Copy Number Variants In Schizophrenia Research Articles

M199. COPY NUMBER VARIANCE (CNV) ANALYSIS TO DETERMINE OPTIMAL ANTIPSYCHOTIC DOSAGE IN SCHIZOPHRENIA: A PILOT STUDY

BackgroundThe relationship between genetic polymorphisms of antipsychotic drug-metabolizing agents and drug response has been thoroughly investigated and analyzed. However, from a pharmacokinetic standpoint, few studies have explored the relationship between Copy number variants (CNV) and antipsychotic dosage. The aim of the present study is to test the association between antipsychotic dosage and CNV in schizophrenia (SCZ) patients.MethodsThe current dosage of antipsychotic medications was collected from 263 schizophrenia patients. The dosage was standardized using three different methods: chlorpromazine equivalent(CPZe), defined daily dose (DDD), and percentage of maximum dose (PM %). The patients were then genotyped using the Illumina HumanOmni2.5–8 BeadChip Kit.ResultsThe CNV analysis did not show that CNVs are associated with dosage variation for CPZe, PM %, and DDD.DiscussionIn this pilot sample, we investigated for the first time CNVs and standardized antipsychotic dosage. The relationship between CNV and optimal antipsychotic dosage has far reaching clinical implications. Further analysis is required that utilitze large prescription databases to build on the results presented in this pilot study.

Chromatin remodeling dysfunction extends the etiological spectrum of schizophrenia: a case report

BackgroundThe role of deleterious copy number variations in schizophrenia is well established while data regarding pathogenic variations remain scarce. We report for the first time a case of schizophrenia in a child with a pathogenic mutation of the chromodomain helicase DNA binding protein 2 (CHD2) gene.Case presentationThe proband was the second child of unrelated parents. Anxiety and sleep disorders appeared at the age of 10 months. He presented febrile seizures and, at the age of 8, two generalized tonic-clonic seizures. At the age of 10, emotional withdrawal emerged, along with a flat affect, disorganization and paranoid ideation, without seizures. He began to talk and giggle with self. Eventually, the patient presented daily auditory and visual hallucinations. The diagnosis of childhood onset schizophrenia (DSM V) was then evoked. Brain imaging was unremarkable. Wakefulness electroencephalography showed a normal background and some bilateral spike-wave discharges that did not explain the psychosis features. A comparative genomic hybridization array (180 K, Agilent, Santa Clara, CA, USA) revealed an 867-kb 16p13.3 duplication, interpreted as a variant of unknown significance confirmed by a quantitative PCR that also showed its maternal inheritance. Risperidone (1,5 mg per day), led to clinical improvement. At the age of 11, an explosive relapse of epilepsy occurred with daily seizures of various types. The sequencing of a panel for monogenic epileptic disorders and Sanger sequencing revealed a de novo pathogenic heterozygous transition in CHD2 (NM_001271.3: c.4003G > T).ConclusionsThis case underlines that schizophrenia may be, sometimes, underpinned by a Mendelian disease. It addresses the question of systematic genetic investigations in the presence of warning signs such as a childhood onset of the schizophrenia or a resistant epilepsy. It points that, in the absence of pathogenic copy number variation, the investigations should also include a search for pathogenic variations, which means that some of the patients with schizophrenia should benefit from Next Generation Sequencing tools. Last but not least, CHD2 encodes a member of the chromodomain helicase DNA-binding (CHD) family involved in chromatin remodeling. This observation adds schizophrenia to the phenotypic spectrum of chromodomain remodeling disorders, which may lead to innovative therapeutic approaches.

