Separating Wheat from Chaff in Major Depression Blood Biomarker Studies

项目摘要 重性抑郁症（MDD）患者的血液表达谱有可能改善疾病 分类和诊断，阐明分子机制，提高药物靶向和临床试验 结果。然而，在最大的MDD血液RNA研究中，唯一重要的发现是最近的一项RNAseq分析， 922例MDD患者与健康对照组相比， 与MDD中干扰素反应途径相关的基因。这些数据集的统计分析是 疾病异质性和个体间基因表达变异的来源，如高 人体血细胞类型比例的人与人之间的差异，使得很难找到 严格区分临床群体。我们最近开发了基于潜变量的计算方法 其更有效地对包括血液基因表达数据中的血液比例的异质性进行建模， 改善疾病相关差异表达基因和疾病相关差异的鉴定 细胞类型的比例。我们已经证明，我们的方法增加了功率检测差异 表达的基因，并提高疾病相关的全球RNA表达的单独研究之间的一致性。 我们使用的潜变量框架可以用来解释观察到的变化， 特定的血细胞类型。使用这些方法对最近的大型MDD RNAseq数据集进行初步分析 发现了额外的抑郁症相关的签名和差异表达的RNA的证据，而这些RNA是没有被检测到的。 原创分析。分析这些特征与急性症状的相关性及其随时间的稳定性 表明它们很可能是新的MDD性状标记。我们将应用这种增强的 在这个大型RNAseq数据集中鉴定新的MDD相关基因的方法，通过以下方式确认这些特征： 分析其他MDD公共数据集，调查这些签名的状态/特征和遗传基础，并使用 机器学习生成一个纯粹的临床和生物数据驱动的抑郁症亚组分类。 这项研究有望为血液RNA生物标志物研究提供一个改进的分析框架， 精神疾病，MDD相关细胞类型比例和血液基因表达的重要新见解 特征签名，并确定分子连贯的抑郁症亚组。

Project Summary Blood expression profiling of major depression disorder (MDD) patients has the potential to refine disease classification and diagnosis, elucidate molecular mechanisms, and improve drug targeting and clinical trial outcomes. However, the only significant finding in the largest MDD blood RNA study, a recent RNAseq profiling of 922 individuals comparing MDD patients with healthy controls, was a composite increase in the expression of genes associated with the interferon response pathway in MDD. Statistical analysis of such datasets is complicated by disease heterogeneity and by sources of inter-individual gene expression variation, such as high person-to-person differences in human blood cell type proportions, making it difficult to find measurements that robustly distinguish clinical groups. We have recently developed latent variable based computational approaches that more effectively model heterogeneity including blood proportions in blood gene expression data and that improve the identification of disease associated differentially expressed genes and disease-associated differences in cell type proportions. We have demonstrated that our methods increase the power to detect differentially expressed genes and improve agreement among separate studies of disease-associated global RNA expression. The latent variable framework we use can be exploited for interpreting the observed changes by attributing them to specific blood cell types. Preliminary analysis using these approaches on the large recent MDD RNAseq dataset finds evidence for additional depression related signatures and differentially expressed RNAs not detected by the original analysis. Analysis of the association of these signatures with acute symptoms and their stability over time in individuals suggests that they are most likely novel MDD trait markers. We will apply this enhanced methodology to identify novel MDD-associated genes in this large RNAseq dataset, confirm these signatures by analysis of other MDD public datasets, investigate the state/trait and genetic basis for these signatures and use machine learning to generate a purely clinical and biological data-driven classification of depression subgroups. This study is expected to result in an improved analysis framework for blood RNA biomarkers studies of psychiatric disease, significant new insight into MDD associated cell type proportion and blood gene expression trait signatures, and the identification of molecularly coherent depression subgroups.