Aiden Corvin

Most genetic variants associated with complex heritability phenotypes lie in non-coding regions and are thought to influence disease risk by regulating gene expression. However, most transcriptome-wide association approaches primarily model local ( cis ) genetic effects, leaving much of gene regulation unexplained. Here, we show that incorporating distal ( trans ) regulatory effects improves the prediction of gene expression and the identification of disease-associated genes. Using RNA sequencing data from six human post-mortem brain regions, we developed INGENE and MODULE, two models capturing the combined influence of candidate trans- acting variants within gene coexpression networks. Integrating these models with conventional cis -based predictors improved gene expression imputation (maximum likelihood estimation, α = 0.05) for 18,744 genes across regions. Applying this framework to Psychiatric Genomics Consortium wave 3 genotypes identified 766 genes associated with schizophrenia ( P _FDR < 0.01), including 641 not previously reported by transcriptome-wide analyses. These findings highlight the contribution of distal regulatory mechanisms and gene network interactions to schizophrenia risk.

Obsessive compulsive disorder (OCD) is significantly heritable, but only a fraction of the contributory genetic variation has been identified, and the molecular etiology involved remains obscure. Identifying rare contributory variants of large effect would be an important milestone in helping to elucidate the mechanisms involved. Analysis of densely affected pedigrees is a potentially useful strategy to bypass the sample size challenges of standard case-control approaches. Here we performed whole genome sequencing (WGS) of 25 individuals across two multiplex OCD pedigrees. We prioritised rare variants using a Bayesian inference approach which incorporates variant pathogenicity and co-segregation with OCD. In the first pedigree, we identified a highly deleterious missense variant in NPY5R , carried by the majority of affected individuals. This gene is brain-expressed and has previously been implicated in panic disorder and internet addiction GWAS studies. In the second pedigree, we identified a large deletion of DLGAP1 and a missense variant in MAPK8IP3 , that perfectly co-segregated in a specific branch of the family: both genes have previously been implicated in OCD and autism. Both genes contribute to a protein interaction network including ERBB4 and RAPGEF1 which we had previously identified in a large Tourette Syndrome pedigree. Our analysis suggests that both energy homeostasis and downstream signalling from the post-synaptic density may both be important avenues for future research.

Summary: BICEP is a Bayesian inference model that evaluates how likely a rare variant is to be causal for a genomic trait in pedigree-based analyses. The original prior model in BICEP was designed for single nucleotide variants only. Here, we have developed an extension of the prior models for more comprehensive genomic analysis to include indels and copy number variants. We benchmark the performance of these new priors and show comparable performance accuracy with the existing single nucleotide variant prior model. For copy number variants we evaluate four different input predictors to the models and recommend the best performing ones as the default. Availability and implementation: the updated prior models have been implemented in the current version of BICEP available from: https://github.com/cathaloruaidh/BICEP .

Symptom severity in schizophrenia has been repeatedly associated with thinner cortical gray matter. While global and regional white matter microstructure alterations in schizophrenia are well-documented, their association with clinical symptom severity remains unclear. As this is likely due to methodological heterogeneity across studies, we tested whether symptom severity in schizophrenia was associated with regional and global white matter alterations using standardized methods. We hypothesized that positive symptom severity would be associated with temporal white matter changes and that negative symptom severity would be associated with alterations in frontal white matter. Using a standardized fractional anisotropy (FA) analysis pipeline developed by the ENIGMA consortium, we conducted a meta-analysis of the association between white matter microstructure and symptom severity in n = 1025 (ages 16–68 years; 369 women/656 men) across 19 ENIGMA sites. Where significant heterogeneity was detected across sites, we examined whether variation in association strength between white matter microstructure and symptom severity was explained by duration of illness and/or current antipsychotic use. Positive symptom severity was significantly associated with white matter microstructure as measured using temporal lobe FA and global FA. Negative symptom severity showed no significant association with white matter microstructure as measured using frontal lobe FA or global FA. Significant heterogeneity across sites was observed for the negative symptom analysis, explained partly by duration of illness. Post-hoc exploratory analyses identified one site as disproportionately contributing to this heterogeneity, and when removed, negative symptom severity was significantly associated with both global and frontal FA. These findings support the view of schizophrenia as a disorder of brain connectivity, in a manner relevant to understanding variation in clinical symptom severity.

