PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of wtpaEurope PMCEurope PMC Funders GroupSubmit a Manuscript
 
Mol Psychiatry. Author manuscript; available in PMC Jun 1, 2013.
Published in final edited form as:
PMCID: PMC3272435
EMSID: UKMS36376
NIHMSID: NIHMS330566

Genome-wide Association Study of Alzheimer’s disease with Psychotic Symptoms

Abstract

Psychotic symptoms occur in approximately 40% of subjects with Alzheimer’s disease (AD) and are associated with more rapid cognitive decline and increased functional deficits. They show heritability up to 61% and have been proposed as a marker for a disease subtype suitable for gene mapping efforts. We undertook a combined analysis of three genome-wide association studies (GWAS) to identify loci that a) increase susceptibility to an AD and subsequent psychotic symptoms; or b) modify risk of psychotic symptoms in the presence of neurodegeneration caused by AD. 1299 AD cases with psychosis (AD+P), 735 AD cases without psychosis (AD-P) and 5659 controls were drawn from GERAD1, the NIA-LOAD family study and the University of Pittsburgh ADRC GWAS. Unobserved genotypes were imputed to provide data on > 1.8 million SNPs. Analyses in each dataset were completed comparing a) AD+P to AD-P cases, and b) AD+P cases with controls (GERAD1, ADRC only). Aside from the APOE locus, the strongest evidence for association was observed in an intergenic region on chromosome 4 (rs753129; ‘AD+PvAD-P’ P=2.85 × 10−7; ‘AD+PvControls’ P=1.11 × 10−4). SNPs upstream of SLC2A9 (rs6834555, P=3.0×10−7) and within VSNL1 (rs4038131, P=5.9×10−7) showed strongest evidence for association with AD+P when compared to controls. These findings warrant further investigation in larger, appropriately powered samples in which the presence of psychotic symptoms in AD has been well characterised.

Keywords: Alzheimer’s disease, psychosis, behavioural symptoms, genome-wide association study, genetic

Introduction

Alzheimer’s disease (AD), the most common form of dementia, is highly heritable (heritability of up to 76%) but genetically complex1. Neuropathologically, the disease is characterised by extracellular senile plaques containing β-amyloid (Aβ) and intracellular neurofibrillary tangles containing hyperphosphorylated tau protein1. Prior to 2009, four genes had been definitively implicated in its aetiology. Mutations of the amyloid precursor protein (APP) gene and the presenilin 1 and 2 genes (PSEN1, PSEN2) cause rare, Mendelian forms of the disease usually with an early-onset. Until recently, only apolipoprotein E (APOE) had been established unequivocally as a susceptibility gene for the common late-onset form of AD1.

In 2009 we published a genome-wide association study (GWAS) of AD using the Genetic and Environmental Risk in AD Consortium 1 (GERAD1) sample2, which identified two genome-wide significant susceptibility loci: CLU (P=8.5×10−10) and PICALM (P=1.3×10−9). A second independent AD GWAS performed using the European Alzheimer’s Disease Initiative sample (EADI) showed genome-wide significant evidence for association with CLU (P=7.5×10−9) and CR1 (P=3.7×10−9), and support for PICALM (P=3×10−3). The associations in CLU, PICALM and CR1 have been replicated in several independent datasets3-6 and shown relationships with neurodegenerative processes underlying disease7. In addition, Seshadri and colleagues reported genome-wide significant association for BIN1 (P=1.6×10−11) when combining GERAD1 and EADI data with data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)8.

More recently, members of this consortium have reported findings from an extended study by GERAD (GERAD+), which included 19870 AD cases and 39846 controls and identified genome-wide evidence for association at the ABCA7 locus (P=5.0×10−21) and the MS4A gene cluster (P=1.2×10−16)9. The American Alzheimer’s Disease Genetic Consortium (ADGC) also reported genome-wide significant evidence at the MS4A gene cluster, further support for ABCA7 and suggestive evidence for association with SNPs at the CD33, CD2AP, ARID5B and EPHA1 loci10. When combining data from ADGC and GERAD+ SNPs at CD33 (P=1.6×10−9), CD2AP (P=8.6×10−9) and EPHA1 (P=6.0×10−10) also exceed criteria for genome-wide significant association with AD.

Despite the success of first generation genome-wide association studies, a substantial proportion of the heritability for complex diseases remains unexplained11. It is becoming increasingly apparent that within diagnostic categories, such as bipolar disorder and schizophrenia, extensive aetiological and genetic heterogeneity operates12. As a result, there is increasing emphasis on the use of sub-phenotypes to elucidate genotype-phenotype relationships. Psychotic symptoms have been proposed as a marker for a subtype of AD suitable for gene mapping efforts13. Psychotic symptoms are significantly more common in AD than the general population, affecting around 40% of cases14. They are associated with decreased quality of life for caregivers and patients15, more rapid cognitive16 and functional decline17 and premature institutionalization15. We have previously demonstrated familial aggregation of psychosis in AD in three independent cohorts, and estimate that the heritability of AD+P is around 61%18-21. Candidate genes for other psychiatric disorders in non-demented populations have been investigated in relation to AD+P, implicating alterations in the serotonergic, dopaminergic, catecholaminergic and noradrenaline neurotranmission systems22. However, these findings have not been consistently replicated.

Two possible genetic models of AD+P are a heterogeneity model in which alleles predispose to the development of AD pathology and subsequent psychosis and a disease modifier model in which they modify risk of psychotic symptoms in the presence of neurodegeneration caused by AD 22. The current study, the first GWAS of AD+P, evaluated these two models by contrasting AD+P with a) healthy controls (heterogeneity model) and b) with AD-P subjects (disease modifier model), utilising existing GWAS data from three independent samples: GERAD12, the National Institute on Aging’s Late Onset Alzheimer’s Disease Family Study (NIA-LOAD)21, 23 and the University of Pittsburgh Alzheimer Disease Research Center (ADRC)24.

Methods

Sample Ascertainment

This study included samples from the GERAD12, the NIA-LOAD study 23 and the University of Pittsburgh ADRC24 GWAS datasets.

