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In my last post I said:
I think we can see the direction of travel of the debate, which is that the case for genetics being a part cause of individual differences is gaining ground. It is only doing so because it can increasingly account for some of variance. A decade ago it was not possible to associate the genetic code with intelligent behaviour. Now studies which link snippets of code to intelligence are being published every few months. The pace of discovery is extraordinary. “Nature” and other science journals report frequently on new genetic correlations with important human behaviours, notably mental ability and mental illness and health generally.
I had no idea that my thesis would receive instant support the following day in a paper which begins with a stirring paragraph, worth quoting in full:
Since its discovery in 1904, hundreds of studies have replicated the finding that around 40% of the variance in people’s test scores on a diverse battery of cognitive tests can be accounted for by a single general factor. General cognitive function is peerless among human psychological traits in terms of its empirical support and importance for life outcomes. Individual differences in general cognitive function are stable across most of the life course. Twin studies find that general cognitive function has a heritability of more than 50% from adolescence through adulthood to older age. SNP-based estimates of heritability for general cognitive function are about 20-30%. To date, little of this substantial heritability has been explained; only a few relevant genetic loci have been discovered (Table 1 and Fig.1). Like other highly polygenic traits, a limitation on uncovering relevant genetic loci is sample size; to date, there have been fewer than 100,000 individuals in studies of general cognitive function.
Ninety-nine independent genetic loci influencing general cognitive function include genes associated with brain health and structure (N = 280,360)
Davies et al. (2017)
First of all, it is good to see papers based on f*!^#ff sample sizes. One of the authors, who shall remain nameless so long as he pays me my usual fee, relishes sample sizes large enough to tell doubters that they should go off and multiply. Not only that, but once again a large group of researchers drawn from the Western research world have got together in order to assemble the afore-mentioned large samples. At a quick glance, there are about 200 authors, so each gene takes 2 researchers to find. I have it on good authority that the first and last-named authors spent 3 years of their lives on this project. The authors say:
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 (P<5 x 10-8) 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.
Interestingly, the 9,714 SNPs is entirely in line with the calculation Steve Hsu made that genetics researchers needed ~10k causal variants and ~million sample size to “solve” intelligence. Here we have the requisite causal variations, and have gone a third of the way on sample size, resulting in a magnificent step towards the required goal. Some of the hits overlap with previously identified sections of code, others are novel. Novel genetic correlations were identified between general cognitive function and ADHD rg= -0.36, bipolar disorder rg= -0.09, major depression rg= -0.30, and longevity rg= 0.15. This is now part of a general pattern: the genetic code for ability is associated with psychiatric state, probably because vulnerability to those disorders is association with lower ability. Remember that when one talks of “associations” with genetics, this will involve genes which are positive for ability, and genes which are negative.
The team also looked at reaction times, another one of my bombshell measures of mental ability (true zero, ratio scale) which is both phenotypically and genetically correlated with general cognitive function, and accounts for some of its association with health.
There were 330,069 individuals in the UK Biobank sample with both reaction time and genetic data. GWA results for reaction time uncovered 2,022 significant SNPs in 42 independent genomic regions; 122 of these SNPs overlapped with general cognitive function, with 76 having a consistent direction of effect. These genomic loci showed clear evidence of functionality. Using gene-based GWA, 191 genes attained statistical significance, 28 of which overlapped with general cognitive function.  There was a genetic correlation of 0.227 between reaction time and general cognitive function.
People with higher general cognitive function are broadly healthier; here, we find overlap between genetic loci for general cognitive function and a number of physical health traits. These shared genetic associations may reflect a causal path from cognitive function to disease, cognitive consequences of disease, or pleiotropy. For psychiatric illness, conditions like schizophrenia (and, to a lesser extent, bipolar disorder) are characterised by cognitive impairments, and thus reverse causality (i.e. from cognitive function to disease) is less likely.
As per usual, the papers from this team follow the “two for the price of one” principle, in that they contain the results from the sample of discovery, which they immediately test on other samples. This shows that on 3 test samples they were able to account for 2.37% of the variance in ELSA, 3.96% in Generation Scotland and 4.00% in Understanding Society. “Why so little?” you may ask. Well, “Why so much”, I would reply. These are association studies, the first step in looking for causal links. Yes, causal. Associations are being found by an a-theoretical search process, tantamount to trying to break an enemy code without having any detailed knowledge as to how the enemy forces operate. Testing actual causality may need to involve Petri dishes and selective deletions of bits of code using CRSIP-R. That is what James Lee surmises might be the next step, but association techniques are developing rapidly, and may yet have some way to run.
The authors studied the proportion of variance explained by all common SNPs in four of the largest individual samples, using univariate GCTA-GREML analyses: English Longitudinal Study of Ageing (ELSA: h2= 0.12, SE= 0.06), Understanding Society (h2= 0.17, SE = 0.04), UK Biobank Assessment Centre (h2= 0.25,SE =0.006), and Generation Scotland (h2= 0.20, SE= 0.0519) (Table 2). Genetic correlations for general cognitive function amongst these cohorts, estimated using bivariate GCTA-GREML, ranged from rg= 0.88 to 1.0
There was a genetic correlation (rg) of 0.227 (P= 4.33 × 10-27) between reaction time and general cognitive function.
How to summarize this paper? Well, first note that as the sample sizes increase, the number of reliable genes and SNPs detected increases enormously. Size matters.
Second, note that the association studies are done in a number of different ways, each with their own characteristics, but all contributing to a general picture. It is clear that the genetic signals relate to brain processes, and the identification of those associations is becoming far more specific. We are much closer to detecting actual causal links.
Third, note that we can study correlations in two domains: the correlation between the genetic code and human behaviour (in this case intelligence) and the correlations between different parts of the genetic code. The latter gives rise to the concept of genetic correlation: a way of identifying shared genetic pathways and putative causes.
Fourth, note that the technique used to bring the different intelligence tests onto a common scale is principal components analysis, which is much closer to a simple mathematical process than is factor analysis. The simplest technique is the best for this large-scale comparative study.
The authors conclude:
General cognitive function has prominence and pervasiveness in the human life course, and it is important to understand the environmental and genetic origins of its variation in the population. The unveiling here of many new genetic loci, genes, and genetic pathways that contribute to its heritability—which it shares with many health outcomes, longevity, brain structure, and processing speed—provides a foundation for exploring the mechanisms that bring about and sustain cognitive efficiency through life.
This paper is a major achievement, a magnificent step forwards in cracking the intelligence problem. What happens next? More papers are in the offing, and then we may see the beginnings of an experimental phase, looking at neurone and dendrite development in vitro.