Multidisciplinary evidence support a neurodevelopmental origin for schizophrenia, yet the mechanistic translation of known risk factors remains poorly understood. In this study, we leverage multi-lineage, forebrain-patterned organoids derived from monozygotic twins discordant for schizophrenia, integrating single-cell transcriptomic and epigenomic profiling to uncover disease-associated gene expression and chromatin accessibility topics. By constructing fate probability maps, we identify an accelerated developmental trajectory emerging from a distinct, schizophrenia-associated radial glia cell state, and reveal unique interactions among schizophrenia risk genes through unbiased multimodal analyses. Further, we confirm that schizophrenia-enriched states persist in differentiated lineages and disrupt synaptic programs, accompanied by molecular and cellular phenotypes mimicking observed disease pathology. Collectively, our findings delineate an early disruption in forebrain development, providing novel mechanistic insights into the origins of schizophrenia risk.

Psychiatric conditions share common genes, but mechanisms that differentiate diagnoses remain unclear. We present a multidimensional framework for functional analysis of rare copy number variants (CNVs) across 6 diagnostic categories, including schizophrenia (SCZ), autism (ASD), bipolar disorder (BD), depression (MDD), PTSD, and ADHD (N = 574,965). Using gene-set burden analysis (GSBA), we tested duplication (DUP) and deletion (DEL) burden across 2,645 functional gene sets defined by the intersections of pathways, cell types, and cortical regions. While diagnoses converge on shared pathways, mixed-effects modeling revealed divergence of pathway effects by cell type, brain region, and gene dosage. Factor analysis identified latent dimensions aligned with clinical axes. A primary factor (F1) captured reciprocal dose-dependent effects of DUP and DEL in SCZ reflecting positive and negative effects in excitatory versus inhibitory neurons and association versus sensory cortex. SCZ and ASD were both strongly aligned with F1 but with opposing directionalities. Orthogonal factors highlighted neuronal versus non-neuronal effects in mood disorders (F2) and differential spatial distributions of DEL effects in ADHD and MDD (F3). High-impact CNVs at 16p11.2 and 22q11.2 were enriched for combinations of cell-type-specific genes involved in pathways consistent with our broader findings. These results reveal molecular and cellular mechanisms that are broadly shared across psychiatric traits but differ between diagnostic categories in context and directionality.

Bipolar disorder is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 bipolar disorder risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci and prioritized 17 likely causal SNPs for bipolar disorder. We mapped these SNPs to genes and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci and results from rare variant exome sequencing in bipolar disorder. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment, including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, FKBP2, RASGRP1, FURIN, FES, MED24 and THRA among others in bipolar disorder. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of bipolar disorder polygenic risk scores across diverse populations and present a high-throughput fine-mapping pipeline.

The National Institute of Drug Abuse convened a panel of scientists with expertise in substance use disorders (SUD) and genetic methodologies primarily to determine the feasibility of performing whole genome sequencing utilizing existing pedigree collections with a high density of SUD and psychiatric disorders. A major focus was on determining if there had been any successes in identifying genetic variants for complex traits in family‐based designs. Such information could provide assurance that whole genome sequencing might provide significant pay‐offs particularly in the pursuit of rare variants and copy number variants. An important goal was to discuss and evaluate optimal strategies for studying genetic variants in human samples. Specific topics were (a) to consider whether a smaller number of cases typically available in family studies versus the larger number available in biobanks can reveal unique information; (b) to identify potential gaps in information available in biobank data that might be supplemented with family data; (c) to consider the optimal SUD phenotypic definitions (e.g., quantity of use, problem‐oriented) and data collection instruments (self‐report or clinician administered) that are both practical and efficient to collect, and likely to provide important insights concerning prevention, intervention, and medication development. Conclusions reached by the panel included optimism about the successes that have occurred in the existing family studies ascertained to include densely affected pedigrees. Evaluation of methodologies led, overall, to a panel consensus that steps should be taken to utilize biobank collection in conjunction with family‐based investigations for optimal variant discovery.

Next-generation sequencing is widely applied to the investigation of pedigree data for gene discovery. However, identifying plausible disease-causing variants within a robust statistical framework is challenging. Here, we introduce BICEP: a Bayesian inference tool for rare variant causality evaluation in pedigree-based cohorts. BICEP calculates the posterior odds that a genomic variant is causal for a phenotype based on the variant cosegregation as well as a priori evidence such as deleteriousness and functional consequence. BICEP can correctly identify causal variants for phenotypes with both Mendelian and complex genetic architectures, outperforming existing methodologies. Additionally, BICEP can correctly down-weight common variants that are unlikely to be involved in phenotypic liability in the context of a pedigree, even if they have reasonable cosegregation patterns. The output metrics from BICEP allow for the quantitative comparison of variant causality within and across pedigrees, which is not possible with existing approaches.