ADHD: Restricted Phenotypes Prevalence, Comorbidity, and Polygenic Risk Sensitivity in ABCD Baseline Cohort

Introduction. Estimates of prevalence and comorbidity of ADHD in the United States require additional national, multi-informant data. Further, it is unclear whether the polygenic, neurodevelopmental model of ADHD in DSM-5 is best modeled with a broad or restrictive phenotype definition. Method: In the Adolescent Behavior Cognition Development (ABCD) study baseline data on 9-10 year old children, ADHD prevalence, comorbidity, and association with cognitive functioning and polygenic risk were calculated at four thresholds of definition of ADHD phenotype restrictiveness using multiple measures and informants. Multi-indicator latent variable and composite scores were created and cross validated for ADHD symptoms and for irritability. Missing data, sample nesting, and sampling bias were corrected statistically. Results: Multi-informant estimate of ADHD prevalence by the most restrictive definition was 3.53% when restricted to children in which parent ratings and teacher ratings both converged with KSAD report of current ADHD. As stringency of the phenotype was increased, total comorbidity increased slightly, and associations with cognitive functioning and polygenic risk strengthened. Inclusion of children with past ADHD but now treated increased prevalence estimate without weakening detection of polygenic risk. Irritability and ADHD dimensional composite scores and latent variables achieved satisfactory model fit and expected external correlations. Conclusion: The present report strengthens estimates of ADHD prevalence and comorbidity. Research on polygenic and other correlates of ADHD as a clinical category in the ABCD sample may benefit from using a restrictive, multi-informant operational definition.

A dimensional perspective on the genetics of obsessive-compulsive disorder

This review covers recent findings in the genomics of obsessive-compulsive disorder (OCD), obsessive-compulsive symptoms, and related traits from a dimensional perspective. We focus on discoveries stemming from technical and methodological advances of the past five years and present a synthesis of human genomics research on OCD. On balance, reviewed studies demonstrate that OCD is a dimensional trait with a highly polygenic architecture and genetic correlations to multiple, often comorbid psychiatric phenotypes. We discuss the phenotypic and genetic findings of these studies in the context of the dimensional framework, relying on a continuous phenotype definition, and contrast these observations with discoveries based on a categorical diagnostic framework, relying on a dichotomous case/control definition. Finally, we highlight gaps in knowledge and new directions for OCD genetics research.

Phenotype Definition for “Resisters” to Mycobacterium tuberculosis Infection in the Literature—A Review and Recommendations

Tuberculosis (TB) remains a worldwide problem. Despite the high disease rate, not all who are infected with Mycobacterium Tuberculosis (Mtb) develop disease. Interferon-γ (IFN-γ) specific T cell immune assays such as Quantiferon and Elispot, as well as a skin hypersensitivity test, known as a tuberculin skin test, are widely used to infer infection. These assays measure immune conversion in response to Mtb. Some individuals measure persistently negative to immune conversion, despite high and prolonged exposure to Mtb. Increasing interest into this phenotype has led to multiple publications describing various aspects of these responses. However, there is a lack of a unified “resister” definition. A universal definition will improve cross study data comparisons and assist with future study design and planning. We review the current literature describing this phenotype and make recommendations for future studies.

The Influence of Phenotyping Method on Structural Neuroimaging Associations with Depression in UK Biobank

