Kathryn Paige Harden Profile in The New Yorker (Behavior Genetics)

This is a good profile of behavior geneticist Paige Harden (UT Austin professor of psychology, former student of Eric Turkheimer), with a balanced discussion of polygenic prediction of cognitive traits and the culture war context in which it (unfortunately) exists.

Can Progressives Be Convinced That Genetics Matters? 

The behavior geneticist Kathryn Paige Harden is waging a two-front campaign: on her left are those who assume that genes are irrelevant, on her right those who insist that they’re everything. 

Gideon Lewis-Kraus

Gideon Lewis-Kraus is a talented writer who also wrote a very nice article on the NYTimes / Slate Star Codex hysteria last summer.

Some references related to the New Yorker profile:

1. The paper Harden was attacked for sharing while a visiting scholar at the Russell Sage Foundation: Game Over: Genomic Prediction of Social Mobility 

2. Harden's paper on polygenic scores and mathematics progression in high school: Genomic prediction of student flow through high school math curriculum 

3. Vox article; Turkheimer and Harden drawn into debate including Charles Murray and Sam Harris: Scientific Consensus on Cognitive Ability?

A recent talk by Harden, based on her forthcoming book The Genetic Lottery: Why DNA Matters for Social Equality

Regarding polygenic prediction of complex traits 

I first met Eric Turkheimer in person (we had corresponded online prior to that) at the Behavior Genetics Association annual meeting in 2012, which was back to back with the International Conference on Quantitative Genetics, both held in Edinburgh that year (photos and slides [1] [2] [3]). I was completely new to the field but they allowed me to give a keynote presentation (if memory serves, together with Peter Visscher). Harden may have been at the meeting but I don't recall whether we met. 

At the time, people were still doing underpowered candidate gene studies (there were many talks on this at BGA although fewer at ICQG) and struggling to understand GCTA (Visscher group's work showing one can estimate heritability from modestly large GWAS datasets, results consistent with earlier twins and adoption work). Consequently a theoretical physicist talking about genomic prediction using AI/ML and a million genomes seemed like an alien time traveler from the future. Indeed, I was.

My talk is largely summarized here:

On the genetic architecture of intelligence and other quantitative traits 

https://arxiv.org/abs/1408.3421 

How do genes affect cognitive ability or other human quantitative traits such as height or disease risk? Progress on this challenging question is likely to be significant in the near future. I begin with a brief review of psychometric measurements of intelligence, introducing the idea of a "general factor" or g score. The main results concern the stability, validity (predictive power), and heritability of adult g. The largest component of genetic variance for both height and intelligence is additive (linear), leading to important simplifications in predictive modeling and statistical estimation. Due mainly to the rapidly decreasing cost of genotyping, it is possible that within the coming decade researchers will identify loci which account for a significant fraction of total g variation. In the case of height analogous efforts are well under way. I describe some unpublished results concerning the genetic architecture of height and cognitive ability, which suggest that roughly 10k moderately rare causal variants of mostly negative effect are responsible for normal population variation. Using results from Compressed Sensing (L1-penalized regression), I estimate the statistical power required to characterize both linear and nonlinear models for quantitative traits. The main unknown parameter s (sparsity) is the number of loci which account for the bulk of the genetic variation. The required sample size is of order 100s, or roughly a million in the case of cognitive ability.

The predictions in my 2012 BGA talk and in the 2014 review article above have mostly been validated. Research advances often pass through the following phases of reaction from the scientific community:

1. It's wrong ("genes don't affect intelligence! anyway too complex to figure out... we hope")

2. It's trivial ("ofc with lots of data you can do anything... knew it all along")

3. I did it first ("please cite my important paper on this")

Or, as sometimes attributed to Gandhi: "First they ignore you, then they laugh at you, then they fight you, then you win.”

See also To Predict the Future it is useful to understand the Past.

Technical note: 

In 2014 I estimated that ~1 million genotype | phenotype pairs would be enough to capture most of the common SNP heritability for height and cognitive ability. This was accomplished for height in 2017. However, the sample size of well-phenotyped individuals is much smaller for cognitive ability, even in 2021, than for height in 2017. For example, in UK Biobank the cognitive test is very brief (~5 minutes IIRC, a dozen or so questions), but it has not even been administered to the full cohort as yet. In the Educational Attainment studies the phenotype EA is only moderately correlated (~0.3 ?) or so with actual cognitive ability.

Hence, although the most recent EA4 results use 3 million individuals [1], and produce a predictor which correlates ~0.4 with actual EA, the statistical power available is still less than what I predicted would be required to train a really good cognitive ability predictor.

In our 2017 height paper, which also briefly discussed bone density and cognitive ability prediction, we built a cognitve ability predictor roughly as powerful as EA3 using only ~100k individuals with the noisy UKB test data. So I remain confident that  ~million individuals with good cognitive scores (e.g., SAT, AFQT, full IQ test) would deliver results far beyond what we currently have available. We also found that our predictor, built using actual (albeit noisy) cognitive scores exhibits less power reduction in within-family (sibling) analyses compared to EA. So there is evidence that (no surprise) EA is more influenced by environmental factors, including so-called genetic nurture effects, than is cognitive ability.

A predictor which captures most of the common SNP heritability for cognitive ability might correlate ~0.5 or 0.6 with actual ability. Applications of this predictor in, e.g., studies of social mobility or educational success or even longevity using existing datasets would be extremely dramatic.

Zach Hambrick on Psychometrics and the Science of Expertise -- Manifold Podcast #28

MSU Psychology Professor Zach Hambrick joins Corey and Steve to discuss general cognitive ability, the science of personnel selection, and research on the development of skills and expertise. Is IQ really the single best predictor of job performance? Corey questions whether g is the best predictor across all fields and whether its utility declines at a certain skill level. What does the experience of the US military tell us about talent selection? Is the 10,000 hour rule for skill development valid? What happened to the guy who tried to make himself into a professional golfer through 10,000 hours of golf practice?

