The one and the many: Effects of the Cell Adhesion Molecule Pathway on Neuropsychological function in Psychosis

Abstract

Background

Genetic studies of single gene variants have been criticised as providing a simplistic characterisation of the genetic basis of illness risk that ignores the effects of other variants within the same biological pathways. Of candidate biological pathways for schizophrenia (SZ), cell adhesion molecule (CAM) pathways have repeatedly been linked to both psychosis and neurocognitive dysfunction. Here we tested, using risk allele scores derived from the Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC-SCZ), whether alleles within the CAM pathway were correlated with poorer neuropsychological function in patients.

Method

424 patients with psychosis were assessed in areas of cognitive ability typically found to be impaired in SZ: IQ, memory, working memory and attentional control. CAM pathway genes were identified using the KEGG database. Alleles within these genes identified as significantly associated with SZ risk in the PGC-SCZ were then used to calculate a CAM pathway based polygenic risk allele score for each patient and these scores were tested for association with cognitive ability.

Results

Increased CAM pathway polygenic risk scores were significantly associated with poorer performance on measures of memory and attention, explaining 1–3% of variation on these measures. Notably, the most strongly associated SNP in the CAM pathway (rs9272105 within HLA-DQA1) explained a similar amount of variance in attentional control but not memory as the polygenic risk analysis.

Discussion

These data support a role for the CAM pathway in cognitive function, both at the level of individual SNPs and the wider pathway. In so doing these data highlight the value of pathway-based polygenic risk score studies as well as single gene studies for understanding SZ associated deficits in cognition.

Introduction

The involvement of cell adhesion molecules (CAMs) in the pathophysiology of schizophrenia (SZ) has long been hypothesised. Genes encoding CAMs play an important role in neurodevelopmental processes including axonal and dendritic growth and brain segmentation (e.g. CDH4; Wang et al. 2009), cell-cell binding (e.g. CDH7; Soronen et al. 2010), and synapse formation (e.g. neurexin; Dean et al. 2003). Disruption of several CAM genes has been reported in patients with psychosis, including de novo copy number variants (CNVs) in neurexin-1 (Kirov et al. 2012, Rujescu et al. 2009), neuroligin-2 (Sun et al. 2011) and several others (including members of the DLG gene family; Kirov et al. 2012), each of which has been associated with increased illness risk.

Genes encoding CAMs have also been shown to impact cognitive function. A synthetic peptide derived from the neuronal cell adhesion molecule (NCAM) has been found to influence memory consolidation in an animal model at both a behavioural and hippocampal neuron phenotypes (Cambon et al. 2004). In patients, Soronen and colleagues (Soronen et al 2010) found that the gene cadherin 7 (CDH7) is associated with variation in performance on measures of working memory and visual attention in patients with bipolar disorder. Contactin-associated protein-like 2 (CNTNAP2), which encodes a member of the neurexin family has been implicated in SZ, in associated with epilepsy and intellectual disability (Friedman et al. 2008). The mechanism by which CAMs influence cognition is unknown, particularly whether this occurs via the same biological pathway as is associated with increased psychosis risk.

In addition to the study of single variants, researchers have recently begun to focus on methods to extract data from multiple variants. These include approaches that examine whether associated SNPs are more likely to come from biologically related genes, termed pathway analysis. In a recent pathway analyses by our group of three independent GWAS datasets, we identified using an enrichment of genetic association signals in psychosis for SNPs that were tagged to genes defined as members of the Cell adhesion molecules (CAMs) pathway (KEGG Identifier: HSA04514; O’Dushlaine et al. 2011). Evidence for involvement of CAM pathways has also emerged from two other recent studies taking different analytical approaches (Jia et al. 2012, Lips et al. 2012). A second approach, termed polygenic risk score analysis, has been designed to investigate whether illness associated SNPs from one study (e.g. of schizophrenia) can explain phenotypic variance in an independent sample using either the same illness phenotype (Purcell et al. 2009), or a related phenotype (e.g. cognition, McIntosh et al. 2013). This analysis approach indicates that the composite effect of many small genetic variants contribute substantially to schizophrenia variance (~25%; Purcell et al. 2009, Kirov et al. 2012). Polygene analysis has also provided further evidence of genetic overlap between related phenotypes; for example, McIntosh and colleagues (2013) recently reported that polygenic schizophrenia risk scores could be used to explain variance in longitudinal measures of age related changes in cognitive function.

The purpose of the present study was to investigate the effects of common variants within the CAM pathway on neuropsychological function in patients with psychosis using a combination of pathway and polygenic analysis. We hypothesised that an additive effect of risk allele load from genetic variants located within the CAM pathway would account for a significant percentage of the variance in neuropsychological function in patients. To test this hypothesis we based our analysis on recent case-control analysis from the PGC schizophrenia GWAS.¹⁵ Selecting all SNP variants located within genes from the CAM pathway (defined in our previous work (KEGG Identifier: HSA04514)), we calculated a pathway-specific polygenic risk score based on the number of risk alleles they carried. We then determined the amount of variance in patients’ neuropsychological function explained by these scores. Finally, we compared the amount of variance explained from the full polygene analysis to the amount of variance explained by individual variants from four CAM-related genes that were most strongly associated with SZ risk in the PGC case-control analysis - HLA-DQ1A, CDH4, NRXN1 and CNTNAP2. In doing so we sought to determine both whether polygenic risk scores from the CAM pathway was significantly associated with neuropsychological performance and if so, how the amount of variance explained compared to that explained by individual risk variants within the pathway. Given the previous use of polygene analysis in schizophrenia and psychosis more broadly, a secondary question for our study was whether any significant associations observed were specific to schizophrenia cases, or were shared across the broader psychosis phenotype.

