June 24, 2012

Higher vocabulary ~ higher income

Filed under: data,Data Analysis,GSS,Income,IQ — Razib Khan @ 7:54 pm

Prompted by a comment below I was curious as to the correlation between intelligence and income. To indicate intelligence I used the GSS’s WORDSUM variable, which has a ~0.70 correlation with IQ. For income, I used REALINC, which is indexed to 1986 values (so it is inflation adjusted) and aggregates the household income. Finally, I limited my sample to non-Hispanic whites over the age of 30 (for what it’s worth, this choice also limited the data set to respondents from the year 2000 and later).

The results don’t get at the commenter’s assertions, because 10 out of 10 on WORDSUM does not imply that you’re that smart really. But the trendline is suggestive. Note that aggregated 0-4 because the sample size at the lower values is small indeed.

April 15, 2012

Common variant for “IQ gene”?

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

A few people have forwarded me this paper, Identification of common variants associated with human hippocampal and intracranial volumes:

…Whereas many brain imaging phenotypes are highly heritable…identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).

Look at the sample sizes. Beware of behavior genomics with small sample sizes. Paul Thompson, one of the many authors of this paper, is giving media interviews. To me that’s a good sign, as he’s a very smart guy. He has some confidence in this study. Here’s the section which is resulting in ...

January 1, 2012

Too smart to be a good cop

Filed under: IQ,Psychology — Razib Khan @ 3:35 pm

Several readers have pointed me to this amusing story, Court OKs Barring High IQs for Cops:

A man whose bid to become a police officer was rejected after he scored too high on an intelligence test has lost an appeal in his federal lawsuit against the city.

“This kind of puts an official face on discrimination in America against people of a certain class,” Jordan said today from his Waterford home. “I maintain you have no more control over your basic intelligence than your eye color or your gender or anything else.”

Jordan, a 49-year-old college graduate, took the exam in 1996 and scored 33 points, the equivalent of an IQ of 125. But New London police interviewed only candidates who scored 20 to 27, on the theory that those who scored too high could get bored with police work and leave soon after undergoing costly training.

The average score nationally for police officers is 21 to 22, the equivalent of an IQ of 104, or just a little above average.

But the U.S. District Court found that New London had “shown a rational basis for the policy.” In a ruling dated Aug. 23, the 2nd Circuit agreed. The court said the policy might be unwise but was a rational way to reduce job turnover.


First, is the theory empirically justified? If so, I can see where civil authorities are coming from. That being said, it’s obvious that there are some areas where “rational discrimination” is socially acceptable, and others where it is not. The same arguments used to be applied to women, in terms of the actuarial probabilities that they would get pregnant and so have to leave the workforce. And disparate impact always looms large in the utilization of these sorts of tests.

Second, can’t you just fake a lower score on an intelligence test? Do police departments hire statisticians to smoke out evidence of conscious selection of incorrect scores? I doubt it. Jordan may be smart, but perhaps he lacks common sense if the upper bound for IQ was well known.

My initial thought was that an IQ of 104 seemed too low for a median police officer, but poking around it does seem plausible as a descriptive statistic. Honestly I don’t have much acquaintance with the police, so I’ll trust the scholars no this. That being said, is it in our social interest for police officers to be so average? I don’t know. Though is it in the social interest that someone with an IQ as high as Robert Jordan’s ends up a prison guard?

June 27, 2010

Psychometrics, epigenetics and economics

Filed under: IQ,Psychology,Psychometrics — Razib Khan @ 10:26 am

Two papers of interest. IQ in the Production Function: Evidence from Immigrant Earnings (ungated). And Human Intelligence and Polymorphisms in the DNA Methyltransferase Genes Involved in Epigenetic Marking. My impression is that the focus on epigenetics has a higher-order social motive; even the sort of humanists who are involved with N + 1 have asked me about the topic. But how many people know what methylation is?