Transcript

Science of Expertise

Zach Hambrick (Faculty Profile)

Armed Services Vocational Aptitude Battery

Project 100,000 (1960s DoD Program)

Test Validity Study Report (CLA)

The Validity and Utility of Selection Methods in Personnel Psychology

Feynman and Tukey (Working Memory); Dom and Brexit

I received this message over the weekend.

Dear Dr. Hsu,

With great interest I regularly read your excellent Information Processing Blog. With regard to your assessment of Dom Cummings' achievements I am at variance with yours. But I guess you will like the anecdote referring to Feynman.

I tried to comment directly on your blog but the whole procedure was somewhat cumbersome, so I mail my comment directly to you. Please feel free to post it at the comment section under my full name. See the comment attached.

I am a retired psychology prof from University of Mannheim, Germany specializing in intelligence research, research methodology, assessment and evaluation research.

Best regards
Werner W. Wittmann

The letter:

IQ makes the difference

If you want to learn more about what kind of difference differences in IQ make read the research of Dave Lubinski and Camilla Benbow what differences highly gifted youngsters accomplish after several decades. Dave makes their publications available at https://my.vanderbilt.edu/smpy/publications/david-lubinski/

But let me turn to a funnier anecdote for physicists like Steve Hsu

A 35 year gap:

Physicists are among the smartest high IQ people, there is no doubt. If you want a single case example take Richard Feynman. If we could have lured him to psychology an important concept probably would have been published 35 years earlier.

In 1939 Feynman as a graduate student at Princeton experimented just for fun together with his friend John Tukey (who later became the famous statistician) to assess the ability of measuring time by counting.(Gleick,1992) They run stairs up and down to accelerate their heartbeats and trained themselves at the same time to count seconds and steps. Feynman’s performance deteriorated when he talked but not when he read. Tukey instead performed well when he recited poems aloud and worse when he read. So both have detected what is now known as the two slave systems of working memory, namely the phonological loop and the visuo-spatial sketchpad. Now you get a feeling how much more psychology would have been advanced if brains like theirs had been invested in my discipline at that time.

As a true and convinced European I am really sorry that the English left us, the Scots and the Northern-Irish didn’t want it and maybe one day the fame of tearing the United Kingdom into parts goes to Cummings as well?

What I would say to Cummings:

“If a thing is not worth doing, it is not worth doing well.” ― John W. Tukey

But he did it and now…

Boris Johnson probably to Cummings: “The moor has pled guilty the moor can go” ?

References:

Gleick,J.(1992) Genius. The life and science of Richard Feynman. New York: Pantheon Books. 

Lubinski, D., Benbow, C.P., & Kell, H.J. (2014). Life paths and accomplishments of mathematically precocious males and females four decades later. Psychological Science, 25, 2217–2232.

From Wikipedia about Working Memory 

In 1974, Baddeley and Hitch[11] introduced the multicomponent model of working memory. The theory proposed a model containing three components: the central executive, the phonological loop, and the visuospatial sketchpad with the central executive functioning as a control center of sorts, directing info between the phonological and visuospatial components.[12] The central executive is responsible inter alia for directing attention to relevant information, suppressing irrelevant information and inappropriate actions, and coordinating cognitive processes when more than one task is simultaneously performed. A "central executive" is responsible for supervising the integration of information and for coordinating "slave systems" that are responsible for the short-term maintenance of information. One slave system, the phonological loop (PL), stores phonological information (that is, the sound of language) and prevents its decay by continuously refreshing it in a rehearsal loop. It can, for example, maintain a seven-digit telephone number for as long as one repeats the number to oneself again and again.[13] The other slave system, the visuospatial sketchpad, stores visual and spatial information. It can be used, for example, for constructing and manipulating visual images and for representing mental maps. The sketchpad can be further broken down into a visual subsystem (dealing with such phenomena as shape, colour, and texture), and a spatial subsystem (dealing with location).

Re: Brexit, see these remarks from Now it can be told: Dominic Cummings and the Conservative victory 2019

I don't know enough to have a high confidence or high conviction opinion concerning Brexit. Intelligent and thoughtful people disagree strongly over whether it is a good idea or a potential disaster.

Nevertheless, I can admire Dom's effectiveness as a political strategist and chief advisor to the Prime Minister. I do know him well enough to state with high confidence that his intentions are idealistic, not selfish, and that he (someone who has spent decades thinking about UK government, foreign policy, relations with Europe) sincerely thinks Brexit is in the best interests of the British people. Dom has deeper insights and better intuition about these issues than I do!

Being a rationalist, Dom has pointed out on his own blog that it is impossible to know with high confidence what the future implications of most political decisions are... In that sphere one cannot avoid decision making under extreme uncertainty.

The epistemically careful may end up like Zhou Enlai. When asked about consequences of the French Revolution, the late premier is reported to have said: Too early to tell. Be prepared to find that thoughtful people, pressed for an opinion, can disagree...

Annals of Psychometry: 35 years of talent selection

David Lubinski kindly shared the recent paper linked below. He and I will both be at ISIR 2019, the annual meeting of the International Society for Intelligence Research.

Psychological Constellations Assessed at Age 13 Predict Distinct Forms of Eminence 35 Years Later (Psychological Science 2019, Vol. 30(3) 444–454).

The paper studies two populations:

1. 13 year olds identified through talented and gifted programs, all of whom scored in the top 1% in at least one of Mathematical or Verbal ability (based on SAT score; some scored at the 1 in 10k level). They were also assessed using a preference inventory (SOV = Study of Values). About 10% of this cohort of 677 were identified 35 years later as having achieved "eminence" in their careers -- e.g., full professor at R1 university, senior executive status, ...

2. Exceptional STEM graduate students at top 15 PhD programs, evaluated using GRE and SOV. If I'm not mistaken many or all of these students were NSF Graduate Fellows. About 20% of this population of 605 had achieved STEM eminence 25 years later.

I would estimate that only about one in a thousand individuals drawn randomly from the general population attains eminence as defined in the paper. Thus, the talent selection used to form cohorts 1&2 (e.g., SAT administered at age 13) produced success rates as much as 100 times higher than in the base population.