Method

Neuropsychological sample characteristics

424 cases who had completed a full neuropsychological assessment battery and for whom full genome-wide SNP data was available were analysed (Irish Schizophrenia Genomics Consortium 2012). Cases consisted of clinically stable patients with a DSM-IV diagnosis of schizophrenia (SZ), schizoaffective disorder (SZA), bipolar disorder (BP), major depressive disorder with psychotic features (MDD) or psychosis not otherwise specified (PNOS) (see Table 1 for details) recruited from five sites across Ireland. Inclusion criteria required that participants were clinically stable at the time of neuropsychological assessment, aged 18 to 65 years, had no history of co-morbid psychiatric disorder, no substance abuse in the preceding six months, no prior head injury with loss of consciousness and no history of seizures. Diagnosis was confirmed by trained psychiatrists using the Structured Clinical Interview for DSM-IV Axis 1 Diagnoses (SCID; First et al, 2002). Due to the range of psychotic illness present in the sample and differences in cognitive deficits associated with these, we based our analysis on both (1) a narrow definition of SZ and SZA (n=340) and (2) a broad definition of psychosis which encompassed all those meeting the criteria for psychosis (n=424). Additional diagnostic details and clinical sample characteristics ascertained at time of interview include medication dosage and symptom severity. This was calculated based on a factor analysis of Operational Criteria Checklist for Psychotic Illness (OPCRIT; McGuffin et al. 1991), as previously described for this sample (Cummings et al. 2013). All assessments were conducted in accordance with the relevant ethics committees’ approval from each participating site. All patients were of Irish ancestry (i.e. four grandparents born in Ireland) and all provided written informed consent.

Table 1:

Patient demographic characteristics.

	Narrow Psychosis Dx	Broad Psychosis Dx
Total	N=340	N=424
Psychosis subtype:
Schizophrenia	N = 282	N = 282
Schizoaffective disorder	N = 58	N = 58
Bipolar disorder I	NA	N = 61
Major depressive disorder	NA	N = 11
Psychosis not otherwise specified	NA	N = 12
Gender (ratio; M:F)	2.6:1	2.2:1
Age (years; mean (SD))	41.3 (12.2)	41.3 (12.4)
Age at onset (years; mean(SD))	22.8 (7.2)	23.2 (7.5)
Medication (Chlorpromazine equiv. mg/day; mean(SD))	589.8 (562.4)	555.5 (540.7)
SAPS/SANS factor scores:
Manic (mean (SD))	−0.18 (0.95)	0.04(1.09)
Depression (mean (SD))	0.16 (1.07)	0.23(1.06)
Positive (mean (SD))	−0.02 (0.99)	−0.12(0.95)
Disorganised (mean (SD))	−0.22 (0.76)	−0.31(0.78)
Negative (mean (SD))	0.39 (0.90)	0.32(0.87)
Full scale IQ (mean (SD))	89.6 (17.8)	90.3(18.3)

Cognitive assessment

All patients completed a full neuropsychological assessment battery designed to target the cognitive deficits typically reported in SZ – namely deficits in general cognitive function, memory function, working memory and attentional control. Where possible both a verbal and a visuo-spatial measure of each construct were included.

Pre-morbid and current General cognitive functioning (IQ) was measured using the Wechsler Test of Adult Reading (WTAR) and selected subtests (Vocabulary, Similarities, Block Design and Matrix Reasoning) from the Wechsler Adult Intelligence Scale, 3^rd edition (Wechsler 1997a). Verbal and visual episodic memory were assessed using the logical memory subtest from the Wechsler Memory Scale, 3rd edition (WMS-III; Wechsler 1997b) and the Paired Associate Learning (PAL) task from the Cambridge Automated Neuropsychological Test Battery (CANTAB; Robbins et al. 1994) respectively. Working memory was assessed using the spatial working memory task (SWM) from the CANTAB and letter number sequencing (LNS) from WMS-III. Attentional control was assessed using the continuous performance task identical pair’s version (CPT-IP; Cornblatt et al. 1988) and the sustained attention to response task (SART; Robertson 1994).

Genotyping

Genetic analysis for patient samples was conducted on DNA extracted from whole blood. SNP data for these samples was available from a recent genome-wide association study using the Affymetrix SNP Array 6.0 as previously described (Bellenguez et al. 2012).

Calculating risk allele load

Polygenic scores for variants located within the CAM pathway were calculated in four steps. Firstly, all available SNPs within 20Kb of genes in the CAM pathway were identified. CAM pathway genes were identified based on data from the KEGG database as previously described by us (O’Dushlaine et al. 2011). A total of 132 genes were identified (see Table S1); five of these could not be tagged with genotyped SNPs using the above criteria. Secondly, alleles within these SNPs were identified as risk or non-risk using data from the PGC SZ GWAS analysis according to three different thresholds: p<10⁻⁵, p<0.05, p<0.5. Using a variety of threshold cut-off points for determining risk is in line with proceedures used in previous polygenic analysis (Purcell et al. 2009). Regarding the three thresholds we selected, these are arbitrary and were pragmatically selected to reflect a distribution including strong (10e-5), nominal(.05) and non-significant baseline (.5) associations. The SNPs were all coded so that the “target” allele was the one positively associated with schizophrenia; hence, the directionality (increased risk) is the same for all SNPs. Thirdly, to account for differences between variants in the effect size of the association with illness, each risk allele was weighted as the log10 of the effect size described in the PGC dataset (W_SNP= log₁₀ (OR_PGC)). Included variants were not LD pruned; inclusion of all variants has previously been recommended to capture all variation associated with risk (ISC,2009). Finally, a risk score for each individual was calculated based on the number of weighted risk alleles they carried at each of the three p-value thresholds using the equation: Score(p<threshold) = Ʃj(S_SNP )/(j-m), where j = number of SNPs at P<threshold and m= number of SNPs with missing genotypes. A risk score for each of the CAM SNPs was calculated as (S_SNP = W_SNP * Risk Allele Count). The number of missing genotypes was consistently low within each p-value threshold (for p<10⁻⁵ it was 6 (2.5%), for p<0.05 it was 44 (0.85%), and for p<0.5 it was 254 (0.49%)).

