Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function

ARTICLE Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function Gail Davies1, Max Lam et al. # General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N=300,486; age 16–102) and find 148 genome-wide significant independent loci (P<5×10 −8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function. DOI: 10.1038/s41467-018-04362-x OPEN Correspondence and requests for materials should be addressed to I.D. (email: [email protected]c.uk) #A full list of authors and their affliations appears at the end of the paper. NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications 1 1234567890():,; Some individuals have generally higher cognitive function than others. These individual differences are quite persistent across the life course from later childhood onwards. Individuals with higher measured general cognitive function tend to live longer and be less deprived. Retaining general cognitive function is an important aspect of healthy ageing. The population variance in this medicallyand socially-important trait has environmental and genetic aetiologies. The details of the genetic contributions are, as-yet, poorly understood. Since the discovery of general cognitive ability (or ‘g’) in 19041, hundreds of studies have replicated the finding that around 40% ofthevarianceinsubjects’scores on a diverse battery of cognitive tests can be accounted for by a single general factor2. Some variance is also attributable to individual cognitive domains (e.g., reasoning, memory, processing speed, and spatial ability), and some is attributable to specificcognitiveskills associated with individual mental tests. However, all cognitive tests rely to a greater or lesser extent on general cognitive ability for successful execution. Figure 1illustrates and explains this hierarchical model of cognitive ability differences3. Therefore, using a general cognitive function phenotype in a genetically-informative design is supported by the observation that the well-established positive manifold of cognitive tests may be represented by a substantially heritable, higher-order, latent general cognitive function phenotype2,4,5. There are two commonly-used routes that are used to obtain general cognitive ability scores for each participant in a sample. First, if all members of a sample have taken the same set of diverse cognitive tests, then a data reduction procedure (such as principal components analysis (PCA) or factor analysis) can be applied. Typically, this finds that all tests load on (i.e., correlate positively with) the first unrotated component, or factor, and scores on this component can be calculated for each person; this gives each person a gscore. Second, some mental tests —usually those involving complex mental work, and often those with a variety of item types—have a high gloading2. That is, scores on some individual cognitive tests can be used to obtain an acceptable proxy for general cognitive ability. An example of the latter is the Moray House Test of verbal and numerical reasoning, which has a high correlation with a PCA-derived general cognitive function score6. General cognitive function is peerless among human psychological traits in terms of its empirical support and importance for life outcomes7,8. Individuals who have higher cognitive function in childhood and adolescence tend to stay longer in education, gain higher educational qualifications, progress to more professional and better-paid jobs, live healthier lives, and live longer. Individual differences in general cognitive function show phenotypic and genetic stability across most of the life course9–11. The phenotypic correlation between general cognitive function scores on the same people at age 11 and age 70–80 years is almost 0.7, and remains above 0.5 when age 11 versus age 90 scores are correlated. Twin studies find that general cognitive function has a heritability of more than 50% from adolescence through adulthood to older age4,5,12. SNP-based estimates of heritability for general cognitive function are about 20–30%13. However, these estimates might increase to about 50% when family-based designs are used to retain the contributions made by rarer SNPs14. To date, little of this substantial heritability has been explained, i.e., only a few relevant genetic loci have been discovered (Table 1; Supplementary Fig. 1). As has been found with other highly polygenic traits, a limitation on uncovering relevant genetic loci is sample size15; to date, there have been fewer than 100,000 individuals in studies of general cognitive function13,16. The MTAG (multi-trait analysis of genome-wide association studies) method has been used to corral cognitive function and associated traits to expand the number of loci associated with general cognitive function17. However, the present study uses only cognitive function phenotypes, and amasses a total sample size of over 300,000. The present study also tests for genetic contributions to reaction time, and examines its genetic relationship with general cognitive function. Reaction time is both phenotypically and genetically correlated with general