Multi-scale systems genomics analysis predicts pathways, cell 1 types and drug targets involved in normative human cognition 2 variation 3

Abstract


Introduction
The growth in genomics and functional annotation resources over the past decade provides an opportunity to build models of how changing genotype affects multiple levels of system organization underlying a phenotype, from genes and molecules through to pathway, cell, cell circuit, anatomy and physiology system levels (systems-genomics analysis).This opportunity complements a conceptual shift to systems-level thinking in many biomedical fields.For example, a major drive in psychiatry is the reconceptualization of mental illnesses as brain disorders treatable by neurobiological systemgrounded therapies, such as working memory deficits in schizophrenia 1 .As a shared guide for the field, the U.S. National Institute of Mental Health has developed a "genes-to-behaviour" framework that deconstructs human behaviour into neurobehavioural domains, such as cognition and social processing 2 .Each of these constructs has subconstructs and these are linked to a variety of systems level concepts.While the genetic architecture of overall cognitive ability (i.e., intelligence) has been studied by large-scale GWAS 3-5 , little is known about the molecular basis of more detailed neurocognitive phenotypes.
In this work, we identify genetic variants associated with normative variation in nine cognitive phenotypes measured in the Philadelphia Neurodevelopmental Cohort (PNC).We selected this study for our systems-genomics analysis as the phenotypes measured were designed around systems-level neuroscience theory.For example, tasks requiring use of working memory, a type of short-term memory that recruits a cortical-subcortical network including the dorsolateral prefrontal cortex, shows a genetic component in twins, and is impaired in schizophrenia 6-8 .Thus, we hypothesize that this data will yield systems-genomics signal, that is genetic variants linked to one or more system level scales of phenotype-related organization.To our knowledge, there have been no reports of genotypephenotype analyses on the PNC dataset.Using diverse functional genomics resources, we link variants to genes, pathways, brain cell types, brain systems, predicted drug targets, and diseases, providing a systems-level view of the genetics of the neurodevelopmental phenotypes under study.With standardized and well-controlled cognitive tests and genotyping on over 8,000 community youths aged 8-21 years, the PNC is the largest publicly-available dataset for genotype-phenotype analysis of developmental cognition 9,10 .All participants have computerized neurocognitive test battery scores, measuring speed and accuracy in multiple cognitive domains.These measures have neurobehavioural validity 11 , SNP-based heritability 12 , and disease relevance 11,13 .Multiple cognitive test scores in the PNC demonstrate significant SNP-based heritability 12 , and reduced test scores are correlated with increased genetic risk of psychiatric disease 13 .Moreover, the PNC captures the age range through which some cognitive abilities, such as working memory, mature to stable adult levels 14,15 .Despite the relatively small size of this dataset by GWAS standards, we reasoned that the PNC dataset provides a valuable opportunity to study the molecular and systems basis of cognitive tasks impaired in disease and evaluate how a systems-genomics approach can increase statistical and interpretive power compared to standard SNP and gene-based analysis approaches, both of which are performed here to enable us to compare these approaches.

Methods
Cognitive assessment was performed using the Penn Computerized Neurocognitive Battery (CNB), which was customized and shortened for a pediatric population.Performance is measured by a session of trials containing items with varying levels of difficulty, which allows the test to capture nuances in speed and accuracy measures.Tests were also developed through evaluation by