Single gene variant selection

As we hypothesized that polygene risk scores would better explain variance in neuropsychological function than would be explained by single SNP analyses, we planned to follow up any significant neurocognitive findings from the CAM polygene analysis by characterising the effects of single SNPs within CAM on neurocognition. To do this, we selected the most strongly associated single SNPs from each of the four CAM genes most strongly associated with SZ risk in the PGC analysis. These were: HLA-DQA1 (rs9272105; OR = 0.87; p = 9.97×10⁻⁹), CDH4 (rs2427104; OR = 0.90; p = 0.00006), NRXN1 (rs1819972; OR = 1.1; p = 0.0004), and CNTNAP2 (rs1548743; OR = 0.92; p = 0.0005). Selecting these 4 variants for analysis was based on an arbitrary cut-off point for statistical association with SZ of p≤0.0005. Furthermore, while 11 other HLA genes exceeded this cut-off threshold we only included the most strongly associated given the high LD in this region.

Statistical Analysis

Associations between CAM pathway polygenic risk allele scores and the phenotypes of IQ, episodic memory, working memory and attention were tested in a series of multiple regression analyses implemented in SPSS 17 (SPSS 2008). In each case, scores for each neuropsychological subtest were entered as dependent variables, and where appropriate age and gender were entered on the first step of the analysis as effects of no interest, followed by CAM pathway risk allele score on the second step. Exactly the same approach was taken in the analysis of the single variants, with the risk genotype score (0, 1, or 2 alleles) in each case replacing the polygenic score as the independent variable. The R² value, or variance explained, was calculated between these nested models; namely, a model containing the intercept plus sex and age (when appropriate) and the model containing these same terms plus the pathway score. We report this pathway score-specific R² plus the corresponding F-test p-value. Finally, effects sizes for all significant effects were calculated using Cohen’s d in ClinTools (ClinTools 2005) to enable comparison between the polygenic scores analysis and analysis of the individual SNPS considered.

Results

Demographic and clinical characteristics for all patients appear in Table 1. No differences were observed between the narrow psychosis group (SZ and SZA only) and broad psychosis group (all patients with psychosis) in terms of age, gender, age at onset, or general cognitive ability as indexed by full-scale IQ. Based on factor scores previously calculated for symptom severity based on OPCRIT (Cummings et al. 2013), no differences were observed between the two groups in severity of symptoms of depression, positive symptoms, negative symptoms or disorganisation. Differences were however observed for on the ‘mania’ factor, with the broad psychosis group scoring significantly higher on the manic scale than the narrow psychosis group. No between group differences were observed in medication dosage as measured by chlorpromazine equivalents.

The effects of CAM pathway risk allele load on cognition

R² change and p-values from the regression analyses for each of the 3 cognitive domains of IQ, memory and attention by CAM pathway risk allele load are presented in Figure 1 and Table 2. Across both narrow sense and broad sense psychosis groups, higher polygenic risk scores within the CAM pathway was significantly associated with deficits in both memory function (as measured by the CANTAB paired associate learning test) and sustained attention (as measured by the SART). For the broad diagnoses groups, CAM pathway risk allele load was also associated with poorer verbal episodic memory function as measured by WMS-III logical memory task. In the narrow psychosis group, this effect was observed at trend level only. By contrast, the narrow psychosis group showed a nominal association between preserved premorbid IQ and higher CAM pathway risk polygene scores (but only for SNPs thresholded at p=10×⁻⁵), whereas this association was only observed at trend level in the broad psychosis group. The amount of variance in cognitive performance explained by CAM pathway polygenic risk scores ranged between 1–3% in regression models that were significant, with the highest percentage of variance explained on the SART attentional control task. Calculated effect sizes (Cohen’s d) for this variance explained ranged from 0.23 to 0.37 with a mean effect size of 0.29.

Figure 1:

Plot of regression analyses of neuropsychological variables showing r2 values and associated significance for each polygenic risk threshold.

Table 2:

CAM pathway polygenic score regression analysis for each neuropsychological variable. CAM risk alleles included were thresholded at (a) p=10×⁻⁵ (b) p=.05 (c) p=.5

	Neuropsych variable	All patients with psychosis			Patients with SZ or SZA
		10×−5	0.05	0.5	10×−5	0.05	0.5
		r2 (p)	r2 (p)	r2 (p)	r2 (p)	r2 (p)	r2 (p)
IQ	Premorbid IQ	.008(.07)	.005(.17)	.005(.168)	.013(.045)	.006(.178)	.004(.239)
	Verbal IQ	.000(.854)	.000(.675)	.001(.63)	.000(.944)	.001(.633)	.000(.693)
	Performance IQ	.000(.954)	.000(.807)	.003(.309)	.000(.776)	.000(.777)	.005(.201)
	Full scale IQ	.000(.985)	.000(.713)	.002(.419)	.000(.791)	.000(.715)	.002(.403)
Memory	Logical memory 1	.002(.407)	.004(.206)	.005(.176)	.000(.768)	.005(.24)	.006(.181)
	Logical memory 2	.008(.079)	.013(.023)	.014(.021)	.003(.36)	.011(.063)	.013(.051)
	PAL total errors	.011(.044)	.021(.005)	.032(.001)	.004(.27)	.01(.083)	.021(.013)
Attention	SART reaction time	.006(.201)	.026(.005)	.024(.008)	.009(.141)	.029(.009)	.027(.011)
	CPT d’Prime 2 digit	.004(.304)	.000(.957)	.000(.94)	.012(.103)	.002(.549)	.001(.57)
	CPT d’Prime 3 digit	.000(.798)	.007(.16)	.004(.296)	.004(.346)	.001(.69)	.000(.76)