See related posts: 1 2 3

Psychological Constellations Assessed at Age 13 Predict Distinct Forms of Eminence 35 Years Later

Psychological Science 2019, Vol. 30(3) 444–454

Brian O. Bernstein, David Lubinski, and Camilla P. Benbow
Department of Psychology & Human Development, Vanderbilt University

Abstract
This investigation examined whether math/scientific and verbal/humanistic ability and preference constellations, developed on intellectually talented 13-year-olds to predict their educational outcomes at age 23, continue to maintain their longitudinal potency by distinguishing distinct forms of eminence 35 years later. Eminent individuals were defined as those who, by age 50, had accomplished something rare: creative and highly impactful careers (e.g., full professors at research-intensive universities, Fortune 500 executives, distinguished judges and lawyers, leaders in biomedicine, award-winning journalists and writers). Study 1 consisted of 677 intellectually precocious youths, assessed at age 13, whose leadership and creative accomplishments were assessed 35 years later. Study 2 constituted a constructive replication—an analysis of 605 top science, technology, engineering, and math (STEM) graduate students, assessed on the same predictor constructs early in graduate school and assessed again 25 years later. In both samples, the same ability and preference parameter values, which defined math/scientific versus verbal/humanistic constellations, discriminated participants who ultimately achieved distinct forms of eminence from their peers pursuing other life endeavors.

Note that even within both cohorts SAT / GRE were useful in predicting achievement outcomes. Click figures below for larger versions.

Jordan Peterson: Identity Politics, IQ, Harvard and Asian admissions

First ~9min: Trump, the US Left and Right, Identity Politics

@10min: IQ

@24min: Harvard and Asian admissions. "The Asians are the wildcard..."

@37min: Nazism, Communism; UK Leftist: "I don't love Obama. I'm literally a communist, you idiot."

Coincidentally (or perhaps not) I know the room they are sitting in very well. I'll be there later today ;-)

Cognitive ability predicted from fMRI (Caltech Neuroscience)

Caltech researchers used elastic net (L1 and L2 penalization) to train a predictor using cognitive scores and fMRI data from ~900 individuals. The predictor captures about 20% of variance in intelligence; the score correlates a bit more than 0.45 with actual intelligence. This may validate earlier work by Korean researchers in 2015, although the Korean group claimed much higher predictive correlations.

Press release:

In a new study, researchers from Caltech, Cedars-Sinai Medical Center, and the University of Salerno show that their new computing tool can predict a person's intelligence from functional magnetic resonance imaging (fMRI) scans of their resting state brain activity. Functional MRI develops a map of brain activity by detecting changes in blood flow to specific brain regions. In other words, an individual's intelligence can be gleaned from patterns of activity in their brain when they're not doing or thinking anything in particular—no math problems, no vocabulary quizzes, no puzzles.

"We found if we just have people lie in the scanner and do nothing while we measure the pattern of activity in their brain, we can use the data to predict their intelligence," says Ralph Adolphs (PhD '92), Bren Professor of Psychology, Neuroscience, and Biology, and director and Allen V. C. Davis and Lenabelle Davis Leadership Chair of the Caltech Brain Imaging Center.

To train their algorithm on the complex patterns of activity in the human brain, Adolphs and his team used data collected by the Human Connectome Project (HCP), a scientific endeavor funded by the National Institutes of Health (NIH) that seeks to improve understanding of the many connections in the human brain. Adolphs and his colleagues downloaded the brain scans and intelligence scores from almost 900 individuals who had participated in the HCP, fed these into their algorithm, and set it to work.

After processing the data, the team's algorithm was able to predict intelligence at statistically significant levels across these 900 subjects, says Julien Dubois (PhD '13), a postdoctoral fellow at Cedars-Sinai Medical Center. But there is a lot of room for improvement, he adds. The scans are coarse and noisy measures of what is actually happening in the brain, and a lot of potentially useful information is still being discarded.

"The information that we derive from the brain measurements can be used to account for about 20 percent of the variance in intelligence we observed in our subjects," Dubois says. "We are doing very well, but we are still quite far from being able to match the results of hour-long intelligence tests, like the Wechsler Adult Intelligence Scale,"

Dubois also points out a sort of philosophical conundrum inherent in the work. "Since the algorithm is trained on intelligence scores to begin with, how do we know that the intelligence scores are correct?" The researchers addressed this issue by extracting a more precise estimate of intelligence across 10 different cognitive tasks that the subjects had taken, not only from an IQ test. ...

Paper:

A distributed brain network predicts general intelligence from resting-state human neuroimaging data

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of long-term life success, and since individual differences in a similar broad ability are found across animal species. The most replicated neural correlate of human intelligence to date is total brain volume. However, this coarse morphometric correlate gives no insights into mechanisms; it says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project dataset (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state fMRI data. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.

New Statesman: "like it or not, the debate about whether genes affect intelligence is over"

Science writer Philip Ball, a longtime editor at Nature, writes a sensible article about the implications of rapidly improving genomic prediction for cognitive ability.

Philip Ball is a freelance science writer. He worked previously at Nature for over 20 years, first as an editor for physical sciences (for which his brief extended from biochemistry to quantum physics and materials science) and then as a Consultant Editor. His writings on science for the popular press have covered topical issues ranging from cosmology to the future of molecular biology.

Philip is the author of many popular books on science, including works on the nature of water, pattern formation in the natural world, colour in art, the science of social and political philosophy, the cognition of music, and physics in Nazi Germany.

... Philip has a BA in Chemistry from the University of Oxford and a PhD in Physics from the University of Bristol.

I recommend the whole article -- perhaps it will stimulate a badly needed discussion of this rapidly advancing area of science.

The IQ trap: how the study of genetics could transform education (New Statesman)

The study of the genes which affect intelligence could revolutionise education. But, haunted by the spectre of eugenics, the science risks being lost in a political battle.

