Monday, 25 April 2016

Genetics of mental ability: greater power

I distinctly remember hearing from a colleague in July 2011 that earlier that afternoon Ian Deary had presented a paper in London which claimed that 1% of intelligence could be explained by genomic analysis. Although both he and I were excited by this result, the general reaction was one of respectful scepticism. A proven link between genes and intelligence had never been achieved before, so the result was a 100% improvement on all that had gone before. Welcome as it was, it seemed too good to be true, and if confirmed, worthy of a prize.

Davies et al. (2011) Genome-wide association studies establish that human intelligence is highly heritable and polygenic.


These gene hunters had gone many steps better than most psychologists. Psychology gets published with sample sizes of about 80, but gene hunters habitually report on 100,000 people. Even more important, instead of just reporting their results on the main sample, clocking up a publication, and then leaving replication to others while they bask in glory, they always take the sobering step of moving from the “sample of discovery” to the “sample of testing”. Two papers for the price of one: a harsh reality check, and best practice.

So, as regards the 2011 paper, the conventional way of reporting it in psychology would have been “genetics explains 40% to 51% of intelligence”. It is only when one moves from the sample of discovery to the sample of testing that it turns out that 1% can be explained in new samples, which is the acid test of having found a real relationship in nature.

What does the picture look like almost 5 years later?

Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112151)

G Davies, RE Marioni, DC Liewald, WD Hill, SP Hagenaars, SE Harris, SJ Ritchie, M Luciano, C Fawns-Ritchie, D Lyal, B Cullen, SRCox, C Hayward, DJ Porteous, J Evans, AM McIntosh, J Gallacher, N Craddock, JP Pell, DJ Smith, CR Gale and IJ Deary. Molecular Psychiatry (2016) 1-10.

People’s differences in cognitive functions are partly heritable and are associated with important life outcomes. Previous genome-wide association (GWA) studies of cognitive functions have found evidence for polygenic effects yet, to date, there are few replicated genetic associations. Here we use data from the UK Biobank sample to investigate the genetic contributions to variation in tests of three cognitive functions and in educational attainment. GWA analyses were performed for verbal–numerical reasoning (N= 36 035), memory (N= 112 067), reaction time (N= 111 483) and for the attainment of a college or a university degree (N= 111 114). We report genome-wide significant single-nucleotide polymorphism (SNP)-based associations in 20 genomic regions, and significant gene-based findings in 46 regions. These include findings in the ATXN2, CYP2DG, APBA1 and CADM2 genes. We report replication of these hits in published GWA studies of cognitive function, educational attainment and childhood intelligence. There is also replication, in UK Biobank, of SNP hits reported previously in GWA studies of educational attainment and cognitive function. GCTA-GREML analyses, using common SNPs (minor allele frequency 0.01), indicated significant SNP-based heritabilities of 31% (s.e.m. = 1.8%) for verbal–numerical reasoning, 5% (s.e.m. = 0.6%) for memory, 11% (s.e.m. = 0.6%) for reaction time and 21% (s.e.m. = 0.6%) for educational attainment. Polygenic score analyses indicate that up to 5% of the variance in cognitive test scores can be predicted in an independent cohort. The genomic regions identified include several novel loci, some of which have been associated with intracranial volume, neurodegeneration, Alzheimer’s disease and schizophrenia.

Molecular Psychiatry

advance online publication, 5 April 2016; doi:10.1038/mp.2016.45


Cognitive functions have important roles in human mental and physical well-being. Better cognitive function in youth is associated with lower risk of some psychiatric disorders and physical illness later in the life course, and with reduced mortality risk. The reverse is also true; some mental and physical illnesses are associated with a lowering of cognitive capabilities in youth and over the life course. Higher cognitive ability in youth is associated also with higher educational attainment and adult social position. Domains of cognitive functioning differ in their associations with ageing; some have trajectories of decline (for example, processing speed and some types of memory), whereas others (for example, knowledge-based tests) hold their levels better over the adult life course. Therefore, it is important to understand the causes of people’s differences in cognitive functions.

One source of cognitive differences is genetic variation. Cognitive functions have a substantial heritability. This has been found by using twin and family studies, and by molecular genetic methods, such as Genome-wide Complex Trait Analysis (GCTA-GREML), which estimates heritability based on common single-nucleotide polymorphisms (SNPs).