Genetic imputation
The samples (n=8,719) were all genotyped using Illumina or Affymetrix SNP-array platforms by the Center for Applied Genomics at The Children's Hospital of Philadelphia.16 The workflow for genomic imputation is shown in Supplementary Figure 1.Genotypes for the four most frequent microarray genotyping platforms were downloaded from dbGaP (phs000607.v1).We performed genetic imputation for the Illumina Human610-Quad BeadChip, the Illumina HumanHap550 Genotyping BeadChip v1.1, Illumina HumanHap550 Genotyping BeadChip v3, and the Affymetrix AxiomExpress platform (Supplementary Table 1, total of 6,502 samples before imputation), using the protocol recommended by the EMERGE consortium 17 .Imputation was performed as follows: Step 1: Platform-specific plink quality control: Quality control was first performed for each microarray platform separately.Single nucleotide polymorphisms (SNPs) were limited to those on chr1-22.SNPs in linkage disequilibrium (LD) were excluded (--indep-pairwise 50 5 0.2), and alleles were recoded from numeric to letter (ACGT) coding.Samples were excluded if they demonstrated heterozygosity > 3 standard deviations (SD) from the mean, or if they were missing >=5% genotypes.
Where samples had pairwise Identity by Descent (IBD) > 0.185, one of the pair was excluded.Variants with minor allele frequency (MAF) < 0.05 were excluded, as were those failing Hardy-Weinberg equilibrium with p < 1e-6 and those missing in >=5% samples.
Step 2: Convert coordinates to hg19.LiftOver 18 was used to convert SNPs from human genome assembly version hg18 to hg19; Hap550K v1 data was in hg17 and was converted from this build to hg19.
Step 3: Strand-match check and prephasing: ShapeIt v2.r790 19 was used to confirm that the allelic strand in the input data matched that in the reference panel; where it did not, allele strands were flipped (shapeit "-check" flag).ShapeIt was used to prephase the variants using the genetic_b37 Step 4: Imputation: Genotypes were imputed using Impute2 v2.3.2 20 and a reference panel from the Step 5: Post-imputation quality control: The HapMap3 panel was used to assign genetic ancestry for samples, using steps from 21 (Supplementary Figure 3).Individuals within 5 SD of the centroid of the

Phenotype processing
Phenotype data was downloaded from dbGaP for 8,719 individuals.637 individuals with severe medical conditions (Medical rating=4) were excluded to avoid confounding the symptoms of their conditions with performance on the cognitive tests 12 .Linear regression was used to regress out the effect of age at test time (variable name: "age at cnb") and sex from sample-level phenotype scores, and the residualized phenotype was used for downstream analysis.
The nine phenotypes selected for systems-genomics analysis are measures of overall performance accuracy in the Penn Computerized Neurocognitive Test Battery (CNB; Supplementary Table 3) and represent major cognitive domains.Tasks mapped to domains in the following manner: verbal reasoning, nonverbal reasoning, and spatial reasoning measured complex cognition; attention allocation and working memory measured executive function; recall tests for faces, words and objects measured declarative memory, and emotion identification measured social processing.Following regression, none of the phenotypes were significantly correlated with age after Bonferroni correction, indicating that the age effect had been reduced (Supplementary Table 4).Following guidelines from previous analyses on these data 13 , individuals with scores more than four standard deviations from the mean for a particular test, representing outliers, were excluded from the analysis of the corresponding phenotype.For a given phenotype, only samples with a code indicating a valid test score (codes "V" or "V2") were included; e.g. for pfmt_tp (Penn Face Memory Test), only samples with pfmt_valid = "V" or "V2" were retained; the rest had scores set to NA.Finally, each phenotype was dichotomized so that samples in the bottom 33 rd percentile were relabeled as "poor" performers and those in the top 33 rd were set to be "good" performers; for a given phenotype, this process resulted in ~1,000 samples in each group (Supplementary Table 3).Where an individual had good or poor performance in multiple phenotypes, they were included in the corresponding group for each of those phenotypes.

Genetic association analysis
For each of nine CNB phenotypes, marginal SNP-level association was calculated using a mixed-effects linear model (MLMA), using the leave-one-chromosome-out (LOCO) method of estimating polygenic contribution (GCTA v1.97.7beta software 22 ).In this strategy, a mixed-effect model is fit for each SNP: where y is the binarized label (good/poor performer on a particular task), x measures the effect of genotype (indicator variable coded as 0, 1 or 2), grepresents the polygenic contribution of all the SNPs in the genome (here, the ~4.89M imputed SNPs), and e represents a vector of residual effects.In the LOCO variation, gis calculated using a chromosome-specific genetic relatedness matrix, one that excludes the chromosome on which the candidate SNP is located 22 .SNPs and associated genes were annotated as described in Supplementary Notes 1-4.

