Complement genes contribute sex-biased vulnerability in diverse disorders.

Many common illnesses, for reasons that have not been identified, differentially affect men and women. For instance, the autoimmune diseases systemic lupus erythematosus (SLE) and Sjögren's syndrome affect nine times more women than men1, whereas schizophrenia affects men with greater frequency and severity relative to women2. All three illnesses have their strongest common genetic associations in the major histocompatibility complex (MHC) locus, an association that in SLE and Sjögren's syndrome has long been thought to arise from alleles of the human leukocyte antigen (HLA) genes at that locus3-6. Here we show that variation of the complement component 4 (C4) genes C4A and C4B, which are also at the MHC locus and have been linked to increased risk for schizophrenia7, generates 7-fold variation in risk for SLE and 16-fold variation in risk for Sjögren's syndrome among individuals with common C4 genotypes, with C4A protecting more strongly than C4B in both illnesses. The same alleles that increase risk for schizophrenia greatly reduce risk for SLE and Sjögren's syndrome. In all three illnesses, C4 alleles act more strongly in men than in women: common combinations of C4A and C4B generated 14-fold variation in risk for SLE, 31-fold variation in risk for Sjögren's syndrome, and 1.7-fold variation in schizophrenia risk among men (versus 6-fold, 15-fold and 1.26-fold variation in risk among women, respectively). At a protein level, both C4 and its effector C3 were present at higher levels in cerebrospinal fluid and plasma8,9 in men than in women among adults aged between 20 and 50 years, corresponding to the ages of differential disease vulnerability. Sex differences in complement protein levels may help to explain the more potent effects of C4 alleles in men, women's greater risk of SLE and Sjögren's syndrome and men's greater vulnerability to schizophrenia. These results implicate the complement system as a source of sexual dimorphism in vulnerability to diverse illnesses.

BCFtools/liftover: an accurate and comprehensive tool to convert genetic variants across genome assemblies.

MOTIVATION: Many genetics studies report results tied to genomic coordinates of a legacy genome assembly. However, as assemblies are updated and improved, researchers are faced with either realigning raw sequence data using the updated coordinate system or converting legacy datasets to the updated coordinate system to be able to combine results with newer datasets. Currently available tools to perform the conversion of genetic variants have numerous shortcomings, including poor support for indels and multi-allelic variants, that lead to a higher rate of variants being dropped or incorrectly converted. As a result, many researchers continue to work with and publish using legacy genomic coordinates. RESULTS: Here we present BCFtools/liftover, a tool to convert genomic coordinates across genome assemblies for variants encoded in the variant call format with improved support for indels represented by different reference alleles across genome assemblies and full support for multi-allelic variants. It further supports variant annotation fields updates whenever the reference allele changes across genome assemblies. The tool has the lowest rate of variants being dropped with an order of magnitude less indels dropped or incorrectly converted and is an order of magnitude faster than other tools typically used for the same task. It is particularly suited for converting variant callsets from large cohorts to novel telomere-to-telomere assemblies as well as summary statistics from genome-wide association studies tied to legacy genome assemblies. AVAILABILITY AND IMPLEMENTATION: The tool is written in C and freely available under the MIT open source license as a BCFtools plugin available at http://github.com/freeseek/score.

Ancestry-agnostic estimation of DNA sample contamination from sequence reads.

Detecting and estimating DNA sample contamination are important steps to ensure high-quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes. We propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele frequencies projected from reference genotypes onto principal component coordinates. Our method can also be used for estimating genetic ancestries, similar to LASER or TRACE, but simultaneously accounting for potential contamination. We demonstrate that our method robustly estimates contamination rates and genetic ancestries across populations and contamination scenarios. We further demonstrate that, in the presence of contamination, genetic ancestry inference can be substantially biased with existing methods that ignore contamination, while our method corrects for such biases.

Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder.

BACKGROUND: Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders. METHODS: We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific. RESULTS: We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). CONCLUSIONS: Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.