The GERAD11 sample comprised 543 AD+P cases, 454 AD-P Cases and 4701 controls. All AD cases and 955 controls were genotyped at the Sanger Institute on the Illumina 610-quad chip. These data were combined with 3,746 unscreened population controls from the 1958 British Birth Cohort (1958BC; http://www.b58cgene.sgul.ac.uk) and UK Blood Service control group genotyped using either the Illumina HumanHap550 BeadChip (n=2683) or the Illumina HumanOmni1-Quad (n=1063). All AD cases met criteria for either probable (NINCDS-ADRDA25, DSM-IV) or definite (CERAD26) AD. All elderly controls were screened for dementia using the MMSE or ADAS-cog or were determined to be free from dementia at neuropathological examination. All individuals in the GERAD sample were of Caucasian ancestry.

Recruitment for the NIA-LOAD cohort has been described previously21, 27. In brief, 18 AD centers throughout the US, each of which had received approval by their institutional review board, participated. The recruitment criteria included a family with multiple members affected with late-onset AD that could provide clinical information and a biological sample for DNA extraction. The current report drew from among all such subjects those who had been previously genotyped by the Center for Inherited Disease Research using the Illumina 610-quad chip (Illumina, San Diego, CA, USA). The minimum genotype completion rate for any sample released by CIDR was 98.3%. Blind duplicate reproducibility was 99.99 % based on 118 paired samples23. A total of 260 AD+P and 125 AD-P subjects from 264 families were analyzed. Those in the NIA sample were predominantly of Caucasian ancestry (96.1%), but also included 12 (3.1%) individuals classified as African American and 2 (0.5%) individuals of Native American descent.

University of Pittsburgh ADRC subjects were recruited from the University of Pittsburgh AD Research Center, where all subjects undergo a standard assessment28 and received diagnoses of possible or probable AD with age of onset ≥ 60. Autopsy confirmation rates in similarly diagnosed subjects at this center are >90%29. A total of 496 AD+P, 156 AD-P subjects and 958 age-matched screened controls of Caucasian ancestry were included. All AD cases met criteria for either probable (NINCDS-ADRDA25, DSM-IV) or definite (CERAD26) AD. Control subjects included volunteers at the University of Pittsburgh ADRC who underwent the same assessment and were found to be cognitively normal and also populuation-based controls who were found to be cognitively normal using a previously described neuropsychological screening battery30, which included the MMSE and several additional tests tapping cognitive domains known to be affected in dementia. Genotyping for Pittsburgh subjects was performed using the Illumina Omni1-Quad chip (containing probes for 1.13 million SNPs).

Characterisation of Psychotic Symptoms

The Neuropsychiatric Inventory (NPI)31 was used to assess behavioural symptoms in all GERAD cases. The NPI is an established and commonly used informant-based rating scale that evaluates 12 common behavioural symptoms in AD32. The severity and frequency of each symptom are rated from 0-3 and 0-4, respectively, and were scored to reflect the worst episode of each symptom over the lifetime of the illness. Frequency and severity scores are multiplied to give an overall domain score for each symptom ranging from 0-12. 42% of the GERAD sample were assessed annually for ≥2 assessments. Where multiple NPI ratings were available the highest delusions and hallucinations domains scores were used. AD+P was defined as either the presence of delusions and hallucinations, or where only one symptom was present a delusions domain score ≥4 or a hallucinations domain score ≥2. A more stringent cut-off for delusions was adopted to avoid phenocopy due to transient confabulations13. AD cases with delusion and hallucination domain scores of 0 were coded as ‘Alzheimer’s with no psychosis’ (AD-P). Individuals with intermediate scores were excluded from analysis.

ADRC and NIA-LOAD subjects were rated for psychotic symptoms on the informant-based CERAD behavioural rating scale (CBRS)33. 69% and 35% of the ADRC and NIA-LOAD sample were assessed on more than one occasion. A delusion was defined as a false belief based on incorrect inference about external reality, resistant to persuasion or contrary evidence, and not attributable to social or cultural mores. Hallucinations were defined as sensory perceptions for which there was no basis in reality. Discrete hypnagogic and hypnopompic hallucinations, as well as symptoms occurring only during episodes of delirium, were not rated. The CBRS was administered at initial and annual visits and in some subjects between annual visits by telephone21, 34. AD+P was considered present when any of the CBRS items #33 - #45 were rated as occurring ≥3 times in the past month at any visit. Individuals with scores of 0 on the same CBRS items at all visits were classified as AD-P. Inter-rater reliability of the psychosis assessments used, including telephone assessments, has been previously described for the ADRC22 and NIA-LOAD21 Cohorts.

Psychotic symptoms typically emerge in the moderate stages of AD21, 35, therefore those categorised as AD-P who were in the mild stages of disease at their last assessment (Global Deterioration Scale36 score<4, Clinical Dementia Rating37 score <1 or mini-mental state examination score38 >=20) were considered to be at substantial risk of going on to develop delusional or hallucinatory behaviour. These individuals were therefore excluded from the analysis. As such a total of 219, 5 and 32 individuals were excluded from the GERAD, NIA-LOAD and ADRC samples respectively. Subjects with a known history of mood disorders, bipolar disease, unipolar disease, or an anxiety disorder were also excluded from all analyses.

Quality Control

Quality control (QC) of the GERAD1 sample has been described in detail elsewhere2. Briefly, individuals were retained if they had a missing genotype rates < 0.01, with mean autosomal heterozygosity between 0.33 and 0.34, and mean X-chromosome heterozygosity either <0.02 for males, or between 0.25 and 0.40 for females. Genetic outliers and those showing evidence of relatedness (IBD estimate ≥ 0.125) or non-European ancestry based on genotype data were also excluded. Following QC 543 AD+P cases, 454 AD-P cases and 4701 controls were retained. Markers were excluded if they had a minor allele frequency (MAF) < 0.01 or a Hardy-Weinberg P≤1×10−5. SNPs with a MAF≥0.05 were excluded if they had a genotype missing rate of >0.03; for SNPs with a MAF between 0.01 and 0.05, a more stringent genotype missing rate threshold of 0.01 was employed.

Individuals in the NIA-LOAD sample were retained if they had a missing genotype rate < 0.05. 260 AD+P and 125 AD-P subjects falling in 264 families passed QC. Markers were excluded if they had a minor allele frequency (MAF) < 0.05 or if they had a genotype missing rate of >0.02. No modifications were made for Hardy-Weinberg equilibrium given the small sample size. 516,835 SNPs passed QC.