Cognitive analysis of SZ-associated individual SNPs within the CAM pathway genes

HLA-DQA1, CDH4, NRXN1 and CNTNAP2

We next tested whether variance in neurocognitive function explained by CAM pathway scores was comparable to that explained by individual SNPs within the CAM pathway. As described above, this was based on the analysis of SNPS within each of the four genes most strongly associated with SZ risk in the PGC analysis - rs9272105 within HLA-DQA1, rs2427104 within CDH4, rs1819972 within NRXN1 and rs1548743 within CNTNAP2 (See Table 3). Only the HLA-DQA1 SNP (the most strongly associated CAM pathway SNP from the PGC analysis and the only variant achieving genome wide associated significance) was observed to be associated with variation in neurocognitive performance – specifically attentional control in both the narrow and broad diagnosis groups, and pre-morbid IQ in the narrow diagnosis group only. No association with memory function was observed. None of the other variants within CDH4, NRXN1 or CNTNAP2 were associated with variation on any of these three neurocognitive measures. Similarly, a combined regression analysis that included all four SNPs failed to explain a significant amount of variation on any of these three neurocognitive measures.

Table 3:

Regression analysis for SNPs within HLA-DQA1, NRXN1, CNTNAP2 and CDH4.

	Neuropsych variable	r square	Adjusted r square	F	p
HLA-DQA1 rs9272105	All patients with psychosis
	Premorbid IQ	0.01	0.007	3.7	0.055
	Logical memory 2	0.035	−0.002	0.44	0.508
	SART Reaction time	0.028	0.021	4.0	0.019
	PAL Total errors	0.003	0.001	0.898	0.344
	Patients with SZ + SZA only
	Premorbid IQ	0.021	0.017	6.25	0.013
	Logical memory 2	.009	.005	2.38	0.123
	SART Reaction time	0.036	0.027	3.98	0.02
	PAL Total errors	0	−0.003	0.083	0.773
NRXN1 rs1819972	All patients with psychosis
	Premorbid IQ	0	−0.002	0	0.996
	Logical memory 2	0.032	−0.002	0.386	0.535
	SART Reaction time	0.012	0.008	3.538	0.061
	PAL Total	0.008	0.005	2.819	0.094
	Patients with SZ + SZA only
	Premorbid IQ	0	−0.003	0	0.983
	Logical memory 2	.003	−0.001	.831	0.363
	SART RT	0.012	0.008	2.81	0.095
	PAL Total errors	0.011	0.007	3.1	0.079
CNTNAP2 rs1548743	All patients with psychosis
	Premorbid IQ	0	−0.002	0.188	0.664
	Logical memory 2	0.01	−0.003	0.038	0.845
	SART Reaction time	0	−0.003	0.066	0.797
	PAL Total errors	0.006	0.004	2.43	0.119
	Patients with SZ + SZA only
	Premorbid IQ	0.001	−0.002	0.271	0.603
	Logical memory 2	0.03	0.001	0.324	0.57
	SART Reaction time	0	−0.004	0.005	0.946
	PAL Total	0.008	0.005	2.4	0.122
CDH4 rs2427104	All patients with psychosis
	Premorbid IQ	0	−0.002	0.154	0.695
	Logical memory 2	0.042	0.002	0.656	0.419
	SART Reaction time	0.001	−0.002	0.311	0.577
	PAL Total errors	0	−0.002	0.113	0.737
	Patients with SZ + SZA only
	Premorbid IQ	0.001	−0.002	0.411	0.522
	Logical memory 2	0.085	0.007	2.19	0.14
	SART Reaction time	0.003	−0.001	0.785	0.377
	PAL Total errors	0	−0.003	0.055	0.815

Given the significant contribution of rs9272105 to explaining variation in neuropsychological functioning, we re-ran our CAM pathway polygenic regression analysis to exclude all variants at this gene locus (resulting in the exclusion of 3 SNPs). When we re-calculated CAM pathway polygenic risk allele load scores minus these three HLA-DQA1 variants, the association between the CAM polygene score and memory largely remained significant (see Table 4). By comparison the association with attentional control was no longer significant. The nominal association with pre-morbid IQ seen in the narrow diagnosis group using the p=10x−5 threshold also became non-significant.

Table 4:

Recomputation of the regression analyses for the CAM pathway polygeneic risk scores without 3 SNPs mapped to the HLA-DQA1 gene.

	Neuropsych variable	All patients with psychosis			Patients with SZ or SZA
		10×−5	0.05	0.5	10×−5	0.05	0.5
		r2(p)	r2(p)	r2(p)	r2(p)	r2(p)	r2(p)
IQ	Premorbid IQ	.006(.123)	.004(.236)	.003(.259)	.005(.192)	.003(.296)	.002(.414)
Memory	Logical memory 2	.006(.128)	.012(.029)	.010(.049)	.002(.389)	.010(.077)	.007(.151)
	PAL total errors	.008(.079)	.012(.031)	.015(.017)	.003(.357)	.006(.166)	.008(.114)
Attention	SART reaction time	.001(.53)	.002(.416)	.002(.393)	.000(.883)	.000(.755)	.001(.702)

Discussion

This study used polygeneic risk allele scores derived from the PGC schizophrenia GWAS case-control analysis to investigate whether risk variants within the CAM pathway were associated with poorer neuropsychological function amongst 424 patients with either narrow sense or broad sense psychosis. We further compared the variation in neuropsychological performance explained by CAM pathway polygenic risk allele scores to the individual CAM pathway variants identified by the PGC as most strongly associated with SZ risk (CDH4, NRXN1, HLA-DQA1, and CNTNAP2). Based on these analyses we found that (1) polygenic scores for the CAM pathway explained a statistically significant proportion of variance in neuropsychological function; (2) one risk SNP – HLA-DQA1 was also individually associated with variation in neuropsychological function, and (3) after removal of this gene from the polygene analysis, polygenic risk scores continued to explain variants in memory, but not attentional control. To our knowledge this is the first study to characterise the effects on neurocognition of risk variants within the CAM pathway – or any other pathway – in patients with SZ.

