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Improving the interpretability of genetic studies of major depressive disorder to identify risk genes

提高重度抑郁症基因研究的可解释性以识别风险基因

基本信息

批准号：
10504696
负责人：
JONATHAN FLINT
金额：
$ 61.99万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-16 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10504696
关键词：
Biological Biological databases Biology Bipolar Disorder Clinical Code Computerized Medical Record Data Development Diagnosis Disease Electronic Health Record Ensure Etiology Factor Analysis Genes Genetic Genetic Risk Genetic study Genotype Goals Gold Heritability Heterogeneity Individual International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10)Interview Joints Light Major Depressive Disorder Measures Mental Depression Mental disorders Methods Moods Pathogenicity Pathway interactions Patients Phenotype Process Research Residual state Risk Sample Size Sampling Schizophrenia Sequence Analysis Signal Transduction Silver Specificity Statistical Methods Symptoms Testing Time Tissues Variant base biobank case control clinical diagnosis cognitive change cohort comorbidity cost deep learning diagnosis standard disability dysphoria effective therapy exome family genetics genetic approach genetic architecture genetic risk factor genetic testing genome wide association study improved insight loss of function meetings negative affect non-genetic novel phenotyping algorithm portability rare variant risk variant secondary outcome trait

项目摘要

Project Summary This project aims to advance our understanding of major depressive disorder (MDD) through the analysis of electronic medical records, biobanks and associated genetic data. MDD is the commonest psychiatric disorder and recognized as the world’s leading cause of disability, yet current treatments are relatively ineffective: only about half of patients will show signs of improvement after three months of therapy. Genetic approaches are a proven path to identifying causal factors and hence finding novel treatments, but they are hard to apply to MDD without obtaining large samples of cases. We propose using the very large numbers of cases available through electronic medical records by applying statistical methods that accurately identify MDD. Our methods provide a “best-guess” diagnosis by a process known as imputation. We then identify features that are specific to MDD. Our insight is that since non-genetic and non-specific factors explain large components of variability in traditional MDD phenotypes, algorithmically removing them increases the signal from the core biological drivers. We assume that non-specificity can be attributed to latent factors capturing the relationship between MDD, comorbid disease, and pleiotropic factors. By identifying and removing these signals, we increase specificity, and thus identify features that reflect the episodic severe shifts of mood, associated with neurovegetative and cognitive changes, that are central to MDD. Our project has three aims: first, to impute phenotypes of a large sample of MDD cases and controls in biobank data and determine the best approximation to MDD; second, to identify and characterise specific and non-specific genetic effects on MDD, and finally to identify genes involved in MDD by associating the cases defined via our first two aims with rare coding variants.

项目总结