QC and analysis of the ADRC sample has been described in detail elsewhere (Kamboh et al. manuscript in preparation). Briefly, individuals were retained if they had a missing genotype rate <0.02, with mean X-chromosome heterozygosity either <0.02 for males or between 0.25 and 0.4 for females. Genetic outliers and those showing evidence of relatedness (IBD estimate ≥ 0.4) or non-European ancestry based on genotype data were excluded. Following QC, 496 AD+P cases, 156 AD-P cases and 958 unaffected controls were retained. Markers were excluded if they had a minor allele frequency (MAF) <0.01 or a Hardy-Weinberg P≤1×10−6 in controls. SNPs were also excluded if they had a genotype missing rate of >0.02. Markers were examined to determine if the rate of missing data was found to depend on case/control status or the genotyping batch. No additional exclusions were needed based on these analyses.

Following quality control this study comprised a total of 1299 cases with AD+P, 735 individuals characterised as AD-P and 5659 controls.

Imputation

As there is only moderate overlap between the Illumina 550/610 arrays (used in the GERAD1 and NIA-LOAD GWAS) and the Illumina Omni1-Quad array (used in the ADRC GWAS) unobserved genotypes were imputed. The GERAD genotype data was imputed with MACH v.1.0, using haplotypes released from initial low coverage sequencing of 112 European ancestry samples in the 1000 genomes project (ftp://ftp.sanger.ac.uk/pub/1000genomes/REL-0908/LowCov/) as a reference sample. The phasing step included 200 individuals to calculate the specific maps based upon the sample recombination rates. Imputation generated data for >8.2 million SNPs. On the investigation of several QQ – plots from an explorative association analysis, an a-posteriori filter was applied to exclude SNPs with MAF<0.01 or r2<0.3. The ADRC data was also imputed with MACH v.1.0, using haplotypes from the HapMap v3 data release as a reference sample. Imputation generated data for >3 million SNPs that were subsequently filtered to exclude SNPs with r2<0.3. Imputed genotypes were not available for the NIA-LOAD dataset

Association analyses

GERAD1

Separate analyses were completed to compare AD+P with unaffected controls and AD-P cases. Following QC 4659431 and 4479280 SNPs were tested in the ‘AD+PvControl’ and ‘AD+PvAD-P’ analyses, respectively. Analyses were completed using logistic regression assuming an additive model. The first four principal components extracted from an EIGENSTRAT39 analysis were included as covariates.

ADRC

Separate analyses were performed to compare AD+P with unaffected controls and AD-P cases in the ADRC sample. Following imputation and QC, a total of 2543888 SNPs were tested in each analysis. Analyses were performed using logistic regression assuming an additive model in PLINK40. Age (age at exam for unaffected controls or age-at-diagnosis for AD cases), sex and the first four principal components extracted from a multi-dimensional scaling analysis (also implemented in PLINK) were included as covariates in the analyses.

NIA-LOAD

The NIA-LOAD sample included AD cases sampled from multiplex families. As some of the subjects were related, the contrast of AD+P versus AD-P was performed using Generalized Estimating Equations or GEE methods. For simplicity we assumed all relative pairs were related as full-siblings, a conservative assumption, and accounted for their estimated covariance in tests contrasting AD+P versus AD-P allele frequencies. To account for differences in genetic ancestry, cases and controls were matched based on genetic distances calculated from the 8 significant dimensions of ancestry as determined by SpectralGem41.

Meta-analyses

An inverse variance weighted fixed effects meta-analysis was used to test for association with AD+P in the GERAD1, NIA-LOAD and ADRC datasets. Separate meta-analyses were completed for ‘AD+PvControls’ (including summary statistics from GERAD1 and ADRC) and ‘AD+PvAD-P’ (including summary statistics from GERAD1, NIA-LOAD and ADRC). Cochran’s Q-test was performed and I2 calculated to assess heterogeneity. SNPs present, and passing all QC filters, in 2 or more studies were included. All analyses were completed using METAL (http://www.sph.umich.edu/csg/abecasis/metal/)42.

Results

A total of 1299 AD+P cases, 735 AD-P cases and 5659 controls were included in the meta-analysis. Clinical characteristics of the sample can found in Table 1. The final meta-analysis of the ‘AD+PvControl’ and ‘AD+PvAD-P’ datasets included 1847262 and 1882172 SNPS, respectively. The genomic control inflation factors (λ)43 were 1.002 and 1.004 respectively, suggesting little evidence for residual stratification. Table 2 shows loci associated (P<1.0×10−5) with AD+P when compared either controls or AD-P cases. Summary statistics for SNPs with P-values ≤ 1×10−4 can be found for ‘AD+Pvcontrols’ and ‘AD+PvAD-P analyses in Supplementary Tables 1 and 2, respectively. Figure 1 includes manhatten plots of the ‘AD+PvControls’ and ‘AD+PvAD-P’ GWAS.

Figure 1
Scatterplot of chromosomal position (x-axis) against −log 10 GWAS P-value (yaxis). a) AD+P vs Controls, b) AD+P vs AD-P. The threshold for genome-wide significance (p ≤ 9.5×10−8) is indicated by the red horizontal line. ...
Table 1
Descriptive characteristics for all samples.
Table 2
Loci associated with AD+P at P<1×10−5 in either the ‘AD+PvControl’ or ‘AD-PvAD-P’ analysis.

Excluding the APOE region, the most significant SNP (rs753129) had a P-value of 2.85 × 10−7 (OR= 0.66) and was associated with AD+P when compared to AD-P cases. rs753129 is in a intergenic region on chromosome 4. The same SNP showed some evidence for association in analysis of ‘AD+PvControls’, OR= 0.81, P=1.11×10−4. The most significant SNP outside the APOE locus in the ‘AD+PvControl’ analysis was rs6834555, which sits just upstream of the solute carrier family 2 (facilitated glucose transporter), member 9 (SLC2A9) gene (OR=1.39, P=3.0×10−7). This SNP was not associated with AD+P when compared to AD-P (OR=1.13, P=0.18). The most significant intragenic SNP in the ‘AD+PvControl’ analysis was rs4038131, an intronic SNP in the visinin-like 1 (VSNL1) gene (OR: 0.65, P=5.9×10−7). This SNP was also associated with AD+P when compared to AD-P cases, (OR= 0.72, P=1.84×10−2).