Cultured Mesenchymal Cells from Nasal Turbinate as a Cellular Model of the Neurodevelopmental Component of Schizophrenia Etiology.

The study of neurodevelopmental molecular mechanisms in schizophrenia requires the development of adequate biological models such as patient-derived cells and their derivatives. We previously utilized cell lines with neural progenitor properties (CNON) derived from the superior or middle turbinates of patients with schizophrenia and control groups to study schizophrenia-specific gene expression. In this study, we analyzed single-cell RNA seq data from two CNON cell lines (one derived from an individual with schizophrenia (SCZ) and the other from a control group) and two biopsy samples from the middle turbinate (MT) (also from an individual with SCZ and a control). We compared our data with previously published data regarding the olfactory neuroepithelium and demonstrated that CNON originated from a single cell type present both in middle turbinate and the olfactory neuroepithelium and expressed in multiple markers of mesenchymal cells. To define the relatedness of CNON to the developing human brain, we also compared CNON datasets with scRNA-seq data derived from an embryonic brain and found that the expression profile of the CNON closely matched the expression profile one of the cell types in the embryonic brain. Finally, we evaluated the differences between SCZ and control samples to assess the utility and potential benefits of using CNON single-cell RNA seq to study the etiology of schizophrenia.

Systematic Review and Meta-Analysis of Mass Spectrometry Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Schizophrenia.

Mass spectrometry (MS)-based techniques can be a powerful tool to identify neuropsychiatric disorder biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids of schizophrenia (SCZ) patients to identify disease biomarkers and relevant networks of biological pathways. Following PRISMA guidelines, a search was performed for studies that used MS proteomics approaches to identify proteomic differences between SCZ patients and healthy control groups (PROSPERO database: CRD42021274183). Nineteen articles fulfilled the inclusion criteria, allowing the identification of 217 differentially expressed proteins. Gene ontology analysis identified lipid metabolism, complement and coagulation cascades, and immune response as the main enriched biological pathways. Meta-analysis results suggest the upregulation of FCN3 and downregulation of APO1, APOA2, APOC1, and APOC3 in SCZ patients. Despite the proven ability of MS proteomics to characterize SCZ, several confounding factors contribute to the heterogeneity of the findings. In the future, we encourage the scientific community to perform studies with more extensive sampling and validation cohorts, integrating omics with bioinformatics tools to provide additional comprehension of differentially expressed proteins. The produced information could harbor potential proteomic biomarkers of SCZ, contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

Systematic Review and Meta-Analysis on MS-Based Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Bipolar Disorder.

Bipolar disorder (BD) is a clinically heterogeneous condition, presenting a complex underlying etiopathogenesis that is not sufficiently characterized. Without molecular biomarkers being used in the clinical environment, several large screen proteomics studies have been conducted to provide valuable molecular information. Mass spectrometry (MS)-based techniques can be a powerful tool for the identification of disease biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids to assess BD biomarkers and identify relevant networks of biological pathways. Following PRISMA guidelines, we searched for studies using MS proteomics to identify proteomic differences between BD patients and healthy controls (PROSPERO database: CRD42021264955). Fourteen articles fulfilled the inclusion criteria, allowing the identification of 266 differentially expressed proteins. Gene ontology analysis identified complement and coagulation cascades, lipid and cholesterol metabolism, and focal adhesion as the main enriched biological pathways. A meta-analysis was performed for apolipoproteins (A-I, C-III, and E); however, no significant differences were found. Although the proven ability of MS proteomics to characterize BD, there are several confounding factors contributing to the heterogeneity of the findings. In the future, we encourage the scientific community to use broader samples and validation cohorts, integrating omics with bioinformatics tools towards providing a comprehensive understanding of proteome alterations, seeking biomarkers of BD, and contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

A rare functional noncoding variant at the GWAS-implicated MIR137/MIR2682 locus might confer risk to schizophrenia and bipolar disorder.