... Researchers are now becoming confident enough to claim that the information available from sequencing a person’s genome – the instructions encoded in our DNA that influence our physical and behavioural traits – can be used to make predictions about their potential to achieve academic success. “The speed of this research has surprised me,” says the psychologist Kathryn Asbury of the University of York, “and I think that it is probable that pretty soon someone – probably a commercial company – will start to try to sell it in some way.” Asbury believes “it is vital that we have regulations in place for the use of genetic information in education and that we prepare legal, social and ethical cases for how it could and should be used.”

... Some kids pick things up in a flash, others struggle with the basics. This doesn’t mean it’s all in their genes: no one researching genes and intelligence denies that a child’s environment can play a big role in educational attainment. Of course kids with supportive, stimulating families and motivated peers have an advantage, while in some extreme cases the effects of trauma or malnutrition can compromise brain development.

... Robert Plomin of King’s College London, one of the leading experts on the genetic basis of intelligence, and his colleague Sheila Walker. They surveyed almost 2,000 primary school teachers and parents about their perceptions of genetic influence on a number of traits, including intelligence, and found that on the whole, both teachers and parents rated genetics as being just as important as the environment. This was despite the fact that 80 per cent of the teachers said there was no mention of genetics in their training. Plomin and Walker concluded that educators do seem to accept that genes influence intelligence.

Kathryn Asbury supports that view. When her PhD student Madeline Crosswaite investigated teachers’ beliefs about intelligence, Asbury says she found that “teachers, on average, believe that genetic factors are at least as important as environmental factors” and say they are “open to a role for genetic information in education one day, and that they would like to know more”.

... But now it’s possible to look directly at people’s genomes: to read the molecular code (sequence) of large proportions of an individual’s DNA. Over the past decade the cost of genome sequencing has fallen sharply, making it possible to look more directly at how genes correlate with intelligence. The data both from twin studies and DNA analysis are unambiguous: intelligence is strongly heritable. Typically around 50 per cent of variations in intelligence between individuals can be ascribed to genes, although these gene-induced differences become markedly more apparent as we age. As Ritchie says: like it or not, the debate about whether genes affect intelligence is over.

... Genome-wide polygenic scores can now be used to make such predictions about intelligence. They’re not really reliable at the moment, but will surely become better as the sample sizes for genome-wide studies increase. They will always be about probabilities, though: “Mrs Larkin, there is a 67 per cent chance that your son will be capable of reaching the top 10 per cent of GCSE grades.” Such exam results were indeed the measure Plomin and colleagues used for one recent study of genome-based prediction. They found that there was a stronger correlation between GPS and GCSE results for extreme outcomes – for particularly high or low marks.

... Using GPSs from nearly 5,000 pupils, the report assesses how exam results from different types of school – non-selective state, selective state grammar, and private – are correlated with gene-based estimates of ability for the different pupil sets. The results might offer pause for thought among parents stumping up eyewatering school fees: the distribution of exam results at age 16 could be almost wholly explained by heritable differences, with less than 1 per cent being due to the type of schooling received. In other words, as far as academic achievement is concerned, selective schools seem to add next to nothing to the inherent abilities of their pupils. ...

The Genetics of Human Behavior (The Insight podcast)

Intelligence researcher Stuart Ritchie interviewed by genomicists Razib Khan and Spencer Wells. Highly recommended! Thanks to a commenter for the link.

Sweet Tweet Treats

For mysterious reasons, this old tweet has attracted almost 200k impressions in the last day or so:

Nature, Nurture, and Invention: analysis of patent, SES, IQ data from Finland. "IQ matters more than all family background variables combined" ... "IQ direct effect ... almost five times as large as that of having a high-income father"https://t.co/O6txxW4tTL pic.twitter.com/YN8EWzWqcZ

— steve hsu (@hsu_steve) December 13, 2017

If you like that tweet, this one might be of interest as well:

Low SES does not decrease heritability of cognitive ability (N=300k). PNAS, editor J. Heckman, 24k twins! "largest study to date... matched birth and school administrative records... no evidence of SES moderation of genetic influence on test scores..." https://t.co/KGIFiwzOrf pic.twitter.com/rHvqA1eUSa

— steve hsu (@hsu_steve) December 19, 2017

I'm always amazed that so many people have strong opinions on topics like Nature vs Nurture, How the World Works, How Civilization Advances (or does not), without having examined the evidence.

Nature, Nurture, and Invention: analysis of Finnish data

What is the dominant causal mechanism for the results shown above? Is it that better family environments experienced by affluent children make them more likely to invent later in life? Is it that higher income fathers tend to pass on better genes (e.g., for cognitive ability) to their children? Obviously the explanation has important implications for social policy and for models of how the world works.

The authors of the paper below have access to patent, income, education, and military IQ records in Finland. (All males are subject to conscription.) By looking at brothers who are close in age but differ in IQ score, they can estimate the relative importance of common family environment (such as family income level or parental education level, which affect both brothers) versus the IQ difference itself. Their results suggest that cognitive ability has a stronger effect than shared family environment. Again, if one just looks at probability of invention versus family income or SES (see graph), one might mistakenly conclude that family environment is the main cause of increased likelihood of earning a patent later in life. In fact, higher family SES is also correlated to superior genetic endowments which can be passed on to the children.

The Social Origins of Inventors
Philippe Aghion, Ufuk Akcigit, Ari Hyytinen, Otto Toivanen
NBER Working Paper No. 24110
December 2017

In this paper we merge three datasets - individual income data, patenting data, and IQ data - to analyze the determinants of an individual's probability of inventing. We find that: (i) parental income matters even after controlling for other background variables and for IQ, yet the estimated impact of parental income is greatly diminished once parental education and the individual's IQ are controlled for; (ii) IQ has both a direct effect on the probability of inventing an indirect impact through education. The effect of IQ is larger for inventors than for medical doctors or lawyers. The impact of IQ is robust to controlling for unobserved family characteristics by focusing on potential inventors with brothers close in age. We also provide evidence on the importance of social family interactions, by looking at biological versus non-biological parents. Finally, we find a positive and significant interaction effect between IQ and father income, which suggests a misallocation of talents to innovation.

From the paper:

... IQ has both a direct effect on the probability of inventing which is almost five times as large as that of having a high-income father, and an indirect effect through education ...

