Summary Background Findings from family and twin studies suggest that genetic contributions to psychiatric disorders do not in all cases map to present diagnostic categories. We aimed to identify specific variants underlying genetic effects shared between the five disorders in the Psychiatric Genomics Consortium: autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia. Methods We analysed genome-wide single-nucleotide polymorphism (SNP) data for the five disorders in 33 332 cases and 27 888 controls of European ancestory. To characterise allelic effects on each disorder, we applied a multinomial logistic regression procedure with model selection to identify the best-fitting model of relations between genotype and phenotype. We examined cross-disorder effects of genome-wide significant loci previously identified for bipolar disorder and schizophrenia, and used polygenic risk-score analysis to examine such effects from a broader set of common variants. We undertook pathway analyses to establish the biological associations underlying genetic overlap for the five disorders. We used enrichment analysis of expression quantitative trait loci (eQTL) data to assess whether SNPs with cross-disorder association were enriched for regulatory SNPs in post-mortem brain-tissue samples. Findings SNPs at four loci surpassed the cutoff for genome-wide significance (p<5×10−8) in the primary analysis: regions on chromosomes 3p21 and 10q24, and SNPs within two L-type voltage-gated calcium channel subunits, CACNA1C and CACNB2. Model selection analysis supported effects of these loci for several disorders. Loci previously associated with bipolar disorder or schizophrenia had variable diagnostic specificity. Polygenic risk scores showed cross-disorder associations, notably between adult-onset disorders. Pathway analysis supported a role for calcium channel signalling genes for all five disorders. Finally, SNPs with evidence of cross-disorder association were enriched for brain eQTL markers. Interpretation Our findings show that specific SNPs are associated with a range of psychiatric disorders of childhood onset or adult onset. In particular, variation in calcium-channel activity genes seems to have pleiotropic effects on psychopathology. These results provide evidence relevant to the goal of moving beyond descriptive syndromes in psychiatry, and towards a nosology informed by disease cause. Funding National Institute of Mental Health.

The Functional Single Nucleotide Polymorphism (F-SNP) database integrates information obtained from 16 bioinformatics tools and databases about the functional effects of SNPs. These effects are predicted and indicated at the splicing, transcriptional, translational and post-translational level. As such, the database helps identify and focus on SNPs with potential deleterious effect to human health. In particular, users can retrieve SNPs that disrupt genomic regions known to be functional, including splice sites and transcriptional regulatory regions. Users can also identify non-synonymous SNPs that may have deleterious effects on protein structure or function, interfere with protein translation or impede post-translational modification. A web interface enables easy navigation for obtaining information through multiple starting points and exploration routes (e.g. starting from SNP identifier, genomic region, gene or target disease). The F-SNP database is available at http://compbio.cs.queensu.ca/F-SNP/.

Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide single-nucleotide polymorphism (SNP) data for asthma and allergic diseases in 33,593 cases and 76,768 controls of European ancestry from the UK Biobank. Two publicly available independent genome wide association studies (GWAS) were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg = 0.75, P = 6.84×10−62). Cross trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance our understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases.

Individual differences in affective and social processes may arise from variability in amygdala-medial prefrontal (mPFC) circuitry and related genetic heterogeneity. To explore this possibility in humans, we examined the structural correlates of trait negative affect in a sample of 1050 healthy young adults with no history of psychiatric illness. Analyses revealed that heightened negative affect was associated with increased amygdala volume and reduced thickness in a left mPFC region encompassing the subgenual and rostral anterior cingulate cortex. The most extreme individuals displayed an inverse correlation between amygdala volume and mPFC thickness, suggesting that imbalance between these structures is linked to negative affect in the general population. Subgroups of participants were further evaluated on social (n = 206) and emotional (n = 533) functions. Individuals with decreased mPFC thickness exhibited the poorest social cognition and were least able to correctly identify facial emotion. Given prior links between disrupted amygdala–mPFC circuitry and the presence of major depressive disorder (MDD), we explored whether the individual differences in anatomy observed here in healthy young adults were associated with polygenic risk for MDD (n = 438) using risk scores derived from a large genome-wide association analysis (n = 18,759). Analyses revealed associations between increasing polygenic burden for MDD and reduced cortical thickness in the left mPFC. These collective findings suggest that, within the healthy population, there is significant variability in amygdala–mPFC circuitry that is associated with poor functioning across affective and social domains. Individual differences in this circuitry may arise, in part, from common genetic variability that contributes to risk for MDD.