No evidence of association was observed at the APOE locus when analyzing ‘AD+PvAD-P’ (all P>0.235). Six SNPs in close proximity to APOE showed genome-wide significant (GWS) associations when comparing AD+P cases with controls. All of these markers showed similar patterns of effect and more compelling evidence of association in the primary analysis of the GERAD+ Stage 1 analysis, which did not condition on presence or absence of psychotic symptoms (P-values ranging from 3.5 × 10−39 – 1.6 ×10−215)44. Furthermore none of these SNPs showed evidence of association with AD+P when compared to AD-P (P>0.330), indicating that the APOE locus is associated with AD, unconditional of psychosis status. Results for these SNPs at the APOE locus can be found in Supplementary Table 3.

Recent GWAS studies of AD have identified compelling evidence for nine novel risk loci for AD: CLU, PICALM, CR1, BIN1, ABCA7, MS4A, CD2AP, CD33 and EPHA12, 8-10, 45. In this study, comprising a subset of the GERAD9, NIA-LOAD10 and University of Pittsburgh ADRC24 samples included in the studies which identified these loci, we noted association (P<0.05) with BIN1 (rs744373, P=2.6×1−5, OR=0.80), EPHA1 (rs11767557, P=0.002, OR=1.22), CLU (rs11136000, P=0.005, OR 0.86), MS4A (rs670139, P = 0.028, OR=1.12) and PICALM (rs3851179, P=7.2×10−5, OR=0.81) when comparing AD+P cases with controls. The pattern of effect was consistent with that observed in the primary analysis of the GERAD+ dataset and none of these SNPs showed evidence of association when comparing AD+P and AD-P cases, suggesting that these loci are associated with AD, irrespective of presence or absence of psychotic symptoms (see Supplementary Table 4).

Finally, we investigated whether putative risk loci for schizophrenia (SZ) or bipolar disorder (BP), both of which feature prominent psychotic symptoms, were associated with AD+P when compared to AD-P or Controls (Table 3). We tested those SNPs that have shown genome-wide significant evidence for association (P≤ 5.0 × 10−8) in GWAS of SZ or BP. Eleven SNPs were chosen, six of which were identified by GWAS of schizophrenia46-48 five by GWAS of BP49-51. Although individually GWA SNPs showed only limited association with AD+PvAD-P, as a group there was a trend towards association, combined P=0.109 (using an unweighted Z-transformation52, excluding rs1938526 and rs13194053 as they are in strong LD with rs10994336 and rs6932590 respectively) We also identified 5 additional loci without genome-wide significant evidence for association in SZ or BP, but implicated by meta-analysis as listed in the top 10 SZGene loci on 1st June 2011 (http://www.szgene.org/). These SNPs showed no evidence of association with AD+PvAD-P individually, or as a group (Combined P=0.90), see Table 3.

Table 3
Analysis of putative bipolar disorder (BP) and schizophrenia (Sz) loci

Discussion

A substantial body of evidence indicates that the presence of psychotic symptoms in AD identifies a subgroup of subjects who undergo more rapid cognitive and functional decline22. The distinctive nature of the AD+P phenotype, in conjunction with evidence of the familial aggregation and heritability of psychosis in AD, has led to the hypothesis that AD+P may have a genetic architecture which diverges from that of AD-P13. There are two plausible genetic models of AD+P. First, a heterogeneity model in which alleles predispose to the development of AD pathology and subsequent psychosis and secondly, a disease modifier model in which alleles increase the risk of psychosis conditioned on the presence of AD22. The current study evaluated these two models by contrasting AD+P with healthy controls and with AD-P subjects.

Although we did not observe novel genome-wide significant evidence of association with AD+P when compared to healthy controls or AD-P cases, several additional considerations apply. Despite testing for association in one of the largest cohorts of AD+P and AD-P subjects studied to date, the power of the present study to detect loci of the magnitude commonly observed in complex traits is limited. For example, for common variants (MAF= 0.3) the current study has 0.02 and 0.06 power to detect disease susceptibility loci with OR of 1.2 in the ‘AD+PvAD-P’ and ‘AD+PvControl’ analyses respectively, and power is even less for lower frequency alleles that confer this effect size. It is therefore possible that many SNPs in the current study, while failing to reach this stringent level of statistical significance, could reflect true disease loci with resulting implications for the biology of AD+P. For example, the most significant intragenic SNP in the ‘AD+P v Control’ analysis was rs4038131, an intronic SNP in the visinin-like 1 (VSNL1) gene. VSNL1 utilizes a calcium-myristoyl switch mechanism that upon stimulation by intracellular calcium causes VSNL1 to translocate to cell membranes, including the transGolgi apparatus, where it may influence a number of cell signalling pathways53. VSNL1 concentrations are elevated in the cerebrospinal fluid (CSF) of individuals with AD, where they correlate with CSF tau concentrations and with the degree of cognitive impairment54. Whether VSNL1 expression in cerebral cortex of AD+P subjects is reduced in comparison to AD-P subjects is not known, but VSNL1 mRNA and protein expression are reduced in cerebral cortex of subjects with schizophrenia55, 56. Another SNP that showed suggestive evidence of association when comparing AD+P cases with controls was rs6834555, which is located just 5′ of the urate transporter, SLC2A9. Variation in SLC2A9 has been shown to influence serum urate concentrations across populations57, 58. Of interest, reduced serum urate concentration has been identified in AD59 and in schizophrenia60, 61. Serum and cerebrospinal fluid urate have also been shown to be positively correlated59. How altered urate concentration may relate to the brain pathologies observed in schizophrenia and AD is not known, though the protective antioxidant effects of urate have been suggested as one potential mechanism59-61. Relatively less evidence currently exists to suggest that the SNPs identified in the ‘AD+PvAD-P’ analysis may be tied to the neurobiology of AD or psychosis. One exception may be serine/threonine kinase 11 (STK11). An unusually large scale STK11 deletion has been described in one case in which Peutz-Jeghers syndrome, mental retardation, and schizophrenia co-occurred62 and reduced copy numbers of STK11 have been reported in affected siblings with schizophrenia63.

In contrast to the above genes, we found little evidence to suggest that previously identified AD risk genes may contribute selectively to the risk of psychosis within AD. Several of these loci showed association when contrasting AD+P cases with controls, however the effect sizes were comparable to those obtained when comparing AD cases unselected for psychotic symptoms with controls. Furthermore, none of these loci showed association in the ‘AD+PvAD-P’ analysis. This conclusion should be tempered by awareness of the limited power in our ‘AD+P v AD-P’ comparison.