BackgroundDepression is assessed in many different ways, with large population studies often relying on minimal phenotyping approaches. Genetic results suggest that more formal clinical diagnoses and simpler self-report measures of depression show some core similarities, but also important differences. It is not yet clear whether this is also the case for neuroimaging measures.MethodsWe studied 39,300 UK Biobank imaging participants (20,701 female; aged 44.6 to 82.3 years, M = 64.1, SD = 7.5) with structural neuroimaging (T1 and DTI) and depression data. Depression phenotypes included a minmal single-item self-report measure, an intermediate symptom-based measure of ‘probable’ depression, and a more clinically robust measure based on DSM-IV criteria. We tested i) associations between brain structural measures and each depression phenotype, and ii) the effects of depression phenotype on these associations.ResultsSmall depression-brain structure associations (β < 0.1) were significant after FDR correction for many global and regional metrics for all three phenotypes. The most consistent imaging associations across depression phenotypes were for measures of white matter integrity. There were small but significant effects of phenotype definition primarily for cortical thickness, which showed stronger negative associations with Self-reported Depression than the symptom-based measures.ConclusionSimilar to previous genetic studies, we found some consistent associations indicating a core component of depression across phenotypes, and some additional associations that were phenotype-specific. Although these specific results did not relate to depth of phenotyping as expected, effects of phenotype definition are still an important consideration for future depression research.

Phe2vec: Automated Disease Phenotyping based on Unsupervised Embeddings from Electronic Health Records

ObjectiveWe introduce Phe2vec, an automated framework for disease phenotyping from electronic health records (EHRs) based on unsupervised learning. We assess its effectiveness against standard rule-based algorithms from the Phenotype KnowledgeBase (PheKB).Materials and MethodsPhe2vec is based on pre-computing embeddings of medical concepts and patients’ longitudinal clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are similarly linked to a disease if their embedded representation is close to the phenotype. We evaluated Phe2vec using 49,234 medical concepts from structured EHRs and clinical notes from 1,908,741 patients in the Mount Sinai Health System. We assessed performance on ten diverse diseases having a PheKB algorithm, and one disease without, namely Lyme disease.ResultsPhe2vec phenotypes derived using Word2vec, GloVe, and Fasttext embeddings led to promising performance in disease definition and patient cohort identification as compared with standard PheKB definitions. When comparing head-to-head Phe2vec and PheKB disease patient cohorts using chart review, Phe2vec performed on par or better in nine out of ten diseases in terms of predictive positive values. Additionally, Phe2vec effectively identified phenotype definition and patient cohort for Lyme disease, a condition not covered in PheKB.DiscussionPhe2vec offers a solution to improve time-consuming phenotyping pipelines. Differently from other automated approaches in the literature, it is fully unsupervised, can easily scale to any disease and was validated against widely adopted expert-based standards.ConclusionPhe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.

Phenotypic Subtyping and Re-Analysis of Existing Methylation Data from Autistic Probands in Simplex Families Reveal ASD Subtype-Associated Differentially Methylated Genes and Biological Functions

Autism spectrum disorder (ASD) describes a group of neurodevelopmental disorders with core deficits in social communication and manifestation of restricted, repetitive, and stereotyped behaviors. Despite the core symptomatology, ASD is extremely heterogeneous with respect to the severity of symptoms and behaviors. This heterogeneity presents an inherent challenge to all large-scale genome-wide omics analyses. In the present study, we address this heterogeneity by stratifying ASD probands from simplex families according to the severity of behavioral scores on the Autism Diagnostic Interview-Revised diagnostic instrument, followed by re-analysis of existing DNA methylation data from individuals in three ASD subphenotypes in comparison to that of their respective unaffected siblings. We demonstrate that subphenotyping of cases enables the identification of over 1.6 times the number of statistically significant differentially methylated regions (DMR) and DMR-associated genes (DAGs) between cases and controls, compared to that identified when all cases are combined. Our analyses also reveal ASD-related neurological functions and comorbidities that are enriched among DAGs in each phenotypic subgroup but not in the combined case group. Moreover, relational gene networks constructed with the DAGs reveal signaling pathways associated with specific functions and comorbidities. In addition, a network comprised of DAGs shared among all ASD subgroups and the combined case group is enriched in genes involved in inflammatory responses, suggesting that neuroinflammation may be a common theme underlying core features of ASD. These findings demonstrate the value of phenotype definition in methylomic analyses of ASD and may aid in the development of subtype-directed diagnostics and therapeutics.