May 4, 2010

WORDSUM & IQ & the correlation

Filed under: Blog,data,Data Analysis,GSS,IQ,WORDSUM — Razib Khan @ 2:08 pm

Every time I use the WORDSUM variable from the GSS people will complain that a score on a 10-question vocabulary test is not a good measure of intelligence. The reality is that “good” is too imprecise a term. The correlation between adult IQ and WORDSUM = 0.71. The source for this number is a 1980 paper, The Enduring Effects of Education on Verbal Skills. I’ve reproduced the relevant table…

Estimated Correlations for Variables in a Model of Enduring Effects of Education for White, Native-Born People 25 to 72 Years Old in the Contemporary [1970s] United States
 Child IQAgeSexFather’s EducFather’s SEIEducAdult IQWORDSUM
Child IQ-000.310.300.510.80-
Age--0.026-0.304-0.130-0.304-0.42-0.005
Sex----0.0540.0580.0500-0.121
Father’s Educ----0.4880.4690.300.302
Father’s SEI-----0.3470.310.285
Educ------0.660.511
Adult IQ-------0.71
WORDSUM------ -
         

Obviously since the WORDSUM test was not given to those under 18 you can’t calculate the correlation between childhood IQ and WORDSUM score. Additionally, I suspect since 1980 there’s been a bit more cognitive stratification by education. I notice in the GSS sample that there are many older people, especially women, who have high WORDSUM scores but no college education. In the younger age cohorts this pattern is not as evident because if you are intelligent the probability is much higher that you’ll obtain a university education.

A correlation of 0.71 is not mind-blowing, there’s a significant difference between IQ and WORDSUM as they relate to each other linearly. But I think it’s good enough to get a sense that WORDSUM is a serviceable substitute for a more rigorous measure of g in lieu of any alternatives, and not so clumsy a proxy so as to be useless. Though that call is up to you, and readers are free to disagree with the methodology of the model used to obtain this correlation. Additionally, I would point out that WORDSUM is a subset of the vocabulary subsection of the Wechsler Adult Intelligence Scale. WORDSUM is in effect a slice of an IQ test.

I am bookmarking this post so that in the future I can simply place a link in the comment threads in response to objections to WORDSUM.

Note: Thanks to Bryan Caplan for pointing me to this paper.

Citation: Lee M. Wolfle, Sociology of Education, Vol. 53, No. 2 (Apr., 1980), pp. 104-114

February 1, 2010

Half Sigma’s flawed post on DTNBP1

Filed under: Behavior Genetics,IQ — ben g @ 10:33 am

A while back, Mark and I were working on a comprehensive post which would try to tally the results of the various IQ-gene studies to see what they said about racial differences. We began this quest bright-eyed and hopeful that we would help contribute to ending a calamitous debate that has gone on for way too long. However, as we learned more about genetics, and these studies in particular, we came to realize that it’s too early to take IQ-genes seriously.

We began with an approach similar to what Half Sigma did 2 years ago with the DTNBP1 gene. However, we soon learned that this approach was incredibly flawed and misleading. I wasn’t going to write this post, but recently Half Sigma’s DTBP1 post was linked from Reddit and tens of thousands of people are viewing it. When I saw that, I frustratedly criticized HS. He responded that I should give a more diplomatic and reasoned response, so here it is:

  1. You cannot simply add up SNPs from the same gene or chromosome. Half Sigma simply adds the observed effects of the SNPs to one another, ignoring that the alleles are highly correlated with one another, and not independently inherited, which is referred to as linkage disequilibrium (LD). The study that Half Sigma used provides the following table of LD for its SNPs:

    rs2619539rs3213207rs1011313rs2619528rs760761rs2619522rs2619538
    rs26195390.1560.1110.00.00.0010.055
    rs32132071.00.0140.3340.4030.340.076
    rs10113130.9161.00.0370.0330.0360.081
    rs26195280.0240.9551.00.8380.7370.128
    rs7607610.01511.00.960.8540.166
    rs26195220.040.9551.00.8670.960.182
    rs26195380.2420.8230.8250.6480.7720.778

    As can be seen in this table, pairwise LD goes as high as 1.0, meaning that two of the alleles are always inherited together. Adding these SNP’s together is therefore like counting them twice.

  2. Group comparisons require replication in both groups. Because different populations have systematic genetic and environmental differences, an effect in one group may not occur in another. The study that Half Sigma uses relies primarily on a (small) sample of Dutch people. It is unclear whether these effects would exist in a population of African ancestry, let alone another European one.
  3. Candidate-gene association studies are not reliable. This is the most important point. Candidate gene association studies have largely failed to replicate. In fact, there have been no common IQ polymorphisms which have been replicated. Genome-wide association studies, which don’t suffer as severely the various biases of candidate-gene association studies like publication bias or the winner’s curse have not shown common SNP-associations with IQ.

