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Original Investigation |

Differential Effects of Common Variants in SCN2A on General Cognitive Ability, Brain Physiology, and messenger RNA Expression in Schizophrenia Cases and Control Individuals FREE

Dwight Dickinson, PhD1; Richard E. Straub, PhD2; Joey W. Trampush, PhD1; Yuan Gao, PhD2; Ningping Feng, PhD1; Bin Xie, PhD2; Joo Heon Shin, PhD2; Hun Ki Lim, PhD2; Gianluca Ursini, MD2,3; Kristin L. Bigos, PhD2; Bhaskar Kolachana, PhD1; Ryota Hashimoto, MD4,5; Masatoshi Takeda, MD4,5; Graham L. Baum, BS1; Dan Rujescu, MD6,7; Joseph H. Callicott, MD1; Thomas M. Hyde, MD, PhD1,2; Karen F. Berman, MD1; Joel E. Kleinman, MD, PhD1,2; Daniel R. Weinberger, MD1,2,8,9,10,11
[+] Author Affiliations
1Clinical Brain Disorders Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
2Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, Maryland
3Psychiatric Neuroscience Group, Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
4Molecular Research Center for Children’s Mental Development, United Graduate School of Child Development, Osaka University, Osaka, Japan
5Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
6Department of Psychiatry, Ludwig-Maximilians University, Munich, Germany
7Department of Psychiatry, Martin Luther University Halle-Wittenberg, Halle, Germany
8Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
9Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
10Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
11McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
JAMA Psychiatry. 2014;71(6):647-656. doi:10.1001/jamapsychiatry.2014.157.
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Importance  One approach to understanding the genetic complexity of schizophrenia is to study associated behavioral and biological phenotypes that may be more directly linked to genetic variation.

Objective  To identify single-nucleotide polymorphisms associated with general cognitive ability (g) in people with schizophrenia and control individuals.

Design, Setting, and Participants  Genomewide association study, followed by analyses in unaffected siblings and independent schizophrenia samples, functional magnetic resonance imaging studies of brain physiology in vivo, and RNA sequencing in postmortem brain samples. The discovery cohort and unaffected siblings were participants in the National Institute of Mental Health Clinical Brain Disorders Branch schizophrenia genetics studies. Additional schizophrenia cohorts were from psychiatric treatment settings in the United States, Japan, and Germany. The discovery cohort comprised 339 with schizophrenia and 363 community control participants. Follow-up analyses studied 147 unaffected siblings of the schizophrenia cases and independent schizophrenia samples including a total of an additional 668 participants. Imaging analyses included 87 schizophrenia cases and 397 control individuals. Brain tissue samples were available for 64 cases and 61 control individuals.

Main Outcomes and Measures  We studied genomewide association with g, by group, in the discovery cohort. We used selected genotypes to test specific associations in unaffected siblings and independent schizophrenia samples. Imaging analyses focused on activation in the prefrontal cortex during working memory. Brain tissue studies yielded messenger RNA expression levels for RefSeq transcripts.

Results  The schizophrenia discovery cohort showed genomewide-significant association of g with polymorphisms in sodium channel gene SCN2A, accounting for 10.4% of g variance (rs10174400, P = 9.27 × 10−10). Control individuals showed a trend for g/genotype association with reversed allelic directionality. The genotype-by-group interaction was also genomewide significant (P = 1.75 × 10−9). Siblings showed a genotype association with g parallel to the schizophrenia group and the same interaction pattern. Parallel, but weaker, associations with cognition were found in independent schizophrenia samples. Imaging analyses showed a similar pattern of genotype associations by group and genotype-by-group interaction. Sequencing of RNA in brain revealed reduced expression in 2 of 3 SCN2A alternative transcripts in the patient group, with genotype-by-group interaction, that again paralleled the cognition effects.

Conclusions and Relevance  The findings implicate SCN2A and sodium channel biology in cognitive impairment in schizophrenia cases and unaffected relatives and may facilitate development of cognition-enhancing treatments.

Figures in this Article

Schizophrenia is a heritable neurodevelopmental disorder characterized by disturbed patterns of behavior and abnormalities of brain function.1,2 Genomewide association studies (GWASs) are beginning to yield insights into the genetic architecture of schizophrenia, although effect sizes for individual genes are modest.35 However, to our knowledge, few GWASs have examined behavioral or biological traits associated with the disorder, which may reflect more penetrant effects of common genetic variation.

Broad cognitive impairment is common in schizophrenia.68 Subtle cognitive differences are often measurable years before psychotic symptoms or exposure to medications913 and impairment is seen in an attenuated form in unaffected relatives,6,7,1416 suggesting that impaired cognition is an intermediate phenotype related to the genetic risk for schizophrenia.17 Studies in nonclinical groups,1820 and in patients with schizophrenia,6,21,22 have indicated that cognitive data are characterized by a hierarchical structure, in which individual measures group into domain-specific cognitive factors (eg, working memory), which underlie a higher-order construct referred to as general cognitive ability or g. General cognitive ability is reliably indexed with standard measurement tools,23 stable over time,24,25 and associated with life outcomes from academic and vocational success2630 to health and mortality.31,32 Physiologically, g is closely related to the efficiency of the prefrontal cortex (PFC),33,34 an important focus of schizophrenia research.35

The heritability of g has been estimated at between 40% and 80%,25,3638 but genetic associations with cognitive performance in nonclinical samples have been difficult to find and replicate27,39 likely owing to the interaction of multiple genetic and environmental influences on brain development and function. Gene-cognition associations within clinical groups present additional complexities because of the potential role of illness epiphenomena (eg, medication) but may be enriched for illness-specific mechanisms of cognitive impairment (eg, apolipoprotein-E4 in Alzheimer disease samples). A fast-emerging, but inconsistent, literature has explored the association of cognitive performance with suspected genetic markers of schizophrenia.4046 One twin study suggested significant overlap in the genes that contribute to cognition and schizophrenia,47 whereas another concluded that overlap was more limited.48 Thus, it remains unclear to what degree the set of genes that leads to schizophrenia risk also impacts brain systems that underlie cognitive performance.

Here, we report a GWAS of cognition in Americans of European ancestry with DSM-IV schizophrenia and community control participants from the Clinical Brain Disorders Branch (CBDB)/National Institute of Mental Health (NIMH) Study of Schizophrenia Genetics (D.R.W., principal investigator). In the sodium channel gene, SCN2A (gene identification: 6326)—previously associated with seizure disorders, intellectual disability, and autism4953—we have identified single-nucleotide polymorphisms (SNPs rs10174400 and rs10182570) that show GWAS-significant association with general cognitive ability in schizophrenia. We found consistent evidence in a sample of the unaffected siblings of these probands and in independent schizophrenia samples. Further support comes from analyses of blood oxygen level–dependent (BOLD) functional magnetic resonance imaging (fMRI) during working memory and of RNA sequencing in postmortem PFC tissue samples.