Late-onset Alzheimer's disease (AD) is 50-70% heritable with complex genetic underpinnings. In addition to Apoliprotein E (APOE) ε4, the major genetic risk factor, recent genome-wide association studies (GWAS) have identified a growing list of sequence variations associated with the disease. Building on a prior large-scale AD GWAS, we used a recently developed analytic method to compute a polygenic score that involves up to 26 independent common sequence variants and is associated with AD dementia, above and beyond APOE. We then examined the associations between the polygenic score and the magnetic resonance imaging-derived thickness measurements across AD-vulnerable cortex in clinically normal (CN) human subjects (N = 104). AD-specific cortical thickness was correlated with the polygenic risk score, even after controlling for APOE genotype and cerebrospinal fluid (CSF) levels of β-amyloid (Aβ(1-42)). Furthermore, the association remained significant in CN subjects with levels of CSF Aβ(1-)(42) in the normal range and in APOE ε3 homozygotes. The observation that genetic risk variants are associated with thickness across AD-vulnerable regions of interest in CN older individuals, suggests that the combination of polygenic risk profile, neuroimaging, and CSF biomarkers may hold synergistic potential to aid in the prediction of future cognitive decline.

Motivation: Identifying single nucleotide polymorphisms (SNPs) that underlie common and complex human diseases, such as cancer, is of major interest in current molecular epidemiology. Nevertheless, the tremendous number of SNPs on the human genome requires computational methods for prioritizing SNPs according to their potentially deleterious effects to human health, and as such, for expediting genotyping and analysis. As of yet, little has been done to quantitatively assess the possible deleterious effects of SNPs for effective association studies. Results: We propose a new integrative scoring system for prioritizing SNPs based on their possible deleterious effects within a probabilistic framework. We applied our system to 580 diseasesusceptibility genes obtained from the OMIM (Online Mendelian Inheritance in Man) database, which is one of the most widely used databases of human genes and genetic disorders. The scoring results clearly show that the distribution of the functional significance (FS) scores for already known disease-related SNPs is significantly different from that of neutral SNPs. In addition, we summarize distinct features of potentially deleterious SNPs based on their FS score, such as functional genomic regions where they occur or biomolecular functions that they mainly affect. We also demonstrate, through a comparative study, that our system improves upon other function-assessment systems for SNPs, by assigning significantly higher FS scores to already known disease-related SNPs than to neutral SNPs.

Historically, bipolar disorder and schizophrenia have been considered distinct disorders with different etiologies. Growing evidence suggests that overlapping genetic influences contribute to risk for these disorders and that each disease is genetically heterogeneous. Using cluster analytic methods, we empirically identified homogeneous subgroups of patients, their relatives, and controls based on distinct neurophysiologic profiles. Seven phenotypes were collected from two independent cohorts at two institutions. K-means clustering was used to identify neurophysiologic profiles. In the analysis of all participants, three distinct profiles emerged: “globally impaired”, “sensory processing”, and “high cognitive”. In a secondary analysis, restricted to patients only, we observed a similar clustering into three profiles. The neurophysiological profiles of the SZ and BPD patients did not support the DSM diagnostic distinction between these two disorders. Smokers in the globally impaired group smoked significantly more cigarettes than those in the sensory processing or high cognitive groups. Our results suggest that empirical analyses of neurophysiological phenotypes can identify potentially biologically relevant homogenous subgroups independent of diagnostic boundaries. We hypothesize that each neurophysiology subgroup may share similar genotypic profiles, which may increase statistical power to detect genetic risk factors.

We propose a novel method to infer genetic networks by alleviating the shortage of available mRNA expression data with prior knowledge. We call the proposed method 'modularized network learning' (MONET). Firstly, the proposed method divides a whole gene set to overlapped modules considering biological annotations and expression data together. Secondly, it infers a Bayesian network for each module, and integrates the learned subnetworks to a global network. An algorithm that measures a similarity between genes based on hierarchy, specificity and multiplicity of biological annotations is presented. The proposed method draws a global picture of inter-module relationships as well as a detailed look of intra-module interactions. We applied the proposed method to analyze Saccharomyces cerevisiae stress data, and found several hypotheses to suggest putative functions of unclassified genes. We also compared the proposed method with a whole-set-based approach and two expression-based clustering approaches.