    IQ is highly heritable, so the problem is the current methods, not the search for genes. With the development of sequencing technology and huge cohorts, we will be able to see the genes that are really behind normal IQ variation. With replication in multiple ethnicities and races, we will also see to what extent various genes and environments are responsible for group differences. There’s no need to make proclamations of victory for hereditarianism or environmentalism in the mean time.

December 8, 2009

Does the family matter for adult IQ?

Filed under: Behavior Genetics,IQ — ben g @ 5:57 am

A frequent claim in the IQ debates is that which family you are raised in has no lasting impact on your IQ. Jensen argues in The g Factor that the only causes of IQ similarities between adult identical twins are genetic. Many researchers go so far as to argue that by 12 years of age, the shared environment has no impact.

Based on my limited knowledge of the behavior genetic research, I used to hold this position as well. But thanks to some recent in depth reading, I have come to the conclusion that which family you are raised in matters significantly for your IQ as an adult, especially so for people of lower socioeconomic status. I’ll detail the behavior genetic evidence here, and argue that it points to significant shared environmental influences on adult IQ scores.

Twin Studies

The most recent and comprehensive survey of twin studies on IQ comes from Haworth et al (2009). Using pooled twin data from around the world, they modeled genetic and environmental influences as a function of age. Here is what they found regarding the effects of the shared environment:

[S]hared environment shows a decrease from childhood (33%) to adolescence (18%) but remained at that modest level in young adulthood (16%).

In an email exchange with Dr. McGue (one of the co-authors of the paper) he told me that while the latest data may not fit with earlier estimates, it’s actually more reliable due to the unprecedented sample size (11,000 pairs of twins).

One failing of this study, though, is that it doesn’t go far enough into adulthood. The young adult group ranges from 14 to 34 years of age, with an average age of 17. In contrast, McGue (1993) looked seperately at data on adults over 20 years of age. He found that the shared environment diminished to zero impact at that point. Here’s his chart:
Looking at that chart, you might quickly conclude that shared environmental influence evaporates by age 20. However, this conclusion is premature. Twin studies make a great number of assumptions, some of which increase and others of which decrease estimates of the shared environment. A straightforward way of bypassing these assumptions is to compare monozygotic twins reared apart (MZAs) to monozygotic twins reared together (MZTs). The following data comes from a comparison of MZTs and MZAs, of average age 41, in Bouchard (1990):

MeasureMZA correlationMZT correlation
WAIS IQ-Full Scale0.690.88
WAIS IQ-Verbal0.640.88
WAIS IQ-Performance0.710.79

Differences between MZA’s and MZT’s on Raven’s Progressive Matrices follow the same pattern but are even more extreme. Bouchard (1981) reported a median correlation of only 0.58 for adult MZA’s on the Raven’s. Curiously, though, MZA’s are equally if not more correlated than MZT’s on the Mill-Hill vocabulary test. Apparently, the pattern is that more g-loaded tests tend to show stronger evidence of lasting shared environmental impact.

It’s worth noting that MZT vs. MZA comparisons are actually biased towards an underestimation of shared environmental impact. Bouchard’s study of twins reared apart found an environmental correlation of .22 for MZAs on various environmental measures, with some having a small but significant correlation with IQ scores. Also MZA’s share the womb. To summarize: when the assumptions of the twin method are effectively controlled for, lasting shared environmental impacts are revealed.

Adoption Studies

To date, most adoption studies of IQ have concluded that being adopted by a new and typically well-off family has no effect on adult IQ scores. Here is a chart of adoption studies from Bouchard (2009):As you can see by clicking it, the IQ correlation between unrelated individuals in the same family decreases (on average) from .26 in childhood to .04 in adulthood (which begins at age 17 for the purposes of this graph).

However, as with the previous chart, the quick conclusion that shared environmental influences don’t matter in adulthood shouldn’t be so quickly accepted. To begin with, we can see that the adoption data underestimates the shared environment relative to the twin literature. This most likely occurs because of the assumptions that go into adoption studies.

Stoolmiller (1999), for example, highlighted the issue of range restriction– the idea that the limited range of adoptee and adoptive family environments will lower estimates of the shared environment. This idea is supported by studies which make the extra effort to include individuals of lower SES. The French adoption studies that made such an effort buck the trendline seen above, in finding that nurture matters almost as much as nature for the IQ of 14 year olds. Scarr (1993) is the outlier in the adoption graph above, finding a .19 correlation between unrelated adolescent siblings. Perhaps her results differed from others because her sample was multi-racial and therefore less range restricted. Lastly, there are other lines of evidence supporting the idea of range restriction, such as Turkheimer’s work on SES and cognitive ability.

