Patrick F Sullivan

ObjectiveSETD1A encodes a histone methyltransferase involved in various cell cycle regulatory processes. Loss-of-function SETD1A variants have been associated with numerous neurodevelopmental phenotypes, including intellectual disability and schizophrenia. While the association between rare coding variants in SETD1A and schizophrenia has achieved genome-wide significance by rare variant burden testing, only a few studies have described the psychiatric phenomenology of such individuals in detail. This systematic review and case report aims to characterize the neurodevelopmental and psychiatric phenotypes of SETD1A variant-associated schizophrenia.MethodsA PubMed search was completed in July 2022 and updated in May 2023. Only studies that reported individuals with a SETD1A variant as well as a primary psychotic disorder were ultimately included. Additionally, another two previously …

Understanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets.

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification, and holds great potential in mental disorders. However, several important factors are needed to transform current practice into a “precision psychiatry” framework. Most important are (1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, (2) the development and validation of advanced analytical tools for stratification and prediction, and (3) the development of clinically useful management platforms for patient monitoring that can be integrated into healthcare systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements – well-powered samples from large biobanks, integrated with electronic health records and health registry data using novel artificial intelligence algorithms – to predict …

BackgroundThe study of biological age acceleration may help identify at-risk individuals and reduce the rising global burden of age-related diseases. Using DNA methylation (DNAm) clocks, we investigated biological aging in schizophrenia (SCZ), a mental illness that is associated with an increased prevalence of age-related disabilities and morbidities. In a whole blood DNAm sample of 1090 SCZ cases and 1206 controls across four European cohorts, we performed a meta-analysis of differential aging using three DNAm clocks (i.e., Hannum, Horvath, and Levine). To dissect how DNAm aging contributes to SCZ, we integrated information on duration of illness and SCZ polygenic risk, as well as stratified our analyses by chronological age and biological sex.ResultsWe found that blood-based DNAm aging is significantly altered in SCZ independent from duration of the illness since onset. We observed sex-specific …

Genome-wide association studies across diverse populations may help validate and confirm genetic contributions to risk of disease. We estimated the extent of population stratification as well as the predictive accuracy of polygenic scores (PGS) derived from European samples to a data set from India. We analysed 2685 samples from two data sets, a population neurodevelopmental study (cVEDA) and a hospital-based sample of bipolar affective disorder (BD) and obsessive-compulsive disorder (OCD). Genotyping was conducted using Illumina's Global Screening Array. Population structure was examined with principal component analysis (PCA), uniform manifold approximation and projection (UMAP), support vector machine (SVM) ancestry predictions, and admixture analysis. PGS were calculated from the largest available European discovery GWAS summary statistics for BD, OCD, and externalizing traits using two Bayesian methods that incorporate local linkage disequilibrium structures (PGS-CS-auto) and functional genomic annotations (SBayesRC). Our analyses reveal global and continental PCA overlap with other South Asian populations. Admixture analysis revealed a north-south genetic axis within India (FST 1.6%). The UMAP partially reconstructed the contours of the Indian subcontinent. The Bayesian PGS analyses indicates moderate-to-high predictive power for BD. This was despite the cross-ancestry bias of the discovery GWAS dataset, with the currently available data. However, accuracy for OCD and externalizing traits was much lower. The predictive accuracy was perhaps influenced by the sample size of the discovery …

Internet-delivered cognitive behavioural therapy (ICBT) is an effective and accessible treatment for mild to moderate depression and anxiety disorders. However, up to 50% of patients do not experience sufficient symptom relief. Identifying patient characteristics predictive of higher post-treatment symptom severity is crucial for devising personalized interventions to avoid treatment failures and reduce healthcare costs. Using the new Swedish multimodal database MULTI-PSYCH, we expand upon established predictors of treatment outcome and assess the added benefit of utilizing polygenic risk scores (PRS) and nationwide register data in a combined sample of 2668 patients treated with ICBT for major depressive disorder (n= 1300), panic disorder (n= 727), and social anxiety disorder (n= 641). We present two linear regression models: a baseline model using six well-established predictors and a full model incorporating six clinic-based, 32 register-based predictors, and PRS for seven psychiatric disorders and traits. First, we assessed predictor importance through bivariate associations and then compared the models based on the proportion of variance explained in post-treatment scores. Our analysis identified several novel predictors of higher post-treatment severity, including comorbid ASD and ADHD, receipt of financial benefits, and prior use of some psychotropic medications. The baseline model explained 27% of the variance in post-treatment symptom scores, while the full model offered a modest improvement, explaining 34%. Developing a machine learning model that can capture complex non-linear associations and interactions …

Vocal production learning (“vocal learning”) is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical, and neurophysiological data from the Egyptian fruit bat (Rousettus aegyptiacus) with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal motor cortical region in the Egyptian fruit bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning …

CRISPR epigenomic editing technologies enable functional interrogation of non-coding elements. However, current computational methods for guide RNA (gRNA) design do not effectively predict the power potential, molecular and cellular impact to optimize for efficient gRNAs, which are crucial for successful applications of these technologies. We present "launch-dCas9" (machine LeArning based UNified CompreHensive framework for CRISPR-dCas9) to predict gRNA impact from multiple perspectives, including cell fitness, wildtype abundance (gauging power potential), and gene expression in single cells. Our launchdCas9, built and evaluated using experiments involving >1 million gRNAs targeted across the human genome, demonstrates relatively high prediction accuracy (AUC up to 0.81) and generalizes across cell lines. Method-prioritized top gRNA(s) are 4.6-fold more likely to exert effects, compared to other gRNAs in the same cis-regulatory region. Furthermore, launchdCas9 identifies the most critical sequence-related features and functional annotations from >40 features considered. Our results establish launch-dCas9 as a promising approach to design gRNAs for CRISPR epigenomic experiments.

Background:Individuals with mental illness are at higher risk of severe COVID-19 outcomes. However, previous studies on the uptake of COVID-19 vaccination in this population have reported conflicting results. Therefore, we aimed to investigate the association between mental illness and COVID-19 vaccination uptake, using data from five countries.Methods:Data from seven cohort studies (N= 325,298), and the Swedish registers (8,080,234), were used to identify mental illness and COVID-19 vaccination uptake. Multivariable modified Poisson regression models were conducted to calculate the prevalence ratio (PR) and 95% CIs of vaccination uptake among individuals with vs without mental illness. Results from the cohort studies were pooled using random effects meta-analyses.Findings:Most of the meta-analyses performed using the COVIDMENT study population showed no significant association between mental illness and vaccination uptake. In the Swedish register study population, we observed a very small reduction in the uptake of both the first (prevalence ratio [PR] 0.98, 95% CI 0.98-0.99, p< 0.001) and second dose among individuals with mental illness; the reduction was however greater among those not using pyschiatric medication (PR 0.91, 95% CI 0.91-0.91, p< 0.001).Conclusions:The high uptake of COVID-19 vaccination observed among individuals with most types of mental illness highlights the comprehensiveness of the vaccination campaign, however lower levels of vaccination uptake among subgroups of individuals with unmedicated mental illness warrants attention in future vaccination campaigns.

Bipolar disorder (BD) features heterogenous clinical presentation and course of illness. It remains unclear how subphenotypes associate with genetic loadings of BD and related psychiatric disorders. We investigated associations between the subphenotypes and polygenic risk scores (PRS) for BD, schizophrenia, and major depressive disorder (MDD) in two BD cohorts from Sweden (N = 5180) and the UK (N = 2577). Participants were assessed through interviews and medical records for inter-episode remission, psychotic features during mood episodes, global assessment of functioning (GAF, function and symptom burden dimensions), and comorbid anxiety disorders. Meta-analyses based on both cohorts showed that inter-episode remission and GAF-function were positively correlated with BD-PRS but negatively correlated with schizophrenia-PRS (SCZ-PRS) and MDD-PRS. Moreover, BD-PRS was …

Background It remains unknown why ~30% of patients with psychotic disorders fail to respond to treatment. Previous genomic investigations of treatment-resistant psychosis have been inconclusive, but some evidence suggests a possible link between rare disease-associated copy number variants (CNVs) and worse clinical outcomes in schizophrenia. Here, we identified schizophrenia-associated CNVs in patients with treatment-resistant psychotic symptoms and then compared the prevalence of these CNVs to previously published schizophrenia cases not selected for treatment resistance. Methods CNVs were identified using chromosomal microarray (CMA) and whole exome sequencing (WES) in 509 patients with treatment-resistant psychosis (a lack of clinical response to ≥3 adequate antipsychotic medication trials over at least 5 years of psychiatric hospitalization …

PurposeDepression and anxiety afflict millions worldwide causing considerable disability. MULTI-PSYCH is a longitudinal cohort of genotyped and phenotyped individuals with depression or anxiety disorders who have undergone highly structured internet-based cognitive-behaviour therapy (ICBT). The overarching purpose of MULTI-PSYCH is to improve risk stratification, outcome prediction and secondary preventive interventions. MULTI-PSYCH is a precision medicine initiative that combines clinical, genetic and nationwide register data.ParticipantsMULTI-PSYCH includes 2668 clinically well-characterised adults with major depressive disorder (MDD) (n=1300), social anxiety disorder (n=640) or panic disorder (n=728) assessed before, during and after 12 weeks of ICBT at the internet psychiatry clinic in Stockholm, Sweden. All patients have been blood sampled and genotyped. Clinical and genetic data have …

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.