Participants in the CBDB/NIMH Sample

The GWAS discovery sample included 363 community control individuals and 339 people with DSM-IV schizophrenia54,55 after exclusions and genotyping quality control (QC) (Table 1). The main findings were tested further in a sample of full siblings of 147 of these probands (eTable 1 in Supplement provides details regarding inclusion and exclusion of participants). All research participants were competent adults and provided written informed consent pursuant to the National Institutes of Health neuroscience institutional review board–reviewed and –approved protocols.

Table Graphic Jump LocationTable 1.  Descriptive Statistics for NIMH/CBDB Sample
Cognitive Phenotypes for the CBDB/NIMH Sample

Cognitive phenotypes were composites of individual measures constructed to represent verbal memory, visual memory, N-back, processing speed, card sorting, working memory span, and g (eTable 2 in Supplement). All composites were unweighted and were calculated in exactly the same way for probands, control participants, and unaffected siblings.6

Genotyping and QC for the CBDB/NIMH Sample

DNA samples were genotyped using Illumina HumanHap550K/610Quad Bead Chips, according to the manufacturer’s protocol (eAppendix 1 in Supplement). After QC procedures (eAppendix 1 in Supplement), 495 089 high-quality autosomal SNPs were available for analysis. Quality control of individual genotyping results (eAppendix 1 in Supplement) left a total of 933 individuals with good genotype information. Of these, 339 probands and 363 control individuals had cognitive test data and were retained for discovery analyses (g could not be calculated for 5 probands because of missing data).

For siblings, SCN2A rs10174400 genotypes were determined using the 5′ exonuclease TaqMan assay. Single-nucleotide polymorphisms probe and primer sets were acquired from Applied Biosystems. Genotype accuracy was assessed by regenotyping within a subsample and reproducibility was routinely greater than 99%.

Statistical Analysis for the CBDB/NIMH Sample

We performed multidimensional scaling on the matrix of genomewide identity by state pairwise distances using PLINK56 version 1.07 and, to control for population stratification, included the first 4 multidimensional scaling axes as covariates in GWAS analyses. Analyses of the associations of 495 089 SNPs with 7 cognitive variables were performed in PLINK, assuming an additive genetic model and also controlling for age and sex. We did not control for education because it is confounded with illness and with g.57 Analyses in unaffected siblings were conducted using PASW Statistics version 18.0 (IBM).

Additional Samples and Cognitive Variables

Study design details for the multisite CATIE (Clinical Antipsychotic Trials of Intervention Effectiveness) schizophrenia antipsychotic effectiveness trial have been published including details of cognitive assessments, genotyping, and genotype QC methods.5860 (Details related to the current comparison sample are in eAppendix 1 in Supplement.) Details of data collection for the Japanese sample have been previously published.61 The cognitive battery was comparable with the CBDB/NIMH battery. Genotyping and QC are described in eAppendix 1 in Supplement. Genetic and cognitive data were available for 95 people (eTables 1 and 2 in Supplement). The German sample consisted of 294 clinically stable individuals of European ancestry with DSM-IV schizophrenia, as described previously (details are in eAppendix 1 in Supplement).62

Statistical Analysis for Additional Samples

Genotype-cognition association analyses in independent schizophrenia samples were conducted using PASW Statistics version 18.0. We performed unidirectional tests (ie, 1 tailed), assuming a minor allele disadvantage in schizophrenia, and using an additive genetic model, controlling for age and sex. For meta-analysis of effect sizes across schizophrenia samples, we calculated sample-weighted effect sizes with a bias correction for the small number of samples combined.

BOLD fMRI Analyses

To test the relationship between SCN2A rs10174400 and cognition-related activation patterns as measured by BOLD fMRI, we studied 397 control participants and 87 schizophrenia cases from the CBDB/NIMH sibling study who were genotyped and completed the N-back working memory task while scanned at 3 T (details are in eAppendix 1 in Supplement). After quality screening and correction for covariates of no interest (eg, head motion), we used analyses of covariance controlling for age and sex to test SCN2A genotype within each diagnostic group and the interaction of diagnosis by genotype. Genotype groups within diagnoses did not differ in terms of demographic and performance variables. Thus, differences in activation are thought to reflect neural efficiency (ie, less activation at similar performance implying greater efficiency)—such differences representing a familial and heritable phenotype.6366 Given our interest in prefrontal information processing efficiency, we used a prefrontal region of interest with small-volume statistical correction (familywise error).

RNA Sequencing in Independent Postmortem Brain Samples

RNA sequencing was performed on postmortem PFC gray matter from 61 adult control participants (51 males; mean [SD] age, 44 [14.6] years) and 64 adult probands (51 males; mean [SD] age, 44.3 [14.8] years), all of European ancestry. Detailed brain-tissue collection methods used by the Lieber Institute and CBDB/NIMH have been published67 and details of RNA sequencing are described in eAppendix 1 in Supplement. The relative abundances of the 3 common SCN2A RefSeq transcripts—NM_21007, NM_001040142, and NM_001040143—were estimated by Cufflinks version 2.0.2 and compared with Illumina iGenome gene annotation. The 3 transcripts can be differentiated based on unique 5′ exons, thus allowing a reliable estimation of relative abundance of each specific transcript. We used analyses of covariance, with age and sex covariates, to investigate main effects and interactions among diagnosis, SCN2A rs10174400 genotype, and SCN2A transcript levels for the 3 transcripts. Analyses were also corrected for postmortem interval and RNA integrity number. We calculated Cohen d effect sizes. With the low number of rs10174400 minor allele homozygotes (8 probands and 7 control participants), we combined heterozygotes with minor allele homozygotes (T carriers) for analyses based on genotype.

Supplementary Analyses

In Supplement, eAppendix 1 describes covariate sensitivity analyses (ie, medication, chronicity, age at onset, and family socioeconomic status); analysis of the potential role in current findings of low-frequency exonic SNPs; and tests of the association of g with SNP sets representing the whole SCN2A gene, other sodium channel genes, and the whole sodium and calcium channel gene families.

