Bipolar disorder (BPD) shares genetic components with other psychiatric disorders; however, uncertainty remains about where in the psychiatric spectra BPD falls. To understand the etiology of BPD, we studied the familial aggregation of BPD and co-aggregation between BPD and schizophrenia, depression, anxiety disorders, attention-deficit hyperactivity disorder, drug abuse, personality disorders, and autism spectrum disorders.
Darier disease is an autosomal dominant skin disorder caused by mutations in the ATPase, Ca++ transporting, cardiac muscle, slow twitch 2 (ATP2A2) gene and previously reported to cosegregate with bipolar disorder and schizophrenia in occasional pedigrees. It is, however, unknown whether these associations exist also in the general population, and the objective of this study was to examine this question.
Schizophrenia is a common disease with a complex aetiology, probably involving multiple and heterogeneous genetic factors. Here, by analysing the exome sequences of 2,536 schizophrenia cases and 2,543 controls, we demonstrate a polygenic burden primarily arising from rare (less than 1 in 10,000), disruptive mutations distributed across many genes. Particularly enriched gene sets include the voltage-gated calcium ion channel and the signalling complex formed by the activity-regulated cytoskeleton-associated scaffold protein (ARC) of the postsynaptic density, sets previously implicated by genome-wide association and copy-number variation studies. Similar to reports in autism, targets of the fragile X mental retardation protein (FMRP, product of FMR1) are enriched for case mutations. No individual gene-based test achieves significance after correction for multiple testing and we do not detect any alleles of moderately low frequency (approximately 0.5 to 1 per cent) and moderately large effect. Taken together, these data suggest that population-based exome sequencing can discover risk alleles and complements established gene-mapping paradigms in neuropsychiatric disease.
Schizophrenia is a genetically and clinically heterogeneous disorder. Genetic risk factors for the disorder may differ between the sexes or between multiply affected families compared to cases with no family history. Additionally, limited data support a genetic basis for variation in onset and severity, but specific loci have not been identified. We performed genome-wide association studies (GWAS) examining genetic influences on age at onset (AAO) and illness severity as well as specific risk by sex or family history status using up to 2762 cases and 3187 controls from the International Schizophrenia Consortium (ISC). Subjects with a family history of schizophrenia demonstrated a slightly lower average AAO that was not significant following multiple testing correction (p=.048), but no differences in illness severity were observed by family history status (p=.51). Consistent with prior reports, we observed earlier AAO (p=.005) and a more severe course of illness for men (p=.002). Family history positive analyses showed the greatest association with KIF5C (p=1.96×10(-8)), however, genetic risk burden overall does not differ by family history. Separate association analyses for males and females revealed no significant sex-specific associations. The top GWAS hit for AAO was near the olfactory receptor gene OR2K2 (p=1.52×10(-7)). Analyses of illness severity (episodic vs. continuous) implicated variation in ST18 (p=8.24×10(-7)). These results confirm recognized demographic relationships but do not support a simplified genetic architecture for schizophrenia subtypes based on these variables.
Schizophrenia is an idiopathic mental disorder with a heritable component and a substantial public health impact. We conducted a multi-stage genome-wide association study (GWAS) for schizophrenia beginning with a Swedish national sample (5,001 cases and 6,243 controls) followed by meta-analysis with previous schizophrenia GWAS (8,832 cases and 12,067 controls) and finally by replication of SNPs in 168 genomic regions in independent samples (7,413 cases, 19,762 controls and 581 parent-offspring trios). We identified 22 loci associated at genome-wide significance; 13 of these are new, and 1 was previously implicated in bipolar disorder. Examination of candidate genes at these loci suggests the involvement of neuronal calcium signaling. We estimate that 8,300 independent, mostly common SNPs (95% credible interval of 6,300-10,200 SNPs) contribute to risk for schizophrenia and that these collectively account for at least 32% of the variance in liability. Common genetic variation has an important role in the etiology of schizophrenia, and larger studies will allow more detailed understanding of this disorder.
Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohns disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.
Large genomic copy number variations have been implicated as strong risk factors for schizophrenia. However, the rarity of these events has created challenges for the identification of further pathogenic loci, and extremely large samples are required to provide convincing replication.
Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n=3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to this ranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.
Disrupted in Schizophrenia-1 (DISC1) is a candidate gene for psychiatric disorders and has many roles during brain development. Common DISC1 polymorphisms (variants) are associated with neuropsychiatric phenotypes including altered cognition, brain structure, and function; however, it is unknown how this occurs. Here, we demonstrate using mouse, zebrafish, and human model systems that DISC1 variants are loss of function in Wnt/GSK3? signaling and disrupt brain development. The DISC1 variants A83V, R264Q, and L607F, but not S704C, do not activate Wnt signaling compared with wild-type DISC1 resulting in decreased neural progenitor proliferation. In zebrafish, R264Q and L607F could not rescue DISC1 knockdown-mediated aberrant brain development. Furthermore, human lymphoblast cell lines endogenously expressing R264Q displayed impaired Wnt signaling. Interestingly, S704C inhibited the migration of neurons in the developing neocortex. Our data demonstrate DISC1 variants impair Wnt signaling and brain development and elucidate a possible mechanism for their role in neuropsychiatric phenotypes.
The XVIIIth World Congress of Psychiatric Genetics, sponsored by The International Society of Psychiatric Genetics took place in Athens, Greece on October 3-7, 2010. Approximately 950 participants gathered to discuss the latest findings in this rapidly advancing field. The following report was written by junior travel awardees, as well as others who were volunteers from student meeting attendees. Each was assigned sessions as rapporteurs. This report represents some of the areas covered in oral presentation during the conference, and reports on some of the notable major new findings described at this 2010 World Congress of Psychiatric Genetics.