... an R-squared decomposition shows that IQ matters more than all family background variables combined; moreover, IQ has both a direct and an indirect impact through education on the probability of inventing, and finally the impact of IQ is larger and more convex for inventors than for medical doctors or lawyers. Third, to address the potential endogeneity of IQ, we focused on potential inventors with brothers close in age. This allowed us to control for family-specific time-invariant unobservables. We showed that the effect of visuospatial IQ on the probability of inventing is maintained when adding these controls.

More on the close brothers analysis (p.24).

We look at the effect of an IQ differential between the individual and close brother(s) born at most three years apart.16 This allows us to include family fixed effects and thereby control for family-level time-invariant unobservables, such as genes shared by siblings, parenting style, and fixed family resources. Table 4 shows the results from the regression with family-fixed effects. The first column shows the baseline OLS results using the sample on brothers born at most three years apart. Notice that we include a dummy for the individual being the first born son in the family to account for birth-order effects. The second column shows the results from a regression where we introduce family fixed effects. We lose other parental characteristics than income due to their time-invariant nature.17 The main finding in Table 4 is that the coefficients on "IQ 91-95" and "IQ 96-100" [ these are percentiles, not IQ scores ] in Column 2 (i.e. when we perform the regression with family fixed effects) are the same as in the OLS Column 1. This suggests that these coefficients capture an effect of IQ on the probability of inventing which is largely independent of unobserved family background characteristics, as otherwise the OLS coefficients would be biased and different from the fixed effects estimates.

Note Added: Finland is generally more egalitarian than the US, both in terms of wealth distribution and access to education. But the probability of invention vs family income graph is qualitatively similar in both countries (see Fig 1 in the paper). The figure below is from recent US data; compare to the Finland figure at top.

Thanks to some discussion (see comments) I noticed that in the Finnish data the probability of invention seems to saturate at high incomes (see top figure, red circle), whereas it continues to rise strongly at top IQ scores (middle figure above; also perhaps in the US data above?). It would be interesting to explore this in more detail...

Feynman, Schwinger, and Psychometrics

Slate Star Codex has a new post entitled Against Individual IQ Worries.

I write a lot about the importance of IQ research, and I try to debunk pseudoscientific claims that IQ “isn’t real” or “doesn’t matter” or “just shows how well you do on a test”. IQ is one of the best-studied ideas in psychology, one of our best predictors of job performance, future income, and various other forms of success, etc.

But every so often, I get comments/emails saying something like “Help! I just took an IQ test and learned that my IQ is x! This is much lower than I thought, and so obviously I will be a failure in everything I do in life. Can you direct me to the best cliff to jump off of?”

So I want to clarify: IQ is very useful and powerful for research purposes. It’s not nearly as interesting for you personally.

I agree with Scott's point that while g is useful as a crude measurement of cognitive ability, and a statistical predictor of life outcomes, one is better off adopting the so-called growth mindset. ("Individuals who believe their talents can be developed through hard work, good strategies, and input from others have a growth mindset.")

Inevitably the question of Feynman's IQ came up in the discussion. I wrote to Scott about this (slightly edited):

Dear Scott,

I enjoyed your most recent SSC post and I agree with you that g is better applied at a statistical level (e.g., by the Army to place recruits) than at an individual level.

I notice Feynman came up again in the discussion. I have written more on this topic (and have done more research as well). My conclusions are as follows:

1. There is no doubt Feynman would have scored near the top of any math-loaded test (and he did -- e.g., the Putnam).

2. I doubt Feynman would have scored near the ceiling on many verbally loaded tests. He often made grammatical mistakes, spelling mistakes (even of words commonly used in physics), etc. He occasionally did not know the *meanings* of terms used by other people around him (even words commonly used in physics).

3. By contrast, his contemporary and rival Julian Schwinger wrote and spoke in elegant, impeccable language. People often said that Schwinger "spoke in entire paragraphs" that emerged well-formed from his mouth. My guess is that Schwinger was a more balanced type for that level of cognitive ability. Feynman was verbally creative, colorful, a master communicator, etc. But his score on the old SAT-V might not have been above top few percentile.

More people know about Feynman than Schwinger, but not just because Feynman was more colorful and charismatic. In fact, very little that Schwinger ever said or wrote was comprehensible to people below a pretty high IQ threshold, whereas Feynman expressed himself simply and intuitively. I think this has a bit to do with their verbal IQs. Even really smart physics students have an easier time understanding Feynman's articles and lectures than Schwinger's!

Schwinger had read (and understood) all of the existing literature on quantum mechanics while still a HS student -- this loads on V, not just M. Feynman's development path was different, partially because he had trouble reading other people's papers.

Schwinger was one of the subjects in Anne Roe's study of top scientists. His verbal score was above +4 SD. I think it's extremely unlikely that Feynman would have scored that high.

See links below for more discussion, examples, etc.

Hope you are enjoying Berkeley!

Best,
Steve

Feynman's Cognitive Style

Feynman and the Secret of Magic

Feynman's War

Schwinger meets Rabi

Roe's Scientists

Here are some (accessible) Schwinger quotes I like.

The pressure for conformity is enormous. I have experienced it in editors’ rejection of submitted papers, based on venomous criticism of anonymous referees. The replacement of impartial reviewing by censorship will be the death of science.


Is the purpose of theoretical physics to be no more than a cataloging of all the things that can happen when particles interact with each other and separate? Or is it to be an understanding at a deeper level in which there are things that are not directly observable (as the underlying quantized fields are) but in terms of which we shall have a more fundamental understanding?


To me, the formalism of quantum mechanics is not just mathematics; rather it is a symbolic account of the realities of atomic measurements. That being so, no independent quantum theory of measurement is required -- it is part and parcel of the formalism.

[ ... recapitulates usual von Neumann formulation: unitary evolution of wavefunction under "normal" circumstances; non-unitary collapse due to measurement ... discusses paper hypothesizing stochastic (dynamical) wavefunction collapse ... ]

In my opinion, this is a desperate attempt to solve a non-existent problem, one that flows from a false premise, namely the vN dichotomization of quantum mechanics. Surely physicists can agree that a microscopic measurement is a physical process, to be described as would any physical process, that is distinguished only by the effective irreversibility produced by amplification to the macroscopic level. ...