Characterization of Single Gene Copy Number Variants in Schizophrenia

BackgroundGenetic studies of schizophrenia have implicated numerous risk loci including several copy number variants (CNVs) of large effect and hundreds of loci of small effect. In only a few cases has a specific gene been clearly identified. Rare CNVs affecting a single gene offer a potential avenue to discovering schizophrenia risk genes. MethodsCNVs were generated from exome sequencing of 4913 schizophrenia cases and 6188 control subjects from Sweden. We integrated two CNV calling methods (XHMM and ExomeDepth) to expand our set of single-gene CNVs and leveraged two different approaches for validating these variants (quantitative polymerase chain reaction and NanoString). ResultsWe found a significant excess of all rare CNVs (deletions: p = .0004, duplications: p = .0006) and single-gene CNVs (deletions: p = .04, duplications: p = .03) in schizophrenia cases compared with control subjects. An expanded set of CNVs generated from integrating multiple approaches showed a significant burden of deletions in 11 of 21 gene sets previously implicated in schizophrenia and across all genes in those sets (p = .008), although no tests survived correction. We performed an extensive validation of all deletions in the significant set of voltage-gated calcium channels among CNVs called from both exome sequencing and genotyping arrays. In total, 4 exonic, single-gene deletions were validated in schizophrenia cases and none in control subjects (p = .039), of which all were identified by exome sequencing. ConclusionsThese results point to the potential contribution of single-gene CNVs to schizophrenia, indicate that the utility of exome sequencing for CNV calling has yet to be maximized, and note that single-gene CNVs should be included in gene-focused studies using other classes of variation.

SU94 - EXPLORING THE SOMATIC VARIATION IN SCHIZOPHRENIA

Background The presence of somatic variation in the human brain has been elucidated over the past several decades. Most recently, single-cell whole-genome sequencing studies have provided unequivocal evidence that individual human brain cells harbor unique genetic variants. The functional significance of this phenomenon is unclear, but some studies have suggested that it contributes to risk of neuropsychiatric disease. Currently, cost and labor requirement render difficult the sequencing of individual genomes at the scale that will likely be necessary to fully uncover the extent and role of somatic variation in the brain. A scalable alternative, however, has been developed in the field of cancer genomics wherein somatic variants are identified by comparing the frequencies of variant alleles in tumor and normal bulk tissue specimens from the same individual. Here, we apply this approach to investigate the role of somatic variation in schizophrenia in a pilot study of 9 individuals (5 schizophrenia cases and 4 unaffected controls). Methods Specimens from the prefrontal cortex and temporal muscle of 5 schizophrenia cases and 4 controls were obtained from the Mount Sinai NIH Brain and Tissue Repository. Deep whole-exome sequencing (250X coverage) was performed using DNA extracted from neuronal and non-neuronal nuclei, which were isolated from cortical specimens by fluorescent-activated nuclear sorting. We followed standard protocols for mapping and filtering reads, then called somatic Single Nucleotide Variants (sSNVs) using a suite of 6 calling algorithms. For each individual, the calling algorithms were utilized to call variants in the exomes of both the neuronal and non-neuronal cells of the brain by comparing them to a matched “normal” sample from the same individual (i.e., the temporal muscle). Variants were then filtered using an in-house quality control pipeline, and a subset of the remaining calls were validated with digital PCR (dPCR) and parallel sequencing of multiple clones (clone-seq). Results We identified 26 sSNVs in neuronal and non-neuronal cells from the brains of 9 individuals. We chose 10 of 26 for experimental validation using dPCR and clone-seq, observing a validation rate of 80%. We noted that 13 of the 26 sSNVs were found in a single schizophrenia case; as such, 4 of the 10 sSNVs included in validation experiments were from this individual, 3 of which were successfully validated. For 5 sSNVs, only the neuronal exome of the individual had the variant, for 17 only the non-neuronal exome and for 4 both the neuronal and non-neuronal exomes. At least 1 sSNV was observed in all 5 of the cases and in 2 of the 4 controls. We noted that 2 of the 23 somatic SNVs in cases fell within one of the 16 copy number variants implicated in schizophrenia by the largest study of copy number variation in schizophrenia to date. Several of the sSNVs observed in our cohort fall within genes known to function in determining cellular fate and development, including WNT10B, SMAD1 and FGF1. Discussion Here, we present a pipeline suitable for the large-scale investigation of somatic variation in the human brain. We show evidence that somatic variation occurs in a cell-type specific manner in the human brain. Genes affected by sSNVs in the brain have known roles in determining cell fate. Greater number of sSNVs in schizophrenia cases compared to controls was observed, and multiple case sSNVs fall within known schizophrenia CNVs. Larger sample sizes are needed so as to further elucidate the extent and role of somatic variation in the human brain.