Schizophrenia (SZ) genome-wide association studies (GWASs) have identified common risk variants in >100 susceptibility loci; however, the contribution of rare variants at these loci remains largely unexplored. One of the strongly associated loci spans MIR137 (miR137) and MIR2682 (miR2682), two microRNA genes important for neuronal function. We sequenced ∼6.9 kb MIR137/MIR2682 and upstream regulatory sequences in 2,610 SZ cases and 2,611 controls of European ancestry. We identified 133 rare variants with minor allele frequency (MAF) <0.5%. The rare variant burden in promoters and enhancers, but not insulators, was associated with SZ (p = 0.021 for MAF < 0.5%, p = 0.003 for MAF < 0.1%). A rare enhancer SNP, 1:g.98515539A>T, presented exclusively in 11 SZ cases (nominal p = 4.8 × 10(-4)). We further identified its risk allele T in 2 of 2,434 additional SZ cases, 11 of 4,339 bipolar (BP) cases, and 3 of 3,572 SZ/BP study controls and 1,688 population controls; yielding combined p values of 0.0007, 0.0013, and 0.0001 for SZ, BP, and SZ/BP, respectively. The risk allele T of 1:g.98515539A>T reduced enhancer activity of its flanking sequence by >50% in human neuroblastoma cells, predicting lower expression of MIR137/MIR2682. Both empirical and computational analyses showed weaker transcription factor (YY1) binding by the risk allele. Chromatin conformation capture (3C) assay further indicated that 1:g.98515539A>T influenced MIR137/MIR2682, but not the nearby DPYD or LOC729987. Our results suggest that rare noncoding risk variants are associated with SZ and BP at MIR137/MIR2682 locus, with risk alleles decreasing MIR137/MIR2682 expression.

Paternal age effect: Replication in schizophrenia with intriguing dissociation between bipolar with and without psychosis.

Advanced paternal age (APA) is a risk factor for schizophrenia (Sz) and bipolar disorder (BP). Putative mechanisms include heritable genetic factors, de novo mutations, and epigenetic mechanisms. Few studies have explored phenotypic features associated with APA. The Genomic Psychiatry Cohort established a clinically characterized repository of genomic samples from subjects with a Sz-BP diagnosis or unaffected controls, 12,975 with parental age information. We estimated relative risk ratios for Sz, schizoaffective depressed and bipolar types (SA-D, SA-B), and BP with and without history of psychotic features (PF) relative to the control group, comparing each paternal age group to the reference group 20-24 years. All tests were two-sided with adjustment for multiple comparisons. Subjects with fathers age 45+ had significantly higher risk for all diagnoses except for BP w/o PF. APA also bore no significant relation to family psychiatric history. In conclusion, we replicated APA as a risk factor for Sz. To our knowledge, this is the first published report of APA in a BP sample stratified by psychosis history, extending this association only in BP w/PF. This suggests that phenotypic expression of the APA effect in Sz-BP spectrum is psychosis, per se, rather than other aspects of these complex disorders. The lack of a significant relationship between paternal age and familial disease patterns suggests that underlying mechanisms of the paternal age effect may involve a complex interaction of heritable and non-heritable factors. The authors discuss implications and testable hypotheses, starting with a focus on genetic mechanisms and endophenotypic expressions of dopaminergic function. © 2015 Wiley Periodicals, Inc.

Symptoms of psychosis in schizophrenia, schizoaffective disorder, and bipolar disorder: A comparison of African Americans and Caucasians in the Genomic Psychiatry Cohort.