(See Schwinger on Quantum Foundations ;-)

Schwinger survived both Feynman and Tomonaga, with whom he shared the Nobel prize for quantum electrodynamics. He began his eulogy for Feynman: "I am the last of the triumvirate ..."

Ninety-nine genetic loci influencing general cognitive function

The paper below has something like 200 authors from over 100 institutions worldwide.

Many people claimed just a few years ago (or more recently!) that results like this were impossible. Will they admit their mistake?

In Scientific Consensus on Cognitive Ability? I described the current consensus among experts as follows.

0. Intelligence is (at least crudely) measurable
1. Intelligence is highly heritable (much of the variance is determined by DNA)
2. Intelligence is highly polygenic (controlled by many genetic variants, each of small effect)
3. Intelligence is going to be deciphered at the molecular level, in the near future, by genomic studies with very large sample size

See figures below for a summary of progress over the last six years. Note 4% of total variance = 1/25 and sqrt(1/25) = 1/5, so a predictor built from these variants would correlate ~0.2 with actual cognitive ability. There is still much more variance to be discovered with larger samples, of course.

Ninety-nine independent genetic loci influencing general cognitive function include genes associated with brain health and structure (N = 280,360)

General cognitive function is a prominent human trait associated with many important life outcomes including longevity. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants. Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N=280,360). We found 9,714 genome-wide significant SNPs in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function were SGCZ, ATXN1, MAPT, AUTS2, and P2RY6. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI. Gene-based analyses found 536 genes significantly associated with general cognitive function; many were highly expressed in the brain, and associated with neurogenesis and dendrite gene sets. Genetic association results predicted up to 4% of general cognitive function variance in independent samples. There was significant genetic overlap between general cognitive function and information processing speed, as well as many health variables including longevity.

Scientific Consensus on Cognitive Ability?

From the web site of the International Society for Intelligence Research (ISIR): a summary of the recent debate involving Charles Murray, Sam Harris, Richard Nisbett, Eric Turkheimer, Paige Harden, Razib Khan, Bo and Ben Winegard, Brian Boutwell, Todd Shackelford, Richard Haier, and a cast of thousands! ISIR is the main scientific society for researchers of human intelligence, and is responsible for the Elsevier journal Intelligence.

If you click through to the original, there are links to resources in this debate ranging from podcasts (Harris and Murray), to essays at Vox, Quillette, etc.

I found the ISIR summary via a tweet by Timothy Bates, who sometimes comments here. I wonder what he has to say about all this, given that his work has been cited by both sides :-)

TALKING ABOUT COGNITIVE ABILITY IN 2017

[ Click through for links. ]

2017 has already seen more science-lead findings on cognitive ability, and public discussion about the origins, and social and moral implications of ability, than we have had in some time, which should be good news for those seeking to understand and grow cognitive ability. This post brings together some of these events linking talk about differences in reasoning that are so near to our sense of autonomy and identity.

Middlebury
Twenty years ago, when Dr Charles Murray co-authored a book with Harvard Psychologist Richard Herrnstein he opened up a conversation about the role of ability in the fabric of society, and in the process made him famous for several things (most of which that he didn‘t say), but for which he, and that book – The Bell Curve – came to act as lightning rods, for the cauldron of mental compression of complex ideas, multiple people, into simpler slogans. 20 years on, Middlebury campus showed this has made even speaking to a campus audience fraught with danger.

Waking Up
In the wake of this disrupted meeting, Sam Harris interviewed Dr Murray in a podcast listened (and viewed on youtube) by and audience of many thousands, creating a new audience and new interest in ideas about ability, its measurement and relevance to modern society.

Vox populi
The Harris podcast lead a response in turn, published in Vox in which IQ, genetics, and social psychology experts Professors Eric Turkheimer, Paige Harden, and Richard Nisbett responded critically to the ideas raised (and those not raised) which they argue are essential for informed debate on group differences.

Quillette
And that lead in turn lead to two more responses: First by criminologists and evolutionary psychologists Bo and Ben Winegard, Brian Boutwell, and Todd Shackelford in Quillette, and a second post at Quillette, also supportive of the Murray-Harris interaction, from past-president of ISIR and expert intelligence research Professor Rich Haier.

And that lead to a series of planned essays by Professor Harden (first of which is now published here) and Eric Turkheimer (here). Each of these posts contains a wealth of valuable information, links to original papers, and they are responsive to each other: Addressing points made in the other posts with citations, clarifications, and productive disagreement where that still exists. They’re worth reading.

The answer, in 2017, may be a cautious “Yes, – perhaps we can talk about differences in human cognitive ability”. And listen, reply, and perhaps even reach a scientific consensus.

[ Added: 6/15 Vox response from Turkheimer et al. that doesn't appear to be noted in the ISIR summary. ]

In a recent post, NYTimes: In ‘Enormous Success,’ Scientists Tie 52 Genes to Human Intelligence, I noted that scientific evidence overwhelmingly supports the following claims:

0. Intelligence is (at least crudely) measurable
1. Intelligence is highly heritable (much of the variance is determined by DNA)
2. Intelligence is highly polygenic (controlled by many genetic variants, each of small effect)
3. Intelligence is going to be deciphered at the molecular level, in the near future, by genomic studies with very large sample size

I believe that, perhaps modulo the word near in #3, every single listed participant in the above debate would agree with these claims.

(0-3) above take no position on the genetic basis of group differences in measured cognitive ability. That is the where most of the debate is focused. However, I think it's fair to say that points (0-3) form a consensus view among leading experts in 2017.

As far as what I think the future will bring, see Complex Trait Adaptation and the Branching History of Mankind.

NYTimes: In ‘Enormous Success,’ Scientists Tie 52 Genes to Human Intelligence

The Nature Genetics paper below made a big splash in today's NYTimes: In ‘Enormous Success,’ Scientists Tie 52 Genes to Human Intelligence. The picture above is of a UK Biobank storage facility for blood (DNA) samples.

