Things are moving so fast in genetic research on intelligence that one cannot take a coffee break without missing important announcements. By way of small compensation, even the biggest breakthroughs are based on previous breakthroughs, so most stories in science are about a pattern of results rather than a single paper, and that pattern eventually becomes familiar territory, an increase in understanding which, once digested, may eventually become the new orthodoxy.

Where are we now, in the continuing story of the genetics of intelligence? Usually, one goes to a meta-analysis to discern the pattern of results.

A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. W. D. Hill, R. E. Marioni, O. Maghzian, S. J. Ritchie, S. P. Hagenaars, A. M. McIntosh, C. R. Gale, G. Davies & I. J. Deary

https://www.nature.com/articles/s41380-017-0001-5

They say:

Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (r g = 0.70). We used these findings as foundations for our use of a novel approach—multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)—to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination—as well as genes expressed in the synapse, and those involved in the regulation of the nervous system—may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.

However, the authors not only give us a meta-analysis, they follow the same value-for-money approach traditional for this team: they test their predictions on an independent sample. Keep reading.

Intelligence, also known as general cognitive function or simply g, describes the shared variance that exists between diverse measures of cognitive ability[1]. In a population with a range of cognitive ability, intelligence accounts for around 40% of the variation between individuals in scores on diverse cognitive tests[2]. Intelligence is predictive of health states, including mortality; [3, 4] a lower level of cognitive function in youth is associated with earlier death over the next several decades[5]. Intelligence is a heritable trait, with twin- and family-based estimates of heritability indicating that between 50–80% of differences in intelligence can be explained by genetic factors[6]. These genetic factors make a greater contribution to phenotypic differences as age increases from childhood to adulthood[7]. Heritability estimates derived from molecular genetic data using the GREML-SC [8, 9] method indicate that around 20–30% of variation can be explained by variants in linkage disequilibrium (LD) with genotyped single nucleotide polymorphisms (SNPs)[10]. Some of the association between intelligence and health is due to genetic variants that act across traits [11, 12]. More recent methods to measure heritability, such as GREML-KIN[13], and GREML-MS[14] using imputed SNPs, have found that some of the heritability of intelligence can be found in variants that are in poor LD with genotyped variants; by taking these into consideration, SNP heritability estimates of 0.54 (GREML-KIN) and 0.50 (GREML-MS)[15] have been found.

For those who, like me, don’t take easily to genetic jargon, just think of all this as computer code. If you look through traditional computer code you will find sub-routines and Go To instructions. Some of the code is embedded in sub-routines, some code acts as signposting, and some code does the essential processing work along the way. All these sections of code can develop a flavour: by the time you get to a distant sub-routine your variable names will have drifted further down the alphabet; line numbers will be higher, the types of calculations will have altered, and will involve the products of farprevious sub-routines. Of course, the genetic code is nothing like this, but linkage can be close or , common or unusual, and if a piece of code gets picked out because it is particularly useful, it can carry some neighbouring useless code with it, like fluff on a toffee. New ways of understanding and analyzing the code are being developed fast, so new findings may well arise when analysis moves from exploratory association work to laboratory manipulations of individual genes in Petri dishes.

The authors got their large sample size by using a crafty technique, combining an intelligence test data sample with the proxy phenotype of educational attainment in another sample, and thus getting far more analytic power. The genetic correlation between intelligence and education is 0.7 which is what assists this alignment and pasting-together technique.

Seven novel biological systems associated with intelligence differences were found.

1 Neurogenesis, the process by which neurons are generated from neural stem cells.

2 Genes expressed in the synapse, consistent with previous studies showing a role for synaptic plasticity.

3 Regulation of nervous system development.

4 Neuron projection

5 Neuron differentiation

6 Central nervous system neuron differentiation.

7 Oligodendrocyte differentiation.

In addition to these novel results, the finding that regulation of cell development (gene-set size = 808 genes, P-value 9.71 × 10−7) is enriched for intelligence was replicated.

In summary, if further proof were needed that these bits of the genetic code were associated with brainpower, the list homes in on everything likely to be required for a fast-thinking powerful biological system.

Here is a heat map of the results, which you will find similar to previous studies. (You may need to click on this image to enlarge it).

Here is a more detailed picture of the tissues involved:

They canter to a conclusion:

We found 187 independent associations for intelligence in our GWAS, and highlighted the role of 538 genes being involved in intelligence, a substantial advance on the 18 loci previously reported.

We used our meta-analytic GWAS data to predict almost 7% of the variation in intelligence in one of three independent samples. The range of similar estimates across the three independent samples was 3.6 to 6.8%. Previous estimates of prediction have been ∼5% at most. We report the novel finding that the polygenic signal across our GWAS dataset clusters in genes involved in the process of neurogenesis, genes expressed in the synapse, and genes involved in the development of the nervous system, as well as those involved in myelination within the central nervous system due to their role in oligodendrocyte differentiation. This provides a rationale for a theory of how genetic differences, via their influence on physiological differences, contribute to variation in intelligence. The finding of neurogenesis gene-set enrichment for intelligence is persuasive, because neurogenesis has been linked to cognitive processes—particularly pattern separation and cognitive flexibility—in rodent models. New neurons are continually made in humans in the subgranular zone of the hippocampus and in the striatum; in rodent studies, experimentally reducing analogous neurogenesis results in a poorer ability to discriminate between highly similar patterns, whereas increasing the number of new neurons produced results in an increased ability to successfully discriminate between highly similar stimuli. Oligodendrocyte differentiation was also identified by gene-set analysis as being involved in intelligence differences. The central nervous system of humans contains a very high percentage (~50%) of white matter, which is maintained by the action of oligodendrocytes. Abnormalities in white matter are also associated with psychiatric disorders such as schizophrenia and autism, conditions that have previously been shown to be genetically linked to differences in intelligence. By finding that genes involved in the myelination of the central nervous system are associated with cognitive variation, we provide a molecular genetic basis for the link between white matter tract structure and intelligence. Finally, we showed, using genetic correlations with 29 other traits, that our meta-analytic intelligence GWAS had a highly similar genetic architecture to that of intelligence alone. The genetic correlations that were produced using the meta-analytic intelligence GWAS did differ for some traits; this was most evident for schizophrenia, for which positive genetic correlations have been observed with education[12], but negative associations with intelligence.

This paper is largely a meta-analysis, but it certainly confirms a picture which has been shown in previous studies. Indeed, I think it brings the picture into much sharper focus, because it provides potential physiological mechanisms and an overall rationale for arguing that the revealed associations are causative. That is the whole point of studying DNA. It is causative, and the predictions being made arise from DNA alone. It is possible that various means of gene expression will account for further variance, and presumably these would count as a particular type of environmental effect: call it a close cousin of genetics. That remains to be shown for human intelligence.

This paper also brings a considerable benefit: it shows that on the basis of DNA alone, it is possible to predict 7% of the variance in intelligence in a new sample. Not enough, you may say, but it is a massive advance on the 0% achieved in previous millennia.