cognitive function, and accounts for some of its association with health18–20. By making these comparisons between general cognitive function and reaction time, we identify regions of the genome that have a shared correlation with general cognitive function and more elementary cognitive tasks21. Domain 1 e.g., Reasoning Domain 2 e.g., Speed Domain 3 e.g., Memory Domain 4 e.g., Spatial …Domain n N Individual reasoning tests N Individual speed tests N Individual memory tests N Individual spatial tests N Individual domain n tests g Level 3: variance in g Level 2: cognitive domain variance Level 1: specific-test and error variance Fig. 1 The hierarchical model of cognitive function variance. At level 1, individuals differ in specific tests that assess the various cognitive domains. Scores on all the tests correlate positively. It is found that there are especially strong correlations among the tests of the same domain, so a latent trait at the domain level can be extracted to represent this common variance. It is then found that individuals who do well in one domain also tend to do well in the other domains, so a general cognitive latent trait called gcan be extracted. This model allows researchers to partition cognitive performance variance into these different levels. They can then explore the causes and consequences of variance at different levels of cognitive specificity-generality. For example, there are genetic and ageing effects on gand on some specific domains, such as memory and speed of processing. Note that the specific-test-level variance contains variation in the performance of skills that are specific to the individual test and also contains error variance. (Reproduced, with permission, from ref. 3) ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04362-x 2NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications Results General cognitive function phenotypes. The psychometric characteristics of the general cognitive component from each cohort in the CHARGE consortium are shown in Supplementary Note 1. In order to address the fact that different cohorts had applied different cognitive tests, we previously showed that two general cognitive function components extracted from different sets of cognitive tests on the same participants correlate highly13. The cognitive test from the large UK Biobank sample was the socalled ‘fluid’test, a 13-item test of verbal-numerical reasoning, which has a high genetic correlation with general cognitive function22.WiththeCHARGEandCOGENTsamples’general cognitive function scores and UK Biobank’s verbal-numerical reasoning scores, there were 300,486 participants included in the present report’s meta-analysis of genome-wide association studies (GWASs). Note that we included four UK Biobank samples, i.e. three assessment centre-tested samples, and one online-tested sample. The genetic correlation between CHARGE’s-COGENT’s general cognitive function component and UK Biobank’s verbalnumerical reasoning test, calculated for the present study using linkage disequilibrium score (LDSC) regression, was estimated at 0.87 (SE =0.03). This indicates very substantial overlap between the genetic variants associated with cognitive function in these two groups. SNP-based meta-analyses of cognitive function GWASs.We performed an N-weighted meta-analysis of general cognitive function which included all of the CHARGE, COGENT, and UK Biobank samples. Meta-analysis of the results for the general cognitive function GWASs found 11,600 significant (P<5×10 −8) SNP associations, and 21,855 at a suggestive level (1 × 10−5>P≥ 5×10 −8); see Fig. 2a, Supplementary Fig. 2a, and Supplementary Data 1and 2. There were 434 ‘independent’significant SNPs; see Methods section for description of independent SNP selection criteria, distributed within 148 loci across all autosomal chromosomes. Note that, for consistency, we use the term ‘independent’here according to the definition that is used in the relevant analysis package. A comparison of these 148 loci with results from the largest previous GWASs of cognitive function16, and educational attainment24, and an MTAG analysis of cognitive function17—all of which included a subsample of individuals contributing to the present study—confirmed that 11 of 18, 24 of 74, and 89 of 187 of these were, respectively, genome-wide significant in the present study (Supplementary Data 3). Of the 148 loci found in the present study, 58 have not been reported previously in other GWA studies of cognitive function or educational attainment (novel loci are indicated in Supplementary Data 4). One hundred and seventy-eight lead SNPs were identified within these 148 loci. For the 434 independent significant SNPs and tagged SNPs, a summary of previous SNP associations is listed in Supplementary Data 5. They have been associated with many physical (e.g., BMI, height, weight), medical (e.g., lung cancer, Crohn’s disease, blood pressure), and psychiatric (e.g., bipolar disorder, schizophrenia, autism) traits. Of the 58 new loci, we highlight previous associations with schizophrenia (2 loci), Alzheimer’s disease (1 locus), and Parkinson’s disease (1 locus). We sought to