Some explanation is required regarding cognitive phenotypes.All tests of cognitive ability correlate positively, though not perfectly; that is, people who do well on one type of cognitive test tend to do well on the others. It is this regularity that is the basis for the construct of general cognitive ability, which is usually abbreviated to g. There are also separable domains of cognitive functioning. Differences in individual cognitive test score performances may be due to: (1) differences in general cognitive function described by the variance shared by all cognitive domains; (2) differences in test performance specific to a cognitive domain; and (3) differences specific to a particular test.

Twin and SNP-based GCTA-GREML studies have found that there is substantial heritability for general cognitive function, and also some heritability for cognitive domains and specific cognitive skills. They also find that there are significant genetic correlations among tests of different cognitive domains, and between cognitive abilities and education, which also shows substantial heritability.

Genome-wide association studies (GWAS) of cognitive functions have been successful in estimating SNP-based heritability, and in using summary GWAS data to make predictions of cognitive phenotypes in independent samples.However, they have been less successful in identifying the specific genetic variants that cause cognitive differences. The largest studies to date have been the CHARGE-Cognitive Working Groups studies and those on educational phenotypes by the Social Science Genetics Association Consortium. In a study of 53 949 individuals with data on general cognitive function, there were three genome-wide significant hits in three genomic regions, with the closest genes being APOE/TOMM40, AKAP6 and MIR2113.

In a study of 32 070 individuals with data on processing speed (mostly digit-symbol substitution-type tests) there was one genome-wide significant hit, near CADM2. In a study of 29 076 individuals with data on verbal declarative memory there were three genome-wide significant hits, near APOE and genes associated with immune response.

The present study directly addresses the limitations of previous molecular genetic studies of cognitive functions. It presents genome-wide association analyses of reasoning, processing speed, declarative memory, and educational attainment in the UK Biobank sample. The number of subjects is over 100 000 for most analyses. All participants took the same cognitive tests with the same instructions. All participants included in the current analysis were of white British ancestry. Genotyping was also standardised across the same arrays and QC procedures. The study addresses three important cognitive domains and educational attainment in a single report. These advantages are likely contributors to the relative success in finding many new genetic variants associated with cognitive functions.

Cognitive assessment .Verbal–numerical reasoning. Verbal–numerical reasoning was measured using a 13-item test presented on a touchscreen computer. The test included six verbal and seven numerical questions, all with multiple-choice answers, and had a time limit of two minutes in total. An example verbal item is: ‘If Truda’s mother’s brother is Tim’s sister’s father, what relation is Truda to Tim?’ (possible answers:  aunt/sister/niece/cousin/no relation/do not know/prefer not to answer). An example numerical item is: ‘If
60 is more than half of 75, multiply 23 by 3. If not subtract 15 from 85’ (possible answers: ‘68/69/70/71/72/do not know/prefer not to answer’). The verbal–numerical reasoning score was the total score out of 13. The Cronbach α-coefficient for the 13 items was 0.62.

Here is the variance explained for each of the main cognitive variables:

GWAS and variance explained

To my eye the memory test isn’t reliable, and only the verbal-numerical intelligence test is up to standard.

The most important novel contribution of the present study is the discovery of many new genome-wide significant genetic variants associated with reasoning ability, cognitive processing speed and the attainment of a college or
university degree. The study provided robust estimates of the
SNP-based heritability of the four cognitive variables and their
genetic correlations. The study makes important steps toward
genetic consilience, because several of the genomic regions
identified by the present analyses have previously been associated in GWASs of general cognitive function, executive function, educational attainment, intracranial volume, neurodegenerative disorders and Alzheimer’s disease. The study was successful in using the GWAS results from UK Biobank to predict cognitive variation in new samples.

The SNP-based estimate of heritability for verbal–numerical
reasoning (31%) was highly consistent with previous estimates
based on a general cognitive ability phenotype that had been
composed using three or more diverse cognitive tests.

Using the summary GWAS data from the present study to predict cognitive variation in independent samples (Supplementary Table S4) produced the largest R2 values in this field to date, with sometimes over 5% of variance explained, especially in the more crystallized cognitive functions such as vocabulary. Previously, values of 1 to 2% have been typical.