A specific criticism of the single variant approach in studying both illness phenotypes and intermediate phenotypes (including cognition) is that as illness risk is polygenically determined¹³ the function of single variants cannot be understood in isolation from either other risk variants or from the biological pathways in which they function. In this context, we investigated both whether pathway based risk estimates significantly accounts for variation in neuropsychological deficits and if so, how the amount of variance explained compared to that explained by individual SNP genotypes. When compared to results from single SNP analysis from the four most strongly associated CAM pathway genes, CAM pathway polygene scores explained a greater percentage of variance in memory function than the HLA-DQA1 SNP most strongly associated with risk in the PGC analysis, and this variance explained was undimished after the HLA-DQA1 SNP was removed from the polygene score analysis. For attentional control, however, variation on this variable was better explained by the SNP than the pathway.

The CAM pathway has previously been implicated in a variety of neurocognitive processes, including memory. Consistent with this, an association with variation on a neurocognitively-associated phenotype has been reported for three of the four individual genes characterised: CDH4 has been associated with total brain volume (Seshadri et al. 2007), NRXN1 with white matter volume (Voineskos et al. 2011), and CNTNAP2 with language processing (Kos et al. 2012). This is the first study to our knowledge that specifically implicates the HLA-DQA1 gene in neuropsychological performance in humans. Beyond these single variant analyses, this is the first study to our knowledge that demonstrates the relevance of CAM genes to cognition at the pathway level. Specifically, it highlights the role of previously identified SZ risk variants within the CAM pathway on cognition in a manner that goes beyond the effects of individual variants. While making this point, two caveats regarding the involvement of HLA-DQA1 are relevant. First, while the HLA-DQA1 gene is located within the KEGG cell adhesion pathway, its demonstrated roles in cell adhesion have been confined to the immune system. This is noteworthy because a role for immune related MHC class 1 molecules in neuronal plasticity (which is strongly linked to learning and memory) has long been speculated (Shatz 2009). It should, of course also be noted that the HLA-DQA1 resides in an area of strong linkage disequilibrium encompassing many genes, many of which are not involved in cell adhesion. In summary, therefore, while this study highlights the role of CAM genes in cognition, the observed cognitive effects of HLA-DQA1 may occur independently of this pathway.

This finding that pathway based polygenic risk scores for SZ explain variance in cognition is relevant to the recent debate regarding the genetic overlap between cognitive performance and schizophrenia in particular and psychosis in general may also be important. While a high degree of overlap between cognitive deficits and schizophrenia have previously been inferred from twin studies (Toulopoulou et al. 2007), recent evidence by Fowler and colleagues (Fowler et al. 2012) has suggested that this may not be quite as high when based on non-biased epidemiological samples. In a polygenic analysis of age related changes in cognition in healthy adults over time, McIntosh and colleagues (2013) found that polygene scores (again based on the PGC SZ dataset) significant predicted ~1% variance in cognitive change. In the present study we extend these findings by demonstrated that polygenic risk scores also predict variance in SZ related cognitive deficits in patients (~3%). Furthermore by focusing on a previously implicated biological pathway our data highlights the relevance of polygenic risk in the CAM pathway in particular to cognitive deficits in this group. The CAM pathway is unlikely to be unique in this regard - polygenic risk scores analysis based on other pathways is likely to also be informative about cognitive deficits in this group. For example, a recent study by Greenwood et al. (2011) of 94 candidate SZ genes suggests that glutamate pathway genes are also likely to be important. The approach taken in our study is equally applicable to the study of these other pathways, and may well be informative not just for explaining cognitive deficits in patients, but also for explaining the genetics architecture of cognition in the healthy population.

Conclusion

In conclusion this study, building on previous literature adopting either pathway based or polygenic based approaches to psychosis risk and cognitive dysfunction, investigated the role of pathway-specific risk variants in the cognitive deficits associated with psychosis. The data supports a role for the CAM pathway in memory formation and attention related reaction time. It also provided evidence that a single SNP – in HLA-DQA1 – was also associated with variation in attentional control, and to a lesser extent pre-morbid IQ, in psychotic patients. In doing so, this study demonstrates the feasibility of pathway specific polygenic risk analysis to studying an intermediate phenotype for psychosis, while at the same time suggesting that single gene analysis nonetheless continues to be informative about the effects of gene effects on cognition.

Members of Wellcome Trust Case Control Consortium 2

Management Committee

Peter Donnelly (Chair)^1,2, Ines Barroso (Deputy Chair)³, Jenefer M Blackwell^{4, 5}, Elvira Bramon⁶, Matthew A Brown⁷, Juan P Casas⁸, Aiden Corvin⁹, Panos Deloukas³, Audrey Duncanson¹⁰, Janusz Jankowski¹¹, Hugh S Markus¹², Christopher G Mathew¹³, Colin NA Palmer¹⁴, Robert Plomin¹⁵, Anna Rautanen¹, Stephen J Sawcer¹⁶, Richard C Trembath¹³, Ananth C Viswanathan¹⁷, Nicholas W Wood¹⁸

Data and Analysis Group

Chris C A Spencer¹, Gavin Band¹, Céline Bellenguez¹, Colin Freeman¹, Garrett Hellenthal¹, Eleni Giannoulatou¹, Matti Pirinen¹, Richard Pearson¹, Amy Strange¹, Zhan Su¹, Damjan Vukcevic¹, Peter Donnelly^1,2

DNA, Genotyping, Data QC and Informatics Group

Cordelia Langford³, Sarah E Hunt³, Sarah Edkins³, Rhian Gwilliam³, Hannah Blackburn³, Suzannah J Bumpstead³, Serge Dronov³, Matthew Gillman³, Emma Gray³, Naomi Hammond³, Alagurevathi Jayakumar³, Owen T McCann³, Jennifer Liddle³, Simon C Potter³, Radhi Ravindrarajah³, Michelle Ricketts³, Matthew Waller³, Paul Weston³, Sara Widaa³, Pamela Whittaker³, Ines Barroso³, Panos Deloukas³.

