A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence

W D Hill,S J Ritchie,O Maghzian,S P Hagenaars,R E Marioni,G Davies,I J Deary,A M Mcintosh,C R Gale

doi:10.1038/s41380-017-0001-5

W D Hill, S J Ritchie + Show 7 more

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https://doi.org/10.1038/s41380-017-0001-5

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Journal: Molecular Psychiatry	Publication Date: Jan 11, 2018
Citations: 252	License type: open-access

Affiliation: Harvard University, University of Edinburgh

Abstract

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 (rg = 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.

Highlights

Intelligence, known as general cognitive function or g, describes the shared variance that exists between diverse measures of cognitive ability [1]
To maximise sample size in our intelligence dataset, four additional genome-wide association studies (GWASs) were performed on the verbal-numerical reasoning (VNR) test in UK Biobank
Genetic correlations performed for each of the four VNR groups whose genetic data were released in the second release of the UK Biobank genetic data against the Sniekers dataset [16] indicated no evidence of sample overlap; this is shown by there being an intercept of around zero for each comparison

Summary

Introduction

Intelligence, known as general cognitive function or g, describes the shared variance that exists between diverse measures of cognitive ability [1]. The sparsity of genome-wide significant SNPs discovered so far, combined with the substantial heritability estimate, suggests a phenotype with a highly polygenic architecture, where the total effect of all associated variants is substantial, but in which each individual variant exerts only a small influence This is compelling evidence that the number of uncovered genomewide significant loci associated with intelligence can be increased by raising the sample size—and the statistical power—of GWASs, as has been the case for other phenotypes such as height [17] and schizophrenia [18]. In the case of UK Biobank, a test of verbal and numerical reasoning shows a high genetic correlation with intelligence [23] as derived from psychometrically validated test batteries [16]

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