Hi-C Data Processing
We downloaded publicly-available Hi-C data from human prefrontal cortex tissue 23,24

SNP to gene mapping for annotation and enrichment analyses
SNPs were mapped to genes using a combination of genome position information (i.e.closest gene), brain-specific expression Quantitative Trait Locus (eQTL) and higher-order chromatin interaction (Hi-C) information.For 3D chromatin interaction mapping (Hi-C), we downloaded long-range chromatin interaction data from the adult cortex 24 and human developing brain 30 (Interactions to TSS for cortical plate and germinal zone, Tables S22 and S23 of Won et al. 30 ).The enhancer region of these enhancer-promoter interactions was intersected with brain enhancers (see above) to only keep enhancer-promoter interactions overlapping known active brain enhancers.Then, the promoter region of these filtered enhancer-promoter interactions was mapped to a gene if it intersected with the region 250bp upstream and 500bp downstream of the corresponding gene transcription start site.SNPs were mapped to a gene if they overlapped the promoter of the filtered enhancer-promoter sites.

Gene
Finally, SNPs were positionally mapped to the nearest gene if the shortest distance to either transcription start site or end site was 60kb.This cutoff was selected because it maps the majority (90%) of SNPs to their nearest gene, following a distance distribution analysis.
The order of SNP-gene mapping was as follows: SNPs that mapped to a gene via brain eQTL or Hi-C interactions were prioritized and not also positionally mapped to a gene.A SNP was allowed to map to genes using both eQTL and Hi-C.SNPs without eQTL or Hi-C mappings were positionally mapped to a gene.Where a SNP positionally mapped to multiple genes, all associations were retained.These SNPgene mappings were used for the pathway and gene set enrichment analysis described below, as well as to annotate SNPs from the GWAS analysis.
. CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made available under The copyright holder for this preprint (which was not this version posted March 15, 2021.; https://doi.org/10.1101/751933doi: bioRxiv preprint Using these criteria, 7.7% of SNPs mapped to genes using non-positional information (246,357 by eQTL and 16,923 by HiC, for a total of 263,280 SNPs); 2,917,948 SNPs mapped solely by positional information (89.2%).In total, SNPs mapped to 18,782 genes.1,711,969 SNPs did not map to any genes (34.9%).

Gene set enrichment analysis
For each of the nine CNB phenotypes, gene set enrichment analysis was performed using an implementation of GSEA for genetic variants 31,32 .GSEA was selected as it computes pathway enrichment scores using all available SNP information, which improves sensitivity, rather than using a hypergeometric model limited to SNPs passing a specific GWAS p-value cutoff.Moreover, pathway significance is ascertained using sample permutation, which corrects false-positives arising due to mapping of a few high-ranking SNPs to multiple nearby genes in the same pathway 33 .All SNPs were mapped to genes (as described in the "SNP-gene mapping for annotation and enrichment analyses" section above) and the gene score was defined as the best GWAS marginal p-value of all mapped SNPs for each gene.For each pathway, GSEA computes an enrichment score (ES) using the rank-sum of gene scores.The set of genes that appear in the ranked list before the rank-sum reaches its maximum deviation from zero, is called the "leading edge subset", and is interpreted as the core set of genes responsible for the pathway's enrichment signal.Following computation of the ES, we created a null distribution for each pathway by repeating genome-wide association tests with randomly labelpermuted data and by computing ES from these permuted data; in this work, we use 100 permutations to reduce computational burden.As a test of sensitivity to this parameter, we increased this value to 1000 for the working memory phenotype (lnb_tp2).Finally, the ES on the original data is normalized to the score computed for the same gene set for label-permuted data (Z-score of real ES relative to mean of ES in label-permuted data), resulting in a Normalized Enrichment Score (NES) per pathway.
The nominal p-value for the NES score is computed based on the null distribution and FDR correction is used to generate a q-value.
We used enrichment analysis to perform pathway analysis using pathway information compiled from HumanCyc 34  S14).Only gene sets with 20-500 genes were included in the analysis; 421 gene sets met these criteria and were included in the enrichment analysis.

