An expanded framework for RNA quality correction in expression analyses in the human brain

人脑表达分析中 RNA 质量校正的扩展框架

• 批准号: 9809058
• 负责人: Andrew Ellis Jaffe
• 金额: $ 27.99万
• 依托单位: LIEBER INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9809058
• 关键词: Affect; Alzheimer' s Disease; Amygdaloid structure; Anterior; Archives; Autopsy; Biological; Biological Process; Bipolar Disorder; Birth; Brain; Brain Diseases; Brain region; Cessation of life; Chromatin; Clinical; Data; Data Set; Disease; Drug abuse; Etiology; Family; Gene Expression; Genes; Genetic; Genetic study; Genomic Segment; Genotype-Tissue Expression Project; Heritability; Hippocampus (Brain); Human; Individual; Lead; Libraries; Literature; Major Depressive Disorder; Medial; Modeling; Molecular; Molecular Profiling; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Post-Traumatic Stress Disorders; Prefrontal Cortex; Preparation; Public Health; Publishing; RNA; Research Personnel; Risk; Sampling; Schizophrenia; Statistical Data Interpretation; Statistical Models; Temperature; Time; Tissue Sample; Tissues; Transcript; Validation; Variant; autism spectrum disorder; base; brain tissue; caudate nucleus; cingulate cortex; clinically relevant; differential expression; experimental study; frontal lobe; genetic association; genome wide association study; improved; insight; interest; neuropsychiatry; new therapeutic target; non-genetic; novel; risk variant; severe mental illness; tissue processing; tool; transcriptome; transcriptome sequencing

Project Summary/Abstract Serious brain disorders like schizophrenia, bipolar disorder, major depression, autism spectrum disorder, and Alzheimer’s disease are debilitating illnesses that are substantial burdens on both the families of affected individuals and the public health. While they all have high degrees of heritability, the etiology underlying these disorders in the majority of patients has been difficult to characterize. The strongest clues for the etiological underpinnings of these disorders, particularly neuropsychiatric and neurodevelopmental, come from recent genetic studies which have identified hundreds of common loci that each contribute to small effects of risk, but the mechanisms guiding any individual risk locus remain largely unknown. These common variants are therefore hypothesized to manifest at the gene pathway- and network-level, but there has been substantial variability in the pathways associated with the illness based on the genetic association results. Many groups, including our own, have therefore utilized postmortem human brain tissue to better understand the molecular correlates of the both genetic and non-genetic effects of these disorders, as gene expression levels may better illuminate mechanisms of risk. However, in this proposal we point out a damaging and often overlooked issue related to confounding effects of RNA quality in comparing postmortem tissue between patients and controls – we have identified strong confounding effects of RNA quality found in the majority of published, and our own, datasets. We first describe these widespread RNA quality effects, demonstrate that existing statistical approaches do not remove this confounding, and show these RNA quality effects drive inference in co-expression and network analyses – using both simulated and real data, we identify hundreds of false positive network edges while discovering only few true edges. In this application, to better understand the molecular etiology of these debilitating disorders, we propose a framework to accurately model RNA quality in gene expression datasets based on molecular degradation experiments across the human brain. This framework, called “quality surrogate variable analysis”, will be applied to better identify molecular signatures at the gene and network level for debilitating brain disorders to improve replication and interpretability from these large publicly available datasets. Gene networks resulting from our RNA quality-corrected framework will be interrogated for biological functionality and clinical relevance using pre-defined gene sets. These results can illuminate potentially novel biological associations underlying serious mental illness. We hypothesize that removing the biases induced by RNA quality will result in the strongest enrichment with these gene sets at the gene and network levels. Correctly modeling potential RNA quality effects in postmortem gene expression data will be an important tool in the statistical analysis of gene and network level analyses to improve concordance and biological inference across rich datasets that can potentially lead to novel therapeutic targets to treat these disorders.

项目总结/摘要 严重的大脑疾病，如精神分裂症，躁郁症，重度抑郁症，自闭症谱系 疾病和阿尔茨海默病是使人衰弱的疾病，对两个家庭都是沉重的负担。 影响个人和公众健康。虽然它们都具有高度的遗传性，但病因学 在大多数患者中，这些疾病的潜在原因一直难以描述。最有力的线索 这些疾病的病因学基础，特别是神经精神和神经发育， 最近的遗传学研究发现了数百个共同的基因座，每个基因座都有很小的影响， 风险，但机制指导任何个人的风险位点仍然在很大程度上未知。这些常见的变体 因此，假设在基因通路和网络水平上表现出来，但已经有大量的 基于遗传关联结果的与疾病相关的途径的可变性。很多团体， 包括我们自己的，因此利用死后的人脑组织，以更好地了解分子 这些疾病的遗传和非遗传效应的相关性，因为基因表达水平可能更好， 阐明风险机制。 然而，在这个建议中，我们指出了一个与混淆有关的破坏性和经常被忽视的问题。 RNA质量在比较患者和对照组死后组织中的作用-我们已经确定了 在大多数已发表的和我们自己的数据集中发现了RNA质量的强混杂效应。我们首先 描述了这些广泛的RNA质量效应，证明了现有的统计方法不能消除 这种混淆，并显示这些RNA质量效应驱动共表达和网络分析中的推断- 使用模拟和真实的数据，我们识别了数百个假阳性网络边缘， 一些真正的边缘。在本申请中，为了更好地理解这些使人衰弱的疾病的分子病因， 我们提出了一个框架，以准确建模RNA质量的基因表达数据集的基础上，分子 在人脑中进行的降解实验。这一框架被称为“质量替代变量分析”， 将被应用于更好地识别基因和网络水平的分子签名， 疾病，以改善复制和解释这些大型公开可用的数据集。 从我们的RNA质量校正框架产生的基因网络将被询问生物学特性。 功能性和临床相关性。这些结果可以阐明潜在的新的 严重精神疾病背后的生物学联系我们假设，消除由 RNA质量将导致这些基因组在基因和网络水平上的最强富集。 正确地建模潜在的RNA质量影响死后基因表达数据将是一个重要的工具 在基因和网络水平分析的统计分析中， 这些数据集可能会导致新的治疗靶点来治疗这些疾病。