1/4 Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disorders

1/4 利用 EHR 连接的生物库进行精神疾病的深度表型分析、多基因风险评分建模和结果分析

• 批准号: 10015337
• 负责人: Joseph John Mann
• 金额: $ 26.49万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-10 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10015337
• 关键词: Address; Anxiety; Anxiety Disorders; Architecture; Big Data; Clinic; Clinical; Clinical Data; Collaborations; Complex; Computerized Medical Record; Data; Data Set; Disease; Electronic Health Record; Employment; Environmental Risk Factor; European; Evaluation; Feeling suicidal; Funding; General Population; Genetic; Genetic Determinism; Genetic Research; Genetic Risk; Genetic Variation; Genotype; Geography; Goals; Health Care Costs; Health system; Heritability; Hospitalization; Individual; Knowledge; Link; Machine Learning; Major Depressive Disorder; Measures; Medical; Medical center; Mental Health; Mental disorders; Methods; Modeling; Natural Language Processing; New York City; Outcome; Participant; Patients; Performance; Persons; Phenotype; Population; Population Heterogeneity; Research; Risk; Risk stratification; Role; Sampling; Scoring Method; Site; Substance Use Disorder; Suicide attempt; Symptoms; Text; Variant; base; biobank; care outcomes; clinical care; clinical practice; cohort; comorbidity; deep learning; disorder risk; functional disability; genetic epidemiology; genetic risk factor; genome wide association study; genome-wide; health care service utilization; improved; infancy; interest; large datasets; learning strategy; mortality; mortality risk; neuropsychiatric disorder; pleiotropism; polygenic risk score; population based; psychogenetics; response; social determinants; social health determinants; structured data; suicidal behavior; technique development; therapy resistant; trait; treatment-resistant depression

PROJECT ABSTRACT Major depressive disorder (MDD), anxiety disorders, and substance use disorders (SUDs) are common, complex psychiatric traits that frequently co-occur and are associated with significant functional impairment, increased healthcare utilization and cost, and higher mortality risk. Not only are these three conditions highly prevalent in the general population and generate a huge societal burden, but recent studies by our team and others have shown that shared covariance from common genetic variation significantly contributes to these psychiatric comorbidities. Large data sets are needed to understand how the multifaceted interplay of genetics, including polygenic risk scores (PRSs), and social determinants of health factors, such as employment and educational attainment, can increase the risk of these psychiatric disorders and clinical outcomes, such as multiple psychiatric hospitalizations. PRSs have shown potential for risk prediction, but the clinical utility of PRSs for psychiatric conditions is just starting to be explored. Use of Electronic Health Records (EHRs) offers the promise of large data sets to examine these relationships in cohorts of patients seen in clinical practice. However, the use of EHRs is in its infancy in the study of psychiatric disorders and their treatment. This study will address critical knowledge gaps in “genotype-psychiatric phenotype” relationships in large, demographically and geographically diverse population-based samples derived from EHR-linked biobanks across four medical centers - Columbia, Cornell, Mayo Clinic and Mount Sinai. Our objectives are to (1) develop improved methods for EHR phenotyping of MDD, anxiety, and SUDs, and related outcomes based on a data-set of >30 million EHRs, (2) evaluate associations between PRSs and these conditions, as well as (3) assess the association between PRSs and outcomes including treatment resistance in MDD and healthcare utilization in patients with MDD, anxiety and SUD. The PRS analyses will utilize data from biobanks with >50,000 persons with both EHR and GWAS data. Successful completion of this study will generate new data in improving our understanding of the clinical utility of PRSs for commonly occurring psychiatric disorders.

项目摘要