Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Intelligence has been associated with a wide variety of human behaviors and brain anatomy.

“Intelligence has been associated with a wide variety of human behaviors 15 and brain anatomy 27”

• cites [15] Deary, I. J., Penke, L. & Johnson, W. The neuroscience of human intelligence differences. Nat. Rev. Neurosci. 11, 201–211 (2010). sourceSupported
• cites [27] Posthuma, D. et al. The association between brain volume and intelligence is of genetic origin. Nat. Neurosci. 5, 83–84 (2002). sourceSupported

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Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence.

“Recent genome-wide metaanalyses have identified 24 genomic loci linked to variation in intelligence 3-7”

• cites [3] Davies, G. et al. Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N = 53949). Mol. Psychiatry 20, 183–192 (2015). sourcePartly supported

  The Results report 13 genome-wide significant SNPs in three genomic regions associated with general cognitive function. This supports the claim that a genome-wide meta-analysis identified linked regions, but this paper does not establish the cited sentence’s total of 24 loci across multiple analyses. (LLM, conf=0.90)

  Experimental — accuracy not yet measured
• cites [4] Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N = 112 151). Mol. Psychiatry 21, 758–767 (2016). sourcePartly supported

  The Introduction (page unavailable) reports genome-wide significant associations with general cognitive function, and the Results section on verbal–numerical reasoning (page unavailable) identifies three associated genomic regions. These passages support the link between genomic loci and cognitive variation, but the paper does not establish the claimed total of 24 loci identified by recent meta-analyses. (LLM, conf=0.85)

  Experimental — accuracy not yet measured
• cites [5] Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49, 1107–1112 (2017). sourcePartly supported

  The Letters section reports that this genome-wide meta-analysis identified loci associated with intelligence, supporting the general point. It reports 18 independent significant loci, not 24; the cited paper alone therefore does not substantiate the sentence’s total across multiple studies. No page number is supplied in our copy. (LLM, conf=0.88)

  Experimental — accuracy not yet measured
• cites [6] Trampush, J. W. et al. GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium. Mol. Psychiatry 22, 336–345 (2017). sourcePartly supported

  The Results (GWAS of general cognitive function; page unavailable) report two genome-wide-significant loci, supporting the claim that a meta-analysis identified loci associated with cognitive performance. The Abstract reports that 24 is the number of cohorts studied, not the number of loci identified; it also reports three additional loci from gene-based analysis. This source therefore does not support the count of 24 genomic loci. (LLM, conf=0.90)

  Experimental — accuracy not yet measured
• cites [7] Zabaneh, D. et al. A genome-wide association study for extremely high intelligence. Mol. Psychiatry 23, 1226–1232 (2018). sourcePartly supported

  The Results (Analysis of genome-wide SNPs; page unavailable) identify three significant SNPs at one ADAM12 locus, and the Introduction discusses earlier genome-wide studies of intelligence. Neither passage supports the specific claim that meta-analyses identified 24 loci. (LLM, conf=0.83)

  Experimental — accuracy not yet measured

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Intelligence is a major determinant of human health and well-being.

“a major determinant of human health and well-being 2”

• cites [2] Wraw, C., Deary, I. J., Gale, C. R. & Der, G. Intelligence in youth and health at age 50. Intelligence 53, 23–32 (2015). sourcePartly supported

  The Abstract’s Results & conclusion and the Discussion (page numbers not provided) link higher intelligence in youth with better physical health and fewer chronic conditions at age 50. They describe associations, not intelligence as a major determinant of human health generally, and the study does not establish an effect on well-being. (LLM, conf=0.85)

  Experimental — accuracy not yet measured

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Previous studies implicate bidirectional causal effects between cognitive ability and educational attainment.

“Such findings are consistent with previous studies implicating bidirectional causal effects 30,31”

• cites [30] Johnson, W., Deary, I. J. & Iacono, W. G. Genetic and environmental transactions underlying educational attainment. Intelligence 37, 466–478 (2009). sourceSupported
• cites [31] Richards, M. & Sacker, A. Is education causal? Yes. Int. J. Epidemiol. 40, 516–518 (2011). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Conserved and regulatory regions have previously been implicated in cognitive functioning, but coding regions have not.

“Conserved and regulatory regions have previously been implicated in cognitive functioning 22 , but coding regions have not.”