It’s worth noting, however, that McGue (2007) looked for evidence of range restriction effects within the “broad middle class” and did not find any. He used statistical methods that are over my head to estimate the effects of range restriction based on a range restricted sample and state census data. Unfortunately there are no studies which have critiqued his as of yet. Any commenters who are familiar with the statistics involved are invited to comment. Even if McGue is right about restriction of range, my point stands that assumptions inherent in the adoption studies deflate c^2 estimates.

Future Directions

Future work will help sort out the still unanswered question of shared environmental influences on adult IQ scores. There are large longitudinal adoption studies currently under way, and I believe that Haworth’s twin study will be followed-up on and include data on older twins. There are also interesting (albeit less methodologically agreed upon) studies coming out like this one, which find significant shared effects on IQ in adulthood.

My reading of the available evidence is that there is a significant shared environmental input to adult IQ, and that it is associated with socioeconomic status. To what extent it’s the neighborhood or the parents themselves that matters is unclear. Just as the most g-loaded tests show the most shared environmental effects in the MZA-MZT comparison, so too does the Flynn effect occur on the most g-loaded tests, suggesting that whatever is loading onto the “shared environment” within generations is also responsible for differences between them.

Does the family matter for adult IQ?

Filed under: Behavior Genetics,IQ — ben g @ 5:57 am

A frequent claim in the IQ debates is that which family you are raised in has no lasting impact on your IQ. Jensen argues in The g Factor that the only causes of IQ similarities between adult identical twins are genetic. Many researchers go so far as to argue that by 12 years of age, the shared environment has no impact.

Based on my limited knowledge of the behavior genetic research, I used to hold this position as well. But thanks to some recent in depth reading, I have come to the conclusion that which family you are raised in matters significantly for your IQ as an adult, especially so for people of lower socioeconomic status. I’ll detail the behavior genetic evidence here, and argue that it points to significant shared environmental influences on adult IQ scores.

Twin Studies

The most recent and comprehensive survey of twin studies on IQ comes from Haworth et al (2009). Using pooled twin data from around the world, they modeled genetic and environmental influences as a function of age. Here is what they found regarding the effects of the shared environment:

[S]hared environment shows a decrease from childhood (33%) to adolescence (18%) but remained at that modest level in young adulthood (16%).

In an email exchange with Dr. McGue (one of the co-authors of the paper) he told me that while the latest data may not fit with earlier estimates, it’s actually more reliable due to the unprecedented sample size (11,000 pairs of twins).

One failing of this study, though, is that it doesn’t go far enough into adulthood. The young adult group ranges from 14 to 34 years of age, with an average age of 17. In contrast, McGue (1993) looked seperately at data on adults over 20 years of age. He found that the shared environment diminished to zero impact at that point. Here’s his chart:
Looking at that chart, you might quickly conclude that shared environmental influence evaporates by age 20. However, this conclusion is premature. Twin studies make a great number of assumptions, some of which increase and others of which decrease estimates of the shared environment. A straightforward way of bypassing these assumptions is to compare monozygotic twins reared apart (MZAs) to monozygotic twins reared together (MZTs). The following data comes from a comparison of MZTs and MZAs, of average age 41, in Bouchard (1990):

MeasureMZA correlationMZT correlation
WAIS IQ-Full Scale0.690.88
WAIS IQ-Verbal0.640.88
WAIS IQ-Performance0.710.79

Differences between MZA’s and MZT’s on Raven’s Progressive Matrices follow the same pattern but are even more extreme. Bouchard (1981) reported a median correlation of only 0.58 for adult MZA’s on the Raven’s. Curiously, though, MZA’s are equally if not more correlated than MZT’s on the Mill-Hill vocabulary test. Apparently, the pattern is that more g-loaded tests tend to show stronger evidence of lasting shared environmental impact.

It’s worth noting that MZT vs. MZA comparisons are actually biased towards an underestimation of shared environmental impact. Bouchard’s study of twins reared apart found an environmental correlation of .22 for MZAs on various environmental measures, with some having a small but significant correlation with IQ scores. To summarize: when the assumptions of the twin method are effectively controlled for, lasting shared environmental impacts are revealed.