Leading edge gene interaction network
Genes contributing to pathway enrichment results (leading edge genes) were obtained in our GSEA analysis for genetic variants 31

Results
We developed a systems-genomics analysis workflow to identify genetic variants associated with normal cognitive phenotypes (Figure 1).Briefly, genotypes were imputed using a reference panel from the 1,000 Genomes Project 53 , and samples were limited to those of European genetic ancestry (Supplementary Figure 1-3, Supplementary Table 1).11 .In all instances, age and sex was regressed out of the phenotype (Supplementary Table 4) and samples were thereafter binarized into poor and good performers (bottom and top 33% percentile, respectively) resulting in ~1,000 samples per group for each phenotype (Supplementary Figure 4,5, Supplementary Table 3).
For each of the nine phenotypes, we first performed SNP-level genome-wide association analysis, as a comparative baseline following traditional methods.We used a mixed-effects linear model that included genome-wide genetic ancestry as a covariate (GCTA 22 ).Among the nine phenotypes, 661 SNPs had suggestive levels of significance at the genome-wide level (p < 10 -5 ; Figure 1b,c, Supplementary Figure 6,7,8, Supplementary Table 6).Over half of these SNPs are associated with tasks related to complex cognition, i.e. verbal reasoning, non-verbal reasoning and spatial reasoning (377 SNPs or 57%).27% were associated with executive function (177 SNPs), which included attention allocation and working memory.13% SNPs were associated with declarative memory tasks (83 SNPs), which included face recall, word recall and object recall.4% of SNPs were associated with emotion identification (24 SNPs), a measure of social processing.More generally, SNPs associated with PNC cognitive phenotypes at suggestive significance levels (p<10 -5 ) map to genes previously associated with diseases of the nervous system and/or mark cell-types in the fetal and newborn brain 41,43 (Supplementary Figure 8, Supplementary Table 7).We predict that one-sixth of suggestive peaks (112 SNPs or 17%) are linked to a functional consequence in brain tissue, including non-synonymous changes to protein sequence (Supplementary Fig. 8), presence in brain-specific promoters and enhancers, or association with changes in gene expression (Supplementary Table 6).
. CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made available under The copyright holder for this preprint (which was not this version posted March 15, 2021.; https://doi.org/10.1101/751933doi: bioRxiv preprint Nonverbal reasoning was the only phenotype with SNPs passing the cutoff for genome-wide significance (rs77601382 and rs5765534, p = 4.6x10 -8 ) (Figure 2).The peak is located in a ~33kb region (chr22:45,977,415-46,008,175) overlapping the 3' end of the Fibulin-1 (FBLN1) gene, including the last intron and exon (Figure 2b).To better understand the significance of this gene in brain function, we examined FBLN1 expression in published fetal and adult transcriptomes, and single-cell data 29,43,45 .FBLN1 transcription in the human brain is highest in the early stages of fetal brain development, with little to no expression in the adult (Figure 2c, Supplementary Figure 8); this is consistent with single-cell assays showing FBLN1 to be a marker for dividing progenitor cells in the fetal brain 43 .FBLN1 encodes a glycoprotein present in the extracellular matrix; this protein is a direct interactor of proteins involved in neuronal diseases, such as Amyloid Precursor Protein-1 54 (Supplementary Figure 9 55 ).FBLN1 expression is upregulated in the brain in schizophrenia and has been previously associated with genetic risk for bipolar disorder (Figure 1d, 56,57 ).Therefore, we conclude that FBLN1, associated with nonverbal reasoning test performance, shows characteristics of a gene involved in neurodevelopment and the dysregulation of which could increase risk for psychotic disorders of neurodevelopmental origin.
Pathway analysis is an established systems-genomics technique used to improve the statistical power of subthreshold univariate signal by aggregation of signal and reduction of multiple hypothesis testing burden, as well as to provide mechanistic insight into cellular processes that affect phenotypic outcome.Pathway analysis has been successfully used to link genetic disease risk to cellular processes for diseases in various domains, including schizophrenia 58 , breast cancer 59 and type 2 diabetes 60 .We performed pathway analysis for the nine phenotypes using a rank-based pathway analysis strategy (GSEA 31,38 , 500 permutations; 4,102 pathways tested).SNPs were mapped to genes using brain- specific eQTL, chromatin interaction and positional information, using the same method as described above.The working memory phenotype demonstrated significant enrichment of top-ranking genetic variants in a developmental pathway (q < 0.05; Supplementary Tables 8-10), showing biologically relevant signal where our univariate SNP-based baseline analysis did not.An advantage of the rankbased pathway analysis over those based on hypergeometric or binomial tests, is that the former provides a list of "leading-edge" genes driving the pathway-level enrichment signal, which can be further interpreted.We annotated leading-edge genes with prior knowledge about genetic associations with nervous system disorders, transcription in brain cell types 41-43,51 and known drug interactions 50 .Out of 53 leading edge genes of this gene set, roughly one-half are known brain cell markers (25 genes or 47%), roughly one-third have known drug interactions (17 genes or 36%), and ~11% are associated with nervous system disease (6 genes) (pathway q < 0.05, Figure 3a, Supplementary Table 10, Supplementary Figure 11).Among disease-associated genes were those associated with autism (CSDE1) and Parkinson's disease (LHFPL2).
To perform a brain system and disease analysis, we performed a second enrichment analysis using gene sets curated from the literature, including transcriptomic and proteomic profiles of the developing and adult healthy brain and brains affected by mental illness, brain-related genome-wide association studies, and terms from a phenotype ontology (421 gene sets tested, Supplementary Note 5, Supplementary Table 5, Supplementary Data 1).Two gene sets pertaining to general nervous system dysfunction were significantly enriched (q<0.05;GSEA, 500 permutations), again related to working memory (Figure 3c, Supplementary Table 11).Roughly 17% of the 71 leading edge genes of these gene sets are associated with nervous system disorders (12 genes), roughly one-third have predicted drug targets (22 genes, 31%), and over half (43 genes or 61%) are markers of brain celltypes (Figure 3b,c; Supplementary Table 12,13).Two genes have all three attributes: SNCA and LRRK2 (Figure 3c, Supplementary Table 13).Leading edge genes have genetic associations, including those with schizophrenia, autism spectrum disorder, Parkinson's disease, Alzheimer's disease, depression and mood disorders (Figure 3c, Supplementary Table 13).In summary, we identified many genetic