The Genomics and Metagenomics of Asthma Severity (GEMAS) Study: Rationale and Design

Asthma exacerbations are a major contributor to the global disease burden, but no significant predictive biomarkers are known. The Genomics and Metagenomics of Asthma Severity (GEMAS) study aims to assess the role of genomics and the microbiome in severe asthma exacerbations. Here, we present the design of GEMAS and the characteristics of patients recruited from March 2018 to March 2020. Different biological samples and demographic and clinical variables were collected from asthma patients recruited by allergy and pulmonary medicine units in several hospitals from Spain. Cases and controls were defined by the presence/absence of severe asthma exacerbations in the past year (oral corticosteroid use, emergency room visits, and/or asthma-related hospitalizations). A total of 137 cases and 120 controls were recruited. After stratifying by recruitment location (i.e., Canary Islands and Basque Country), cases and controls did not differ for most demographic and clinical variables (p > 0.05). However, cases showed a higher proportion of characteristics inherent to asthma exacerbations (impaired lung function, severe disease, uncontrolled asthma, gastroesophageal reflux, and use of asthma medications) compared to controls (p < 0.05). Similar results were found after stratification by recruitment unit. Thereby, asthma patients enrolled in GEMAS are balanced for potential confounders and have clinical characteristics that support the phenotype definition. GEMAS will improve the knowledge of potential biomarkers of asthma exacerbations.

Implicit bias of encoded variables: frameworks for addressing structured bias in EHR–GWAS data

The ‘discovery’ stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur ‘outside’ the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias.

Phenoflow: Portable Workflow-based Phenotype Definitions

Clinical phenotyping is an effective way to identify patients with particular characteristics within a population. In order to enhance the portability of a phenotype, it is often defined abstractly, with users expected to realise the phenotype computationally before executing it against a local dataset. However, complex definitions, which also provide little information about how best to implement a phenotype in practice, mean that this process is often not easy. To address this issue, we propose a new multi-layer model for a phenotype definition, which is realised as a workflow, and can be combined with different implementation units in order to produce a computable form. A novel authoring architecture, Phenoflow, supports the generation of these structured definitions. To illustrate the utility of our approach, we re-author a diabetes phenotype definition, and then compare its portability to the original definition, in the context of a population of 26,406 patients at Northwestern University.

Phenome-wide association study of a comprehensive health check-up database in a Korea population: Clinical application & trans-ethnic comparison

AbstractsBackgroundThe expanding use of the phenome-wide association study (PheWAS) faces challenges in the context of using International Classification of Diseases billing codes for phenotype definition, imbalanced study population ethnicity, and constrained application of the results to clinical practice or research.MethodsWe performed a PheWAS utilizing deep phenotypes corroborated by comprehensive health check-ups in a Korean population, along with trans-ethnic comparisons through the UK Biobank and Biobank Japan Project. Network analysis, visualization of cross-phenotype mapping, and causal inference mapping with Mendelian randomization were conducted in order to make robust, clinically applicable interpretations.ResultsOf the 136 phenotypes extracted from the health check-up database, the PheWAS associated 65 phenotypes with 14,101 significant variants (P < 4.92×10−10). In the association study for body mass index, our population showed 583 exclusive loci relative to the Japanese population and 669 exclusive loci relative to the European population. In the meta-analysis with Korean and Japanese populations, 72.5% of phenotypes had uniquely significant variants. Tumor markers and hematologic phenotypes had a high degree of phenotype-phenotype pairs. By Mendelian randomization, one skeletal muscle mass phenotype was causal and two were outcomes. Among phenotype pairs from the genotype-driven cross-phenotype associations, 71.65% also demonstrated penetrance in correlation analysis using a clinical database.ConclusionsThis comprehensive analysis of PheWAS results based on a health check-up database will provide researchers and clinicians with a panoramic overview of the networks among multiple phenotypes and genetic variants, laying groundwork for the practical application of precision medicine.