We previously identified 22 studies, comprising more than 5,200 subjects with AD, that examined the association of the APOE є4 allele with AD+P, with nine reporting significant associations22. Though not supportive of a true assocation of APOE with AD+P, such a pattern could result from false negatives due to variation between studies in the in subject populations, sample sizes, definitions of AD+P and analytic approaches, or due to limited power to detect small, but real effects (e.g. O.R. 1.1-1.2). A recent examination of the largest sample to date of AD subjects uniformly characterized for psychosis (N=2317) found that neither APOE є4 carrier status nor allele number was associated with psychosis64. Alternatively, the variable pattern of association might reflect causal association with genetic variation in linkage disequilibrium with the APOE ε4 allele. The current study, which found no association with AD+P of SNPs throughout the chromosome 19 region containing APOE, tested in more than 2000 AD cases characterized for psychosis, does not support this alternative. Similarly, when examined in samples which largely overlap with the current ADRC cohort, there was no evidence of association of AD+P with a poly-T repeat length variation in TOMM40 which is in linkage disequilibrium with APOE65. Taken as a whole, these data do not support a role of APOE in risk for psychosis within AD subjects, a finding consistent with a current meta-analysis of 26 studies which finds no association of APOE ε4 alleles with risk for schizophrenia (www.szgene.org).

These analyses are largely hypothesis generating. It is now important to follow up these findings, either through replication or meta analysis of other AD GWAS datasets where psychotic symptoms have been well characterised. As such we will make the complete genome-wide meta-analysis results for ‘AD+PvAD-P’ and ‘AD+PvControls’ available to researchers upon application. It also notable that this study was limited to mainly those of Caucasian origin. Future studies may seek to investigate AD+P within other ethnic groups. It is interesting to note some overlap between schizophrenia and psychosis in AD was observed. There is increasing evidence that there is genetic overlap between the major psychiatric66, 67 and nonpsychiatric68 disorders. The largest family study of bipolar disorder and schizophrenia has recently been published. They showed that there are increased risks of both schizophrenia and bipolar disorder to first-degree relatives of probands with either disorder, which was largely due to additive genetic effects66. Furthermore, a recent study provides strong evidence that schizophrenia is largely polygenic, involving many common SNPs that are substantially shared with bipolar disorder67. It is plausible that this overlap will extend to other disease phenotypes with prominent psychotic symptoms. A vital part of dissecting the large amounts of GWAS data available in psychiatric and neuropsychiatric populations will be working toward more biologically valid classification approaches, which will allow genetic mappings of symptoms domains and dimensions which will undoubtedly lead to greater understanding of clinical phenotypes69.

It is important to note that there are no established criteria for defining AD+P. We took several approaches in order to minimize the possibility of misclassification of subjects. Because rates of AD+P increase with disease progression, we used all available longitudinal structured ratings of behavioural pathology within subjects to identify AD+P as the occurrence of symptoms at any time point. We excluded individuals with transient mild psychotic symptoms to avoid phenocopies due to misunderstanding of confusional episodes. Similarly, we excluded non-psychotic individuals in the earliest stages of illness, who may have been at risk of expressing psychosis only after their illness progressed. These operationalised criteria for AD+P map closely to those used in our earlier studies that demonstrated familial aggregation of AD+P in AD18, 21. Nevertheless, we cannot rule out the possibility that because the extent of longitudinal data varied for individuals, some individuals may have been misclassified with regard to their psychosis status. It is also notable that there were different proportions of individuals in each sample categorised as AD+P and AD-P. This is likely reflects differences in the availability of longitudinal ratings and disease severity, subtle differences in rating instruments and also differences resulting from the underlying populations studies. By performing, analyses in each cohort, controlling for population substructure, and combining results through meta-analyses we limit the effect these differences will have on the results of this study.

This study also included unscreened, population-based controls in the AD+P v Control analysis. Unscreened controls are commonly used in GWAS studies and can substantially increase power to detect true genetic associations where disease prevalence is low (Kp<0.2)70. It is noteworthy that the GERAD GWAS, which includes a large proportion of unscreened controls, has already produced compelling evidence for new susceptibility genes for AD: CLU, PICALM, BIN1, CR1, ABCA7, MS4A, CD2AP, CD33 & EPHA1. Of which CLU, PICALM, BIN1, CR1, ABCA7 and MS4A have since been replicated in independent samples3, 5, 7, 71-73. Despite the pragmatic value of unscreened controls, there are potential limitations to their use. Though the population prevalence of AD+P is low, that largely reflects its age dependency, thus a high proportion of individuals among unscreened controls may carry genetic risk for AD+P and would be anticipated to manifest this syndrome if they were evaluated through the age of risk. As such, contrasts between AD+P and unscreened controls may fail to detect some meaningful genetic associations. Also, due to age differences between AD subjects and unscreened controls some SNPs associated with AD+P in the ‘ADPvControl’ comparison may represent confounds due to a true association with a survival advantage. These potential limitations are mitigated somewhat in the current study by the inclusion of screened elderly controls. Similarly, loci with evidence of association in both the ‘AD+PvControl’ and the ‘AD+PvAD-P’ (e.g. VSNL1) are less likely to result from this confounding.

In conclusion, this study does not identify any SNPs that meet strict criteria for genome-wide significant association with AD+P, when compared to controls or AD-P cases. However a number of sub threshold associations were observed that a) show patterns of effect that are stronger than those generally observed in AD GWAS, b) are interesting biological and position candidates for AD+P and c) show some overlap with others psychiatric disorders with psychotic features. As such these findings warrant further investigation in larger, appropriately powered samples in which the presence of psychotic symptoms in AD is well characterised.

Supplementary Material

2

Acknowledgements

GERAD1: We thank the patients and families who took part in this research. The Cardiff University group was supported by the Wellcome Trust, Medical Research Council, Alzheimer’s Research Trust and the Welsh Assembly Government. The Alzheimer’s Research Trust also supported and funded DNA sample collections at the Institute of Psychiatry, Cambridge University, University of Nottingham and University of Belfast. The University of Belfast group are supported by the Alzheimer’s Society, Alzheimer’s Research Trust, Ulster Garden Villages, N Ireland R&D Office and the Royal College of Physicians/Dunhill Medical Trust. The Trinity College Dublin sample was supported by the MRC and Mercer’s Institute for Research on Ageing. The LASER-AD study was funded by Lundbeck SA. GR is supported by a program grant from the MRC (G0800509). We would also like to thank Advanced Research Computing @ Cardiff (ARCCA) who facilitated data analysis.