Discussion
To our knowledge, this is the first study to identify genetic variants that may contribute to normal human variation in multiple, diverse cognitive domains, and to link these to various levels of brain system organization, including genes, pathways, cell types, brain regions, diseases and known drug targets.
These suggest that FBLN1, the gene we find associated with genome-wide significant SNPs for nonverbal reasoning, is dysregulated in brain-related disease.In addition to the evidence provided in our results (Figure 2c, Supplementary Figure 8,9), the FBLN1 gene has been associated with a rare genetic syndrome that includes various cognitive impairments, and protein levels of FBLN1 have been associated with altered risk for ischaemic stroke 61,62 .However, the mechanism by which FBLN1 contributes to normal brain function is not known.We also do not exclude the possibility that suggestive peaks we identified within FBLN1 may affect the function of neighbouring or otherwise linked genes, which may instead or in combination affect the phenotype.One such gene is Ataxin-10 (ATXN10), which is the next neighboring downstream gene to FBLN1, in which a pentanucleotide repeat expansion causes spinocerebellar atrophy and ataxia 63 .The FBLN1 locus was not significantly enriched in a large GWAS study of general cognitive ability 64 , suggesting that this locus may be influencing a specialized trait.
A limitation of the current study is the relatively small size of the patient cohort -roughly 1,000 cases and controls each per phenotype -compared to contemporary GWAS studies which may include over 100,000 individuals.The reduced sample size is partly because we chose to limit the analysis to individuals with European genetic ancestry, to maintain the largest number of samples while avoiding the confound with genetic ancestry.Furthermore, we dichotomized the phenotype into bottom and top performers, ignoring samples in the middle, as our goal was to work with a subset enriched for extremes within typical phenotypic variation, to strengthen signal.For all phenotypes tested in this work, we also performed genome-wide association tests using continuously-valued measures, instead of binarized phenotypes; none of the associations resulted in significant results (data not shown).This lack of association is consistent with the strategy to binarize outcomes for improved contrast; binarization includes only the top and bottom thirds of performance measures, and ignores the measures in between.
This work contributes towards an understanding of the molecular and systems-level underpinnings of individual cognitive tasks.These associations will need to be validated in better-powered datasets, possibly using newer neurobehavioural measurement standards in the field Figure 1.Framework for multi-scale systems-genomics analysis for neurocognitive phenotypes the Philadelphia Neurodevelopmental Cohort.a. Workflow for genome-wide association an (GWAS).Genotypes were imputed (1KGP reference), and limited to European samples.Samples severe medical conditions were removed and invalid test scores excluded.Nine neurocognitiv scores were binarized after regressing out age and sex.GWAS was performed using the acc measure as a phenotype for each of these nine phenotypes.b.Framework to organize variant associations into a multi-scale systems view in health (blue) and disease (red).Existing func genomic resources used for annotation shown in brown.