Several studies have reported differences between African Americans and Caucasians in relative proportion of psychotic symptoms and disorders, but whether this reflects racial bias in the assessment of psychosis is unclear. The purpose of this study was to examine the distribution of psychotic symptoms and potential bias in symptoms assessed via semi-structured interview using a cohort of 3,389 African American and 5,692 Caucasian participants who were diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder. In this cohort, the diagnosis of schizophrenia was relatively more common, and the diagnosis of bipolar disorder and schizoaffective disorder-bipolar type was less relatively common, among African Americans than Caucasians. With regard to symptoms, relatively more African Americans than Caucasians endorsed hallucinations and delusions symptoms, and this pattern was striking among cases diagnosed with bipolar disorder and schizoaffective-bipolar disorder. In contrast, the relative endorsement of psychotic symptoms was more similar among cases diagnosed with schizophrenia and schizoaffective disorder-depressed type. Differential item function analysis revealed that African Americans with mild psychosis over-endorsed "hallucinations in any modality" and under-endorsed "widespread delusions" relative to Caucasians. Other symptoms did not show evidence of racial bias. Thus, racial bias in assessment of psychotic symptoms does not appear to explain differences in the proportion of symptoms between Caucasians and African Americans. Rather, this may reflect ascertainment bias, perhaps indicative of a disparity in access to services, or differential exposure to risk factors for psychosis by race. © 2015 Wiley Periodicals, Inc.

Nicotine dependence and psychosis in Bipolar disorder and Schizoaffective disorder, Bipolar type.

Patients with Bipolar disorder smoke more than the general population. Smoking negatively impacts mortality and clinical course in Bipolar disorder patients. Prior studies have shown contradictory results regarding the impact of psychosis on smoking behavior in Bipolar disorder. We analyzed a large sample of Bipolar disorder and Schizoaffective disorder, Bipolar Type patients and predicted those with a history of psychosis would be more likely to be nicotine dependent. Data from subjects and controls were collected from the Genomic Psychiatry Cohort (GPC). Subjects were diagnosed with Bipolar disorder without psychosis (N = 610), Bipolar disorder with psychosis (N = 1544). Participants were classified with or without nicotine dependence. Diagnostic groups were compared to controls (N = 10065) using logistic regression. Among smokers (N = 6157), those with Bipolar disorder had an increased risk of nicotine dependence (OR = 2.5; P < 0.0001). Patients with Bipolar disorder with psychosis were more likely to be dependent than Bipolar disorder patients without psychosis (OR = 1.3; P = 0.03). Schizoaffective disorder, Bipolar Type patients had more risk of nicotine dependence when compared to Bipolar disorder patients with or without psychosis (OR = 1.2; P = 0.02). Bipolar disorder patients experiencing more severity of psychosis have more risk of nicotine dependence. © 2015 Wiley Periodicals, Inc.

Substance use associated with short sleep duration in patients with schizophrenia or schizoaffective disorder.

STUDY OBJECTIVES: To examine the association between substance use and short sleep duration in individuals with schizophrenia or schizoaffective disorder, depressive type (SADD). DESIGN: Cross-sectional, retrospective study. SETTING: Urban, suburban, and rural centers across the United States. PARTICIPANTS: 2,462 consented, adult individuals with schizophrenia or schizoaffective disorder, depressive type (SADD). Participants included inpatients in acute or chronic care settings as well as outpatients and residents in community dwellings. MEASUREMENTS: Substance use was assessed with 10 questions adopted from well-validated measures (e.g., CAGE questionnaire) for alcohol, marijuana, and illicit drugs. Short sleep duration was defined as <6 hr of self-reported sleep per night. RESULTS: Close to 100% of our sample used nicotine while 83% used substances other than nicotine. More importantly, there was a significant association between substance use and short sleep duration. Interestingly, this association was strongest among African-Americans with schizophrenia or SADD. CONCLUSIONS: Because psychiatric medications often target chemical receptors involved with both sleep and substance use, understanding the association between short sleep duration and substance use in individuals with schizophrenia and SADD is important. Given that the majority of premature deaths in individuals with psychotic illness are due to medical conditions associated with modifiable risk factors, prospective studies designed to examine the effect of short sleep duration on behaviors like substance use should be undertaken. Finally, analyzing genetic and environmental data in a future study might help illuminate the strong association found between short sleep duration and substance use in African-Americans with schizophrenia and SADD. © 2015 Wiley Periodicals, Inc.