NIA-LOAD: Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. Samples used in this study were obtained from the National Cell Repository for Alzheimer’s Disease (NCRAD). Jennifer Williamson, Susan LaRusse Eckert and Stephanie Doan (Columbia University), Michele Goodman (Indiana University), and Elise Weamer (University of Pittsburgh) helped coordinate the project across the United States. We would especially like to acknowledge the support and guidance of Creighton H. Phelps, PhD, at the National Institute on Aging.

ADRC: The following investigators and Alzheimer’s Disease Centers participated in the Study: Boston University Robert Green, Neil Kowall, Lindsay Farrer; Columbia University Jennifer Williamson, Vincent Santana; Duke University Donald Schmechel, Peter Gaskel; Indiana University, Bernardino Ghetti, Martin R. Farlow, Kelly Horner; Massachusetts General Hospital John H. Growdon, Deborah Blacker, Rudolph E. Tanzi, Bradley T. Hyman; Mayo Clinic-Rochester Bradley Boeve, Karen Kuntz, Lindsay Norgaard, Nathan Larson; Mayo Clinic-Jacksonville Dana Kistler, Fracine Parfitt, Jenny Haddow; Mount Sinai School of Medicine Jeremy Silverman, Michal Schnaider Beeri, Mary Sano, Joy Wang, Rachel Lally; Northwestern University Nancy Johnson, Marcel Mesulum, Sandra Weintraub, Eileen Bigio; Oregon Health and Science University Jeffery Kaye, Patricia Kramer, Jessica Payne-Murphy ; Rush University David Bennett, Holli Jacobs, Jeen-Soo Chang, Danielle Arends; University of Alabama at Birmingham Lindy Harrell; University of California, Los Angeles George Bartzokis, Jeffery Cummings, Po H Lu, Usha Toland; University of Kentucky William Markesbery, Charles Smith, Alise Brickhouse; University of Pennsylvania John Trojanowski, Vivianna Van Deerlin, Elisabeth McCarty Wood; University of Pittsburgh Oscar L. Lopez, Robert A. Sweet; University of Southern California I Helena Chui, Arousiak Varpetian; University of Texas Southwestern Ramon Diaz-Arrastia, Roger Rosenberg, Barbara Davis; University of Washington Thomas Bird, Malia Rumbaugh, Gerard D. Schellenberg, Murray Raskind; Washington University at St Louis Alison Goate, John Morris, Joanne Norton, Denise Levitch, Betsy Grant, Mary Coats.

This study was supported by the following federal grants: U24AG026395 (NIA-LOAD Family Study); U24AG021886 (National Cell Repository for Alzheimer’s Disease); R01AG027224, R01AG030653 and P50AG005133 University of Pittsburgh; P30AG10161 Rush University Medical Center; P30AG013846 Boston University; P50AG08702 Columbia University; P30AG028377 Duke University; P30AG010133 Indiana University; P50AG05134 Massachusetts General Hospital; P50AG165574 Mayo Clinic, Rochester and Mayo Clinic, Jacksonville; P01AG05138, P01AG02219, and P50AG05138 Mount Sinai School of Medicine; P30AG13854 Northwestern University Medical School; P30AG008017 Oregon Health and Science University; P50AG016582 University of Alabama at Birmingham; P50AG016579 David Geffen School of Medicine, University of California, Los Angeles; P30AG028383 University of Kentucky, Lexington; P30AG10124 University of Pennsylvania; P50AG05142 University of Southern California; P30AG12300 The University of Texas Southwestern Medical Center; P50AG05136 University of Washington; and P50AG05681 and P01AG03991 Washington University School of Medicine.

Collection and ascertainment of the ADRC subjects was supported by USPHS grants AG027224.

We thank contributors who collected samples used in this study, and we particularly thank the patients and their families, whose help and participation made this work possible.

