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Meta-Analysis
, 50 (7), 912-919

Genome-wide Association Meta-Analysis in 269,867 Individuals Identifies New Genetic and Functional Links to Intelligence

Jeanne E Savage  1 Philip R Jansen  1   2 Sven Stringer  1 Kyoko Watanabe  1 Julien Bryois  3 Christiaan A de Leeuw  1 Mats Nagel  4 Swapnil Awasthi  5 Peter B Barr  6 Jonathan R I Coleman  7   8 Katrina L Grasby  9 Anke R Hammerschlag  1 Jakob A Kaminski  5   10 Robert Karlsson  3 Eva Krapohl  7 Max Lam  11 Marianne Nygaard  12   13 Chandra A Reynolds  14 Joey W Trampush  15   16 Hannah Young  17 Delilah Zabaneh  7 Sara Hägg  3 Narelle K Hansell  18 Ida K Karlsson  3 Sten Linnarsson  19 Grant W Montgomery  9   20 Ana B Muñoz-Manchado  19 Erin B Quinlan  21 Gunter Schumann  21 Nathan G Skene  19   22 Bradley T Webb  23   24 Tonya White  2 Dan E Arking  25 Dimitrios Avramopoulos  25   26 Robert M Bilder  27 Panos Bitsios  28 Katherine E Burdick  29   30   31 Tyrone D Cannon  32 Ornit Chiba-Falek  33 Andrea Christoforou  34 Elizabeth T Cirulli  35 Eliza Congdon  27 Aiden Corvin  36 Gail Davies  37   38 Ian J Deary  37   38 Pamela DeRosse  39   40   41 Dwight Dickinson  42 Srdjan Djurovic  43   44 Gary Donohoe  45 Emily Drabant Conley  46 Johan G Eriksson  47 Thomas Espeseth  48   49 Nelson A Freimer  27 Stella Giakoumaki  50 Ina Giegling  51 Michael Gill  36 David C Glahn  52 Ahmad R Hariri  53 Alex Hatzimanolis  54   55   56 Matthew C Keller  57 Emma Knowles  52 Deborah Koltai  58 Bettina Konte  51 Jari Lahti  59   60 Stephanie Le Hellard  34   44 Todd Lencz  39   40   41 David C Liewald  38 Edythe London  27   61 Astri J Lundervold  62   63 Anil K Malhotra  39   40   41 Ingrid Melle  44   49 Derek Morris  45 Anna C Need  64 William Ollier  65 Aarno Palotie  66   67   68 Antony Payton  69 Neil Pendleton  70 Russell A Poldrack  71 Katri Räikkönen  72 Ivar Reinvang  48 Panos Roussos  29   30   73 Dan Rujescu  51 Fred W Sabb  74 Matthew A Scult  53 Olav B Smeland  75 Nikolaos Smyrnis  54   55 John M Starr  37   76 Vidar M Steen  34   44 Nikos C Stefanis  54   55   56 Richard E Straub  77 Kjetil Sundet  48   49 Henning Tiemeier  2   78 Aristotle N Voineskos  79 Daniel R Weinberger  77 Elisabeth Widen  66 Jin Yu  39 Goncalo Abecasis  80   81 Ole A Andreassen  49   75   82 Gerome Breen  7   8 Lene Christiansen  12   13 Birgit Debrabant  13 Danielle M Dick  6   83   84 Andreas Heinz  5 Jens Hjerling-Leffler  19 M Arfan Ikram  78 Kenneth S Kendler  23   24   83 Nicholas G Martin  9 Sarah E Medland  9 Nancy L Pedersen  3 Robert Plomin  7 Tinca J C Polderman  1 Stephan Ripke  5   85   86 Sophie van der Sluis  4 Patrick F Sullivan  3   87 Scott I Vrieze  17 Margaret J Wright  18   88 Danielle Posthuma  89   90
Affiliations
Meta-Analysis

Genome-wide Association Meta-Analysis in 269,867 Individuals Identifies New Genetic and Functional Links to Intelligence

Jeanne E Savage et al. Nat Genet.

Abstract

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

Conflict of interest statement

Competing Financial Interests Statement

PF Sullivan reports the following potentially competing financial interests: Lundbeck (advisory committee), Pfizer (Scientific Advisory Board member), and Roche (grant recipient, speaker reimbursement). G Breen reports consultancy and speaker fees from Eli Lilly and Illumina and grant funding from Eli Lilly. J Hjerling-Leffler reports interests from Cartana (Scientific Advisor) and Roche (grant recipient). TD Cannon is a consultant to Boehringer Ingelheim Pharmaceuticals and Lundbeck A/S. All other authors declare no financial interests or potential conflicts of interest.

