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

项目摘要 本项目旨在通过对抑郁症的分析， 电子病历、生物库和相关遗传数据。抑郁症是最常见的精神疾病 被认为是世界上导致残疾的主要原因，但目前的治疗方法相对无效： 约半数病人在接受三个月的治疗后会有改善的迹象。遗传学方法是一种 这是一条确定因果因素并因此找到新治疗方法的有效途径，但它们很难应用于MDD 而不需要获得大量的案例样本。我们建议使用大量的情况下，可通过 通过应用准确识别MDD的统计方法来建立电子病历。我们的方法提供了 “最佳猜测”的诊断通过一个过程称为插补。然后，我们确定特定于MDD的功能。 我们的观点是，由于非遗传和非特异性因素解释了传统遗传学中的大部分变异性， MDD表型，通过算法去除它们增加了来自核心生物驱动因素的信号。我们 假设非特异性可归因于潜在因素，这些因素捕获了MDD、共病 疾病和多效性因素。通过识别和去除这些信号，我们增加了特异性， 识别反映情绪的情节性严重变化的特征，与神经植物性和认知相关 这些变化是MDD的核心。我们的项目有三个目标：第一，估算大样本的表型， 生物库数据中的MDD病例和对照，并确定MDD的最佳近似值;第二， 研究MDD的特异性和非特异性遗传效应，最后通过基因测序鉴定MDD相关基因。 将通过我们的前两个目标定义的情况与罕见的编码变体相关联。

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.