References

1. Hollingworth P, Harold D, Jones L, Owen MJ, Williams J. Alzheimer’s disease genetics: current knowledge and future challenges. Int J Geriatr Psychiatry. 2010 [PubMed]
2. Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet. 2009;41(10):1088–1093. [PMC free article] [PubMed]
3. Corneveaux JJ, Myers AJ, Allen AN, Pruzin JJ, Ramirez M, Engel A, et al. Association of CR1, CLU and PICALM with Alzheimer’s disease in a cohort of clinically characterized and neuropathologically verified individuals. Hum Mol Genet. 2010;19(16):3295–3301. [PMC free article] [PubMed]
4. Zhang Q, Yu JT, Zhu QX, Zhang W, Wu ZC, Miao D, et al. Complement receptor 1 polymorphisms and risk of late onset Alzheimer’s disease. Brain Res. 2010 [PubMed]
5. Carrasquillo MM, Belbin O, Hunter TA, Ma L, Bisceglio GD, Zou F, et al. Replication of CLU, CR1, and PICALM associations with alzheimer disease. Arch Neurol. 2010;67(8):961–964. [PMC free article] [PubMed]
6. Jun G, Naj AC, Beecham GW, Wang LS, Buros J, Gallins PJ, et al. Meta-analysis Confirms CR1, CLU, and PICALM as Alzheimer Disease Risk Loci and Reveals Interactions With APOE Genotypes. Arch Neurol. 2010 [PMC free article] [PubMed]
7. Biffi A, Anderson CD, Desikan RS, Sabuncu M, Cortellini L, Schmansky N, et al. Genetic variation and neuroimaging measures in Alzheimer disease. Arch Neurol. 2010;67(6):677–685. [PMC free article] [PubMed]
8. Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M, et al. Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA. 2010;303(18):1832–1840. [PMC free article] [PubMed]
9. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC, Carrasquillo MM, et al. Common variants in ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet. 2011 In Press. [PMC free article] [PubMed]
10. Naj AC. Common variants in MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet. 2011 In Press. [PMC free article] [PubMed]
11. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–753. [PMC free article] [PubMed]
12. O’Donovan MC, Craddock NJ, Owen MJ. Genetics of psychosis; insights from views across the genome. Hum Genet. 2009;126(1):3–12. [PubMed]
13. Sweet RA, Nimgaonkar VL, Devlin B, Jeste DV. Psychotic symptoms in Alzheimer disease: evidence for a distinct phenotype. Mol Psychiatry. 2003;8(4):383–392. [PubMed]
14. Ropacki SA, Jeste DV. Epidemiology of and risk factors for psychosis of Alzheimer’s disease: a review of 55 studies published from 1990 to 2003. Am J Psychiatry. 2005;162(11):2022–2030. [PubMed]
15. Shin IS, Carter M, Masterman D, Fairbanks L, Cummings JL. Neuropsychiatric Symptoms and Quality of Life in Alzheimer Disease. Am J Geriatr Psychiatry. 2005;13(6):469–474. [PubMed]
16. Wilkosz PA, Seltman HJ, Devlin B, Weamer EA, Lopez OL, Dekosky ST, et al. Trajectories of cognitive decline in Alzheimer’s disease. Int Psychogeriatr. 2009:1–10. [PMC free article] [PubMed]
17. Lopez OL, Wisniewski SR, Becker JT, Boller F, DeKosky ST. Psychiatric medication and abnormal behavior as predictors of progression in probable Alzheimer disease. Arch Neurol. 1999;56(10):1266–1272. [PubMed]
18. Hollingworth P, Hamshere ML, Holmans PA, O’Donovan MC, Sims R, Powell J, et al. Increased familial risk and genomewide significant linkage for Alzheimer’s disease with psychosis. Am J Med Genet B Neuropsychiatr Genet. 2007;144B(7):841–848. [PubMed]
19. Sweet RA, Nimgaonkar VL, Devlin B, Lopez OL, DeKosky ST. Increased familial risk of the psychotic phenotype of Alzheimer disease. Neurology. 2002;58(6):907–911. [PubMed]
20. Bacanu SA, Devlin B, Chowdari KV, DeKosky ST, Nimgaonkar VL, Sweet RA. Heritability of psychosis in Alzheimer disease. Am J Geriatr Psychiatry. 2005;13(7):624–627. [PubMed]
21. Sweet RA, Bennett DA, Graff-Radford NR, Mayeux R. Assessment and familial aggregation of psychosis in Alzheimer’s disease from the National Institute on Aging Late Onset Alzheimer’s Disease Family Study. Brain. 2010;133(Pt 4):1155–1162. [PMC free article] [PubMed]
22. DeMichele-Sweet MA, Sweet RA. Genetics of psychosis in Alzheimer’s disease: a review. J Alzheimers Dis. 2010;19(3):761–780. [PMC free article] [PubMed]
23. Wijsman EM, Pankratz ND, Choi Y, Rothstein JH, Faber KM, Cheng R, et al. Genome Wide Association of Familial Late Onset Alzheimer’s Disease Replicates BIN1 and CLU, and Nominates CUGBP2 in Interaction With APOE. PLoS Genet. 2011 In Press. [PMC free article] [PubMed]
24. Demichele-Sweet MA, Kleia L, Devlin B, Ferrellb RE, Weamera EA, Emanuela JE, et al. No association of psychosis in Alzheimer disease with neurodegenerative pathway genes. Neurobiol Aging. 2011 In Press. [PMC free article] [PubMed]
25. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984;34(7):939–944. [PubMed]
26. Mirra SS, Heyman A, McKeel D, Sumi SM, Crain BJ, Brownlee LM, et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology. 1991;41(4):479–486. [PubMed]
27. Lee JH, Cheng R, Graff-Radford N, Foroud T, Mayeux R. Analyses of the National Institute on Aging Late-Onset Alzheimer’s Disease Family Study: implication of additional loci. Arch Neurol. 2008;65(11):1518–1526. [PMC free article] [PubMed]
28. Weamer EA, Emanuel JE, Varon D, Miyahara S, Wilkosz PA, Lopez OL, et al. The relationship of excess cognitive impairment in MCI and early Alzheimer’s disease to the subsequent emergence of psychosis. Int Psychogeriatr. 2009;21(1):78–85. [PMC free article] [PubMed]
29. Lopez OL, DeKosky ST. [Neuropathology of Alzheimer’s disease and mild cognitive impairment] Rev Neurol. 2003;37(2):155–163. [PubMed]
30. Ganguli M, Dodge HH, Chen P, Belle S, DeKosky ST. Ten-year incidence of dementia in a rural elderly US community population: the MoVIES Project. Neurology. 2000;54(5):1109–1116. [PubMed]
31. Cummings JL, Mega M, Gray K, Rosenberg-Thompson S, Carusi DA, Gornbein J. The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology. 1994;44(12):2308–2314. [PubMed]
32. Cummings JL. The Neuropsychiatric Inventory: assessing psychopathology in dementia patients. Neurology. 1997;48(5 Suppl 6):S10–16. [PubMed]
33. Tariot PN, Mack JL, Patterson MB, Edland SD, Weiner MF, Fillenbaum G, et al. The Behavior Rating Scale for Dementia of the Consortium to Establish a Registry for Alzheimer’s Disease. The Behavioral Pathology Committee of the Consortium to Establish a Registry for Alzheimer’s Disease. Am J Psychiatry. 1995;152(9):1349–1357. [PubMed]
34. Wilkosz PA, Kodavali C, Weamer EA, Miyahara S, Lopez OL, Nimgaonkar VL, et al. Prediction of psychosis onset in Alzheimer disease: the role of depression symptom severity and the HTR2A T102C polymorphism. Am J Med Genet B Neuropsychiatr Genet. 2007;144B(8):1054–1062. [PMC free article] [PubMed]
35. Hollingworth P, Hamshere ML, Moskvina V, Dowzell K, Moore PJ, Foy C, et al. Four components describe behavioral symptoms in 1,120 individuals with late-onset Alzheimer’s disease. J Am Geriatr Soc. 2006;54(9):1348–1354. [PubMed]