To evaluate previously reported associations of copy number variants (CNVs) with schizophrenia and to identify additional associations, the authors analyzed CNVs in the Molecular Genetics of Schizophrenia study (MGS) and additional available data.
We propose new statistical methods for analyzing genetic case/control association data in which cases can be further classified into subtypes, for example, based on clinical features. The primary utility of our work is the ability to distinguish between subtype-specific and modifier effects of genetic variants within a single testing framework.
The XVII World Congress of Psychiatric Genetics, sponsored by The International Society of Psychiatric Genetics (ISPG) took place in San Diego, California from 4 to 8 November 2009. Approximately 550 participants gathered to discuss the latest molecular genetic findings relevant to serious mental illness, including schizophrenia, mood disorders, substance abuse, autism, and attention deficit disorder. Recent advances in the field were discussed, including the genome-wide association studies results, copy number variation (CNV) in the genome, genomic imaging, and large multicenter collaborations. The following report, written by junior travel awardees who were assigned sessions as rapporteurs represents some of the areas covered in oral presentation during the conference, and reports on some of the notable major new findings described at this 2009 World Congress of Psychiatric Genetics.
Complex diseases invariably involve multiple genes and often exhibit variable symptom profiles. The extent to which disease symptoms, course, and severity differ between affected individuals may result from underlying genetic heterogeneity. Genes with modifier effects may or may not also influence disease susceptibility. In this study, we have simulated data in which a subset of cases differ by some effect size (ES) on a quantitative trait and are also enriched for a risk allele. Power to detect this pseudo-modifier gene in case-only and case-control designs was explored blind to case substructure. Simulations involved 1000 iterations and calculations for 80% power at P<0.01 while varying the risk allele frequency (RAF), sample size (SS), ES, odds ratio (OR), and proportions of the case subgroups. With realistic values for the RAF (0.20), SS (3000) and ES (1), an OR of 1.7 is necessary to detect a pseudo-modifier gene. Unequal numbers of subjects in the case groups result in little decrement in power until the group enriched for the risk allele is <30% or >70% of the total case population. In practice, greater numbers of subjects and selection of a quantitative trait with a large range will provide researchers with greater power to detect a pseudo-modifier gene. However, even under ideal conditions, studies involving alleles with low frequencies or low ORs are usually underpowered for detection of a modifier or susceptibility gene. This may explain some of the inconsistent association results for many candidate gene studies of complex diseases.
The XVI World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics took place in Osaka, Japan, October 2008. Approximately 600 participants gathered to discuss the latest molecular genetic findings relevant to serious mental illnesses, including schizophrenia, bipolar disorder, major depression, alcohol and drug abuse, autism, and attention-deficit disorder. Recently, the field has advanced considerably and includes new genome-wide association studies with the largest numbers of individuals screened and density of markers to date, as well as newly uncovered genetic phenomena, such as copy number variation that may prove to be relevant for specific brain disorders. The following report represents some of the areas covered during this conference and some of the major new findings presented.
We tested four genes [phenylalanine hydroxylase (PAH), the serotonin transporter (SLC6A4), monoamine oxidase B (MAOB), and the gamma-aminobutyric acid A receptor beta-3 subunit (GABRB3)] for their impact on five schizophrenia symptom factors: delusions, hallucinations, mania, depression, and negative symptoms. In a 90 family subset of the Irish Study of High Density Schizophrenia Families, the PAH 232 bp microsatellite allele demonstrated significant association with the delusions factor using both QTDT (F=8.0, p=.031) and QPDTPHASE (chi-square=12.54, p=.028). Also, a significant association between the GABRB3 191 bp allele and the hallucinations factor was detected using QPDTPHASE (chi-square=15.51, p=.030), but not QTDT (chi-square=2.07, p=.560).
Multiple sources of evidence suggest that genetic factors influence variation in clinical features of schizophrenia. The authors present the first genome-wide association study (GWAS) of dimensional symptom scores among individuals with schizophrenia.
Sequencing of gene-coding regions (the exome) is increasingly used for studying human disease, for which copy-number variants (CNVs) are a critical genetic component. However, detecting copy number from exome sequencing is challenging because of the noncontiguous nature of the captured exons. This is compounded by the complex relationship between read depth and copy number; this results from biases in targeted genomic hybridization, sequence factors such as GC content, and batching of samples during collection and sequencing. We present a statistical tool (exome hidden Markov model [XHMM]) that uses principal-component analysis (PCA) to normalize exome read depth and a hidden Markov model (HMM) to discover exon-resolution CNV and genotype variation across samples. We evaluate performance on 90 schizophrenia trios and 1,017 case-control samples. XHMM detects a median of two rare (<1%) CNVs per individual (one deletion and one duplication) and has 79% sensitivity to similarly rare CNVs overlapping three or more exons discovered with microarrays. With sensitivity similar to state-of-the-art methods, XHMM achieves higher specificity by assigning quality metrics to the CNV calls to filter out bad ones, as well as to statistically genotype the discovered CNV in all individuals, yielding a trio call set with Mendelian-inheritance properties highly consistent with expectation. We also show that XHMM breakpoint quality scores enable researchers to explicitly search for novel classes of structural variation. For example, we apply XHMM to extract those CNVs that are highly likely to disrupt (delete or duplicate) only a portion of a gene.
Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.
Numerous genome-wide association studies (GWAS) of schizophrenia have been published in the past 6 years, with a number of key reports published in the last year. The studies have evolved in scale from small individual samples to large collaborative endeavors. This review aims to critically assess whether the results have improved as the sample size and scale of genetic association studies have grown.
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