identify independent significant and tagged SNPs within the 148 significant genomic risk loci associated with general cognitive function that are potentially functional (Fig. 3a; Supplementary Data 4). See Methods section for further details. Across many of the loci there is clear evidence of functionality including involvement in gene regulation, deleterious SNPs, eQTLs, and regions of open chromatin. General cognitive function gene-based and gene-set results.A gene-based association analysis identified 709 genes as significantly associated with general cognitive function (Fig. 2b; Supplementary Fig. 2b; Supplementary Data 6). These 709 genes were compared to gene-based associations from previous studies of general cognitive function and educational attainment13,16,17,25; 418 were replicated in the present study, and 291 were novel. The 291 new gene-based associations are highlighted in Supplementary Data 6. Several of the specific genes associated with general cognitive function are considered in detail in the Discussion, below. Gene-set analysis identified seven significant gene sets associated with general cognitive function: neurogenesis (P= 1.57 × 10−9), regulation of nervous system development (P= 7.52 × 10−7), neuron projection (P=7.89 × 10−7), positive regulation of nervous system development (P=9.42 × 10−7), neuron differentiation (P=1.68 × 10−6), regulation of cell development (P=1.93 × 10−6), and dendrite (P=3.52 × 10−6) (Supplementary Data 7). Gene-property analysis can show if tissue-specific expression levels are associated with a gene’s association with a phenotype. This analysis indicated a significant association between transcription levels in all brain regions—except the brain spinal cord and cervical c1—and the association with general cognitive function. In addition, expression levels in the pituitary were associated with gene-based association with general cognitive function; these results indicate that the genes with the highest expression levels in these regions were those showing the greatest associations with general cognitive function. (Fig. 3b, c; Supplementary Table 1; Supplementary Data 8). The significance of this relationship was greatest in the cerebellum and the cortex. Table 1 Details of GWA studies of general cognitive function to date, including the present study Author; doi Year NGWAS-sig SNP hits GWAS-sig gene hits SNP-based h2 Davies et al. (2011)86 2011 3511 0 1 gene 0.51 (0.11) Lencz et al. (2013)87 2013 5000 0 NA NA Benyamin et al. (2014)88 2014 17,989 0 0 0.46 (0.06) Kirkpatrick et al. (2014)89 2014 7100 0 0 0.35 (0.11) Davies et al. (2015)25 2015 53,949 3 loci (13 SNPs) 1 gene 0.29 (0.05) Davies et al. (2016); results for ‘fluid’test 2016 36,035 3 loci (149 SNPs) 7 loci 17 genes 0.31 (0.02) Trampush et al. (2017)64 2017 35,298 2 loci (7 SNPs) 3 loci 7 genes 0.22 (0.01) Sniekers et al. (2017)16 2017 78,308 18 loci (336 SNPs) 47 genes 0.20 (0.01) Davies et al. (2018); present study 2018 300,486 148 loci (11,600 SNPs) 709 genes 0.25 (0.006) For SNP-based heritability, the value from the largest sample is given NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04362-x ARTICLE NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications 3 SNP-based heritability of general cognitive function. We estimated the proportion of variance explained by all common SNPs using GCTA-GREML in four of the largest individual samples: English Longitudinal Study of Ageing (ELSA: N=6661, h2=0.12, SE =0.06), Understanding Society (N=7841, h2=0.17, SE =0.04), UK Biobank Assessment Centre (N=86,010, h2=0.25, SE =0.006), and Generation Scotland (N=6,507, h2=0.20, SE =0.0523) (Table 2). Genetic correlations for general cognitive function amongst these cohorts, estimated using bivariate GCTA-GREML, ranged from r g =0.88 to 1.0 (Table 2). These results indicate that the same genetic variants contribute to phenotypic differences in general cognitive function across each of these three samples. We investigated the genetic contribution to the stability of individual differences in people’s verbal-numerical reasoning, by examining data from those individuals in UK Biobank who completed the test on two occasions (mean time gap =4.93 years). We found a significant and perfect genetic correlation of r g =1.0 (SE =0.02). Polygenic profile scores and genetic correlations. After omitting them from the meta-analysis of GWASs, we created general cognitive function polygenic profile scores in three of the a b General cognitive function: SNP-based results General cognitive function: gene-based results Chromosome Chromosome –Log10(p-value) –Log10(p-value) 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Fig. 2 Association results for general cognitive function. SNP-based (a) and gene-based (b) association results in 300,486 individuals. The red line indicates the threshold for genome-wide significance: P<5×10 −8for (a), P< 2.75 × 10−6for (b); the blue line in (a) indicates the threshold for suggestive significance: P<1×10 −5 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04362-x 4NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications larger cohorts: ELSA, Generation Scotland, and