36. Reisberg B, Ferris SH, de Leon MJ, Crook T. The Global Deterioration Scale for assessment of primary degenerative dementia. Am J Psychiatry. 1982;139(9):1136–1139. [PubMed]
37. Berg L. Clinical Dementia Rating (CDR) Psychopharmacol Bull. 1988;24(4):637–639. [PubMed]
38. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–198. [PubMed]
39. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–909. [PubMed]
40. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, 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]
41. Lee AB, Luca D, Klei L, Devlin B, Roeder K. Discovering genetic ancestry using spectral graph theory. Genet Epidemiol. 2010;34(1):51–59. [PubMed]
42. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190–2191. [PMC free article] [PubMed]
43. Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55(4):997–1004. [PubMed]
44. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC, Carrasquillo MM, et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet. 2011;43(5):429–435. [PMC free article] [PubMed]
45. Lambert JC, Heath S, Even G, Campion D, Sleegers K, Hiltunen M, et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet. 2009;41(10):1094–1099. [PubMed]
46. Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, et al. Common variants conferring risk of schizophrenia. Nature. 2009;460(7256):744–747. [PMC free article] [PubMed]
47. O’Donovan MC, Craddock N, Norton N, Williams H, Peirce T, Moskvina V, et al. Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet. 2008 [PubMed]
48. Shi J, Levinson DF, Duan J, Sanders AR, Zheng Y, Pe’er I, et al. Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature. 2009;460(7256):753–757. [PMC free article] [PubMed]
49. Ferreira MA, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L, et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet. 2008;40(9):1056–1058. [PMC free article] [PubMed]
50. McMahon FJ, Akula N, Schulze TG, Muglia P, Tozzi F, Detera-Wadleigh SD, et al. Meta-analysis of genome-wide association data identifies a risk locus for major mood disorders on 3p21.1. Nat Genet. 2010;42(2):128–131. [PMC free article] [PubMed]
51. Cichon S, Muhleisen TW, Degenhardt FA, Mattheisen M, Miro X, Strohmaier J, et al. Genome-wide association study identifies genetic variation in neurocan as a susceptibility factor for bipolar disorder. Am J Hum Genet. 2011;88(3):372–381. [PubMed]
52. Whitlock MC. Combining probability from independent tests: the weighted Z-method is superior to Fisher’s approach. J Evol Biol. 2005;18(5):1368–1373. [PubMed]
53. Braunewell KH, Klein-Szanto AJ. Visinin-like proteins (VSNLs): interaction partners and emerging functions in signal transduction of a subfamily of neuronal Ca2+ -sensor proteins. Cell Tissue Res. 2009;335(2):301–316. [PMC free article] [PubMed]
54. Lee JM, Blennow K, Andreasen N, Laterza O, Modur V, Olander J, et al. The brain injury biomarker VLP-1 is increased in the cerebrospinal fluid of Alzheimer disease patients. Clin Chem. 2008;54(10):1617–1623. [PMC free article] [PubMed]
55. Beveridge NJ, Tooney PA, Carroll AP, Gardiner E, Bowden N, Scott RJ, et al. Dysregulation of miRNA 181b in the temporal cortex in schizophrenia. Hum Mol Genet. 2008;17(8):1156–1168. [PubMed]
56. Martins-de-Souza D, Gattaz WF, Schmitt A, Rewerts C, Marangoni S, Novello JC, et al. Alterations in oligodendrocyte proteins, calcium homeostasis and new potential markers in schizophrenia anterior temporal lobe are revealed by shotgun proteome analysis. Journal of neural transmission. 2009;116(3):275–289. [PubMed]
57. Rule AD, de Andrade M, Matsumoto M, Mosley TH, Kardia S, Turner ST. Association between SLC2A9 transporter gene variants and uric acid phenotypes in African American and white families. Rheumatology (Oxford) 2010 [PMC free article] [PubMed]
58. Vitart V, Rudan I, Hayward C, Gray NK, Floyd J, Palmer CN, et al. SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout. Nat Genet. 2008;40(4):437–442. [PubMed]
59. Bowman GL, Shannon J, Frei B, Kaye JA, Quinn JF. Uric acid as a CNS antioxidant. J Alzheimers Dis. 2010;19(4):1331–1336. [PMC free article] [PubMed]
60. Yao JK, Reddy R, van Kammen DP. Reduced level of plasma antioxidant uric acid in schizophrenia. Psychiatry Res. 1998;80(1):29–39. [PubMed]
61. Reddy R, Keshavan M, Yao JK. Reduced plasma antioxidants in first-episode patients with schizophrenia. Schizophr Res. 2003;62(3):205–212. [PubMed]
62. Kam M, Massare J, Gallinger S, Kinzie J, Weaver D, Dingell JD, et al. Peutz-Jeghers syndrome diagnosed in a schizophrenic patient with a large deletion in the STK11 gene. Dig Dis Sci. 2006;51(9):1567–1570. [PubMed]
63. Lee CH, Liu CM, Wen CC, Chang SM, Hwu HG. Genetic copy number variants in sib pairs both affected with schizophrenia. J Biomed Sci. 2010;17:2. [PMC free article] [PubMed]
64. Demichele-Sweet MA, Lopez OL, Sweet RA. Psychosis in Alzheimer’s disease in the national Alzheimer’s disease coordinating center uniform data set: clinical correlates and association with apolipoprotein e. Int J Alzheimers Dis. 2011;2011:926597. [PMC free article] [PubMed]
65. Chu SH, Roeder K, Ferrell RE, Devlin B, DeMichele-Sweet MA, Kamboh MI, et al. TOMM40 poly-T repeat lengths, age of onset and psychosis risk in Alzheimer disease. Neurobiology of Aging. In Press. [PMC free article] [PubMed]
66. Lichtenstein P, Yip BH, Bjork C, Pawitan Y, Cannon TD, Sullivan PF, et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet. 2009;373(9659):234–239. [PubMed]
67. Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009 [PubMed]
68. Lettre G, Rioux JD. Autoimmune diseases: insights from genome-wide association studies. Hum Mol Genet. 2008;17(R2):R116–121. [PMC free article] [PubMed]
69. Craddock N, O’Donovan MC, Owen MJ. Psychosis genetics: modeling the relationship between schizophrenia, bipolar disorder, and mixed (or “schizoaffective”) psychoses. Schizophr Bull. 2009;35(3):482–490. [PMC free article] [PubMed]
70. Moskvina V, Holmans P, Schmidt KM, Craddock N. Design of case-controls studies with unscreened controls. Ann Hum Genet. 2005;69(Pt 5):566–576. [PubMed]
71. Kamboh MI, Minster RL, Demirci FY, Ganguli M, Dekosky ST, Lopez OL, et al. Association of CLU and PICALM variants with Alzheimer’s disease. Neurobiol Aging. 2010 [PMC free article] [PubMed]
72. Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, Buros J, et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet. 2011;43(5):436–441. [PMC free article] [PubMed]
73. Schjeide BM, Schnack C, Lambert JC, Lill CM, Kirchheiner J, Tumani H, et al. The role of clusterin, complement receptor 1, and phosphatidylinositol binding clathrin assembly protein in Alzheimer disease risk and cerebrospinal fluid biomarker levels. Arch Gen Psychiatry. 2011;68(2):207–213. [PubMed]