Impact of IQ on the diagnostic yield of chromosomal microarray in a community sample of adults with schizophrenia

BackgroundSchizophrenia is a severe psychiatric disorder associated with IQ deficits. Rare copy number variations (CNVs) have been established to play an important role in the etiology of schizophrenia. Several of the large rare CNVs associated with schizophrenia have been shown to negatively affect IQ in population-based controls where no major neuropsychiatric disorder is reported. The aim of this study was to examine the diagnostic yield of microarray testing and the functional impact of genome-wide rare CNVs in a community ascertained cohort of adults with schizophrenia and low (< 85) or average (≥ 85) IQ.MethodsWe recruited 546 adults of European ancestry with schizophrenia from six community psychiatric clinics in Canada. Each individual was assigned to the low or average IQ group based on standardized tests and/or educational attainment. We used rigorous methods to detect genome-wide rare CNVs from high-resolution microarray data. We compared the burden of rare CNVs classified as pathogenic or as a variant of unknown significance (VUS) between each of the IQ groups and the genome-wide burden and functional impact of rare CNVs after excluding individuals with a pathogenic CNV.ResultsThere were 39/546 (7.1%; 95% confidence interval [CI] = 5.2–9.7%) schizophrenia participants with at least one pathogenic CNV detected, significantly more of whom were from the low IQ group (odds ratio [OR] = 5.01 [2.28–11.03], p = 0.0001). Secondary analyses revealed that individuals with schizophrenia and average IQ had the lowest yield of pathogenic CNVs (n = 9/325; 2.8%), followed by those with borderline intellectual functioning (n = 9/130; 6.9%), non-verbal learning disability (n = 6/29; 20.7%), and co-morbid intellectual disability (n = 15/62; 24.2%). There was no significant difference in the burden of rare CNVs classified as a VUS between any of the IQ subgroups. There was a significantly (p=0.002) increased burden of rare genic duplications in individuals with schizophrenia and low IQ that persisted after excluding individuals with a pathogenic CNV.ConclusionsUsing high-resolution microarrays we were able to demonstrate for the first time that the burden of pathogenic CNVs in schizophrenia differs significantly between IQ subgroups. The results of this study have implications for clinical practice and may help inform future rare variant studies of schizophrenia using next-generation sequencing technologies.

Potential Value of Genomic Copy Number Variations in Schizophrenia.

Schizophrenia is a devastating neuropsychiatric disorder affecting approximately 1% of the global population, and the disease has imposed a considerable burden on families and society. Although, the exact cause of schizophrenia remains unknown, several lines of scientific evidence have revealed that genetic variants are strongly correlated with the development and early onset of the disease. In fact, the heritability among patients suffering from schizophrenia is as high as 80%. Genomic copy number variations (CNVs) are one of the main forms of genomic variations, ubiquitously occurring in the human genome. An increasing number of studies have shown that CNVs account for population diversity and genetically related diseases, including schizophrenia. The last decade has witnessed rapid advances in the development of novel genomic technologies, which have led to the identification of schizophrenia-associated CNVs, insight into the roles of the affected genes in their intervals in schizophrenia, and successful manipulation of the target CNVs. In this review, we focus on the recent discoveries of important CNVs that are associated with schizophrenia and outline the potential values that the study of CNVs will bring to the areas of schizophrenia research, diagnosis, and therapy. Furthermore, with the help of the novel genetic tool known as the Clustered Regularly Interspaced Short Palindromic Repeats-associated nuclease 9 (CRISPR/Cas9) system, the pathogenic CNVs as genomic defects could be corrected. In conclusion, the recent novel findings of schizophrenia-associated CNVs offer an exciting opportunity for schizophrenia research to decipher the pathological mechanisms underlying the onset and development of schizophrenia as well as to provide potential clinical applications in genetic counseling, diagnosis, and therapy for this complex mental disease.