• cites [22] Hill, W. D. et al. Molecular genetic aetiology of general cognitive function is enriched in evolutionarily conserved regions. Transl. Psychiatry 6, e980 (2016). sourcePartly supported

  The Results (Partitioned heritability; page unavailable) report enrichment for cognitive-function heritability in conserved regions and in regulatory annotations, including weak enhancers and DNase hypersensitivity sites. The paper does not establish the broader claim that coding regions have not been implicated in cognitive functioning. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Previous reports support both a causal link and genetic overlap between intelligence and schizophrenia.

“A number of previous reports support both a causal link and genetic overlap between these phenotypes 32,33”

• cites [32] Kendler, K. S., Ohlsson, H., Sundquist, J. & Sundquist, K. IQ and schizophrenia in a Swedish national sample: their causal relationship and the interaction of IQ with genetic risk. Am. J. Psychiatry 172, 259–265 (2015). sourceSupported
• cites [33] Le Hellard, S. et al. Identification of gene loci that overlap between schizophrenia and educational attainment. Schizophr. Bull. 43, 654–664 (2017). sourcePartly supported

  The Discussion and the ‘Genetic Overlap of SCZ and Educational Attainment’ section report genetic overlap between general cognition and schizophrenia; page numbers are unavailable in our copy. The paper does not establish a causal link between intelligence and schizophrenia, so it supports only the genetic-overlap part of the claim. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

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A false discovery rate of 1 × 10-5 was used to define significant chromatin interactions, based on previous recommendations modified for the cell lines used.

“We used an FDR of 1 × 10 -5 to define significant interactions, based on previous recommendations 51 modified to account for the differences in cell lines used here.”

• cites [51] Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016). sourcePartly supported

  The Experimental Procedures ('Identification of significant Hi-C contacts') support using an FDR threshold to define significant contacts, but specify FDR < 1 × 10⁻⁶, not 1 × 10⁻⁵. The Results ('Identification of Frequently Interacting Regions') discuss variation between samples, but do not describe the citing authors’ cell-line-specific modification. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

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Brain-cell-type expression data used to construct the cell-type gene sets were drawn from single-cell RNA-seq data from mouse brains.

“Briefly, brain-cell-type expression data were drawn from single-cell RNA-seq data from mouse brains.”

• cites [53] Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018). sourceSupported
• cites [37] Coleman, J.R.I. et al. Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals. Mol. Psychiatry https://doi.org/10.1038/s41380-018-0040-6 (2018). sourceSupported

May combine two claims.

Comparison with previous GWAS supported these correlations, showing numerous shared genetic variants across phenotypes.

“Comparison with previous GWAS 28 supported these correlations, showing numerous shared genetic variants across phenotypes”

• cites [28] MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45(D1), D896–D901 (2017). sourcePartly supported

  The Introduction (page not provided) says GWAS Catalog data have been used to find evidence of pleiotropy, which supports the general idea of genetic associations across traits. It does not report the citing authors’ comparison with previous GWAS, confirm their correlations, or show that numerous variants were shared across the phenotypes they studied. (LLM, conf=0.80)

  Experimental — accuracy not yet measured

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A CADD score above 12.37 is the threshold for a variant to be potentially pathogenic.

“A CADD score above 12.37 is the threshold to be potentially pathogenic 23 .”

• cites [23] Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014). sourcePartly supported

  The Technical Report says CADD scores rank known pathogenic variants highly, but it does not establish 12.37 as a pathogenicity threshold. It also says C-scores cannot be precisely calibrated to the likelihood that a variant is pathogenic and are best interpreted as indicating likelihood of deleteriousness. (LLM, conf=0.87)

  Experimental — accuracy not yet measured

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The estimated GSMR causal effect coefficients (bxy) are approximately equal to the natural log odds ratio for a case-control trait.

“The estimated causal effect coefficients (b xy ) are approximately equal to the natural log odds ratio (OR) for a case-control trait 29 .”

• cites [29] Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018). sourceSupported

May combine two claims.

The NCP-weighting method improves power when using an extreme case sampling design such as HiQ and provides a comparable metric to combine information from different analytic designs while accounting for differences in power/effective sample size.

“This method improves power when using an extreme case sampling design such as that in HiQ 35 and provides a comparable metric with which to combine information from different analytic designs while accounting for their differences in power/effective sample size.”

• cites [35] Peloso, G. M. et al. Phenotypic extremes in rare variant study designs. Eur. J. Hum. Genet. 24, 924–930 (2016). sourceSupported

Classified as probably not a substantial claim.

Independently associated loci from the meta-analysis were defined using FUMA.

“Independently associated loci from the metaanalysis were defined using FUMA 24 (see URLs), an online platform for functional mapping of genetic variants.”

• cites [24] Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017). sourceSupported

May combine two claims.