CBDB/NIMH Discovery Sample

The GWAS in the schizophrenia sample identified a strong association signal (Figure 1A). Two linked, intronic SNPs in SCN2A surpassed GWAS significance (ie, P = 5.0 × 10−8) for association with g (rs10174400, P = 9.27 × 10−10; rs10182570, P = 2.56 × 10−9; Table 2; eFigure 1 in Supplement)—accounting for 10.4% of g variance—with no evidence of inflation of test statistics due to population effects (λgenomic control = 1; Figure 1B, eAppendix 2 in Supplement, and eTables 3-6 in Supplement). Performance was least impaired in participants homozygous for the major C allele, intermediate in heterozygotes, and most impaired in participants homozygous for the T allele (Figure 2). In nonindependent analyses, the SCN2A rs10174400 genotype was also associated with the 6 cognitive domain variables in schizophrenia (Table 2). Each of the domains showed directionally consistent and, at least nominally significant, association with rs10174400 genotype, but none met the GWAS threshold.

Place holder to copy figure label and caption
Figure 1.
Manhattan and Quantile-Quantile Plots

A, Manhattan plot for SCN2A genomewide association study (GWAS) findings in 334 people with schizophrenia. Results are from 495 089 single-nucleotide polymorphisms tested for association with general cognitive ability in 334 individuals with schizophrenia. The red line denotes P = 5.0 × 10−8. Chr indicates chromosome. B, Quantile-quantile plot for SCN2A GWAS findings in 334 people with schizophrenia. The plot shows actual vs expected −2log(e)P for general cognitive ability in cases and control individuals. −2Log(e)P follows a χ2 distribution (df = 2) and can be used for statistical inference. Points above the horizontal line indicate an enrichment of low P values beyond what would be expected by chance.

Graphic Jump Location
Table Graphic Jump LocationTable 2.  Association Results in NIMH/CBDB Sample and Additional Cohorts for Analyses of SCN2A rs10174400 and Proxies
Place holder to copy figure label and caption
Figure 2.
Effect of SCN2A rs10174400 Genotype on General Cognitive Ability Composite Performance by Group in 334 Probands, 147 Siblings, and 363 Control Individuals

The triangles (schizophrenia), circles (siblings), and diamonds (controls) represent mean general cognitive ability values by genotype subgroups. The error bars are ±2 SEs.

Graphic Jump Location

For control individuals, no SNP association approached GWAS significance (eAppendix 2, eFigure 2, and eTable 7 in Supplement) and the SCN2A rs10174400 genotype was not a predictor of case/control status (Table 2). Unexpectedly, the allelic trend for the control association with g was in the direction opposite the schizophrenia association (Figure 2), and an analysis of the interaction of rs10174400 genotype by group was also GWAS significant (P = 1.75 × 10−9, Table 2).

Unaffected Siblings

Although not independent of proband results, the sibling analyses addressed the concern that the proband association might be primarily related to illness characteristics (eg, ongoing symptoms) or medications. In unaffected siblings, there was a robust, directionally parallel association between rs10174400 genotype and g, accounting for 3.4% of performance variation, and a significant genotype-by-group interaction (Table 2).

In healthy populations, g has been shown to predict educational attainment,18,27 so a genotype that predicts g might be associated with education. In 147 unaffected siblings, the rs10174400 genotype accounted for 5.7% of the variance in years of education completed (P = .003), with T-allele carriers showing clearly reduced educational attainment compared with C-allele homozygotes (eFigure 3 in Supplement). This association was not present in the full schizophrenia sample (P = .38), likely because of the confounding effect of illness on educational attainment.57

Additional Samples

In 279 schizophrenia cases from the CATIE trial, regression analysis confirmed the association of an rs10174400 proxy to the CATIE neurocognitive composite,68 a general cognitive ability index similar to g, again showing directionality parallel to the discovery analyses (Table 2). Genotype associations to subsidiary composites for processing speed and working memory were also significant and parallel. In 95 Japanese schizophrenia cases, regression analysis yielded a directionally consistent significant association of the same proxy SNP with g, accounting for 3.4% of the variance in performance (Table 2). Post hoc analysis using a recessive model showed an even more pronounced effect, and we observed a similar pattern for a verbal memory composite. Finally, we examined gene/cognition associations in 295 Germans with schizophrenia. Regression analyses failed to replicate the association of rs10174400 with g in schizophrenia in this sample (Table 2). However, there was a parallel genotype association with the working memory span composite in the German cases, which was the strongest domain-specific effect in the discovery sample. Together, the 3 replication samples included 652 people with schizophrenia and g. Across the 3 groups, the rs10174400 genotype accounted for 1.0% of the variance in g (sample-weighted mean effect size). Including the discovery sample with the replication samples (N = 983), genotype accounted for 3.0% of g variance in schizophrenia on average.

BOLD fMRI Analyses

Looking beyond performance, we tested for genotype effects at the level of brain physiology using an N-back working-memory paradigm that robustly engages prefrontal cortical circuitry. The rs10174400 genotype was differentially associated with PFC efficiency in cases and control individuals, analogously to the cognitive results pattern. Among control individuals, TT homozygotes were most efficient; among schizophrenia cases, they were least efficient, and the interaction effect was significant (Montreal Neurological Institute coordinates: x = −36, y = 27, z = 33; familywise error–corrected P = .02; eFigure 4 in Supplement). There were also main effects of SCN2A genotype in both diagnostic groups consistent with the direction of this interaction and with the cognitive associations (eAppendix 2 in Supplement).

RNA Sequencing Analyses

Analysis of RNA sequencing data from postmortem PFC gray matter tissue samples showed significantly reduced expression of SCN2A mRNA in the schizophrenia sample relative to control individuals for 2 of 3 RefSeq transcripts and significant genotype effects and interactions for these 2 transcripts (Table 3 and eFigure 5 in Supplement). The effect sizes for significant findings were small to medium in magnitude. The directions of genotype effects were opposite for the 2 groups and the diagnosis-by-genotype interactions were significant—patterns remarkably similar to those in the cognitive and imaging data.

Table Graphic Jump LocationTable 3.  Results for Analyses of Messenger RNA Expression of SCN2A Alternative Transcripts in PFC Tissue Samples From Schizophrenia Cases and Control Individuals
Supplementary Analyses

Our main findings showed little change in analyses with additional covariates (ie, medication, age at prodrome onset, chronicity, positive and negative symptoms, or family socioeconomic status). Analysis of low-frequency exonic SNPs was inconclusive. Tests of the association of g with SNP sets were an initial step in determining whether the association of sodium channel biology with general cognitive performance extended beyond the influence of the 2 GWAS-significant SNPs (eAppendix 2 and eTables 8-12 in Supplement).