Understanding Society. The polygenic profile score for general cognitive function explained 2.63% of the variance in ELSA (β=0.17, SE =0.01, P=1.70 × 10−51), 3.73% in Generation Scotland (β=0.20, SE =0.01, P=5.02 × 10−68), and 4.31% in Understanding Society (β=0.22, SE =0.01, P=6.17 × 10−88). Full results for all five thresholds are shown in Supplementary Table 2. We tested the genetic correlations between general cognitive function and 52 health-related traits. Thirty-six of these health12,500 a b c Number of SNPs 10,000 7500 5000 2500 0 15 10 –log10(P-value) –log10(P-value) 5 0 15 10 5 0 Brain Pituitary Testis Ovary Uterus Nerve Colon Blood vessel Blood Fallopian tube Stomach Prostate Muscle Tissue type Tissue type Bladder Pancreas Esophagus Vagina Skin Heart Thyroid Kidney Liver Spleen Breast Lung Intergenic Downstream Upstream UTR3 UTR5 Function Intronic Exonic ncRNA_intronic ncRNA_exonic Brain cerebellum Brain cerebellar hemisphere Brain cortex Brain frontal cortex BA9 Brain hippocampus Brain nucleus accumbens basal ganglia Pituitary Testis Ovary Uterus Nerve tibial Colon sigmoid Whole blood Cells transformed fibroblasts Muscle skeletal Esophagus muscularis Artery aorta Cervix ectocervix Artery tibial Colon transverse Adrenal gland Pancreas Fallopian tube Stomach Prostate Small intestine terminal ileum Bladder Artery coronary Skin not sun exposed suprapubic Liver Skin sun exposed lower leg Vagina Heart atrial appendage Spleen Esophagus mucosa Kidney cortex Thyroid Breast mammary tissue Heart left ventricle Adipose visceral omentum Lung Minor salivary gland Adipose subcutaneous Esophagus gastroesophageal junction Cervix endocervix Brain spinal cord cervical c1 Brain substantia nigra Brain putamen basal ganglia Brain caudate basal ganglia Brain amygdala Brain hypothalamus Brain anterior cingulate cortex BA24 Fig. 3 Functional analyses of general cognitive function. Analyses include general cognitive function-associated SNPs, independent significant SNPs, and all SNPs in LD with independent significant SNPs. Functional consequences of SNPs on genes (a) indicated by functional annotation assigned by ANNOVAR. MAGMA gene-property analysis results; results are shown for average expression of 30 general tissue types (b) and 53 specific tissue types (c). The dotted line indicates the Bonferroni-corrected αlevel NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04362-x ARTICLE NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications 5 related traits were significantly genetically correlated with general cognitive function (Supplementary Data 9). We report significant genetic correlations between general cognitive function and: hypertension (r g =−0.15, SE =0.02), grip strength (right hand: r g =0.09, SE =0.02), wearing glasses or contact lenses (r g =0.28, SE =0.04), short-sightedness (r g = 0.32, SE =0.03), long-sightedness (r g =−0.21, SE=0.05), heart attack (r g =−0.17, SE =0.03), angina (r g =−0.18, SE =0.03), lung cancer (r g =−0.26, SE =0.05), and osteoarthritis (r g =−0.24, SE =0.04). We also report a significant genetic correlation with major depressive disorder (r g =−0.30, SE =0.04); this result strengthens previously-reported nonsignificant correlations of around −0.1016,17. We also note the Table 2 Genetic correlations and heritability estimates of a general cognitive function component in three United Kingdom cohorts Cohort ELSA US GS ELSA 0.12 (0.06) US 1.0 (0.33) 0.17 (0.04) GS 1.0 (0.38) 0.88 (0.24) 0.20 (0.05) Below the diagonal, genetic correlations (standard error) of general cognitive function amongst three cohorts are shown: ELSA English Longitudinal Study of Ageing, GS Generation Scotland, US Understanding Society. SNP-based heritability (standard error) estimates appear on the diagonal a b Reaction time: SNP-based results Reaction time: gene-based results Chromosome Chromosome –Log10(p-value) –Log10(p-value) 20 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 18 16 14 12 10 8 7 6 5 4 3 2 1 0 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Fig. 4 Association results for reaction time. SNP-based (a) and gene-based (b) association results in 330,069 individuals. The red line indicates the threshold for genome-wide significance: P<5×10 −8for (a), P< 2.75 × 10−6for (b); the blue line in (a) indicates the threshold for suggestive significance: P<1×10 −5 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04362-x 6NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications important genetic association between general cognitive function and longevity (r g =0.17, SE =0.06). Reaction time results. GWAS results for mean reaction time uncovered 2022 significant SNPs in 42 independent genomic loci (Fig. 4a; Supplementary Fig. 2c; Supplementary Data 10). Suggestive findings are presented in Supplementary Data 11. Both of the significant loci previously reported for this phenotype were replicated13. SNPs within the 42 independent genomic loci showed clear evidence of functionality (Fig. 5a; Supplementary a b c Number of SNPs 2000 2500 1500 1000 500 0 12 8 4 0 10 5 0 Intergenic Downstream Upstream UTR3 UTR5 Function Intronic Exonic ncRNA_intronic ncRNA_exonic –log10(P-value) –log10(P-value) Brain Pituitary Testis Ovary Uterus Nerve Colon Blood Adrenal gland Cervix uteri Stomach Prostate Muscle Tissue type Tissue type Bladder Pancreas Esophagus Vagina Heart Small intestine Salivary gland Adipose tissue Thyroid Kidney Liver Spleen Breast Lung Skin Brain cerebellar hemisphere Brain frontal cortex BA9 Brain cortex Brain cerebellum Brain anterior cingulate cortex BA24 Brain nucleus accumbens basal ganglia Brain hippocampus Brain caudate basal ganglia Brain hypothalamus Brain amygdala Brain putamen basal ganglia Brain substantia nigra Brain spinal cord cervical c1 Pituitary Testis Ovary Calls EBV transformed lymphocytes Nerve tibial Whole blood Uterus Colon sigmoid Muscle skeletal Skin sun exposed lower leg Skin not sun exposed suprapubic Cells transformed fibroblasts Esophagus muscularis Thyroid Esophagus gastroesophageal junction Spleen Adrenal gland Artery tibial Heart left ventricle Colon transverse Cervix ectocervix Cervix endocervix Stomach Prostate Esophagus mucosa Heart atrial appendage Bladder Small intestine terminal ileum Fallopian tube Vagina Artery aorta Liver Artery coronary Adipose subcutaneous Kidney cortex Lung Pancreas Adipose visceral omentum Breast mammary tissue Minor salivary gland Fig. 5 Functional analyses of reaction time. Analyses include reaction time-associated SNPs, independent significant SNPs, and all SNPs in LD with independent significant SNPs. Functional consequences of SNPs on genes (a) indicated by functional annotation assigned by ANNOVAR. MAGMA geneproperty analysis results; results are shown for average expression of 30 general tissue types (b) and 53 specific tissue types (c). The dotted line indicates the Bonferroni-corrected αlevel NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04362-x ARTICLE NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications 7 Data 12). Using gene-based GWA, a total of 191 genes attained statistical significance (Fig. 4b; Supplementary Fig. 2d; Supplementary Data 13), replicating 18 of the 23 genome-wide significant genes found previously for this phenotype13. Gene-set analysis identified no gene sets associated with reaction time (Supplementary Data 14). Gene-property analysis indicated a role for genes expressed in the brain (P=4.66 × 10−13), with this link between gene transcription levels and gene-based association with reaction time being found across the cortex (Fig. 5b, c; Supplementary Table 3; Supplementary Data 15). Gene transcription levels observed in the pituitary gland were also linked to genebased associations with differences in reaction time (P=7.60 × 10−4). The SNP-based heritability of reaction time was 7.42% (SE = 0.29). It should be noted that this estimate is likely to be an underestimation due to the method used (LD score regression)26. Significant overlap was found between the genetic architecture of reaction time and these health outcomes: ADHD, bipolar disorder, schizophrenia, subjective wellbeing, hand grip strength, sleep duration, maternal longevity, hypertension and neuroticism (Supplementary Data 9). The polygenic score for reaction time explained 0.43% of the general cognitive function variance in ELSA (P=1.42 × 10−9), 0.56% in Generation Scotland (P= 2.49 × 10−11), and 0.26% in Understanding Society (P=1.50 × 10 −6). The full results for all five thresholds can be found in Supplementary Table 2. We found a genetic correlation (r g ) of 0.247 (P=1.28 × 10−30) between reaction time and general cognitive function. Overlapping results between the two phenotypes were explored further. Of the 11,600 genome-wide significant SNPs for general cognitive function, 8269 had a consistent direction of effect with reaction time (sign test, P=2.2 × 10−16) (Supplementary Data 1). For reaction time, 1070 of the 2022 significant SNPs were consistent for direction of effect with general cognitive function (sign test, P= 0.0071) (Supplementary Data 10). One hundred and sixty SNPs were genome-wide significant for both general cognitive function and reaction time, with 82 consistent for direction of effect (sign test, NS) (Supplementary Data 16). These overlapping genome-wide findings are located within six genomic loci (genomic loci: 13, 15, 19, 28, 69, 133; see Supplementary Data 4 for details of loci); two of these are novel loci for general cognitive function. In the gene-based analyses of both the general cognitive function and reaction time phenotypes, there were 39 overlapping significant genes; 13 of these are newlyidentified associations with general cognitive function (Supplementary Data 17). Discussion In these meta-analyses of genome-wide association studies for both general cognitive function and reaction time (N=300,486; N=330,069, respectively), we make several original contributions. We report 148 genome-wide significant loci for general cognitive function, of which 58 loci have not been reported before. We report 42 genome-wide significant loci for reaction time, of which 40 have not been reported previously. We also report 291 gene-based associations for general cognitive function, and 173 for reaction time, which have not been reported already. Of these