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

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

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.

1359-4184
1-13
Hill, W.D.
9267a365-018c-4423-92a8-1107729575e8
Marioni, R.E.
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Maghzian, O.
ab017e7b-504b-4b4c-8c6a-ccac1ba2e13e
Ritchie, S.J.
c2376ae7-f32e-474d-b42e-b4534bf0a3ee
Hagenaars, S.P.
f5bef8aa-aeef-44bf-be77-43a1dd2dac9d
McIntosh, A.M.
b95a091e-f188-4a64-826e-99b6c079f69a
Gale, C.R.
5bb2abb3-7b53-42d6-8aa7-817e193140c8
Davies, G.
8f2485ed-6813-4d29-bacd-2cca13301036
Deary, I.J.
e3403cfe-eb5b-4941-903d-87ef0db89c60
Hill, W.D.
9267a365-018c-4423-92a8-1107729575e8
Marioni, R.E.
34fb8c27-c5a0-4a34-b45d-fb7efb7e12c6
Maghzian, O.
ab017e7b-504b-4b4c-8c6a-ccac1ba2e13e
Ritchie, S.J.
c2376ae7-f32e-474d-b42e-b4534bf0a3ee
Hagenaars, S.P.
f5bef8aa-aeef-44bf-be77-43a1dd2dac9d
McIntosh, A.M.
b95a091e-f188-4a64-826e-99b6c079f69a
Gale, C.R.
5bb2abb3-7b53-42d6-8aa7-817e193140c8
Davies, G.
8f2485ed-6813-4d29-bacd-2cca13301036
Deary, I.J.
e3403cfe-eb5b-4941-903d-87ef0db89c60

Hill, W.D., Marioni, R.E., Maghzian, O., Ritchie, S.J., Hagenaars, S.P., McIntosh, A.M., Gale, C.R., Davies, G. and Deary, I.J. (2018) A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Molecular Psychiatry, 1-13. (doi:10.1038/s41380-017-0001-5).

Record type: Article

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.

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Accepted/In Press date: 3 November 2017
e-pub ahead of print date: 11 January 2018

Identifiers

Local EPrints ID: 417428
URI: https://eprints.soton.ac.uk/id/eprint/417428
ISSN: 1359-4184
PURE UUID: 667d923d-258c-44ea-b79c-751097355c27

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Date deposited: 31 Jan 2018 17:30
Last modified: 24 May 2018 16:31

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