In our GWAS analyses of general cognitive ability in patients with schizophrenia, 2 linkage disequilibrium–linked SNPs in SCN2A showed GWAS-significant association. The effect accounted for 10.4% of the variance in overall cognitive performance. A parallel association of the rs10174400 genotype with g in 147 unaffected siblings indicated that the schizophrenia association cannot be attributed solely to illness epiphenomena (eg, medication). Notably, in the siblings, educational attainment also varied with the rs10174400 genotype, accounting for 5.7% of sibling education variance. We found evidence for weaker, but parallel, genotype/cognition associations in independent schizophrenia samples. Across these 3 replication samples, totaling 652 probands, genotype accounted for 1.0% of g variance. Control participants showed a trend for genotype association with allelic directionality opposite to the schizophrenia association, and the rs10174400 genotype-by-group interaction was also GWAS significant.

Neuroimaging findings and RNA sequencing data from postmortem PFC samples provided a measure of biological validation for the behavioral association findings. Analyses of prefrontal information processing efficiency during working memory revealed a genotype-by-diagnosis interaction. The rs10174400 minor (T) allele conferred efficiency advantages for control individuals but maximal inefficiency in schizophrenia. In postmortem RNA sequencing experiments, the schizophrenia sample showed reduced expression of mRNA for 2 of 3 common alternative transcripts and genotype-by-diagnosis interactions analogous to the imaging results. Thus, the pattern of differential rs10174400 genotype associations for cases vs control individuals that was hinted at in the behavioral data (ie, a clear allele dose-dependent effect on cognitive performance in schizophrenia and a weak opposite trend in control individuals) came more clearly into focus in biological analyses. In sum, congruent evidence spanning behavior, physiology, and mRNA expression suggests an interaction between SCN2A genetic markers and schizophrenia-associated phenomena.

Our discovery sample effect was dramatic and likely reflects the winner’s curse seen in some other genetic association studies of relatively small samples. Evidence from independent schizophrenia samples suggested that the SCN2A effect on cognition may generally be smaller—on average, genotype accounted for 1.0% of variance in our replication samples, although in 2 of these 3 samples, the effect was in the range of 1.5% to 3.4%. While smaller, these effects in independent samples were directionally consistent with discovery sample effects—notwithstanding considerable differences in ascertainment, genotyping, and phenotyping. Additionally, the magnitude of the main schizophrenia finding may have reflected enrichment of the CBDB/NIMH sample for a particular form of schizophrenia risk–associated cognitive impairment owing to uniform, restrictive inclusion criteria (eg, IQ>70 and no substance abuse). Across the discovery and replication samples (N = 986), the mean sample-weighted association effect size was 3.0% of g variance. Neuroimaging findings, and mRNA expression findings in wholly independent postmortem brain-tissue samples, offered further, directionally consistent support for the main finding. At the same time, the parallel findings in siblings, although nonindependent, suggested that the schizophrenia findings were not determined by illness epiphenomena. Altogether, the data alleviate concerns that these are not true genotype effects on SCN2A biology. A better understanding of the magnitude of these effects will require further analyses in other samples.

The findings are also plausible both biologically and in terms of known clinical associations. SCN2A encodes the α2 subunit of a voltage-gated sodium ion channel that is widely expressed in the brain and contributes to the initiation and propagation of action potentials.69,70 Na(v)1.2 (the protein encoded by SCN2A) is abundant in parvalbumin-positive gamma-aminobutyric acid–related inhibitory interneurons, at least in the hippocampus and temporal lobe.70 Gamma-aminobutyric acid system abnormalities have been a particular focus of cognitive impairment research in schizophrenia.71,72 Multiple mutations in SCN2A have been associated with childhood epilepsies, sometimes combined with intellectual disability and/or autismlike symptoms,69,73 and antiepileptic medications that block sodium channels (eg, topiramate) have adverse cognitive effects.74 Notably, each of 3 recent whole-exome sequencing studies focused on nonsyndromic intellectual disability found de novo coding mutations in SCN2A (3 of 55 sequenced individuals in one study,52 1 of 12 in the second,51 and 1 of 100 in the third49). Results from a large exome-sequencing study of autism recently identified 279 independent de novo mutations and highlighted SCN2A as the single gene disrupted by 2 of these.53

The hypothesis that cognition is an intermediate phenotype for schizophrenia implies that rs10174400 should discriminate cases from control individuals, at least to some degree.17 No case/control signal was observed in the discovery sample. Therefore, our results suggest that a strong, directionally specific SCN2A association with impaired cognition may emerge in the context of the complex genetic risk architecture of schizophrenia, which is shared by patients and family members, although there is little or no association of SCN2A with cognitive performance in the general adult population. We have very limited evidence as to possible mechanisms, but the involvement of sodium channel biology—and its apparent effect at the most general level of cognitive performance—suggests mediation through low-level and widely acting neural systems. Dysfunction in gamma-aminobutyric acid–related inhibitory systems could fit this description, although there are many possibilities. The findings in unaffected siblings may be quite important in further refining hypotheses about mechanisms. The sibling results clarified that the genotype association to cognition was not driven mainly by illness-specific phenomena. The association was not unique to family members with a schizophrenia diagnosis and was not tightly linked either to positive or negative symptoms, illness chronicity, or antipsychotic medication (eTable 8 in Supplement). Although impaired cognition and psychotic symptoms are defining characteristics of the schizophrenia syndrome, the sibling results reported here frame the question whether these characteristics may be related to distinct genetic components. At the same time, the current study was dramatically smaller than case/control samples that have shown high P value SNP associations with diagnosis. It may be that, with sufficient samples sizes, associations of SCN2A SNPs with the schizophrenia diagnosis will emerge. In the latest published Psychiatric Genomics Consortium analysis of more than 60 000 participants (>21 000 cases),5 several SNPs in SCN2A showed association with schizophrenia at P = 5.0 × 10−3.

Despite ample evidence of heritability for widely used cognitive measures,24 in control individuals, no common variant reached genomewide significance or approached the magnitude of the rs10174400 effect in schizophrenia. Our results echo findings in earlier, larger cognition GWASs.75,76 Perhaps especially for traits as conserved and fundamental as nondisordered cognition, the causal effects of individual, common genetic markers cannot be detected at present amid the complex interaction of genetic, environmental, and random influences that affect individuals over decades of development.77

We have identified common variants in SCN2A that, in the context of schizophrenia and risk for schizophrenia, show substantial and consistent associations with broad cognitive performance, brain physiology, and mRNA expression in the brain. These findings intersect with prominent lines of schizophrenia research and suggest testable hypotheses about the biological roots of cognitive impairment in schizophrenia and avenues for new treatment development.