A pilot study on commonality and specificity of copy number variants in schizophrenia and bipolar disorder.

Schizophrenia (SZ) and bipolar disorder (BD) are known to share genetic risks. In this work, we conducted whole-genome scanning to identify cross-disorder and disorder-specific copy number variants (CNVs) for these two disorders. The Database of Genotypes and Phenotypes (dbGaP) data were used for discovery, deriving from 2416 SZ patients, 592 BD patients and 2393 controls of European Ancestry, as well as 998 SZ patients, 121 BD patients and 822 controls of African Ancestry. PennCNV and Birdsuite detected high-confidence CNVs that were aggregated into CNV regions (CNVRs) and compared with the database of genomic variants for confirmation. Then, large (size⩾500 kb) and small common CNVRs (size <500 kb, frequency⩾1%) were examined for their associations with SZ and BD. Particularly for the European Ancestry samples, the dbGaP findings were further evaluated in the Wellcome Trust Case Control Consortium (WTCCC) data set for replication. Previously implicated variants (1q21.1, 15q13.3, 16p11.2 and 22q11.21) were replicated. Some cross-disorder variants were noted to differentially affect SZ and BD, including CNVRs in chromosomal regions encoding immunoglobulins and T-cell receptors that were associated more with SZ, and the 10q11.21 small CNVR (GPRIN2) associated more with BD. Disorder-specific CNVRs were also found. The 22q11.21 CNVR (COMT) and small CNVRs in 11p15.4 (TRIM5) and 15q13.2 (ARHGAP11B and FAN1) appeared to be SZ-specific. CNVRs in 17q21.2, 9p21.3 and 9q21.13 might be BD-specific. Overall, our primary findings in individual disorders largely echo previous reports. In addition, the comparison between SZ and BD reveals both specific and common risk CNVs. Particularly for the latter, differential involvement is noted, motivating further comparative studies and quantitative models.

Clinically Useful Genetic Markers of Antidepressant Response: How Do We Get There From Here?

Predicting who will respond to an antidepressant—and who will not—remains anurgent but unfulfilled need in psychiatry (1). Clinical indicators of response have a long history, but probably have low predictive value. This state of affairs has fueled an energetic search for biomarkers. Genetic markers are ideal biomarkers, since they do not suffer from reverse causation. Reverse causation arises when abiomarkervalue is actually the consequenceof the outcome, rather than its cause. Genetic markers that represent the inherited sequence of DNA cannot reflect the result of a health outcome. The best genetic markers would identify individuals most likely to benefit from a particular treatment and least likely to suffer adverse events. Years of work with candidate genes have yielded some reproduciblefindings, but their individual effects are small, with little impact on clinical decision making (2). Studies that use genome-wide genotyping technology have implicated additional genes (3), but well-replicated findings that can help guide clinical decision making remain elusive. In this issue, Schatzberg et al. (4) report that common genetic markers in ABCB1 provide some information about which patients are most likely to respond to certain selective serotonin reuptake inhibitors.ABCB1 encodes P-glycoprotein, which influences the efficiency with which certain antidepressants (and other drugs) cross the blood-brain barrier. Common variants in ABCB1 have been associated with antidepressant outcome in some previous studies (5), so replication in an independent sample was the next logical step. Ten common markers in and around ABCB1 were tested in 683 patients with major depressive disorder whowere treated for at least 2 weeks and an additional 576 patients who were treated for 8weeks as part of the International Study to Predict Optimized Treatment in Depression (iSPOT-D) trial. The samples were carefully ascertained and evaluated, with prospective measures of efficacy and side effect burden, using standard instruments. General and emotional cognition were further assessed with a battery of 13 tests. Antidepressants included escitalopram, sertraline, and extended-release venlafaxine, all of which are known to bind P-glycoprotein. One singlenucleotide polymorphism (SNP) upstream of ABCB1 was associated with remission and subjective side effects, and the direction of the effect differed among the medications tested. How far does this take us, and what’s next? Treatment assignmentwas randomized, but not blinded, and therewere no placebo controls and no direct assessments of adherence. Despite these limitations, this study represents one of the few replications in the field of antidepressant pharmacogenomics, and is thus a real step forward. Still, the effect sizes are too small to be useful in clinical decision making. It seems likely that antidepressant response, like depression itself, is a polygenic trait, influenced by numerous genes, each of small effect. Therefore, many more genetic markers will be needed before we can achieve clinically significant prediction. This will require much larger samples than have been studied so far. We now know that genome-wide association studies (GWAS) require very large sample sizes to identify genetic markers of polygenic traits. Samples in the tensof thousandsarenowroutine,yetnoGWASofantidepressant outcome to date has exceeded 2,000. As pharmacogenomics begins to bear fruit in psychiatry, we can hope for a renewed focus on the large sample sizes necessary for robust findings. Theheritability of antidepressant response puts anupper limit onwhatcan be predicted by genetic markers alone. Heritability measures how much of the individual differences in a trait can be explained by inherited genetic differences. This is typically estimated from twin studies, but these are generally impractical for treatment response studies, since twins would only rarely be treated for the same illness in the same way during the time frame of a study. An alternative approach directly compares genetic similarity (estimated from a genome-wide set of SNPs) with phenotypic similarityusingsophisticatedregressionmethods(6).Onestudy used this approach to estimate that antidepressant response was about 40% heritable, but with a large confidence interval (7). Other statistical models show that traits with this degree of heritability cannot be predicted verywell fromgeneticmarkers alone(8).Evenapanelofgeneticmarkersthatexplainsaquarter of the genetic variance—more than anyGWAS todate—gives an area under the curve of only about 64%.Thus, a combination of clinical data and genetic and other biomarkerswill be needed if we are to push predictability into the clinically useful range. Advances in cancer chemotherapy may light the way. We can already tailor treatment for breast cancer based on a Studies like that of Schatzberg et al. are encouraging because they suggest that the ultimate goal of clinically useful outcome prediction can be achieved.