Figure 2 .Figure 3 .
Figure 2. Genome-wide significance of FBLN1 region for binarized performance in nonv reasoning a. Manhattan plot of univariate SNP association with binarized performance in nonverbal reas (N=1,024 poor vs. 1,023 good performers; 4,893,197 SNPs).Plot generated using FU b.Detailed view of hit region at chr22q13.Two SNPs pass genome-wide significance thre rs77601382 and rs74825248 (p=4.64e-8).View using Integrated Genome Viewer (v2.3.93 71,72 red bar indicates the region with increased SNP-level associ c.FBLN1 transcription in the human brain through the lifespan.Data from BrainSpan 45 .transformed normalized expression is shown for cerebellar cortex (CBC), central ganglionic emi (CGE) and lateral ganglionic eminence (LGE), dorsal frontal cortex (DFC), and hippocampus (HIP) Legend as in a.c.Gene-gene interaction network for working memory leading edge genes from enriched (q < brain-related gene sets.Only genes with top SNP p < 5x10 -3 are shown (26 genes).Nodes show and fill colour indicates genes associated with brain cell types, drugs or genetic associations nervous system disorders (colours as in panel a, white indicates absence of association).indicate known interactions (from GeneMANIA 49 ).Genes from the network with disease associ are highlighted with grey description bubbles.4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made available under The copyright holder for this preprint (which was not this version posted March 15, 2021.; https://doi.org/10.1101/751933doi: bioRxiv preprint Page 18 of 18

Figure 4 .
Figure 4. Summary of evidence linking genetic variants associated with cognitive test performa multiple levels of brain organization.Each column shows data for an individual phenotype, group phenotype domain; rows show associations at increasingly more general scales (from top to bot evidence linking variants to healthy system and disease system shown in blue and red, respec Circles indicate relative number of suggestive variant peaks (p < 10 -5 ) from GWAS).Pathways an systems are those identified by gene set enrichment analyses (q<0.05).Cell types are those forFBLN1 is found to be a marker from single-cell transcriptome data 43 .Gene-disease association identified for significant SNPs, using gene-disease mappings from the NHGRI-EBI catalo . CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made available under International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made available underThe copyright holder for this preprint (which was not this version posted March 15, 2021.; https://doi.org/10.1101/751933doi: bioRxiv preprint gene annotation involved identifying interactions with gene promoters, defined as ± 2 kb of a gene TSS.This analysis identified 303,464 DNA-DNA interactions used for our study. (Illumina HiSeq 2000 paired-end raw sequence reads; n=1 sample; 746 Million reads; accession: GSM2322542 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2322542]).We used Trim Galore 25 (v0.4.3) for adapter trimming, HICUP 26 (v0.5.9) for mapping and performing quality control, and GOTHIC 27 for identifying significant interactions (Bonferroni p <0.05), with a 40 kb resolution.Hi-C .CC-BY 4.0 review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made available under ); and genes encoding proteins altered in the schizophrenia synaptosomal proteome 46 .Brain disease gene sets included: genes associated with schizophrenia, bipolar disorder, autism spectrum disorder and major depressive disorder through large-scale genetic association studies by the Psychiatric Genomics Consortium (Supplementary Note 5); genes associated with nervous system disorders by the Human Phenotype Ontology 47 .Genes in the second group were filtered to only include genes with detectable expression in the fetal 48 or adult human brain 44 .A total . CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made available under associated with normative variation in a range of neurocognitive phenotypes enriched in pathways and gene sets related to development, nervous system dysfunction and mental disorders. variants review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.It is made available under 65 but can currently be used as hypotheses to plan biological experiments, or as support for orthogonal methods studying the relevance of genes and pathways we identify for brain biology.Studying the overlap in genetic