Convergent evidence of strong association (z = 9.49) and the highest observed probability of a deleterious protein effect (CADD score = 34) were found for rs13107325.

“Convergent evidence of strong association (z = 9.49) and the highest observed probability of a deleterious protein effect (CADD 23 score = 34) were found for rs13107325.”

• cites [23] Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014). sourceSupported

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

GSMR can help differentiate the likely causal direction of association between two traits but cannot make any statement about the intermediate mechanisms involved in any potential causal process.

“This method can help differentiate the likely causal direction of association between two traits but cannot make any statement about the intermediate mechanisms involved in any potential causal process.”

• cites [29] Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018). sourcePartly supported

  The Methods section describes GSMR as testing whether an exposure causally affects an outcome, and the Results section uses forward and reverse GSMR analyses to assess direction. The Results also say it is difficult to distinguish whether an effect is mediated by other risk factors or driven by them. But the paper does examine associations among risk factors and conditional effects, so the categorical claim that GSMR cannot make any statement about intermediate mechanisms overstates its stated limitation. Page numbers are unavailable in our copy. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

• AssumptionThe authors state this as an assumption: The effect of non-pleiotropic SNPs on an exposure is related to their effect on the outcome in an independent sample only via mediation through the phenotypic causal pathway.

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FUMA contains Hi-C data for 14 tissue types from the study of Schmitt et al.

“FUMA currently contains Hi-C data for 14 tissue types from the study of Schmitt et al. 51 .”

• cites [51] Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016). sourceSupported

May combine two claims.

Age-stratified meta-analyses indicated comparable heritability across age groups, hSNP2 = 0.19-0.22.

“comparable heritability across age groups, as captured by the SNPs included in the analysis (h SNP 2 = 0.19-0.22)”

May combine two claims.

Conditional analysis indicated three independent associations-regulation of nervous system development, central nervous system neuron differentiation, and regulation of synapse structure or activity-that together accounted for the associations of the other sets.

“Conditional analysis indicated that there were three independent associations-regulation of nervous system development, central nervous system neuron differentiation, and regulation of synapse structure or activity-that together accounted for the associations of the other sets.”

Probably not a substantial claim: may not pass S3 (load-bearing).

350 of the GWGAS genes were also mapped by FUMA.

“of which 350 were also mapped by FUMA”

May combine two claims.

The FUMA-mapped genes were enriched for brain tissue expression and several regulatory biological gene sets.

“The FUMA-mapped genes were enriched for brain tissue expression and several regulatory biological gene sets”

May combine two claims.

Cross-locus interactions implicated ELAVL2, PTCH1, ATF4, FBXL17, and MAN2A1 in the left ventricle of the heart, SATB2 in liver tissue, and MEF2C in five tissues.

“Cross-locus interactions implicated ELAVL2, PTCH1, ATF4, FBXL17, and MAN2A1 in the left ventricle of the heart, SATB2 in liver tissue, and MEF2C in five tissues.”

May combine two claims.

rs13107325 is a missense mutation (MAF = 0.065, P = 2.23 × 10^-21) in SLC39A8 and was the lead SNP in locus 71.

“This missense mutation (MAF = 0.065, P = 2.23 × 10 -21 ) in SLC39A8 was the lead SNP in locus 71”

May combine two claims.

Twin and family studies show strong genetic correlations across diverse cognitive domains, suggesting pleiotropy, and across levels of ability.

“twin and family studies show strong genetic correlations across diverse cognitive domains 11 , suggesting pleiotropy, and across levels of ability 11”

• cites [11] Plomin, R. & Kovas, Y. Generalist genes and learning disabilities. Psychol. Bull. 131, 592–617 (2005). sourceNot checked — no full text of the source

Classified as probably not a substantial claim.

The variance common across cognitive tasks can be modeled as a latent factor denoted as g (the general factor of intelligence).

“the variance common across cognitive tasks can be modeled as a latent factor denoted as g (the general factor of intelligence) 9,10”

• cites [9] Carroll, J. B. Human Cognitive Abilities: A Survey of Factor-Analytic Studies. (Cambridge University Press, Cambridge, UK, 1993). sourceNot checked — no full text of the source
• cites [10] Spearman, C. “General intelligence,” objectively determined and measured. Am. J. Psychol. 15, 201–292 (1904). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

The Bonferroni-corrected significance threshold for the 56 functional annotation categories in the stratified LD Score regression was 0.05/56 = 0.0009.

“The Bonferroni-corrected significance threshold was 0.05/56 annotations = 0.0009.”

May combine two claims.