Corresponding Author: Daniel R. Weinberger, MD, Lieber Institute for Brain Development, and Departments of Psychiatry, Neurology, and Neuroscience, and Institute of Genetic Medicine, Johns Hopkins University School of Medicine, 855 N Wolfe St, Baltimore, MD 21205 (drweinberger@libd.org).

Submitted for Publication: June 6, 2013; final revision received December 23, 2013; accepted January 2, 2014.

Published Online: April 9, 2014. doi:10.1001/jamapsychiatry.2014.157.

Author Contributions: Drs Dickinson and Weinberger had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Straub, Trampush, Gao, Takeda, Hyde, Berman, Weinberger.

Acquisition, analysis, or interpretation of data: Dickinson, Straub, Trampush, Gao, Feng, Xie, Shin, Lim, Ursini, Bigos, Kolachana, Hashimoto, Baum, Rujescu, Callicott, Hyde, Berman, Kleinman, Weinberger.

Drafting of the manuscript: Dickinson, Straub, Trampush, Gao, Feng, Ursini, Kolachana, Hashimoto, Baum, Hyde, Weinberger.

Critical revision of the manuscript for important intellectual content: Dickinson, Straub, Trampush, Feng, Xie, Shin, Lim, Ursini, Bigos, Hashimoto, Takeda, Rujescu, Callicott, Hyde, Berman, Kleinman, Weinberger.

Statistical analysis: Dickinson, Straub, Trampush, Gao, Feng, Shin, Lim, Ursini, Bigos, Hashimoto, Baum, Callicott.

Obtained funding: Takeda, Rujescu, Berman, Weinberger.

Administrative, technical, or material support: Dickinson, Trampush, Gao, Bigos, Kolachana, Hashimoto, Takeda, Rujescu, Hyde, Berman, Kleinman, Weinberger.

Study supervision: Dickinson, Straub, Trampush, Gao, Hyde, Berman, Weinberger.

Conflict of Interest Disclosures: Dr Dickinson works for the National Institutes of Health. Drs Hyde, Kleinman, and Weinberger were previously employed by the National Institutes of Health. Dr Weinberger directs the Lieber Institute for Brain Development. No other disclosures were reported.

Funding/Support: This work was supported by the Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, as direct funding of the Clinical Brain Disorders Branch (Dr Weinberger, principal investigator) and the Lieber Institute for Brain Development, Baltimore, Maryland.

Role of the Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Correction: This article was corrected online May 8, 2014, for an error in the Results section.