Publications Committee

Christopher G Mathew (Chair)¹³, Jenefer M Blackwell^4,5, Matthew A Brown⁷, Aiden Corvin⁹, Chris C A Spencer¹

1 Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK; 2 Dept Statistics, University of Oxford, Oxford OX1 3TG, UK; 3 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; 4 Telethon Institute for Child Health Research, Centre for Child Health Research, University of Western Australia, 100 Roberts Road, Subiaco, Western Australia 6008; 5 Cambridge Institute for Medical Research, University of Cambridge School of Clinical Medicine, Cambridge CB2 0XY, UK; 6 Department of Psychosis Studies, NIHR Biomedical Research Centre for Mental Health at the Institute of Psychiatry, King’s College London and The South London and Maudsley NHS Foundation Trust, Denmark Hill, London SE5 8AF, UK; 7 University of Queensland Diamantina Institute, Brisbane, Queensland, Australia; 8 Dept Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT and Dept Epidemiology and Public Health, University College London WC1E 6BT, UK; 9 Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine, Trinity College Dublin, Dublin 2, Eire; 10 Molecular and Physiological Sciences, The Wellcome Trust, London NW1 2BE; 11 Department of Oncology, Old Road Campus, University of Oxford, Oxford OX3 7DQ, UK, Digestive Diseases Centre, Leicester Royal Infirmary, Leicester LE7 7HH, UK and Centre for Digestive Diseases, Queen Mary University of London, London E1 2AD, UK; 12 Clinical Neurosciences, St George’s University of London, London SW17 0RE; 13 King’s College London Dept Medical and Molecular Genetics, King’s Health Partners, Guy’s Hospital, London SE1 9RT, UK; 14 Biomedical Research Centre, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; 15 King’s College London Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Denmark Hill, London SE5 8AF, UK; 16 University of Cambridge Dept Clinical Neurosciences, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK; 17 NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London EC1V 2PD, UK; 18 Dept Molecular Neuroscience, Institute of Neurology, Queen Square, London WC1N 3BG, UK.

Schizophrenia Psychiatric GWAS Consortium co-authors:

Stephan Ripke1, Alan R Sanders2,3, Kenneth S Kendler4–6, Douglas F Levinson7, Pamela Sklar1,8, Peter A Holmans9,10, Dan-Yu Lin11, Jubao Duan2,3, Roel A Ophoff 12–15, Ole A Andreassen16,17, Edward Scolnick18, Sven Cichon19–21, David St. Clair22, Aiden Corvin23, Hugh Gurling24, Thomas Werge25, Dan Rujescu26, Douglas H R Blackwood27, Carlos N Pato28, Anil K Malhotra29–31, Shaun Purcell18, Frank Dudbridge32, Benjamin M Neale18, Lizzy Rossin1, Peter M Visscher33, Danielle Posthuma34,35, Douglas M Ruderfer1, Ayman Fanous5,36,37, Hreinn Stefansson38, Stacy Steinberg38, Bryan J Mowry39,40, Vera Golimbet41, Marc de Hert42, Erik G Jönsson43, István Bitter44, Olli P H Pietiläinen45,46, David A Collier47, Sarah Tosato48, Ingrid Agartz16,49, Margot Albus50, Madeline Alexander7, Richard L Amdur36,37, Farooq Amin51,52, Nicholas Bass24, Sarah E Bergen1, Donald W Black53, Anders D Børglum54,55, Matthew A Brown56, Richard Bruggeman57, Nancy G Buccola58, William F Byerley59,60, Wiepke Cahn61, Rita M Cantor14,15, Vaughan J Carr62, Stanley V Catts63, Khalid Choudhury24, C Robert Cloninger64, Paul Cormican23, Nicholas Craddock9,10, Patrick A Danoy56, Susmita Datta24, Lieuwe de Haan65, Ditte Demontis54, Dimitris Dikeos66, Srdjan Djurovic16,67, Peter Donnelly68,69, Gary Donohoe23,, Sarah Dwyer9,10,, Robert Freedman71, Nelson B Freimer14, Marion Friedl26, Lyudmila Georgieva9,10, Ina Giegling26, Michael Gill23,, Stephanie Godard73, Marian Hamshere9,10, Mark Hansen74, Thomas Hansen25, Annette M Hartmann26, Frans A Henskens75, David M Hougaard76, Christina M Hultman77, Louise K.E. Høffding25, Andrés Ingason25, Assen V Jablensky78,, Maurice Jay79,132,, René S Kahn61, Matthew C Keller81, Gunter Kenis82, Elaine Kenny23, Yunjung Kim83, George K Kirov9,10, Heike Konnerth26, Bettina Konte26, Lydia Krabbendam84, Robert Krasucki24, Virginia K Lasseter85,132, Claudine Laurent79, Jacob Lawrence24, Todd Lencz29–31, F Bernard Lerer86, Kung-Yee Liang87, Paul Lichtenstein77, Jeffrey A Lieberman88, Don H Linszen65, Jouko Lönnqvist89, Carmel M Loughland90, Alan W Maclean27, Brion S Maher4–6, Wolfgang Maier91, Jacques Mallet92, Pat Malloy27, Manuel Mattheisen19,21,93, Morten Mattingsdal16,94, Kevin A McGhee27, John J McGrath39,40, Andrew McIntosh27, Duncan E McLean95, Andrew McQuillin24, Ingrid Melle16,17, Patricia T Michie96,97, Vihra Milanova98, Derek W Morris23, Ole Mors55, Preben B Mortensen99, Valentina Moskvina9,10, Pierandrea Muglia100,101, Inez Myin-Germeys84, Deborah A Nertney39,40, Gerald Nestadt85,, Ivan Nikolov9,10, Merete Nordentoft103, Nadine Norton9,10, Markus M Nöthen19,21, Colm T O’Dushlaine23, Ann Olincy71, Line Olsen25, F Anthony O’neill104, T orben F Ørntoft105,106, Michael J Owen9,10, Christos Pantelis107, George Papadimitriou66, Michele T Pato28, Leena Peltonen45,46,108,132, Hannes Petursson109, Ben Pickard110, Jonathan Pimm24, Ann E Pulver85, Vinay Puri24, Digby Quested111, Emma M Quinn23,, János M Réthelyi44, Robert Ribble4– 6, Marcella Rietschel91,112, Brien P Riley4–6, Anders Rosengren25, Mirella Ruggeri48, Ulrich Schall97,113, Thomas G Schulze112,114, Sibylle G Schwab115–117, Henriette Schmock25, Rodney J Scott118, Jianxin Shi119, Engilbert Sigurdsson109,120, Jeremy M Silverman8,121, Celina Skjødt25, Chris C A Spencer68, Kari Stefansson38, Amy Strange68, Eric Strengman12,13, T Scott Stroup88, Jaana Suvisaari89, Lars Terenius43, Srinivasa Thirumalai122, Johan H Thygesen25,, Draga