Characterizing rare copy number variants in schizophrenia: a clinical, cognitive, and neuroimaging study.

Background: Schizophrenia is a highly heritable disorder affecting approximately 1 in 100 people in Australia and worldwide. The emerging picture is of common and rare genetic variants, as well as environmental factors, contributing to risk. One such rare variant is chromosomal microdeletions, especially large rare deletions. However, little is known about how these deletions affect the phenotype and confer risk for schizophrenia. Method: Clinical data (N=633), cognitive data (N=78), and neuroimaging data (VBM, DTI, & fMRI) (N=35) were collected on patients with schizophrenia with existing genetic information available from a large genome-wide association study (GWAS) pertaining to the presence of large, rare deletions and deletion burden in general. Results: Rare deletions were associated with reduced rates of cannabis abuse, later age at onset of psychosis, greater cognitive dysfunction, and structural (VBM and DTI) profiles intermediate to those identified between healthy controls and schizophrenia patients in general. Rare deletions were associated with reduced functional connectivity between prefrontal cortex and striatum and between regions in the default mode network as identified in rs-fMRI. Conclusions: Rare deletions affect the schizophrenia phenotype across clinical, cognitive, and neuroimaging domains, suggesting an important role in the developmental pathway towards psychosis. Further investigation is warranted, especially in larger samples.

MicroRNAs in Copy Number Variants in Schizophrenia: Misregulation of Genome-wide Gene Expression Programs

MicroRNAs in Copy Number Variants in Schizophrenia: Misregulation of Genome-wide Gene Expression Programs