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Egan  MF, Goldberg  TE, Gscheidle  T,  et al.  Relative risk for cognitive impairments in siblings of patients with schizophrenia. Biol Psychiatry. 2001;50(2):98-107.
PubMed
Goldberg  TE, Torrey  EF, Gold  JM,  et al.  Genetic risk of neuropsychological impairment in schizophrenia: a study of monozygotic twins discordant and concordant for the disorder. Schizophr Res. 1995;17(1):77-84.
PubMed
Snitz  BE, Macdonald  AW  III, Carter  CS.  Cognitive deficits in unaffected first-degree relatives of schizophrenia patients: a meta-analytic review of putative endophenotypes. Schizophr Bull. 2006;32(1):179-194.
PubMed
Meyer-Lindenberg  A, Weinberger  DR.  Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat Rev Neurosci. 2006;7(10):818-827.
PubMed
Carroll  JB. Human Cognitive Abilities: A Survey of Factor-Analytic Studies. New York, NY: Cambridge University Press; 1993.
Deary  IJ.  Human intelligence differences: a recent history. Trends Cogn Sci. 2001;5(3):127-130.
PubMed
Jensen  AR. The g Factor: The Science of Mental Ability. Westport, CT: Praeger; 1998.
Dickinson  D, Ragland  JD, Calkins  ME, Gold  JM, Gur  RC.  A comparison of cognitive structure in schizophrenia patients and healthy controls using confirmatory factor analysis. Schizophr Res. 2006;85(1-3):20-29.
PubMed
Gladsjo  JA, McAdams  LA, Palmer  BW, Moore  DJ, Jeste  DV, Heaton  RK.  A six-factor model of cognition in schizophrenia and related psychotic disorders: relationships with clinical symptoms and functional capacity. Schizophr Bull. 2004;30(4):739-754.
PubMed
Johnson  W, te Nijenhuis  J, Bouchard  TJ  Jr.  Still just 1 g: consistent results from five test batteries. Intelligence. 2008;36(1):81-95. doi:10.1016/j.intell.2007.06.001.
Deary  IJ, Johnson  W, Houlihan  LM.  Genetic foundations of human intelligence. Hum Genet. 2009;126(1):215-232.
PubMed
Deary  IJ, Yang  J, Davies  G,  et al.  Genetic contributions to stability and change in intelligence from childhood to old age. Nature. 2012;482(7384):212-215.
PubMed
Bowie  CR, Reichenberg  A, Patterson  TL, Heaton  RK, Harvey  PD.  Determinants of real-world functional performance in schizophrenia subjects: correlations with cognition, functional capacity, and symptoms. Am J Psychiatry. 2006;163(3):418-425.
PubMed
Deary  IJ.  Intelligence. Annu Rev Psychol. 2012;63:453-482.
PubMed
Gottfredson  LS.  What do we know about intelligence? Am Scholar. 1996;(winter):15-30.
Green  MF.  What are the functional consequences of neurocognitive deficits in schizophrenia? Am J Psychiatry. 1996;153(3):321-330.
PubMed
Green  MF, Kern  RS, Heaton  RK.  Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS. Schizophr Res. 2004;72(1):41-51.
PubMed
Batty  GD, Deary  IJ.  Early life intelligence and adult health. BMJ. 2004;329(7466):585-586.
PubMed
Batty  GD, Deary  IJ, Gottfredson  LS.  Premorbid (early life) IQ and later mortality risk: systematic review. Ann Epidemiol. 2007;17(4):278-288.
PubMed
Duncan  J, Owen  AM.  Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci. 2000;23(10):475-483.
PubMed
Duncan  J, Seitz  RJ, Kolodny  J,  et al.  A neural basis for general intelligence. Science. 2000;289(5478):457-460.
PubMed
Callicott  JH, Bertolino  A, Mattay  VS,  et al.  Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cereb Cortex. 2000;10(11):1078-1092.
PubMed
Davies  G, Tenesa  A, Payton  A,  et al.  Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol Psychiatry. 2011;16(10):996-1005.
PubMed
Devlin  B, Daniels  M, Roeder  K.  The heritability of IQ. Nature. 1997;388(6641):468-471.
PubMed
Plomin  R, Spinath  FM.  Intelligence: genetics, genes, and genomics. J Pers Soc Psychol. 2004;86(1):112-129.
PubMed
Wisdom  NM, Callahan  JL, Hawkins  KA.  The effects of apolipoprotein E on non-impaired cognitive functioning: a meta-analysis. Neurobiol Aging. 2011;32(1):63-74.
PubMed
Burdick  KE, Goldberg  TE, Funke  B,  et al.  DTNBP1 genotype influences cognitive decline in schizophrenia. Schizophr Res. 2007;89(1-3):169-172.
PubMed
Egan  MF, Goldberg  TE, Kolachana  BS,  et al.  Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci U S A. 2001;98(12):6917-6922.
PubMed
Egan  MF, Straub  RE, Goldberg  TE,  et al.  Variation in GRM3 affects cognition, prefrontal glutamate, and risk for schizophrenia. Proc Natl Acad Sci U S A. 2004;101(34):12604-12609.
PubMed
Goldberg  TE, Straub  RE, Callicott  JH,  et al.  The G72/G30 gene complex and cognitive abnormalities in schizophrenia. Neuropsychopharmacology. 2006;31(9):2022-2032.
PubMed
Greenwood  TA, Lazzeroni  LC, Murray  SS,  et al.  Analysis of 94 candidate genes and 12 endophenotypes for schizophrenia from the Consortium on the Genetics of Schizophrenia. Am J Psychiatry. 2011;168(9):930-946.
PubMed
Porteous  DJ, Thomson  P, Brandon  NJ, Millar  JK.  The genetics and biology of DISC1: an emerging role in psychosis and cognition. Biol Psychiatry. 2006;60(2):123-131.
PubMed
Walters  JT, Corvin  A, Owen  MJ,  et al.  Psychosis susceptibility gene ZNF804A and cognitive performance in schizophrenia. Arch Gen Psychiatry. 2010;67(7):692-700.
PubMed
Toulopoulou  T, Picchioni  M, Rijsdijk  F,  et al.  Substantial genetic overlap between neurocognition and schizophrenia: genetic modeling in twin samples. Arch Gen Psychiatry. 2007;64(12):1348-1355.
PubMed
Fowler  T, Zammit  S, Owen  MJ, Rasmussen  F.  A population-based study of shared genetic variation between premorbid IQ and psychosis among male twin pairs and sibling pairs from Sweden. Arch Gen Psychiatry. 2012;69(5):460-466.
PubMed
de Ligt  J, Willemsen  MH, van Bon  BW,  et al.  Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med. 2012;367(20):1921-1929.
PubMed
Meisler  MH, O’Brien  JE, Sharkey  LM.  Sodium channel gene family: epilepsy mutations, gene interactions and modifier effects. J Physiol. 2010;588(pt 11):1841-1848.
PubMed
Need  AC, Shashi  V, Hitomi  Y,  et al.  Clinical application of exome sequencing in undiagnosed genetic conditions. J Med Genet. 2012;49(6):353-361.
PubMed
Rauch  A, Wieczorek  D, Graf  E,  et al.  Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet. 2012;380(9854):1674-1682.
PubMed
Sanders  SJ, Murtha  MT, Gupta  AR,  et al.  De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012;485(7397):237-241.
PubMed
First  M, Spitzer  R, Gibbon  M, Williams  J. Structured Clinical Interview for Axis I DSM-IV. New York, NY: Biometrics Research Dept, New State Psychiatric Institute; 1994.
Spitzer  R, Williams  J, First  M. Structured Clinical Interview for DSM-IV, Patient Version. Arlington, VA: American Psychiatric Press; 1996.
Purcell  S, Neale  B, Todd-Brown  K,  et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559-575.
PubMed
Resnick  SM.  Matching for education in studies of schizophrenia. Arch Gen Psychiatry. 1992;49(3):246.
PubMed
Keefe  RS, Mohs  RC, Bilder  RM,  et al.  Neurocognitive assessment in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) project schizophrenia trial: development, methodology, and rationale. Schizophr Bull. 2003;29(1):45-55.
PubMed
Stroup  TS, McEvoy  JP, Swartz  MS,  et al.  The National Institute of Mental Health Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) project: schizophrenia trial design and protocol development. Schizophr Bull. 2003;29(1):15-31.
PubMed
Sullivan  PF, Lin  D, Tzeng  JY,  et al.  Genomewide association for schizophrenia in the CATIE study: results of stage 1. Mol Psychiatry. 2008;13(6):570-584.
PubMed
Hashimoto  R, Ohi  K, Yasuda  Y,  et al.  The impact of a genome-wide supported psychosis variant in the ZNF804A gene on memory function in schizophrenia. Am J Med Genet B Neuropsychiatr Genet. 2010;153B(8):1459-1464.
PubMed
Stefansson  H, Rujescu  D, Cichon  S,  et al; GROUP.  Large recurrent microdeletions associated with schizophrenia. Nature. 2008;455(7210):232-236.
PubMed
Blokland  GA, McMahon  KL, Hoffman  J,  et al.  Quantifying the heritability of task-related brain activation and performance during the N-back working memory task: a twin fMRI study. Biol Psychol. 2008;79(1):70-79.
PubMed
Blokland  GA, McMahon  KL, Thompson  PM, Martin  NG, de Zubicaray  GI, Wright  MJ.  Heritability of working memory brain activation. J Neurosci. 2011;31(30):10882-10890.
PubMed
Callicott  JH, Weinberger  DR.  Brain imaging as an approach to phenotype characterization for genetic studies of schizophrenia. Methods Mol Med. 2003;77:227-247.
PubMed
Koten  JW  Jr, Wood  G, Hagoort  P,  et al.  Genetic contribution to variation in cognitive function: an FMRI study in twins. Science. 2009;323(5922):1737-1740.
PubMed
Colantuoni  C, Lipska  BK, Ye  T,  et al.  Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature. 2011;478(7370):519-523.
PubMed
Keefe  RS, Bilder  RM, Davis  SM,  et al; CATIE Investigators; Neurocognitive Working Group.  Neurocognitive effects of antipsychotic medications in patients with chronic schizophrenia in the CATIE Trial. Arch Gen Psychiatry. 2007;64(6):633-647.
PubMed
Meisler  MH, Kearney  JA.  Sodium channel mutations in epilepsy and other neurological disorders. J Clin Invest. 2005;115(8):2010-2017.
PubMed
Wang  W, Takashima  S, Segawa  Y,  et al.  The developmental changes of Na(v)1.1 and Na(v)1.2 expression in the human hippocampus and temporal lobe. Brain Res. 2011;1389:61-70.
PubMed
Coyle  JT.  The GABA-glutamate connection in schizophrenia: which is the proximate cause? Biochem Pharmacol. 2004;68(8):1507-1514.
PubMed
Gonzalez-Burgos  G, Lewis  DA.  NMDA receptor hypofunction, parvalbumin-positive neurons, and cortical gamma oscillations in schizophrenia. Schizophr Bull. 2012;38(5):950-957.
PubMed
Catterall  WA, Dib-Hajj  S, Meisler  MH, Pietrobon  D.  Inherited neuronal ion channelopathies: new windows on complex neurological diseases. J Neurosci. 2008;28(46):11768-11777.
PubMed
Salinsky  MC, Storzbach  D, Spencer  DC, Oken  BS, Landry  T, Dodrill  CB.  Effects of topiramate and gabapentin on cognitive abilities in healthy volunteers. Neurology. 2005;64(5):792-798.
PubMed
Cirulli  ET, Kasperaviciūte  D, Attix  DK,  et al.  Common genetic variation and performance on standardized cognitive tests. Eur J Hum Genet. 2010;18(7):815-820.
PubMed
Davis  OS, Butcher  LM, Docherty  SJ,  et al.  A three-stage genome-wide association study of general cognitive ability: hunting the small effects. Behav Genet. 2010;40(6):759-767.
PubMed
Turkheimer  E.  Commentary: variation and causation in the environment and genome. Int J Epidemiol. 2011;40(3):598-601.
PubMed