T oncheva124, Edwin van den Oord125, Jim van Os84, Ruud van Winkel42,82, Jan Veldink126, Dermot Walsh127,, Durk Wiersma57, Dieter B Wildenauer115,129, Hywel J Williams9,10, Nigel M Williams9,10, Brandon Wormley4–6, Stan Zammit9,10, Patrick F Sullivan77,83,130,131, Michael C O’Donovan9,10, Mark J Daly1 & Pablo V Gejman2,3

1Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA. 2Department of Psychiatry and Behavioral Sciences, NorthShore University HealthSystem, Evanston, Illinois, USA. 3Department of Psychiatry and Behavioral Sciences, University of Chicago, Chicago, Illinois, USA. 4Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA. 5Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA. 6Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA. 7Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA. 8Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA. 9Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK. 10Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, UK. 11Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA. 12Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands. 13Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. 14University of California at Los Angeles (UCLA) Center for Neurobehavioral Genetics, University of California at Los Angeles, Los Angeles, California, USA. 15Department of Human Genetics, University of California at Los Angeles, Los Angeles, California, USA. 16Psychiatry Section, Institute of Clinical Medicine, University of Oslo, Oslo, Norway. 17Department of Psychiatry, Oslo University Hospital, Oslo, Norway. 18Broad Institute, Cambridge, Massachusetts, USA. 19Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany. 20Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany. 21Institute of Human Genetics, University of Bonn, Bonn, Germany. 22Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, UK. 23Neuropsychiatric Genetics Research Group, Trinity College Dublin, Dublin, Ireland. 24Molecular Psychiatry Laboratory, Research Department of Mental Health Sciences, University College London Medical School, Windeyer Institute of Medical Sciences, London, UK. 25Institute of Biological Psychiatry, Mental Health Center (MHC) Sct. Hans, Copenhagen University Hospital, Roskilde, Denmark. 26Molecular and Clinical Neurobiology, Department of Psychiatry, Ludwig- Maximilians-University, Munich, Germany. 27Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK. 28Keck School of Medicine, University of Southern California, Los Angeles, California, USA. 29Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of the North Shore-Long Island Jewish Health System, Glen Oaks, New York, USA. 30Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York, USA. 31Department of Psychiatry and Behavioral Science, Albert Einstein College of Medicine of Yeshiva University, New York, New York, USA. 32Department of Non- Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK. 33Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, Brisbane, Queensland, Australia. 34Vrije Universiteit (VU), Center for Neurogenomics and Cognitive Research (CNCR), Department of Functional Genomics, Amsterdam, The Netherlands. 35VU Medical Centre, Department of Medical Genomics, Amsterdam, The Netherlands. 36Washington Veteran’s Affairs Medical Center, Washington, DC, USA. 37Department of Psychiatry, Georgetown University School of Medicine, Washington, DC, USA. 38deCODE Genetics, Reykjavik, Iceland. 39Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. 40Queensland Centre for Mental Health Research, University of Queensland, Brisbane, Queensland, Australia. 41Mental Health Research Center, Russian Academy of Medical Sciences, Moscow, Russia. 42University Psychiatric Centre, Catholic University Leuven, Kortenberg, Belgium. 43Department of Clinical Neuroscience, Human Brain Informatics (HUBIN) Project, Karolinska Institutet and Hospital, Stockholm, Sweden. 44Semmelweis University, Department of Psychiatry and Psychotherapy, Budapest, Hungary. 45Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. 46Department of Medical Genetics, University of Helsinki, Helsinki, Finland. 47Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College, London, UK. 48Section of Psychiatry and Clinical Psychology, University of Verona, Verona, Italy. 49Department of Research, Diakonhjemmet Hospital, Oslo, Norway. 50State Mental Hospital, Haar, Germany. 51Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia, USA. 52Department of Psychiatry and Behavioral Sciences, Atlanta Veterans Affairs Medical Center, Atlanta, Georgia, USA. 53Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA. 54Institute of Human Genetics, University of Aarhus, Aarhus, Denmark. 55Centre for Psychiatric Research, Aarhus University Hospital, Risskov, Denmark.56University of Queensland Diamantina Institute, Princess Alexandra Hospital, University of Queensland, Brisbane, Queensland, Australia. 57University Medical Center Groningen, Department of Psychiatry, University of Groningen, Groningen, The Netherlands. 58School of Nursing, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA. 59Department of Psychiatry, University of California at San Francisco, San Francisco, California, USA. 60NCIRE (Northern California Institute for Research and Education), San Francisco, California, USA. 61Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. 62School of Psychiatry, University of New South Wales and Schizophrenia Research Institute, Sydney, New South Wales, Australia. 63Department of Psychiatry, University of Queensland, Royal Brisbane Hospital, Brisbane, Australia. 