genome-wide significant results, six loci and 39 gene-based associations are genome-wide significant for both general cognitive function and reaction time. We are able to predict, using polygenic scoring, up to 4.31 and 0.56% of the general cognitive function variance in an independent sample, for general cognitive function and reaction time polygenic scores, respectively. We present original and updated estimates of genetic correlations with many health traits for both general cognitive function and reaction time. Gene-set analyses identified significant associations for general cognitive function with gene-sets involved in neural and cell development. Significant enrichments were observed with genes expressed in the cerebellum and the brain’s cortex for both general cognitive function and reaction time. Upon additional exploration of the 58 newly-associated genetic loci, we find that many contain genes that are of further interest. All of the genes discussed below are also genome-wide significant in the general cognitive function gene-based association analysis (P< 2.75 × 10−6; Supplementary Data 6). Significant gene-based associations with general cognitive function have also been previously reported for GATAD2B,SLC39A1, and AUTS216,17. GATAD2B and SLC39A1 are located on chromosome 1; locus 11. Mutations in GATAD2B have been linked to intellectual disability27.SLC39A1 has been implicated in Alzheimer’s Disease28. The ATXN1 gene (chromosome 6; locus 60), encodes a protein containing a polyglutamine tract that has previously been associated with Spinocerebellar Ataxia 129.ATXN1L,ATXN2L, and ATXN7L2 were also located in significant loci that have previously been associated with cognitive function, intelligence, or educational attainment16,17,24. The DCDC2 gene (chromosome 6; locus 64) has previously been associated with cortical morphology30, dyslexia31, and normal variation in reading and spelling32, but not with general cognitive function. TTBK1 (chromosome 6; locus 66) encodes a neuron-specific serine/ threonine and tyrosine kinase, which regulates phosphorylation of tau33. Genetic variants in this gene have been associated with Alzheimer's disease34.AUTS2 (chromosome 7; locus 72) is implicated in a number of neurological disorders35. Mutations in CWF19L1 (chromosome 10; locus 91) have been associated with spinocerebellar ataxia and intellectual disability36. RBFOX1 (chromosome 16; locus 121) encodes a mRNA-splicing factor that interacts with ATXN237, and mutations in this gene lead to neurodevelopmental disorders38. Locus 131, on chromosome 17, has previously been associated with Smith-Magenis Syndrome39. The most significantly-associated SNP (P=2.2 × 10−8) in this locus lies in an intron of the RAI1 gene. RAI1 encodes a protein containing a polymorphic polyglutamine tract that is expressed mainly in neuronal tissues. Variants in the gene are also associated with schizophrenia40. Of the seven significant gene sets identified, one was a new finding: ‘positive regulation of nervous system development’.A more detailed description of this gene-set is: ‘any process that activates, maintains or increases the frequency, rate or extent of nervous system development, the origin and formation of nervous tissue’. The remaining six gene-sets showed replication with previous studies of general cognitive function and/or education16,17,24. Only one, ‘regulation of cell development’, was significant across all four studies16,17,24. Identification of these gene sets is consistent with genes associated with cognitive function regulating the generation of cells within the nervous system, including the formation of neuronal dendrites. A number of not-previously-reported genetic correlations with cognitive function were found here, including with cardiovascular variables. For example, it is already known that there is a phenotypic association between cognitive function in youth and the development of hypertension by age 50 years41; we found a genetic correlation of −0.15. Other genetic correlations between cardiovascular variables and cognitive function were angina (r g = −0.18) and heart attack (r g =−0.17); again, there are known to be phenotypic associations between prior cognitive functioning and various cardiovascular outcomes41,42. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04362-x 8NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications The genetic correlations between general cognitive function and eyesight were in opposite directions depending on the reported reason for wearing glasses or contact lenses; this was despite an overall positive genetic correlation between general cognitive function and wearing glasses (r g =0.28). The result for myopia (short-sightedness; r g =0.32) was consistent with previous evidence of a positive phenotypic43 and genetic44 correlation between this trait and cognitive function. Less genetic work has investigated the links between hyperopia (long-sightedness) and cognitive function, although our finding, a genetic correlation of r g =−0.21, was consistent with the negative phenotypic association between these variables reported in previous literature45. We have