Figures

Place holder to copy figure label and caption
Figure 1.
Manhattan and Quantile-Quantile Plots

A, Manhattan plot for SCN2A genomewide association study (GWAS) findings in 334 people with schizophrenia. Results are from 495 089 single-nucleotide polymorphisms tested for association with general cognitive ability in 334 individuals with schizophrenia. The red line denotes P = 5.0 × 10−8. Chr indicates chromosome. B, Quantile-quantile plot for SCN2A GWAS findings in 334 people with schizophrenia. The plot shows actual vs expected −2log(e)P for general cognitive ability in cases and control individuals. −2Log(e)P follows a χ2 distribution (df = 2) and can be used for statistical inference. Points above the horizontal line indicate an enrichment of low P values beyond what would be expected by chance.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Effect of SCN2A rs10174400 Genotype on General Cognitive Ability Composite Performance by Group in 334 Probands, 147 Siblings, and 363 Control Individuals

The triangles (schizophrenia), circles (siblings), and diamonds (controls) represent mean general cognitive ability values by genotype subgroups. The error bars are ±2 SEs.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Descriptive Statistics for NIMH/CBDB Sample
Table Graphic Jump LocationTable 2.  Association Results in NIMH/CBDB Sample and Additional Cohorts for Analyses of SCN2A rs10174400 and Proxies
Table Graphic Jump LocationTable 3.  Results for Analyses of Messenger RNA Expression of SCN2A Alternative Transcripts in PFC Tissue Samples From Schizophrenia Cases and Control Individuals