First, general cognitive ability, or strong indicators of it, tend to be more heritable than specific cognitive functions such as processing speed and memory.8,10,12,68 Second, tests of verbal ability and reasoning are among those tests that
have higher loadings on the latent trait of general cognitive
ability, and tests of memory and processing speed have lower
loadings.15,69–71 Third, the RT and memory tests in UK Biobank were handicapped further by being very brief. The RT test included a far smaller number of trials than is typical for large surveys in the UK, which have used 40 trials in choice RT
procedures.72,73 The memory test was based on the recall of a
single 12-item matrix with six pairs of stimuli. This is both a brief and unusual type of test in the field of declarative memory; more is known about the psychometric characteristics and genetic foundations of declarative memory tests such as word list and paragraph recall. The test–retest correlation of the memory variable was particularly low (r = 0.15).

This accumulating evidence is consistent with the interpretation that, to some extent, educational attainments are a product of genetic contributions to cognitive ability, but with two emphatic qualifications. First, it is obvious that there are other—especially social—determinants of whether or not people achieve certain educational outcomes. Second, there is evidence that the variation in educational attainments that is caused by genetic differences is shared with traits other than intelligence, such as personality dimensions. Therefore, we predict that not all of the genome-wide significant hits associated with the attainment of a college or university degree in the present study will be associated with cognitive differences; some might be associated with personality and other heritable, educational relevant traits.



This is a strange Table to look at from the point of view of a clinical psychologist. Many of these tests are very familiar to me, and are part of the bread and butter of ability testing, but here are data I never expected to see, showing specific genome-ability links. It is worthy of its own T shirt.

This is a very important paper. It had doubled the amount of variance in mental ability which can be explained by analysis of the genome. Of course we hope for even greater power. UK Biobank data for 500,000 persons will soon become available, which will allow the detection of genetic signals with higher precision.

Although the fact that mental ability is heritable may seem news to some people today, heritability was generally understood by any farmworker in the 19th Century. They knew that, broadly speaking, characteristics in animals and people were inherited, even though they did not know at the genomic level precisely how that was achieved. As people drifted off the farm that natural observation was lost, and as schooling become more available many correctly concluded that education was important, and some incorrectly that all differences between one person and another could be annulled by administering even more education. My experience of psychology was that nurture was given more attention than nature. Mainstream psychology recognised twin studies, but there was an implication that the findings applied to twins, and there weren’t many of them, so the implications were minor. It was always slightly surprising when genetics was suggested as a cause of human differences. The late psychometrician Prof Paul Kline gave a lecture at the University of Exeter in which, with characteristic brio, he announced that almost all winners of the Grand National were descended from a single horse, which he named. I am not a gambling man, so I did not record the name for betting purposes. A lamentable error. Bloodlines have their uses, and I am betting on UK Biobank coming up a winner again and again.





  1. Who knows? Maybe one day our "elites" will grasp what was common knowledge to unschooled illiterate farm hands two hundred years ago.

  2. Kline was probably referring to

    You're also right that the measurement error is biasing these GCTA estimates downward. Shulman points out in that the true verbal-numerical correlation is more like 0.48, which is much higher.

  3. ECLIPSE! It all comes back to me, but too late to place any bets. Now forefather of 95% of thoroughbreds. Thank you for assisting my recollections (lecture was about 25 years ago).
    Would have expected Stephen Hsu to have picked up the low reliability of the memory measure. Pity the tests were too short.

  4. > Pity the tests were too short.

    Yeah. After Shulman brought it up, I looked up what a good IQ test's test-retest reliability is like and with the WAIS-IV, you would get a GCTA estimate of 0.44 rather than their 0.31. I wondered how much moving to a real IQ test would have improved the polygenic score of the Biobank or SSGAC (since education is also a noisy measure of intelligence and is maybe noisy enough that money would be better spent on better phenotyping than in increasing sample sizes) but I wasn't sure how to calculate that offhand.

  5. From Wiki rxy / sqrt(rxx * ryy)
    Or in words: The disattenuated correlation is the raw correlation between x and y (rxy) divided by the square root of the product of the reliability of x (rxx) and the reliability of y (ryy)
    However, this can't really be assumed, so it is a bit of a fix. Jensen often used it in his calculations, but was always absolutely open about the before and after attenuation figures.

  6. Off topic regarding Helmuth Nyborg:

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    Google translate gets the headline wrong but it handles the article itself pretty well.

    Here's the full verdict:

    Here's the official summary:

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