Figures

Figure 1.
Figure 1.. SNP-based associations with intelligence in the GWAS meta-analysis of N=269,867 independent individuals.
(a) Manhattan plot showing the −log10 transformed two-tailed P-value of each SNP from the GWAS meta-analysis (of linear and logistic regression statistics) on the y-axis and base pair positions along the chromosomes on the x-axis. The dotted red line indicates Bonferroni-corrected genome-wide significance (P<5×10−8); the blue line the threshold for suggestive associations (P<1×10−5). Independent lead SNPs are indicated by a diamond. (b) Heritability enrichment of 28 functional annotation categories for SNPs in the meta-analysis, calculated with stratified LD score regression. Error bars show 95% confidence intervals around the enrichment estimate. The dashed horizontal line indicates no enrichment of the annotation category. Red dots indicate significant Bonferroni-corrected two-tailed P-values and beige dots indicate suggestive (P<.05) values. UTR=untranslated region; TSS=transcription start site; CTCF=CCCTC-binding factor; DHS=DNaseI Hypersensitive Site; TFBS=transcription factor binding site; DGF=DNaseI digital genomic footprint. (c) Distribution of functional consequences of SNPs in genomic risk loci in the meta-analysis. (d) Distribution of RegulomeDB score for SNPs in genomic risk loci, with a low score indicating a higher likelihood of having a regulatory function (Online methods). (e) The minimum chromatin state across 127 tissue and cell types for SNPs in genomic risk loci, with lower states indicating higher accessibility and states 1–7 referring to open chromatin states (Online Methods).
Figure 2.
Figure 2.. Cross-locus interactions for genomic regions associated with intelligence in 269,867 independent individuals.
Circos plots showing genes on chromosomes 2 (a), 5 (b), 6 (c), 9 (d), and 22 (e) that were linked to genomic risk loci in the GWAS meta-analysis (blue regions) by eQTL mapping (green lines connecting an eQTL SNP to its associated gene), and/or chromatin interactions (orange lines connecting two interacting regions) and showed evidence of interaction across two independent genomic risk loci. Genes implicated by eQTL are in green, by chromatin interactions in orange, and by both eQTL and chromatin interactions mapping in red. The outer layer shows a Manhattan plot containing the −log10 transformed two-tailed P-value of each SNP from the GWAS meta-analysis (of linear and logistic regression statistics), with genome-wide significant SNPs colored according to linkage disequilibrium patterns with the lead SNP. Circos plots for all chromosomes are provided in Supplementary Fig. 8.
Figure 3.
Figure 3.. Implicated genes, pathways, and tissue- and cell- expression profiles for intelligence in 269,867 independent individuals.
(a) Manhattan plot of the genome-wide gene-based association analysis (GWGAS). The y-axis shows the −log10 transformed two-tailed P-value of each gene from a linear model, and the chromosomal position on the x-axis. The red dotted line indicates the Bonferroni-corrected threshold for genome-wide significance of the gene-based test (P<2.76×10−6; 0.05/18,128 genes), and the blue line indicates the suggestive threshold (P<2.76×10−5; 0.5/18,128 genes) (b) Venn diagram showing overlap of genes implicated by positional mapping, eQTL mapping, chromatin interaction mapping, and GWGAS. (c) Gene expression profiles of associated genes in 53 tissue types. The y-axis shows the −log10 transformed two-tailed P-value of association of GWGAS test statistics with tissue-specific gene expression levels in a linear model. Expression data were extracted from the Genotype-Tissue Expression (GTEx) database. Expression values (RPKM) were log2 transformed with pseudocount 1 after winsorization at 50 and averaged per tissue. The dotted blue line indicates the Bonferroni-corrected significance threshold (P=0.05/7,323 gene-sets=6.83×10−6). (d) Single-cell gene-expression analysis of genes related to intelligence in 24 cell-types. The x-axis shows the −log10 transformed two-tailed P-value of association of GWGAS test statistics with cell-specific gene expression levels in a linear model. The dotted blue line indicates the Bonferroni-corrected significance threshold (P=0.05/7,323 gene-sets=6.83×10−6).

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