BackgroundSuicidality, including suicidal thoughts and attempts, is elevated among individuals with anorexia nervosa (AN), as evidenced by an increased risk of suicide attempts and suicide being one of the leading causes of death in this population. Twin studies suggest that shared genetic factors may underlie the co-occurrence of AN and suicide.MethodsTo further explore the genetic factors that contribute to suicidality in AN, we computed three polygenic risk scores (PRS): for AN, suicide thoughts, and suicide attempts, and evaluated their associations with both ICD-based and self-reported suicidal behaviors in individuals with AN. Using data from the Swedish site of the Anorexia Nervosa Genetics Initiative (ANGI-SE) study comprising 3,189 AN cases born after 1977 with linkage to the Swedish National Registers, we examined the association of AN PRS, suicide thought PRS, and suicide attempt PRS on self …

PurposeDepression and anxiety afflict millions worldwide causing considerable disability. MULTI-PSYCH is a longitudinal cohort of genotyped and phenotyped individuals with depression or anxiety disorders who have undergone highly structured internet-based cognitive-behaviour therapy (ICBT). The overarching purpose of MULTI-PSYCH is to improve risk stratification, outcome prediction and secondary preventive interventions. MULTI-PSYCH is a precision medicine initiative that combines clinical, genetic and nationwide register data.

Genetic dissection of neuropsychiatric disorders can potentially reveal novel therapeutic targets. While genome-wide association studies (GWAS) have tremendously advanced our understanding, we approach a sample size bottleneck (ie, the number of cases needed to identify> 90% of all loci is impractical). Therefore, computationally enhancing GWAS on existing samples may be particularly valuable. Here, we describe DeepGWAS, a deep neural network-based method to enhance GWAS by integrating GWAS results with linkage disequilibrium and brain-related functional annotations. DeepGWAS enhanced schizophrenia (SCZ) loci by~ 3X when applied to the largest European GWAS, and 21.3% enhanced loci were validated by the latest multi-ancestry GWAS. Importantly, DeepGWAS models can be transferred to other neuropsychiatric disorders. Transferring SCZ-trained models to Alzheimer’s disease and …

This correlational study examined the different types of computer-based formative assessments (CBFA) being utilized, frequency of CBFA use, and differences in CBFA usage rates across specified constructs in middle and high schools located in Georgia. 261 middle school and high school academic teachers were provided a Qualtrics survey and descriptive statistics, an ANOVA, and correlations were utilized to analyse the data. Findings noted a positive correlation between CBFA usage rates and teacher comfort with technology and perceived benefit of using technology, and a negative relationship between teacher autonomy to select teaching methods and CBFA usage rates. Additionally, teacher beliefs about the needs of their students are impacting their decisions to use CBFA. Through building awareness of differences in CBFA usage, researchers recommend for school leaders to encourage professional learning that is purposeful, collaborative, and sustainable, which can address the different perceptions educators have about the implementation of instructional technology. Additionally, it is encouraged for teachers to have a voice in the selection of CBFA applications used with their students and incorporating administrative directive to use CBFA applications

Human accelerated regions (HARs) are conserved genomic loci that evolved at an accelerated rate in the human lineage and may underlie human-specific traits. We generated HARs and chimpanzee accelerated regions with an automated pipeline and an alignment of 241 mammalian genomes. Combining deep learning with chromatin capture experiments in human and chimpanzee neural progenitor cells, we discovered a significant enrichment of HARs in topologically associating domains containing human-specific genomic variants that change three-dimensional (3D) genome organization. Differential gene expression between humans and chimpanzees at these loci suggests rewiring of regulatory interactions between HARs and neurodevelopmental genes. Thus, comparative genomics together with models of 3D genome folding revealed enhancer hijacking as an explanation for the rapid evolution of HARs.

Species persistence can be influenced by the amount, type, and distribution of diversity across the genome, suggesting a potential relationship between historical demography and resilience. In this study, we surveyed genetic variation across single genomes of 240 mammals that compose the Zoonomia alignment to evaluate how historical effective population size (Ne) affects heterozygosity and deleterious genetic load and how these factors may contribute to extinction risk. We find that species with smaller historical Ne carry a proportionally larger burden of deleterious alleles owing to long-term accumulation and fixation of genetic load and have a higher risk of extinction. This suggests that historical demography can inform contemporary resilience. Models that included genomic data were predictive of species’ conservation status, suggesting that, in the absence of adequate census or ecological data, genomic …

In Canada, colonisation, both historic and ongoing, increases Indigenous vaccine hesitancy and the threat posed by infectious diseases. This research investigated Indigenous vaccine hesitancy in a First Nation community in Saskatchewan, ways it can be overcome, and the influence of a colonial history as well as modernity. Research followed Indigenous research methodologies, a community-based participatory research design, and used mixed methods. Social media posts (interventions) were piloted on a community Facebook page in January and February (2022). These interventions tested different messaging techniques in a search for effective strategies. The analysis that followed compared the number of likes and views of the different techniques to each other, a control post, and community-developed posts implemented by the community’s pandemic response team. At the end of the research, a sharing …