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.

项目摘要该项目旨在通过分析来促进我们对重大抑郁症（MDD）的理解电子病历，生物库和相关的遗传数据。 MDD是最常见的精神障碍并被认为是世界残疾的主要原因，但目前的治疗效果相对无效：仅三个月的治疗后，约有一半的患者会显示出改善的迹象。遗传方法是证明了识别因果因素并因此找到新的治疗方法的途径，但是它们很难应用于MDD 没有获得大量案件的样本。我们建议使用大量通过通过应用准确识别MDD的统计方法，电子病历。我们的方法提供了通过称为插补的过程“最佳猜测”诊断。然后，我们确定特定于MDD的功能。我们的见解是，由于非遗传和非特异性因素解释了传统的可变性大部分 MDD表型，算法从算法中除去它们会增加来自核心生物驱动因素的信号。我们假设非特异性可以归因于捕获MDD之间关系的潜在因素疾病和多效因素。通过识别和删除这些信号，我们提高了特异性，因此识别反映情节剧集的特征，与神经和认知有关变化是MDD的核心。我们的项目有三个目标：首先，将大量样本的表型算生物库数据中的MDD病例和对照，并确定与MDD的最佳近似值；第二，确定和表征对MDD的特定和非特异性遗传作用，最后确定由MDD涉及的基因通过我们的前两个目标与稀有编码变体相关联的案例。