The results are not especially surprising to people who have been following the subject, but this is the largest sample of genomes and cognitive scores yet analyzed (~80k individuals). SSGAC has assembled a much larger dataset (~750k, soon to be over 1M; over 600 genome-wide significant SNP hits), but are working with a proxy phenotype for cognitive ability: years of education.

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence

Nature Genetics (2017) doi:10.1038/ng.3869
Received 10 January 2017 Accepted 24 April 2017 Published online 22 May 2017

Intelligence is associated with important economic and health-related life outcomes1. Despite intelligence having substantial heritability2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered3, 4, 5. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10−8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10−6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10−6). Despite the well-known difference in twin-based heratiblity2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10−29). These findings provide new insight into the genetic architecture of intelligence.

Perhaps the most interesting aspect of this study is the further evidence it provides that many (the vast majority?) of the hits discovered by SSGAC are indeed correlated with cognitive ability (as opposed to other traits such as Conscientiousness, which might influence educational attainment without affecting intelligence):

To examine the robustness of the 336 SNPs and 47 genes that reached genome-wide significance in the primary analyses, we sought replication. Because there are no reasonably large GWAS for intelligence available and given the high genetic correlation with educational attainment, which has been used previously as a proxy for intelligence7, we used the summary statistics from the latest GWAS for educational attainment21 for proxy-replication (Online Methods). We first deleted overlapping samples, resulting in a sample of 196,931 individuals for educational attainment. Of the 336 top SNPs for intelligence, 306 were available for look-up in educational attainment, including 16 of the independent lead SNPs. We found that the effects of 305 of the 306 available SNPs in educational attainment were sign concordant between educational attainment and intelligence, as were the effects of all 16 independent lead SNPs (exact binomial P < 10−16; Supplementary Table 14). ...

Carl Zimmer did a good job with the Times story. The basic ideas, that

0. Intelligence is (at least crudely) measurable
1. Intelligence is highly heritable (much of the variance is determined by DNA)
2. Intelligence is highly polygenic (controlled by many genetic variants, each of small effect)
3. Intelligence is going to be deciphered at the molecular level, in the near future, by genomic studies with very large sample size 

are now supported by overwhelming scientific evidence. Nevertheless, they are and have been heavily contested by anti-Science ideologues.

For further discussion of points (0-3), see my article On the genetic architecture of intelligence and other quantitative traits.

National Geographic: How Humans Are Shaping Our Own Evolution

See also A Brief History of the Future, as told to the Masters of the Universe and Super-Intelligent Humans are Coming.

National Geographic: How Humans Are Shaping Our Own Evolution:

... Unlike our forebears, we may soon not need to wait for evolution to fix the problem. In 2013 Nick Bostrom and Carl Shulman, two researchers at the Future of Humanity Institute, at Oxford University, set out to investigate the social impact of enhancing intelligence, in a paper for Global Policy. They focused on embryo selection via in vitro fertilization. With IVF, parents can choose which embryo to implant. By their calculations, choosing the “most intelligent embryo” out of any given 10 would increase a baby’s IQ roughly 11.5 points above chance. If a woman were willing to undergo more intensive hormone treatments to produce eggs faster—“expensive and burdensome,” as the study notes with understatement—the value could grow.

The real benefit, though, would be in the compound gain to the recipient’s descendants: After 10 generations, according to Shulman, a descendant might enjoy an IQ as much as 115 points higher than his or her great-great-great-great-great-great-great-great-grandmother’s. As he pointed out to me, such a benefit is built on extremely optimistic assumptions, but at the least the average recipient of this genetic massaging would have the intelligence equal to a genius today. Using embryonic stem cells, which could be converted into sperm or ova in just six months, the paper notes, might yield far faster results. Who wants to wait two centuries to be the scion of a race of geniuses? Shulman also mentioned that the paper omitted one obvious fact: “In 10 generations there will likely be computer programs that outperform even the most enhanced human across the board.”

There’s a more immediate objection to this scenario, though: We don’t yet know enough about the genetic basis for intelligence to select for it. One embryo doesn’t do advanced calculus while another is stuck on whole numbers. Acknowledging the problem, the authors claim that the ability to select for “modest cognitive enhancement” may be only five to 10 years off.

At first glance this would seem improbable. The genetic basis of intelligence is very complex. Intelligence has multiple components, and even individual aspects—computational ability, spatial awareness, analytic reasoning, not to mention empathy—are clearly multigenetic, and all are influenced by environmental factors as well. Stephen Hsu, vice president for research at Michigan State University, who co-founded the Cognitive Genomics Lab at BGI (formerly Beijing Genomics Institute), estimated in a 2014 article that there are roughly 10,000 genetic variants likely to have an influence on intelligence. That may seem intimidating, but he sees the ability to handle that many variants as nearly here—“in the next 10 years,” he writes—and others don’t think you’d need to know all the genes involved to start selecting smarter embryos. “The question isn’t how much we know or don’t know,” Church says. “It’s how much we need to know to make an impact. ..."

On the genetic architecture of intelligence and other quantitative traits
https://arxiv.org/abs/1408.3421

Somewhere ... It's happening...

Everything is Heritable

The figure above comes from the paper below. A quick glance shows that for pairs of individuals: 1. Increasing genetic similarity implies increasing trait similarity (for traits including height, cognitive ability, years of education) 2. Home environments (raised Together vs Apart; Adoptees) have limited impact on the trait (at least in relatively egalitarian Sweden).

It's all here in one simple figure, but still beyond the grasp of most people struggling to understand how humans and human society work... See also The Mystery of Non-Shared Environment.

Genetics and educational attainment

David Cesarini & Peter M. Visscher
NPJ Science of Learning 2, Article number: 4 (2017)
doi:10.1038/s41539-017-0005-6

Abstract: We explore how advances in our understanding of the genetics of complex traits such as educational attainment could constructively be leveraged to advance research on education and learning. We discuss concepts and misconceptions about genetic findings with regard to causes, consequences, and policy. Our main thesis is that educational attainment as a measure that varies between individuals in a population can be subject to exactly the same experimental biological designs as other outcomes, for example, those studied in epidemiology and medical sciences, and the same caveats about interpretation and implication apply.