Genome-wide association study reveals greater polygenic loading for schizophrenia in cases with a family history of illness.

Genome-wide association studies (GWAS) of schizophrenia have yielded more than 100 common susceptibility variants, and strongly support a substantial polygenic contribution of a large number of small allelic effects. It has been hypothesized that familial schizophrenia is largely a consequence of inherited rather than environmental factors. We investigated the extent to which familiality of schizophrenia is associated with enrichment for common risk variants detectable in a large GWAS. We analyzed single nucleotide polymorphism (SNP) data for cases reporting a family history of psychotic illness (N = 978), cases reporting no such family history (N = 4,503), and unscreened controls (N = 8,285) from the Psychiatric Genomics Consortium (PGC1) study of schizophrenia. We used a multinomial logistic regression approach with model-fitting to detect allelic effects specific to either family history subgroup. We also considered a polygenic model, in which we tested whether family history positive subjects carried more schizophrenia risk alleles than family history negative subjects, on average. Several individual SNPs attained suggestive but not genome-wide significant association with either family history subgroup. Comparison of genome-wide polygenic risk scores based on GWAS summary statistics indicated a significant enrichment for SNP effects among family history positive compared to family history negative cases (Nagelkerke's R(2 ) = 0.0021; P = 0.00331; P-value threshold <0.4). Estimates of variability in disease liability attributable to the aggregate effect of genome-wide SNPs were significantly greater for family history positive compared to family history negative cases (0.32 and 0.22, respectively; P = 0.031). We found suggestive evidence of allelic effects detectable in large GWAS of schizophrenia that might be specific to particular family history subgroups. However, consideration of a polygenic risk score indicated a significant enrichment among family history positive cases for common allelic effects. Familial illness might, therefore, represent a more heritable form of schizophrenia, as suggested by previous epidemiological studies.

Scaled and Efficient Derivation of Loss of Function Alleles in Risk Genes for Neurodevelopmental and Psychiatric Disorders in Human iPSC.

Translating genetic findings for neurodevelopmental and psychiatric disorders (NPD) into actionable disease biology would benefit from large-scale and unbiased functional studies of NPD genes. Leveraging the cytosine base editing (CBE) system, here we developed a pipeline for clonal loss-of-function (LoF) allele mutagenesis in human induced pluripotent stem cells (hiPSCs) by introducing premature stop-codons (iSTOP) that lead to mRNA nonsense-mediated-decay (NMD) or protein truncation. We tested the pipeline for 23 NPD genes on 3 hiPSC lines and achieved highly reproducible, efficient iSTOP editing in 22 NPD genes. Using RNAseq, we confirmed their pluripotency, absence of chromosomal abnormalities, and NMD. Interestingly, for three schizophrenia risk genes (SETD1A, TRIO, CUL1), despite the high efficiency of base editing, we only obtained heterozygous LoF alleles, suggesting their essential roles for cell growth. We replicated the reported neural phenotypes of SHANK3-haploinsufficiency and found CUL1-LoF reduced neurite branches and synaptic puncta density. This iSTOP pipeline enables a scaled and efficient LoF mutagenesis of NPD genes, yielding an invaluable shareable resource.

Biological Insights from Schizophrenia-associated Loci in Ancestral Populations.