The phenotypic manifestations of rare CNVs in schizophrenia

There is compelling evidence for the role of copy number variants (CNVs) in schizophrenia susceptibility, and it has been estimated that up to 2–3% of schizophrenia cases may carry rare CNVs. Despite evidence that these events are associated with an increased risk across categorical neurodevelopmental disorders, there is limited understanding of the impact of CNVs on the core features of disorders like schizophrenia. Our objective was to evaluate associations between rare CNVs in differentially brain expressed (BE) genes and the core features and clinical correlates of schizophrenia. The sample included 386 cases of Irish ancestry with a diagnosis of schizophrenia, at least one rare CNV impacting any gene, and a core set of phenotypic measures. Statistically significant associations between deletions in differentially BE genes were found for family history of mental illness (decreased prevalence of all CNVs and deletions, unadjusted and adjusted) and for paternal age (increase in deletions only, unadjusted, among those with later ages at birth of patient). The strong effect of a lack of a family history on BE genes suggests that CNVs may comprise one pathway to schizophrenia, whereas a positive family history could index other genetic mechanisms that increase schizophrenia vulnerability. To our knowledge, this is the first investigation of the association between genome-wide CNVs and risk factors and sub-phenotypic features of schizophrenia beyond cognitive function.

Copy number variation in schizophrenia in Sweden.

Schizophrenia (SCZ) is a highly heritable neuropsychiatric disorder of complex genetic etiology. Previous genome-wide surveys have revealed a greater burden of large, rare copy number variations (CNVs) in SCZ cases and identified multiple rare recurrent CNVs that increase risk of SCZ although with incomplete penetrance and pleiotropic effects. Identification of additional recurrent CNVs and biological pathways enriched for SCZ CNVs requires greater sample sizes. We conducted a genome-wide survey for CNVs associated with SCZ using a Swedish national sample (4719 cases and 5917 controls). High-confidence CNV calls were generated using genotyping array intensity data, and their effect on risk of SCZ was measured. Our data confirm increased burden of large, rare CNVs in SCZ cases as well as significant associations for recurrent 16p11.2 duplications, 22q11.2 deletions and 3q29 deletions. We report a novel association for 17q12 duplications (odds ratio=4.16, P=0.018), previously associated with autism and mental retardation but not SCZ. Intriguingly, gene set association analyses implicate biological pathways previously associated with SCZ through common variation and exome sequencing (calcium channel signaling and binding partners of the fragile X mental retardation protein). We found significantly increased burden of the largest CNVs (>500 kb) in genes present in the postsynaptic density, in genomic regions implicated via SCZ genome-wide association studies and in gene products localized to mitochondria and cytoplasm. Our findings suggest that multiple lines of genomic inquiry--genome-wide screens for CNVs, common variation and exonic variation--are converging on similar sets of pathways and/or genes.

Systematic Prioritization and Integrative Analysis of Copy Number Variations in Schizophrenia Reveal Key Schizophrenia Susceptibility Genes

Schizophrenia is a common mental disorder with high heritability and strong genetic heterogeneity. Common disease-common variants hypothesis predicts that schizophrenia is attributable in part to common genetic variants. However, recent studies have clearly demonstrated that copy number variations (CNVs) also play pivotal roles in schizophrenia susceptibility and explain a proportion of missing heritability. Though numerous CNVs have been identified, many of the regions affected by CNVs show poor overlapping among different studies, and it is not known whether the genes disrupted by CNVs contribute to the risk of schizophrenia. By using cumulative scoring, we systematically prioritized the genes affected by CNVs in schizophrenia. We identified 8 top genes that are frequently disrupted by CNVs, including NRXN1, CHRNA7, BCL9, CYFIP1, GJA8, NDE1, SNAP29, and GJA5. Integration of genes affected by CNVs with known schizophrenia susceptibility genes (from previous genetic linkage and association studies) reveals that many genes disrupted by CNVs are also associated with schizophrenia. Further protein-protein interaction (PPI) analysis indicates that protein products of genes affected by CNVs frequently interact with known schizophrenia-associated proteins. Finally, systematic integration of CNVs prioritization data with genetic association and PPI data identifies key schizophrenia candidate genes. Our results provide a global overview of genes impacted by CNVs in schizophrenia and reveal a densely interconnected molecular network of de novo CNVs in schizophrenia. Though the prioritized top genes represent promising schizophrenia risk genes, further work with different prioritization methods and independent samples is needed to confirm these findings. Nevertheless, the identified key candidate genes may have important roles in the pathogenesis of schizophrenia, and further functional characterization of these genes may provide pivotal targets for future therapeutics and diagnostics.