Genomic Prediction of Cognitive Ability: Dunedin Study

A quiet revolution has begun. We now know enough about the genetic architecture of human intelligence to make predictions based on DNA alone. While it is a well-established scientific fact that variations in human cognitive ability are influenced by genes, many have doubted whether scientists would someday decipher the genetic code sufficiently to be able to identify individuals with above or below average intelligence using only their genotypes. That day is nearly upon us.

The figures below are taken from a recently published paper (see bottom), which examined genomic prediction on a longitudinal cohort of ~1000 individuals of European ancestry, followed from childhood into adulthood. (The study, based in Dunedin, New Zealand, extends over 40 years.) The genomic predictor (or polygenic score) was constructed using SSGAC GWAS analysis of a sample of more than one hundred thousand individuals. (Already, significantly more powerful predictors are available, based on much larger sample size.) In machine learning terminology, the training set includes over a hundred thousand individuals, and the validation set roughly one thousand.


These graphs show that individuals with higher polygenic score exhibit, on average, higher IQ scores than individuals with lower polygenic scores.

This figure shows that polygenic scores predict adult outcomes even when analyses account for social-class origins. Each dot represents ten individuals.

From an earlier post, Genomic Prediction of Adult Life Outcomes:

Genomic prediction of adult life outcomes using SNP genotypes is very close to a reality. This was discussed in an earlier post The Tipping Point. The previous post, Prenatal and pre-implantation genetic diagnosis (Nature Reviews Genetics), describes how genotyping informs the Embryo Selection Problem which arises in In Vitro Fertilization (IVF).

The Adult-Attainment factor in the figure above is computed using inputs such as occupational prestige, income, assets, social welfare benefit use, etc. See Supplement, p.3. The polygenic score is computed using estimated SNP effect sizes from the SSGAC GWAS on educational attainment (i.e., a simple linear model).

A genetic test revealing that a specific embryo is, say, a -2 or -3 SD outlier on the polygenic score would probably give many parents pause, in light of the results in the figure above. The accuracy of this kind of predictor will grow with GWAS sample size in coming years.

Via Professor James Thompson. See also discussion by Stuart Ritchie.

The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development

Psychological Science 2016, Vol. 27(7) 957–972
DOI: 10.1177/0956797616643070

A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children’s polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting DNA sequence with life outcomes may provide targets for interventions to promote population-wide positive development.

One hundred years of research on intellectual precocity

David Lubinski sent me this comprehensive review of 100 years of research on intellectual precocity. Someone has already posted an un-gated copy online at the link below. Many of the stunning SMPY graphs summarizing their longitudinal (30+ year) study of a population of gifted individuals (including one group measured at the 1 in 10,000 ability level at age 13) appear in the paper. More SMPY.

From Terman to Today: A Century of Findings on Intellectual Precocity

David Lubinski
Vanderbilt University

One hundred years of research (1916–2016) on intellectually precocious youth is reviewed, painting a portrait of an extraordinary source of human capital and the kinds of learning opportunities needed to facilitate exceptional accomplishments, life satisfaction, and positive growth. The focus is on those studies conducted on individuals within the top 1% in general or specific (mathematical, spatial, or verbal reasoning) abilities. Early insights into the giftedness phenomenon actually foretold what would be scientifically demonstrated 100 years later. Thus, evidence-based conceptualizations quickly moved from viewing intellectually precocious individuals as weak and emotionally liable to highly effective and resilient individuals. Like all groups, intellectually precocious students and adults have strengths and relative weaknesses; they also reveal vast differences in their passion for different pursuits and their drive to achieve. Because they do not possess multipotentiality, we must take a multidimensional view of their individuality. When done, it predicts well long-term educational, occupational, and creative outcomes.

Genetics, Cognitive Ability, and Education (conversation with Cambridge PhD candidate Daphne Martschenko)

Further conversation with Cambridge PhD candidate Daphne Martschenko concerning genetics of cognitive ability, implications for education policy, etc.

See also earlier conversation: https://www.youtube.com/watch?v=YVqkvHpLfuQ

Dunedin paper referenced in the video (polygenic score prediction of adult success for different SES groups): http://infoproc.blogspot.com/2016/09/genomic-prediction-of-adult-life.html

Flipping DNA switches

The recently published SSGAC study (Nature News) found 74 genome-wide significant hits related to educational attainment, using a discovery sample of ~300k individuals. The UK Biobank sample of ~110k individuals was used as a replication check of the results. If both samples are combined as a discovery sample 162 SNPs are identified at genome-wide significance. These SNPs are likely tagging causal variants that have some effect on cognitive ability.

The SNP hits discovered are common variants -- both (+) and (-) versions are found throughout the general population, neither being very rare. This means that a typical individual could carry 80 or so (-) variants. (A more precise estimate can be obtained using the minor allele frequencies of each SNP.)

Imagine that we knew the actual causal genetic variants that are tagged by the discovered SNPs (we don't, yet), and imagine that we could edit the (-) version to a (+) version (e.g., using CRISPR; note I'm not claiming this is easy to do -- it's a gedanken experiment). How much would the IQ of the edited individual increase? Estimated effect sizes for these SNPs are uncertain, but could be in the range of 1/4 or 1/10 of an IQ point. Multiplying by ~80 gives as a crude estimate of perhaps 10 or 15 IQ points up for grabs, just from the SSGAC hits alone.

Critics of the study point out that only a small fraction of the expected total genetic variance in cognitive ability is accounted for by SSGAC SNPs. But the estimate above shows that the potential biological effect of these SNPs, taken in aggregate, is not small! Indeed, once many more causal variants are known (eventually, perhaps thousands in total), an unimaginably large enhancement of human cognitive ability might be possible.

See also
Super-intelligent humans are coming
On the genetic architecture of intelligence and other quantitative traits

(Super-secret coded message for high g readers: N >> sqrt(N), so lots of SDs are up for grabs! ;-)