All of Us is a landmark initiative for population-scale research into a variety of health conditions, including psychiatric disorders.
To analyze the prevalence, comorbidity, and sociodemographic ...covariates of psychiatric disorders in the All of Us biobank.
We estimated prevalence, overlap, and sociodemographic correlates for psychiatric disorders as reported in electronic health records for All of Us release 5 (N = 331 380).
Social and demographic covariates.
Psychiatric disorders derived from International Statistical Classification of Diseases, Tenth Revision, Clinical Modification, codes across 6 broad domains: mood disorders, anxiety disorders, substance use disorders, stress-related disorders, schizophrenia, and personality disorders.
The analytic sample (N = 329 038) was 60.7% female (mean SD age, 50.9 16.8 years). The prevalence of disorders ranged from 11.00% (95% CI, 10.68% to 11.32%) for any mood disorder to less than 1% (eg, obsessive-compulsive disorder, 0.18%; 95% CI, -0.16% to 0.52%), with mood disorders being the most common and personality disorders being the least. There was substantial overlap among disorders, with the majority of participants with a disorder (30 113/58 806, approximately 51%) having 2 or more registered diagnoses and tetrachoric correlations ranging from 0.43 to 0.74. Comparisons of prevalence across demographic categories revealed that non-Hispanic White people, individuals with low socioeconomic status, women and individuals assigned female at birth, and sexual minority individuals are at greatest risk for most disorders.
Although rates of disorders among the All of Us cohort are lower than in the general population, considerable variation, comorbidity, and disparities exist across social groups. To improve the practice of equitable precision medicine, researchers can use comprehensive health data from large-scale resources such as All of Us.
IMPORTANCE: All of Us is a landmark initiative for population-scale research into a variety of health conditions, including psychiatric disorders. OBJECTIVE: To analyze the prevalence, comorbidity, ...and sociodemographic covariates of psychiatric diagnoses in the All of Us biobank. DESIGN, SETTING, AND PARTICIPANTS: We estimated prevalence, overlap, and sociodemographic correlates for diagnoses of psychiatric disorders as reported in electronic health records for All of Us release 5. EXPOSURES: Social and demographic covariates. MAIN OUTCOMES AND MEASURES: Phecodes for diagnoses derived from International Statistical Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification, codes across 6 broad domains: mood disorders, anxiety disorders, substance use disorders, stress-related disorders, schizophrenia, and personality disorders. RESULTS: The analytic sample (N = 214 206) was 61.3% female (mean SD age, 51.7 16.6 years). The prevalence of diagnoses ranged from 22.14% (95% CI, 21.17% to 22.52%) for any mood disorder to less than 1% (eg, obsessive-compulsive disorder, 0.41%; 95% CI, −0.02% to 0.83%), with mood disorders being the most common and personality disorders being the least. Estimates for diagnoses were lower than nationally representative estimates, except those for mood disorders, sleep disorder, and schizophrenia. There was substantial overlap among disorders, with the majority of participants with a diagnosis (41 840/75 268, approximately 54%) having 2 or more registered diagnoses and tetrachoric correlations ranging from 0.33 to 0.80. Comparisons across demographic categories revealed that non-Hispanic White people, individuals with low socioeconomic status, women and individuals assigned female at birth, and sexual minority individuals are at greatest risk for most disorders. CONCLUSIONS AND RELEVANCE: Although rates for many of the diagnoses among the All of Us cohort in this study were lower than in the general population, considerable variation, comorbidity, and disparities exist across social groups. To improve the practice of equitable precision medicine, researchers can use comprehensive health data from large-scale resources such as All of Us.
Major depressive disorder (MDD), one of the most frequently encountered forms of mental illness and a leading cause of disability worldwide, poses a major challenge to genetic analysis. To date, no ...robustly replicated genetic loci have been identified, despite analysis of more than 9,000 cases. Here, using low-coverage whole-genome sequencing of 5,303 Chinese women with recurrent MDD selected to reduce phenotypic heterogeneity, and 5,337 controls screened to exclude MDD, we identified, and subsequently replicated in an independent sample, two loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene (P = 2.53 × 10(-10)), the other in an intron of the LHPP gene (P = 6.45 × 10(-12)). Analysis of 4,509 cases with a severe subtype of MDD, melancholia, yielded an increased genetic signal at the SIRT1 locus. We attribute our success to the recruitment of relatively homogeneous cases with severe illness.
Abstract
Motivation
Many genetics studies report results tied to genomic coordinates of a legacy genome assembly. However, as assemblies are updated and improved, researchers are faced with either ...realigning raw sequence data using the updated coordinate system or converting legacy datasets to the updated coordinate system to be able to combine results with newer datasets. Currently available tools to perform the conversion of genetic variants have numerous shortcomings, including poor support for indels and multi-allelic variants, that lead to a higher rate of variants being dropped or incorrectly converted. As a result, many researchers continue to work with and publish using legacy genomic coordinates.