Analysis of copy number variations at 15 schizophrenia-associated loci.

BackgroundA number of copy number variants (CNVs) have been suggested as susceptibility factors for schizophrenia. For some of these the data remain equivocal, and the frequency in individuals with schizophrenia is uncertain.AimsTo determine the contribution of CNVs at 15 schizophrenia-associated loci (a) using a large new data-set of patients with schizophrenia (n = 6882) and controls (n = 6316), and (b) combining our results with those from previous studies.MethodWe used Illumina microarrays to analyse our data. Analyses were restricted to 520 766 probes common to all arrays used in the different data-sets.ResultsWe found higher rates in participants with schizophrenia than in controls for 13 of the 15 previously implicated CNVs. Six were nominally significantly associated (P<0.05) in this new data-set: deletions at 1q21.1, NRXN1, 15q11.2 and 22q11.2 and duplications at 16p11.2 and the Angelman/Prader-Willi Syndrome (AS/PWS) region. All eight AS/PWS duplications in patients were of maternal origin. When combined with published data, 11 of the 15 loci showed highly significant evidence for association with schizophrenia (P<4.1×10–4).ConclusionsWe strengthen the support for the majority of the previously implicated CNVs in schizophrenia. About 2.5% of patients with schizophrenia and 0.9% of controls carry a large, detectable CNV at one of these loci. Routine CNV screening may be clinically appropriate given the high rate of known deleterious mutations in the disorder and the comorbidity associated with these heritable mutations.

Copy number variation in schizophrenia in Sweden

Copy number variation in schizophrenia in Sweden

A genome wide survey supports the involvement of large copy number variants in schizophrenia with and without intellectual disability

Copy number variants (CNVs) have been shown to play a role in schizophrenia and intellectual disability. We compared the CNV burden in 66 patients with intellectual disability and no symptoms of psychosis (ID-only) with the burden in 64 patients with intellectual disability and schizophrenia (ID + SCZ). Samples were genotyped on three plates by the Broad Institute using the Affymetrix 6.0 array. For CNVs larger than 100 kb, there was no difference in the CNV burden of ID-only and ID + SCZ. In contrast, the number of duplications larger than 1 Mb was increased in ID + SCZ compared to ID-only. We detected seven large duplications and two large deletions at chromosome 15q11.2 (18.5-20.1 Mb) which were all present in patients with ID + SCZ. The involvement of this region in schizophrenia was confirmed in Scottish samples from the ISC study (N = 2,114; 1,130 cases and 984 controls). Finally, one of the patients with schizophrenia and low IQ carrying a duplication at 15q11.2, is a member of a previously described pedigree with multiple cases of mild intellectual disability, schizophrenia, hearing impairment, retinitis pigmentosa and cataracts. DNA samples were available for 11 members of this family and the duplication was present in all 10 affected individuals and was absent in an unaffected individual. Duplications at 15q11.2 (18.5-20.1 Mb) are highly prevalent in a severe group of patients characterized by intellectual disability and comorbid schizophrenia. It is also associated with a phenotype that includes schizophrenia, low IQ, hearing and visual impairments resembling the spectrum of symptoms described in "ciliopathies."

Pathogenic rare copy number variants in community-based schizophrenia suggest a potential role for clinical microarrays

Individually rare, large copy number variants (CNVs) contribute to genetic vulnerability for schizophrenia. Unresolved questions remain, however, regarding the anticipated yield of clinical microarray testing in schizophrenia. Using high-resolution genome-wide microarrays and rigorous methods, we investigated rare CNVs in a prospectively recruited community-based cohort of 459 unrelated adults with schizophrenia and estimated the minimum prevalence of clinically significant CNVs that would be detectable on a clinical microarray. A blinded review by two independent clinical cytogenetic laboratory directors of all large (>500 kb) rare CNVs in cases and well-matched controls showed that those deemed to be clinically significant were highly enriched in schizophrenia (16.4-fold increase, P < 0.0001). In a single community catchment area, the prevalence of individuals with these CNVs was 8.1%. Rare 1.7 Mb CNVs at 2q13 were found to be significantly associated with schizophrenia for the first time, compared with the prevalence in 23 838 population-based controls (42.9-fold increase, P = 0.0002). Additional novel findings that will facilitate the future clinical interpretation of smaller CNVs in schizophrenia include: (i) a greater proportion of individuals with two or more rare exonic CNVs >10 kb in size (1.5-fold increase, P = 0.0109) in schizophrenia; (ii) the systematic discovery of new candidate genes for schizophrenia; and, (iii) functional gene enrichment mapping highlighting a differential impact in schizophrenia of rare exonic deletions involving diverse functions, including neurodevelopmental and synaptic processes (4.7-fold increase, P = 0.0060). These findings suggest consideration of a potential role for clinical microarray testing in schizophrenia, as is now the suggested standard of care for related developmental disorders like autism.