Restricting interaction-mapped genes to those with an enhancer-promoter overlap reduced the number of genes mapped but increased the likelihood that the identified genes would have a plausible biological function.

“This reduced the number of genes mapped but increased the likelihood that those identified would have a plausible biological function.”

Classified as probably not a substantial claim.

SNP-based replication was carried out using a proxy phenotype of educational attainment in an independent sample.

“SNP-based replication was carried out using a proxy phenotype of educational attainment in an independent sample.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

A P value threshold of 0.01 was used for HEIDI outlier detection in the GSMR analyses, which removes 1% of SNPs by chance if there is no pleiotropic effect.

“We used a threshold P value of 0.01 for the outlier detection analysis in HEIDI, which removes 1% of SNPs by chance if there is no pleiotropic effect.”

Probably not a substantial claim: may not pass S3 (load-bearing).

With the exception of HiQ/HRS, all cohorts used a normally distributed single sum, mean, or factor score from a multidimensional set of cognitive performance tests as the phenotype in a covariate-adjusted GWAS using linear regression.

“With the exception of HiQ/HRS, all cohorts extracted a single sum score, mean score, or factor score from a multidimensional set of cognitive performance tests and used this normally distributed score as the phenotype in a covariate-adjusted (for example, age, sex, ancestry principal components) GWAS using linear regression methods.”

Classified as probably not a substantial claim.

The Bonferroni-corrected significance threshold for the 38 genetic correlation tests was 0.05/38 = 1.32 × 10-3.

“The Bonferroni-corrected significance threshold was 0.05/38 traits = 1.32 × 10 -3 .”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Many of the 1,518 genes linked to intellectual disability and/or developmental delay were identified by non-GWAS sources and are not represented in the NHGRI catalog.

“Many of these have been identified by non-GWAS sources and are not represented in the NHGRI catalog.”

Classified as probably not a substantial claim.

Polygenic scores were calculated from leave-one-out meta-analyses in which four cohorts were separately excluded and reserved for score validation.

“We calculated polygenic scores (PGSs) based on the SNP effect sizes of the leave-one-out meta-analyses, from which four cohorts were (separately) excluded and reserved for score validation.”

May combine two claims.

The X chromosome was treated separately because imputed genotypes were not available for the X chromosome in UKB and there was little overlap between UKB called genotypes and imputed data from other cohorts (n SNPs < 500).

“The X chromosome was treated separately in the meta-analysis because imputed genotypes were not available for the X chromosome in the largest cohort (UKB), and there was little overlap between the UKB called genotypes and imputed data from other cohorts (n SNPs < 500).”

Classified as probably not a substantial claim.

Only called X-chromosome variants in UKB were included for X-chromosome analyses after X-chromosome-specific quality-control steps.

“We therefore included only the called X-chromosome variants in UKB for these analyses after performing X-chromosome-specific quality-control steps 38 .”

• cites [38] König, I. R., Loley, C., Erdmann, J. & Ziegler, A. How to include chromosome X in your genome-wide association study. Genet. Epidemiol. 38, 97–103 (2014). sourceNot checked — no full text of the source

Classified as probably not a substantial claim.

The Bonferroni-corrected significance threshold for the 9,494 gene sets tested by GENE2FUNC was 0.05/9,494 = 5.27 × 10-6.

“The Bonferroni-corrected significance threshold was 0.05/9,494 gene sets = 5.27 × 10 -6 .”

Probably not a substantial claim: may not pass S3 (load-bearing).

A false discovery rate of 0.05 was used to define significant eQTL associations in the gene mapping.

“We used a false discovery rate (FDR) of 0.05 to define significant eQTL associations”

Classified as probably not a substantial claim.

The explained variance (ΔR2) of each polygenic score was derived from a linear model regressing the GWAS intelligence phenotype on the PGS while controlling for the same covariates as in each cohort-specific GWAS, compared to a covariates-only model.

“The explained variance (Δ R 2 ) was derived from a linear model in which the GWAS intelligence phenotype was regressed on each PGS while controlling for the same covariates as in each cohort-specific GWAS, compared to a linear model with GWAS covariates only.”

Probably not a substantial claim: may not pass S3 (load-bearing).

All files were checked for data integrity and accuracy.

“All files were checked for data integrity and accuracy.”

Classified as probably not a substantial claim.

The Bonferroni-corrected significance threshold for the 7,323 gene sets tested with MAGMA was 0.05/7,323 = 6.83 × 10-6.

“The Bonferroni-corrected significance threshold was 0.05/7,323 gene sets = 6.83 × 10 -6 .”

Probably not a substantial claim: may not pass S3 (load-bearing).