Adoption Studies

To date, most adoption studies of IQ have concluded that being adopted by a new and typically well-off family has no effect on adult IQ scores. Here is a chart of adoption studies from Bouchard (2009):As you can see by clicking it, the IQ correlation between unrelated individuals in the same family decreases (on average) from .26 in childhood to .04 in adulthood (which begins at age 17 for the purposes of this graph).

However, as with the previous chart, the quick conclusion that shared environmental influences don’t matter in adulthood shouldn’t be so quickly accepted. To begin with, we can see that the adoption data underestimates the shared environment relative to the twin literature. This most likely occurs because of the assumptions that go into adoption studies.

Stoolmiller (1999), for example, highlighted the issue of range restriction– the idea that the limited range of adoptee and adoptive family environments will lower estimates of the shared environment. This idea is supported by studies which make the extra effort to include individuals of lower SES. The French adoption studies that made such an effort buck the trendline seen above, in finding that nurture matters almost as much as nature for the IQ of 14 year olds. Scarr (1993) is the outlier in the adoption graph above, finding a .19 correlation between unrelated adolescent siblings. Perhaps her results differed from others because her sample was multi-racial and therefore less range restricted. Lastly, there are other lines of evidence supporting the idea of range restriction, such as Turkheimer’s work on SES and cognitive ability.

It’s worth noting, however, that McGue (2007) looked for evidence of range restriction effects within the “broad middle class” and did not find any. He used statistical methods that are over my head to estimate the effects of range restriction based on a range restricted sample and state census data. Unfortunately there are no studies which have critiqued his as of yet. Any commenters who are familiar with the statistics involved are invited to comment. Even if McGue is right about restriction of range, my point stands that assumptions inherent in the adoption studies deflate c^2 estimates.

Future Directions

Future work will help sort out the still unanswered question of shared environmental influences on adult IQ scores. There are large longitudinal adoption studies currently under way, and I believe that Haworth’s twin study will be followed-up on and include data on older twins. There are also interesting (albeit less methodologically agreed upon) studies coming out like this one, which find significant shared effects on IQ in adulthood.

My reading of the available evidence is that there is a significant shared environmental input to adult IQ, and that it is associated with socioeconomic status. To what extent it’s the neighborhood or the parents themselves that matters is unclear. Just as the most g-loaded tests show the most shared environmental effects in the MZA-MZT comparison, so too does the Flynn effect occur on the most g-loaded tests, suggesting that whatever is loading onto the “shared environment” within generations is also responsible for differences between them.

November 21, 2009

Models of IQ & wealth

Filed under: IQ — Razib @ 5:48 pm

Steve Hsu has been interesting of late (interesting like Steve, not Malcolm). So, IQ, compression and simple models and If you’re so smart, why aren’t you rich?. For a theoretical physicist I find Steve to be eminently clear in his exposition of abstract topics (perhaps he has practice from having to talk to experimental physicists?).

November 8, 2009

The quest for common variants & cognition

Filed under: Association,IQ,Population genetics — Razib @ 11:39 am

A genome-wide study of common SNPs and CNVs in cognitive performance in the CANTAB:

Psychiatric disorders such as schizophrenia are commonly accompanied by cognitive impairments that are treatment resistant and crucial to functional outcome. There has been great interest in studying cognitive measures as endophenotypes for psychiatric disorders, with the hope that their genetic basis will be clearer. To investigate this, we performed a genome-wide association study involving 11 cognitive phenotypes from the Cambridge Neuropsychological Test Automated Battery. We showed these measures to be heritable by comparing the correlation in 100 monozygotic and 100 dizygotic twin pairs. The full battery was tested in 750 subjects, and for spatial and verbal recognition memory, we investigated a further 500 individuals to search for smaller genetic effects. We were unable to find any genome-wide significant associations with either SNPs or common copy number variants. Nor could we formally replicate any polymorphism that has been previously associated with cognition, although we found a weak signal of lower than expected P-values for variants in a set of 10 candidate genes. We additionally investigated SNPs in genomic loci that have been shown to harbor rare variants that associate with neuropsychiatric disorders, to see if they showed any suggestion of association when considered as a separate set. Only NRXN1 showed evidence of significant association with cognition. These results suggest that common genetic variation does not strongly influence cognition in healthy subjects and that cognitive measures do not represent a more tractable genetic trait than clinical endpoints such as schizophrenia. We discuss a possible role for rare variation in cognitive genomics.

David Goldstein is one of the authors. I wonder if this influenced his views on the evolution of intelligence.

Powered by WordPress

Do NOT follow this link or you will be banned from the site!