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Goldberg  TE, Torrey  EF, Gold  JM,  et al.  Genetic risk of neuropsychological impairment in schizophrenia: a study of monozygotic twins discordant and concordant for the disorder. Schizophr Res. 1995;17(1):77-84.
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Meyer-Lindenberg  A, Weinberger  DR.  Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat Rev Neurosci. 2006;7(10):818-827.
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Carroll  JB. Human Cognitive Abilities: A Survey of Factor-Analytic Studies. New York, NY: Cambridge University Press; 1993.
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PubMed
Jensen  AR. The g Factor: The Science of Mental Ability. Westport, CT: Praeger; 1998.
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PubMed
Gladsjo  JA, McAdams  LA, Palmer  BW, Moore  DJ, Jeste  DV, Heaton  RK.  A six-factor model of cognition in schizophrenia and related psychotic disorders: relationships with clinical symptoms and functional capacity. Schizophr Bull. 2004;30(4):739-754.
PubMed
Johnson  W, te Nijenhuis  J, Bouchard  TJ  Jr.  Still just 1 g: consistent results from five test batteries. Intelligence. 2008;36(1):81-95. doi:10.1016/j.intell.2007.06.001.
Deary  IJ, Johnson  W, Houlihan  LM.  Genetic foundations of human intelligence. Hum Genet. 2009;126(1):215-232.
PubMed
Deary  IJ, Yang  J, Davies  G,  et al.  Genetic contributions to stability and change in intelligence from childhood to old age. Nature. 2012;482(7384):212-215.
PubMed
Bowie  CR, Reichenberg  A, Patterson  TL, Heaton  RK, Harvey  PD.  Determinants of real-world functional performance in schizophrenia subjects: correlations with cognition, functional capacity, and symptoms. Am J Psychiatry. 2006;163(3):418-425.
PubMed
Deary  IJ.  Intelligence. Annu Rev Psychol. 2012;63:453-482.
PubMed
Gottfredson  LS.  What do we know about intelligence? Am Scholar. 1996;(winter):15-30.
Green  MF.  What are the functional consequences of neurocognitive deficits in schizophrenia? Am J Psychiatry. 1996;153(3):321-330.
PubMed
Green  MF, Kern  RS, Heaton  RK.  Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS. Schizophr Res. 2004;72(1):41-51.
PubMed
Batty  GD, Deary  IJ.  Early life intelligence and adult health. BMJ. 2004;329(7466):585-586.
PubMed
Batty  GD, Deary  IJ, Gottfredson  LS.  Premorbid (early life) IQ and later mortality risk: systematic review. Ann Epidemiol. 2007;17(4):278-288.
PubMed
Duncan  J, Owen  AM.  Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci. 2000;23(10):475-483.
PubMed
Duncan  J, Seitz  RJ, Kolodny  J,  et al.  A neural basis for general intelligence. Science. 2000;289(5478):457-460.
PubMed
Callicott  JH, Bertolino  A, Mattay  VS,  et al.  Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cereb Cortex. 2000;10(11):1078-1092.
PubMed
Davies  G, Tenesa  A, Payton  A,  et al.  Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol Psychiatry. 2011;16(10):996-1005.
PubMed
Devlin  B, Daniels  M, Roeder  K.  The heritability of IQ. Nature. 1997;388(6641):468-471.
PubMed
Plomin  R, Spinath  FM.  Intelligence: genetics, genes, and genomics. J Pers Soc Psychol. 2004;86(1):112-129.
PubMed
Wisdom  NM, Callahan  JL, Hawkins  KA.  The effects of apolipoprotein E on non-impaired cognitive functioning: a meta-analysis. Neurobiol Aging. 2011;32(1):63-74.
PubMed
Burdick  KE, Goldberg  TE, Funke  B,  et al.  DTNBP1 genotype influences cognitive decline in schizophrenia. Schizophr Res. 2007;89(1-3):169-172.
PubMed
Egan  MF, Goldberg  TE, Kolachana  BS,  et al.  Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci U S A. 2001;98(12):6917-6922.
PubMed
Egan  MF, Straub  RE, Goldberg  TE,  et al.  Variation in GRM3 affects cognition, prefrontal glutamate, and risk for schizophrenia. Proc Natl Acad Sci U S A. 2004;101(34):12604-12609.
PubMed
Goldberg  TE, Straub  RE, Callicott  JH,  et al.  The G72/G30 gene complex and cognitive abnormalities in schizophrenia. Neuropsychopharmacology. 2006;31(9):2022-2032.
PubMed
Greenwood  TA, Lazzeroni  LC, Murray  SS,  et al.  Analysis of 94 candidate genes and 12 endophenotypes for schizophrenia from the Consortium on the Genetics of Schizophrenia. Am J Psychiatry. 2011;168(9):930-946.
PubMed
Porteous  DJ, Thomson  P, Brandon  NJ, Millar  JK.  The genetics and biology of DISC1: an emerging role in psychosis and cognition. Biol Psychiatry. 2006;60(2):123-131.
PubMed
Walters  JT, Corvin  A, Owen  MJ,  et al.  Psychosis susceptibility gene ZNF804A and cognitive performance in schizophrenia. Arch Gen Psychiatry. 2010;67(7):692-700.
PubMed
Toulopoulou  T, Picchioni  M, Rijsdijk  F,  et al.  Substantial genetic overlap between neurocognition and schizophrenia: genetic modeling in twin samples. Arch Gen Psychiatry. 2007;64(12):1348-1355.
PubMed
Fowler  T, Zammit  S, Owen  MJ, Rasmussen  F.  A population-based study of shared genetic variation between premorbid IQ and psychosis among male twin pairs and sibling pairs from Sweden. Arch Gen Psychiatry. 2012;69(5):460-466.
PubMed
de Ligt  J, Willemsen  MH, van Bon  BW,  et al.  Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med. 2012;367(20):1921-1929.
PubMed
Meisler  MH, O’Brien  JE, Sharkey  LM.  Sodium channel gene family: epilepsy mutations, gene interactions and modifier effects. J Physiol. 2010;588(pt 11):1841-1848.
PubMed
Need  AC, Shashi  V, Hitomi  Y,  et al.  Clinical application of exome sequencing in undiagnosed genetic conditions. J Med Genet. 2012;49(6):353-361.
PubMed
Rauch  A, Wieczorek  D, Graf  E,  et al.  Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet. 2012;380(9854):1674-1682.
PubMed
Sanders  SJ, Murtha  MT, Gupta  AR,  et al.  De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012;485(7397):237-241.
PubMed
First  M, Spitzer  R, Gibbon  M, Williams  J. Structured Clinical Interview for Axis I DSM-IV. New York, NY: Biometrics Research Dept, New State Psychiatric Institute; 1994.
Spitzer  R, Williams  J, First  M. Structured Clinical Interview for DSM-IV, Patient Version. Arlington, VA: American Psychiatric Press; 1996.
Purcell  S, Neale  B, Todd-Brown  K,  et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559-575.
PubMed
Resnick  SM.  Matching for education in studies of schizophrenia. Arch Gen Psychiatry. 1992;49(3):246.
PubMed
Keefe  RS, Mohs  RC, Bilder  RM,  et al.  Neurocognitive assessment in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) project schizophrenia trial: development, methodology, and rationale. Schizophr Bull. 2003;29(1):45-55.
PubMed
Stroup  TS, McEvoy  JP, Swartz  MS,  et al.  The National Institute of Mental Health Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) project: schizophrenia trial design and protocol development. Schizophr Bull. 2003;29(1):15-31.
PubMed
Sullivan  PF, Lin  D, Tzeng  JY,  et al.  Genomewide association for schizophrenia in the CATIE study: results of stage 1. Mol Psychiatry. 2008;13(6):570-584.
PubMed
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Supplement.

eAppendix 1. Methods

eAppendix 2. Results

eReferences

eFigure 1. Panel A: Regional Plot Centered on SCN2A rs10174400 (+- 2 MB) on Chromosome 2 From GWAS of 334 People With Schizophrenia. Panel B: Schematic diagram of SCN2A alternative Transcripts With SNP Locations

eFigure 2. QQ plot for g (italic) From GWAS of 363 Controls

eFigure 3. Educational Attainment in 147 Unaffected Siblings by SCN2A rs10174400 Genotype

eFigure 4. Interaction Between SCN2A rs10174400 Genotype and Diagnosis for BOLD fMRI Activation During the N-Back Working Memory Task

eFigure 5. RNA Seq mRNA Expression Patterns as a Function of Genotype Carrier Status and Diagnosis in Two RefSeq SCN2A Alternative Transcripts

eTable 1. Descriptive Statistics for Unaffected Siblings and For Schizophrenia Cases From the CATIE Study, Japan, and Germany

eTable 2. Variables Included in Cognitive Composites for NIMH/CBDB Study and Comparison Samples

eTable 3. Partial Correlations Between Cognitive Variables for Schizophrenia Cases and Healthy Controls (Controlling for Age and Sex)

eTable 4. Intraclass Correlations for Cognitive Variables Calculated in 147 Affected/Unaffected Sibling Pairs From the NIMH/CBDB Study

eTable 5. Genomic Inflation Factor (Lambda) Values for Schizophrenia Cases and Controls

eTable 6. Schizophrenia Sample Across Cognitive Phenotypes, SNP Associations Better Than P = 1e-05

eTable 7. Control Sample Across Cognitive Phenotypes, SNP Associations Better Than P = 1e-05

eTable 8. Contrast of the Main Association Finding for SCN2A rs10174400 With Models Accounting for Possible Clinical/Demographic Covariates

eTable 9. HUGO* Sodium and Calcium Channel Gene Families

eTable 10. Sodium and Calcium Ion Channel Sets, Association With g, Gene by Gene, Controlling for Age and Sex, LD-pruned r2=0.25, 1000 Permutations

eTable 11. Sodium and Calcium Ion Channel Gene Sets, Association With g, by Gene Family, Controlling for Age and Sex, LD-pruned r2=0.25, 1000 Permutations

eTable 12. Low-Frequency Exonic SNP Genotype Counts for Two SCN2A Polymorphic Variants for 315 Schizophrenia Cases From CBDB/NIMH Data, Stratified by SCN2A rs10174400 Genotype

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