After combining all data, meta-analysis results were filtered to exclude any variants with n < 50,000.

“After combining all data, meta-analysis results were further filtered to exclude any variants with n < 50,000.”

Classified as probably not a substantial claim. May combine two claims.

Independent significant SNPs were defined as having a Bonferroni-corrected genome-wide significant two-tailed P < 5 × 10−8 and representing signals independent from each other at r2 < 0.6.

“We first identified 'independent significant SNPs' , which had a Bonferroni-corrected genome-wide significant two-tailed P value (P < 5 × 10 -8 ) and represented signals that were independent from each other at r 2 < 0.6.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Indels and SNPs that were duplicated, multiallelic, monomorphic, or ambiguous (A/T or C/G with MAF > 0.4) were excluded.

“Indels and SNPs that were duplicated, multiallelic, monomorphic, or ambiguous (A/T or C/G with MAF > 0.4) were also excluded.”

Classified as probably not a substantial claim.

The Bonferroni-corrected genome-wide significance threshold for the 18,128 genes tested in the GWGAS was 0.05/18,128 = 2.76 × 10-6.

“The Bonferroni-corrected genome-wide significance threshold was 0.05/18,128 genes = 2.76 × 10 -6 .”

Classified as probably not a substantial claim.

To create independent discovery samples for polygenic score validation, meta-analyses were conducted with a leave-one-out strategy excluding each of four validation datasets.

“To create independent discovery samples for use in polygenic score validation, we also conducted meta-analyses with a 'leave-one-out' strategy in which summary statistics from four validation datasets were each excluded from the meta-analysis”

Classified as probably not a substantial claim. May combine two claims.

SNPs were filtered from further analysis if they had imputation quality (INFO/R2) < 0.6, Hardy-Weinberg equilibrium P < 5 × 10−6, study-specific minor allele count < 100, or mismatch of alleles or allele frequency difference greater than 20% from the HRC reference panel.

“SNPs were filtered from further analysis if they met any of the following criteria: imputation quality (INFO/R 2 ) score < 0.6, Hardy-Weinberg equilibrium P < 5 × 10 -6 , study-specific minor allele frequency (MAF) corresponding to a minor allele count (MAC) < 100, and mismatch of alleles or allele frequency difference greater than 20% from the Haplotype Reference Consortium (HRC) genome reference”

• cites [16] Deary, I. J. Intelligence. Annu. Rev. Psychol. 63, 453–482 (2012). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Visual inspection of the distribution of summary statistics was performed, and Manhattan plots and quantile-quantile plots were created for the cleaned summary statistics from each cohort.

“Visual inspection of the distribution of the summary statistics was performed, and Manhattan plots and quantile-quantile plots were created for the cleaned summary statistics from each cohort (Supplementary Fig. 1).”

Probably not a substantial claim: may not pass S3 (load-bearing).

Because chromatin interactions are often defined at a resolution such as 40 kb, an interacting region can span multiple genes.

“Because chromatin interactions are often defined in a certain resolution, such as 40 kb, an interacting region can span multiple genes.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Older samples were screened for cognitive decline to exclude the possibility of dementia affecting performance on cognitive tests.

“Participants ranged from children to older adults, with older samples being screened for cognitive decline to exclude the possibility of dementia affecting performance on cognitive tests.”

Probably not a substantial claim: may not pass S3 (load-bearing).

A random-effects meta-analysis was included for each phase to evaluate potential heterogeneity in SNP association statistics between cohorts.

“We additionally included a random-effects meta-analysis for each phase, as implemented in METAL, to evaluate potential heterogeneity in the SNP association statistics between cohorts.”

Classified as probably not a substantial claim.

For each independent genomic locus, evidence for replication was defined as the lead SNP or another correlated SNP in the region being sign concordant with the corresponding intelligence meta-analysis SNP and having a two-tailed P value of association with educational attainment < 0.05/242 = 0.0002.

“For each independent genomic locus, we considered it to be evidence for replication if the lead SNP or another correlated SNP in the region was sign concordant with the corresponding SNP in the intelligence meta-analysis and had a two-tailed P value of association with educational attainment smaller than 0.05/242 independent tests = 0.0002.”

Classified as probably not a substantial claim.

To test replication of SNPs with educational attainment, a sign concordance test was performed for all genome-wide significant SNPs from the meta-analysis using the two-tailed exact binomial test.

“To test replication of the SNPs with this proxy phenotype, we performed a sign concordance test for all genome-wide significant SNPs from the meta-analysis using the two-tailed exact binomial test.”