Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

Philip R. Jansen; Mats Nagel; Kyoko Watanabe; Yongbin Wei; Jeanne E. Savage; Christiaan A. de Leeuw; Martijn P. van den Heuvel; Sophie van der Sluis; Danielle Posthuma

doi:10.1038/s41467-020-19378-5

Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

Philip R. Jansen
, Mats Nagel
, Kyoko Watanabe
, Yongbin Wei
, Jeanne E. Savage
, Christiaan A. de Leeuw
, Martijn P. van den Heuvel
, Sophie van der Sluis
, Danielle Posthuma^*

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (r_g) between BV and intelligence to be 0.24. We show that the r_g is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV’s shared genetic etiology with intelligence.

Original language	English
Article number	5606
Pages (from-to)	1-12
Number of pages	12
Journal	Nature Communications
Volume	11
Early online date	5 Nov 2020
DOIs	https://doi.org/10.1038/s41467-020-19378-5
Publication status	Published - 2020

Funding

The full GWAS summary statistics from the ENIGMA GWAS meta-analysis were downloaded from http://enigma.ini.usc.edu/research/download-enigma-gwas-results/. Data on infant head circumference has been contributed by HC-GWAS and was downloaded from https://hdl.handle.net/1839/ff12326d-9688-4a46-bc2a-b50cbbd2b20c. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 06/12/2018. This study makes use of data generated by the DECIPHER community. A full list of centers that contributed to the generation of the data is available from http://decipher.sanger.ac.uk and via email from [email protected]. Funding for the project was provided by the Wellcome Trust. This work was funded by The Netherlands Organization for Scientific Research (NWO Brain & Cognition 433-09-228, NWO MagW VIDI 452-12-014, NWO VICI 435-14-005 and 453-07-001, 645-000-003). Analyses were carried out on the Genetic Cluster Computer, which is financed by the Netherlands Scientific Organization (NWO: 480-05-003), by the VU University, Amsterdam, the Netherlands, and by the Dutch Brain Foundation, and is hosted by the Dutch National Computing and Networking Services SurfSARA. This research has been conducted using the UKB Resource (application number 16406). We would like to thank the participants and researchers who collected and contributed to the data.

Funders	Funder number
National Institute of Mental Health
National Human Genome Research Institute
National Institute of Neurological Disorders and Stroke
National Institutes of Health
Wellcome Trust
Vrije Universiteit Amsterdam
National Cancer Institute
National Institute on Drug Abuse
Dutch National Computing and Networking Services
Dutch Brain Foundation
National Heart, Lung, and Blood Institute
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	435-14-005 453-07-001, 480-05-003, 645-000-003, 453-07-001, VICI 435-14-005, MagW VIDI 452-12-014, 433-09-228, 452-12-014
Horizon 2020 Framework Programme	834057

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title = "Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence",

abstract = "The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (rg) between BV and intelligence to be 0.24. We show that the rg is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV{\textquoteright}s shared genetic etiology with intelligence.",

author = "Jansen, \{Philip R.\} and Mats Nagel and Kyoko Watanabe and Yongbin Wei and Savage, \{Jeanne E.\} and \{de Leeuw\}, \{Christiaan A.\} and \{van den Heuvel\}, \{Martijn P.\} and \{van der Sluis\}, Sophie and Danielle Posthuma",

year = "2020",

doi = "10.1038/s41467-020-19378-5",

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AU - Jansen, Philip R.

AU - Nagel, Mats

AU - Watanabe, Kyoko

AU - Wei, Yongbin

AU - Savage, Jeanne E.

AU - de Leeuw, Christiaan A.

AU - van den Heuvel, Martijn P.

AU - van der Sluis, Sophie

AU - Posthuma, Danielle

PY - 2020

Y1 - 2020

N2 - The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (rg) between BV and intelligence to be 0.24. We show that the rg is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV’s shared genetic etiology with intelligence.

AB - The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (rg) between BV and intelligence to be 0.24. We show that the rg is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV’s shared genetic etiology with intelligence.

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Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

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