investigated the six regions of the genome identified as having a shared effect between general cognitive function and more elementary cognitive tasks. Locus 13 on chromosome 1 contains the NMNAT2 gene. NMNAT2 is involved with Wallerian degeneration46,47; this is a neurodegenerative process which occurs after axonal injury in both the peripheral and central nervous system. Locus 15 on chromosome 2 contains ENSG00000271894, a non-coding RNA gene. SLC4A10 and DPP4 are located on chromosome 2 (locus 28). Variants in both SLC4A10 and DPP4 have been linked to schizophrenia48,49; hippocampal volume has also been linked to variants in DPP450. A variant of FOXO3 (chromosome 6; locus 69) has been shown to be associated with longevity in humans51,52; it is found in most centenarians across a variety of populations. MAPT, WNT3, CRHR1, KANSL1, and NSF are located on chromosome 17, locus 133; genetic variants within these genes have been linked to Alzheimer’s disease in APOE e4 carriers53, Parkinson’s disease54–56, neuroticism57, infant head circumference58, intracranial volume59, and subcortical brain region volumes60. Researchers following up the present study's results could prioritise the genetic loci uncovered herein that are associated with general cognitive function and reaction time (Supplementary Data 16 and 17), as well as those that are also associated with brain-related measures in other large GWASs. Such variants, being associated with multiple cognitive and neurological phenotypes, might help to prioritise potentially causal variants, and help to identify how differences in genotypic sequence are linked to such phenotypic consequences. We note limitations with the cognitive phenotypes studied. For general cognitive function, phenotypic heterogeneity is a limitation, due to different tests being used in most samples. We also note the small number of cognitive tests being used in the construction of the general cognitive function phenotype in some cohorts. However, we were able to investigate this further by estimating genetic correlations for general cognitive function amongst some of the larger cohorts. These demonstrated strong positive genetic correlations that ranged from r g =0.88–1.0 (Table 2). There were slight differences in the test questions and the testing environment for the UK Biobank’s‘fluid’(verbalnumerical reasoning) test in the assessment centre versus the online version. We used a bivariate GREML analysis to investigate the genetic contribution to the stability of individual differences in people’s verbal-numerical reasoning; we report a significant perfect genetic correlation. The UK Biobank’s reaction time variable is based on only four trials per participant; this is far fewer trials than would typically be measured. For example, other large UK surveys have used 40 trials in choice RT procedures61,62. Both the overall size of the present study’s meta-analysis of GWASs and the inclusion of a single large sample, UK Biobank, are strengths, which contributed to the abundance of new findings. When compared to an analysis of only UK Biobank herein, the current meta-analysis adds 92 independent significant loci, 51 of which are novel. Yet, as genome-wide studies of other complex traits continue to increase up to and beyond a million individuals, an even larger sample size will be required in order to seek replication of these findings, identify new associations, and generate stronger polygenic predictions15,63 (Supplementary Fig. 1). When compared to previous large studies of cognitive function and education, we replicate a large proportion, but not all, of the previously-reported significant findings. These differences in reported findings might be explained partly by differences in study populations (including age, social status, and ethnicity), phenotypes, and analysis methods. Whereas we know that there is sample overlap in the studies described, each comprises a unique set of contributing cohorts. As described above, there is substantial variation in the cognitive tests that contribute to the construction of a general cognitive function phenotype. Cognitive function is not as simple to measure as, say, height, and it is far from being standardised. This limitation applies across the GWAS meta-analysis studies, as well as within them. The use of different analysis methods—for example MTAG, which includes phenotypes other than the target phenotype—might also contribute to the different findings that have been reported. Finally, it is also possible that, although specific loci reached genome-wide significance in particular studies, there are false positives, highlighting the importance of wellpowered replication studies. Gene-based analysis has been shown to increase the power to detect associations, because the multiple testing burden is reduced, and the effects of multiple SNPs are combined together. From these gene-based analyses, the association of a gene with general cognitive function does not imply that it is causally related to this phenotype, only that the gene is in a region of strong association within a locus. These loci may contain multiple associated genes; therefore, we