Multiplex amplicon quantification screening the ABCA13 gene for copy number variation in schizophrenia and bipolar disorder

Multiplex amplicon quantification screening the ABCA13 gene for copy number variation in schizophrenia and bipolar disorder

Association testing of copy number variants in schizophrenia and autism spectrum disorders.

BackgroundAutism spectrum disorders and schizophrenia have been associated with an overlapping set of copy number variant loci, but the nature and degree of overlap in copy number variants (deletions compared to duplications) between these two disorders remains unclear.MethodsWe systematically evaluated three lines of evidence: (1) the statistical bases for associations of autism spectrum disorders and schizophrenia with a set of the primary CNVs thus far investigated, from previous studies; (2) data from case series studies on the occurrence of these CNVs in autism spectrum disorders, especially among children, and (3) data on the extent to which the CNVs were associated with intellectual disability and developmental, speech, or language delays. We also conducted new analyses of existing data on these CNVs in autism by pooling data from seven case control studies.ResultsFour of the CNVs considered, dup 1q21.1, dup 15q11-q13, del 16p11.2, and dup 22q11.21, showed clear statistical evidence as autism risk factors, whereas eight CNVs, del 1q21.1, del 3q29, del 15q11.2, del 15q13.3, dup 16p11.2, dup 16p13.1, del 17p12, and del 22q11.21, were strongly statistically supported as risk factors for schizophrenia. Three of the CNVs, dup 1q21.1, dup 16p11.2, and dup 16p13.1, exhibited statistical support as risk factors for both autism and schizophrenia, although for each of these CNVs statistical significance was nominal for tests involving one of the two disorders. For the CNVs that were statistically associated with schizophrenia but were not statistically associated with autism, a notable number of children with the CNVs have been diagnosed with autism or ASD; children with these CNVs also demonstrate a high incidence of intellectual disability and developmental, speech, or language delays.ConclusionsThese findings suggest that although CNV loci notably overlap between autism and schizophrenia, the degree of strongly statistically supported overlap in specific CNVs at these loci remains limited. These analyses also suggest that relatively severe premorbidity to CNV-associated schizophrenia in children may sometimes be diagnosed as autism spectrum disorder.

High Frequencies of De Novo CNVs in Bipolar Disorder and Schizophrenia

While it is known that rare copy-number variants (CNVs) contribute to risk for some neuropsychiatric disorders, the role of CNVs in bipolar disorder is unclear. Here, we reasoned that a contribution of CNVs to mood disorders might be most evident for de novo mutations. We performed a genome-wide analysis of de novo CNVs in a cohort of 788 trios. Diagnoses of offspring included bipolar disorder (n= 185), schizophrenia (n= 177), and healthy controls (n= 426). Frequencies of de novo CNVs were significantly higher in bipolar disorder as compared with controls (OR= 4.8 [1.4,16.0], p= 0.009). De novo CNVs were particularly enriched among cases with an age at onset younger than 18 (OR= 6.3 [1.7,22.6], p= 0.006). We also confirmed a significant enrichment of de novo CNVs in schizophrenia (OR= 5.0 [1.5,16.8], p= 0.007). Our results suggest that rare spontaneous mutations are an important contributor to risk for bipolar disorder and other major neuropsychiatric diseases.