Results
Here we present BCFtools/liftover, a tool to convert genomic coordinates across genome assemblies for variants encoded in the variant call format with improved support for indels represented by different reference alleles across genome assemblies and full support for multi-allelic variants. It further supports variant annotation fields updates whenever the reference allele changes across genome assemblies. The tool has the lowest rate of variants being dropped with an order of magnitude less indels dropped or incorrectly converted and is an order of magnitude faster than other tools typically used for the same task. It is particularly suited for converting variant callsets from large cohorts to novel telomere-to-telomere assemblies as well as summary statistics from genome-wide association studies tied to legacy genome assemblies.
Availability and implementation
The tool is written in C and freely available under the MIT open source license as a BCFtools plugin available at http://github.com/freeseek/score.
•The course of major depression is highly varied.•Genetic and environmental risk factors influence the course of depression.•Data mining techniques can be used to predict the course of ...depression.•Replication in new samples is crucial to test these prediction models.•Prediction models may assist clinicians in treatment decisions.
Course of illness in major depression (MD) is highly varied, which might lead to both under- and overtreatment if clinicians adhere to a 'one-size-fits-all' approach. Novel opportunities in data mining could lead to prediction models that can assist clinicians in treatment decisions tailored to the individual patient. This study assesses the performance of a previously developed data mining algorithm to predict future episodes of MD based on clinical information in new data.
We applied a prediction model utilizing baseline clinical characteristics in subjects who reported lifetime MD to two independent test samples (total n = 4226). We assessed the model's performance to predict future episodes of MD, anxiety disorders, and disability during follow-up (1–9 years after baseline). In addition, we compared its prediction performance with well-known risk factors for a severe course of illness.
Our model consistently predicted future episodes of MD in both test samples (AUC 0.68–0.73, modest prediction). Equally accurately, it predicted episodes of generalized anxiety disorder, panic disorder and disability (AUC 0.65–0.78). Our model predicted these outcomes more accurately than risk factors for a severe course of illness such as family history of MD and lifetime traumas.
Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background.
Our prediction model consistently predicted a range of adverse outcomes in MD across two independent test samples derived from studies in different subpopulations, countries, using different measurement procedures. This replication study holds promise for application in clinical practice.
Objective:The extent to which major depression is the outcome of a single biological mechanism or represents a final common pathway of multiple disease processes remains uncertain. Genetic approaches ...can potentially identify etiologic heterogeneity in major depression by classifying patients on the basis of their experience of major adverse events.Method:Data are from the China, Oxford, and VCU Experimental Research on Genetic Epidemiology (CONVERGE) project, a study of Han Chinese women with recurrent major depression aimed at identifying genetic risk factors for major depression in a rigorously ascertained cohort carefully assessed for key environmental risk factors (N=9,599). To detect etiologic heterogeneity, genome-wide association studies, heritability analyses, and gene-by-environment interaction analyses were performed.Results:Genome-wide association studies stratified by exposure to adversity revealed three novel loci associated with major depression only in study participants with no history of adversity. Significant gene-by-environment interactions were seen between adversity and genotype at all three loci, and 13.2% of major depression liability can be attributed to genome-wide interaction with adversity exposure. The genetic risk in major depression for participants who reported major adverse life events (27%) was partially shared with that in participants who did not (73%; genetic correlation=+0.64). Together with results from simulation studies, these findings suggest etiologic heterogeneity within major depression as a function of environmental exposures.Conclusions:The genetic contributions to major depression may differ between women with and those without major adverse life events. These results have implications for the molecular dissection of major depression and other complex psychiatric and biomedical diseases.
Abstract Background The dopamine hypothesis, which posits that dysregulation of the dopaminergic system is etiologic for schizophrenia, is among the most enduring biological theories in psychiatry. ...Although variation within genes related to dopaminergic functioning has been associated with schizophrenia, an aggregate test of variation, using the largest publicly available schizophrenia dataset, has not previously been conducted. Methods We first identified a core set of 11 genes involved in the synthesis, metabolism, and neurotransmission of dopamine. We then extracted summary statistics of markers falling within, or flanking, these genes from the Psychiatric Genomics Consortium's most recent schizophrenia mega-analysis results. We conducted aggregate tests for enrichment of dopamine-related pathways for association with schizophrenia. Results We did not detect significant enrichment of signals across the core set of dopamine-related genes. However, we did observe modest to strong enrichment of genetic signals within the DRD2 locus. Conclusions Within the limits of available power, common sequence variation within core genes of the dopaminergic system is not related to risk of schizophrenia. This does not preclude a role of dopamine, or dopamine-related genes, in the clinical presentation of schizophrenia or in treatment response. However, it does suggest that the genetic risk for schizophrenia is not substantially affected by common variation in those genes which, collectively, critically impact dopaminergic functioning.
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