note that all of the associated genes that we reported may not be independent findings. However, we note that gene-based testing will not be able to detect associations that fall outside of the gene-body. This means that, if SNPs in promoter regions harbour variants that are causal to differences in general cognitive function or reaction time, they will be missed in our gene-based analyses. General cognitive function has prominence and pervasiveness in the human life course, and it is important to understand the environmental and genetic origins of its variation in the population4. The unveiling here of many genetic loci, genes, and genetic pathways that contribute to its heritability (Fig. 2; Supplementary Data 1,6and 7)—which it shares, as we find here, with many health outcomes, longevity, brain structure, and processing speed—provides a foundation for exploring the mechanisms that bring about and sustain cognitive efficiency through life. Methods Participants and cognitive phenotypes. The present study includes 300,486 individuals of European ancestry from 57 population-based cohorts brought together by the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), the Cognitive Genomics Consortium (COGENT) consortia, and UK Biobank (Supplementary Note 2). All individuals were aged between 16 and 102 years. Exclusion criteria included clinical stroke (including self-reported stroke) or prevalent dementia (Supplementary Data 18). General cognitive function, unlike height for example, is not measured the same way in all samples. Here, this was mitigated by applying a consistent method of extracting a general cognitive function component from cognitive test data in the cohorts of the CHARGE and COGENT consortia; all individuals were of European ancestry (Supplementary Note 1). For each of the CHARGE and COGENT cohorts, a general cognitive function component phenotype was constructed from a number of cognitive tasks. Each cohort was required to have tasks that tested at least three different cognitive domains. We avoided taking more than one cognitive test score from any individual cognitive test. Principal component analysis was applied to the cognitive test scores to derive a measure of general cognitive function. Principal component NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04362-x ARTICLE NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications 9 and Mental Health, University of Toronto, Toronto M5T 1L8, Canada. 153 Department of Public Health, University of Helsinki, Helsinki 00014, Finland. 154 Department of Radiology, Erasmus MC, Rotterdam 3015, The Netherlands. 155 Department of Psychiatry and Behavioral Sciences, Faculty of Medicine, University of Crete, Heraklion GR-71003, Greece. 156 Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston 77030 TX, USA. 157 Human Genome Sequencing Center, Baylor College of Medicine, Houston 77030-3411 TX, USA. 158 Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin DO2 AY89, Ireland. 159 Center for Translational and Systems Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York 10032 NY, USA. 160 Program in Medical and Population Genetics, Broad Institute, Cambridge 02142 MA, USA. 161 Department of Neurology, University Hospital of Bordeaux, Bordeaux 33000, France. 162 Department of Global Health, University of Washington, Seattle 98104 WA, USA. 163 Department of Cardiology, Leiden University Medical Center, Leiden 2333, The Netherlands. 164 Institute for Behavioral Genetics, University of Colorado, Boulder 80309 CO, USA. 165 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK. 166 Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki 00014, Finland. 167 Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Manchester Academic Health Science Centre, and Manchester Medical School, Institute of Brain, Behaviour, and Mental Health, University of Manchester, Manchester M13 9PL, UK. 168 Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20520, Finland. 169 School of Medicine and Public Health, University of Newcastle, New South Wales 2308, Australia. 170 Neuroscience Research Australia, Sydney 2031, Australia. 171 Faculty of Medicine, University of New South Wales, Sydney 2052, Australia. 172 Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh EH8 9JZ, UK. 173 Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA. 174 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany. 175 Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig 04103, Germany. 176 Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks 11004 NY, USA. 177 Department of Psychiatry, Hofstra Northwell School of Medicine, Hempstead 11549 NY, USA. 178 Division of Psychiatry, University of Edinburgh, Edinburgh EH10 5HF, UK. 179 MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton SO16 6YD, UK. 180 Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio 78229 TX, USA. These authors contributed equally: Gail Davies, Max Lam. These authors jointly supervised this work: Todd Lencz, Ian J. Deary. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04362-x 16 NATURE COMMUNICATIONS | (2018) 9:2098 |DOI: 10.1038/s41467-018-04362-x |www.nature.com/naturecommunications