64Department of Psychiatry, Washington University, St. Louis, Missouri, USA. 65Academic Medical Centre, University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands. 66Department of Psychiatry, University of Athens Medical School, Athens, Greece. 67Department of Medical Genetics, Oslo University Hospital, Oslo, Norway. 68Wellcome Trust Centre for Human Genetics, Oxford, UK. 69Department of Statistics, University of Oxford, Oxford, UK. 71Department of Psychiatry, University of Colorado Denver, Aurora, Colorado, USA. 73INSERM, Institut de Myologie, Hôpital de la Pitié-Salpêtrière, Paris, France. 74Illumina, Inc., La Jolla, California, USA. 75School of Electrical Engineering and Computing Science, University of Newcastle, Newcastle, New South Wales, Australia. 76Section of Neonatal Screening and Hormones, Department of Clinical Chemistry and Immunology, The State Serum Institute, Copenhagen, Denmark. 77Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 78Centre for Clinical Research in Neuropsychiatry, School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Perth, Western Australia, Australia. 79Department of Child and Adolescent Psychiatry, Pierre and Marie Curie Faculty of Medicine, Paris, France. 81Department of Psychology, University of Colorado, Boulder, Boulder, Colorado, USA. 82Department of Psychiatry and Psychology, School of Mental Health and Neuroscience, European Graduate School of Neuroscience (EURON), South Limburg Mental Health Research and Teaching Network (SEARCH), Maastricht University Medical Centre, Maastricht, The Netherlands. 83Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 84Maastricht University Medical Centre, South Limburg Mental Health Research and Teaching Network, Maastricht, The Netherlands. 85Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 86Department of Psychiatry, Hadassah-Hebrew University Medical Center, Jerusalem, Israel. 87Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA. 88Department of Psychiatry, Columbia University, New York, New York, USA. 89Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, Finland. 90Schizophrenia Research Institute, Sydney and Centre for Brain and Mental Health Research, University of Newcastle, Newcastle, New South Wales, Australia. 91Department of Psychiatry, University of Bonn, Bonn, Germany. 92Laboratoire de Génétique Moléculaire de la Neurotransmission et des Processus Neurodégénératifs, Centre National de la Recherche Scientifique, Hôpital de la Pitié Salpêtrière, Paris, France. 93Institute of Medical Biometry, Informatics and Epidemiology (IMBIE), University of Bonn, Bonn, Germany. 94Department of Research, Sørlandet Hospital, Kristiansand, Norway. 95Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, Queensland, Australia. 96Functional NeuroImaging Laboratory, School of Psychology, University of Newcastle, Sydney, New South Wales, Australia. 97Schizophrenia Research Institute, Sydney, New South Wales, Australia. 98Department of Psychiatry, First Psychiatric Clinic, Alexander University Hospital, Sofia, Bulgaria. 99National Centre for Register-Based Research, University of Aarhus, Aarhus, Denmark. 100Department of Psychiatry, University of Toronto, Toronto, Canada. 101NeuroSearch A/S, Ballerup, Denmark. 103Psychiatric Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark. 104Department of Psychiatry, Queens University, Belfast, Ireland. 105ARoS Applied Biotechnology A/S, Skejby, Denmark. 106Department of Molecular Medicine, Aarhus University Hospital, Skejby, Denmark. 107Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia. 108Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. 109Department of Psychiatry, National University Hospital, Reykjavik, Iceland. 110Strathclyde Institute of Pharmacy and Biomedical Sciences, The John Arbuthnott Building, University of Strathclyde, Glasgow, UK. 111Department of Psychiatry, University of Oxford, Warneford Hospital, Headington, Oxford, UK. 112Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany. 113Priority Centre for Brain and Mental Health Research, University of Newcastle, Sydney, New South Wales, Australia. 114Department of Psychiatry and Psychotherapy, Georg-August-University, Göttingen, Germany. 115School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, Western Australia, Australia. 116Department of Psychiatry, University of Erlangen-Nuremberg, Erlangen, Germany. 117Centre for Medical Research, Western Australian Institute for Medical Research, University of Western Australia, Perth, Western Australia, Australia. 118Centre for Information Based Medicine, University of Newcastle, Hunter Medical Research Institute, Newcastle and Schizophrenia Research Institute, Sydney, New South Wales, Australia. 119Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA. 120Department of Psychiatry, University of Iceland, Reykjavik, Iceland. 121Department of Psychiatry, Veterans Affairs Medical Center, New York, New York, USA. 122West Berkshire National Health Service (NHS) Trust, Reading, UK. 124Department of Medical Genetics, University Hospital Maichin Dom, Sofia, Bulgaria. 125Department of Pharmacy, Virginia Commonwealth University, Richmond, Virginia, USA. 126Rudolf Magnus Institute of Neuroscience, Department of Neurology, Universitair Medisch Centrum (UMC) Utrecht, Utrecht, The Netherlands. 127The Health Research Board, Dublin, Ireland. 129Centre for Clinical Research in Neuropsychiatry, Graylands Hospital, Mt Claremont, Western Australia, Australia. 130Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 131Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 132