Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

/pdf/the-string-database-in-2017-quality-controlled-protein-1u6rx6raii.pdf

The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Genetics and analysis of quantitative traits

The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodellers-most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.

/pdf/synaptic-transcriptional-and-chromatin-genes-disrupted-in-2xzlntcq5u.pdf

Synaptic, transcriptional and chromatin genes disrupted in autism

Introduction: Sexual assault is an urgent public health concern with both immediate and long-lasting health consequences, affecting 44% of women and 25% of men during their lifetimes. Large studies are needed to understand the unique healthcare needs of this patient population. Methods: We mined clinical notes to identify patients with a history of sexual assault in the electronic health record (EHR) at Vanderbilt University Medical Center (VUMC), a large university hospital in the Southeastern USA, from 1989 to 2021 (N = 3,376,424). Using a phenome-wide case-control study, we identified diagnoses co-occurring with disclosures of sexual assault. We performed interaction tests to examine whether sex modified any of these associations. Association analyses were restricted to a subset of patients receiving regular care at VUMC (N = 833,185). Results: The phenotyping approach identified 14,496 individuals (0.43%) across the VUMC-EHR with documentation of sexual assault and achieved a positive predictive value of 93.0% (95% confidence interval = 85.6–97.0%), determined by manual patient chart review. Out of 1,703 clinical diagnoses tested across all subgroup analyses, 465 were associated with sexual assault. Sex-by-trauma interaction analysis revealed 55 sex-differential associations and demonstrated increased odds of psychiatric diagnoses in male survivors. Discussion: This case-control study identified associations between disclosures of sexual assault and hundreds of health conditions, many of which demonstrated sex-differential effects. The findings of this study suggest that patients who have experienced sexual assault are at risk for developing wide-ranging medical and psychiatric comorbidities and that male survivors may be particularly vulnerable to developing mental illness.

Sex Differences in Health Conditions Associated with Sexual Assault in a Large Hospital Population

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/humu.21572

In Search of Genomic Stability: Characterizing Copy Number Stable Regions

Testing the association between genetic scores for Attention Deficit Hyperactivity Disorder (ADHD) and health conditions, can help us better understand its complex etiology. Electronic health records linked to genetic data provide an opportunity to test whether genetic scores for ADHD correlate with ADHD and additional health outcomes in a health care context across different age groups. We generated polygenic scores (ADHD‐PGS) trained on summary statistics from the latest genome‐wide association study of ADHD (N = 55,374) and applied them to genome‐wide data from 12,383 unrelated individuals of African‐American ancestry and 66,378 unrelated individuals of European ancestry from the Vanderbilt Biobank. Overall, only Tobacco use disorder (TUD) was associated with ADHD‐PGS in the African‐American ancestry group (Odds ratio [95% confidence intervals] = 1.23[1.16–1.31], p = 9.3 × 10−09). Eighty‐six phenotypes were associated with ADHD‐PGS in the European ancestry individuals, including ADHD (OR[95%CIs] = 1.22[1.16–1.29], p = 3.6 × 10−10), and TUD (OR[95%CIs] = 1.22[1.19–1.25], p = 2.8 × 10−46). We then stratified outcomes by age (ages 0–11, 12–18, 19–25, 26–40, 41–60, and 61–100). Our results suggest that ADHD polygenic scores are associated with ADHD diagnoses early in life and with an increasing number of health conditions throughout the lifespan (even in the absence of ADHD diagnosis). This study reinforces the utility of applying trait‐specific PGSs to biobank data, and performing exploratory sensitivity analyses, to probe relationships among clinical conditions.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ajmg.b.32911

A phenome‐wide association study of polygenic scores for attention deficit hyperactivity disorder across two genetic ancestries in electronic health record data

Importance: Depression and anxiety are common and highly comorbid, posing a clinical and public health concern because such comorbidity is associated with poorer outcomes. Objective: To evaluate association of genetic risk scores with depression and anxiety diagnosis either in isolation or comorbid with each other. Design: International Classification of Diseases (ICD) ninth and tenth edition codes were extracted from longitudinal electronic health records (EHR) from four EHR-linked biobanks with genetic data available. Data analysis was performed between February 2021 to October 2021. Setting: EHR-linked biorepository study. Participants: Across the four biobanks, 140947 patients (80601 female [57.2%] including 109592 European ancestry [77.8%], 22321 African ancestry [15.8%], and 9034 Hispanic [6.4%]) were included in the study. Main outcomes and measures: Polygenic risk scores (PRS) for depression and anxiety were computed for all participants. They were assessed for diagnosis of depression and anxiety using ICD9/10 codes. The primary outcome was a four-level depression/anxiety diagnosis group variable: neither, depression-only, anxiety-only, and comorbid. Estimated effect measures include odds ratios and the proportion of variance on the liability scale explained by the PRS. Results: 95992 patients had neither diagnosis (68.1%), 14918 depression-only (10.6%), 12682 anxiety-only (9.0%), and 17355 comorbid (12.3%). PRS for depression and anxiety predicted their respective diagnoses within each biobank and each ancestry with the exception of anxiety-PRS not predicting anxiety in any ancestral group from one biobank. In the meta-analysis across participants of European ancestries, both PRSs for depression and anxiety were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR=1.20 per SD increase in PRS; 95% CI= 1.18-1.23) and anxiety-PRS (OR=1.07; 95% CI=1.05-1.09) had the largest effect size for the comorbid group when compared to controls. The confidence interval for the depression-PRS effect did not overlap across groups demonstrating a gradient of genetic risk across the four diagnosis groups. Conclusions and Relevance: The genetic risk of depression and anxiety make distinct contributions to the risk of comorbid depression and anxiety, supporting the hypothesis that the correlated disorders represent distinct nosological entities.

The contribution of genetic risk to the comorbidity of depression and anxiety: a multi-site electronic health records study

Background: Sleep problems are common in individuals with autism spectrum disorder (ASD). This study reviews one detailed approach to querying the electronic health record (EHR).
Methods: We developed methods for identifying individuals with ASD and defined their sleep problems using International Classification of Diseases (ICD) codes or key words. We examined treatment responses to melatonin.
Results: Sleep problems were documented in 86% of patients and using specific keywords yielded more sleep diagnoses than ICD codes alone. About two-thirds of patients benefitted from melatonin.
Conclusions: Our study provides a framework for using deidentified medical records to characterize sleep, a common co-occurring condition, in ASD. Using specific keywords could be helpful in future work that queries the EHR.

https://medrxiv.org/content/10.1101/2021.10.29.21265659v1.full.pdf

Lea K. Davis

Papers

Sex Differences in Health Conditions Associated with Sexual Assault in a Large Hospital Population

In Search of Genomic Stability: Characterizing Copy Number Stable Regions

A phenome‐wide association study of polygenic scores for attention deficit hyperactivity disorder across two genetic ancestries in electronic health record data

The contribution of genetic risk to the comorbidity of depression and anxiety: a multi-site electronic health records study

Characterizing Sleep Disorders in an Autism-Specific Collection of Electronic Health Records