Large-scale genome-wide association studies of schizophrenia have uncovered hundreds of associated loci but with extremely limited representation of African diaspora populations. We surveyed electronic health records of 200,000 individuals of African ancestry in the Million Veteran and All of Us Research Programs, and, coupled with genotype-level data from four case-control studies, realized a combined sample size of 13,012 affected and 54,266 unaffected persons. Three genome-wide significant signals - near PLXNA4 , PMAIP1 , and TRPA1 - are the first to be independently identified in populations of predominantly African ancestry. Joint analyses of African, European, and East Asian ancestries across 86,981 cases and 303,771 controls, yielded 376 distinct autosomal loci, which were refined to 708 putatively causal variants via multi-ancestry fine-mapping. Utilizing single-cell functional genomic data from human brain tissue and two complementary approaches, transcriptome-wide association studies and enhancer-promoter contact mapping, we identified a consensus set of 94 genes across ancestries and pinpointed the specific cell types in which they act. We identified reproducible associations of schizophrenia polygenic risk scores with schizophrenia diagnoses and a range of other mental and physical health problems. Our study addresses a longstanding gap in the generalizability of research findings for schizophrenia across ancestral populations, underlining shared biological underpinnings of schizophrenia across global populations in the presence of broadly divergent risk allele frequencies.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Background: Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD). Aims: Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis. Methods: Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses. Results: While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD. Conclusions: We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P(meta)<1 × 10⁻4, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.

Assessing the impact of population stratification on genetic association studies.

Population stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease. It has been proposed that false positive associations due to stratification can be controlled by genotyping a few dozen unlinked genetic markers. To assess stratification empirically, we analyzed data from 11 case-control and case-cohort association studies. We did not detect statistically significant evidence for stratification but did observe that assessments based on a few dozen markers lack power to rule out moderate levels of stratification that could cause false positive associations in studies designed to detect modest genetic risk factors. After increasing the number of markers and samples in a case-cohort study (the design most immune to stratification), we found that stratification was in fact present. Our results suggest that modest amounts of stratification can exist even in well designed studies.

Association study of 83 candidate genes for bipolar disorder in chromosome 6q selected using an evidence-based prioritization algorithm.

BACKGROUND: Prior genome-scans of bipolar disorder have revealed chromosome 6q22 as a promising candidate region. However, linkage disequilibrium (LD) mapping studies have yet to identify replicated susceptibility loci. METHODS: We analyzed 1,422 LD-tagging single nucleotide polymorphisms (SNPs) in 83 genes to test single-marker and locus-wide evidence of association with bipolar disorder in the NIMH Genetics Initiative bipolar pedigrees and the Portuguese Island Collection (PIC) (N = 1,093 in 528 informative pairs). Both studies previously demonstrated significant evidence of linkage to 6q. SNPs were genotyped using an Illumina iSelect genotyping array which employs the Infinium assay. Evidence of single-marker association was assessed using the generalized disequilibrium test (GDT). Empirical estimates of gene-wide significance were obtained by permutation (via 100,000 gene-dropping simulations) of Fisher's combined test of P-values for each locus. RESULTS: No single variant yielded significant experiment-wide evidence of association, for either the combined sample or in each subsample. Our gene-dropping simulations identified nominally significant gene-wide associations with multiple loci, of which NT5DC1 in the NIMH subsample and CCNC in the PIC were the strongest candidates. However, no one gene consistently exceeded empirical significance criteria in both independent samples or survived Bonferroni correction for the number of genes tested. CONCLUSIONS: Using a gene-based approach to family-based association, we identified gene-wide associations with several genes, though no single locus was significantly associated with bipolar disorder in both cohorts. This suggests that chromosome 6q may harbor multiple susceptibility loci or that complex patterns of LD in this region may confound approaches based on common SNPs. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

The genomic psychiatry cohort: partners in discovery.

The Genomic Psychiatry Cohort (GPC) is a longitudinal resource designed to provide the necessary population-based sample for large-scale genomic studies, studies focusing on Research Domain Criteria (RDoC) and/or other alternate phenotype constructs, clinical and interventional studies, nested case-control studies, long-term disease course studies, and genomic variant-to-phenotype studies. We provide and will continue to encourage access to the GPC as an international resource. DNA and other biological samples and diagnostic data are available through the National Institute of Mental Health (NIMH) Repository. After appropriate review and approval by an advisory board, investigators are able to collaborate in, propose, and co-lead studies involving cohort participants.