Depression susceptibility genes and networks: expression, eQTL and GWAS analysis

抑郁症易感性基因和网络：表达、eQTL 和 GWAS 分析

• 批准号: 7856829
• 负责人: DOUGLAS Frederick LEVINSON
• 金额: $ 187.68万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7856829
• 关键词: Adolescence; Age; Alcohol or Other Drugs use; Algorithms; Alleles; Bioinformatics; Biological; Biological Assay; Blood; Blood Cells; Brain; Childhood; Chronic; Collaborations; DNA; DNA Sequence; Data; Data Set; Diagnosis; Disease; Exercise; Foundations; Gene Expression; Genes; Genetic; Goals; Graph; Heritability; Hippocampus (Brain); Individual; Learning; Machine Learning; Major Depressive Disorder; Meta-Analysis; Methodology; Methods; Modeling; Pathway Analysis; Pathway interactions; Patient Self-Report; Pharmaceutical Preparations; Predisposition; Prefrontal Cortex; Prevention strategy; Procedures; Psychological Transfer; Quality Control; Quantitative Trait Loci; RNA; Recording of previous events; Recruitment Activity; Recurrence; Regulator Genes; Research; SNP genotyping; Sampling; Sampling Studies; Severities; Smoking Status; Source; Specimen; Statistical Methods; Surveys; Susceptibility Gene; Telephone Interviews; Testing; Time; Transcription factor genes; Weight; Whole Blood; Work; brain tissue; case control; depression; disability; early onset; genome wide association study; genome-wide; improved; mind control; non-genetic; novel; novel strategies; population based; programs; protein protein interaction; public health relevance; recurrent depression; sex; success

DESCRIPTION (provided by applicant): Major depressive disorder (MDD) is the leading cause of disability in the world (WHO). The severe, recurrent form often onsets during childhood or adolescence and becomes chronic. Heritability (35-70%) is similar to that of other common disorders for which genetic methodologies have yielded major discoveries. However, genome-wide association (GWA) studies have not yet produced significant results, and success may require much larger samples than are available. Alternative discovery strategies are urgently needed. Here, we will integrate genome-wide gene expression data from fresh blood with GWAS data to detect expression quantitative trait loci (eQTLs - SNP loci that influence gene expression) with the goal of discovering new MDD susceptibility loci using two novel analytic strategies, including a gene network analysis method that will be substantially developed in the context of this work. We will also seek to refine and confirm the results with genome-wide expression data from brain tissue. We will recruit a new, population-based sample of 500 new recurrent MDD cases and 500 depression-free controls, using a novel strategy in collaboration with a survey research company. Using RNA extracted from RNA-stabilized whole blood, genome-wide gene expression levels will be assayed with Illumina HT-12 array, and common SNP genotypes will be assayed (Illumina 660W) from DNA. (We will also assay brain tissue from two regions from 39 case and 27 control specimens for confirmatory studies.) Potential non-genetic sources of variability will be considered and statistically controlled. Two novel analytic strategies will be used to discover MDD susceptibility alleles, genes and gene regulatory networks. (1) A univariate discovery strategy will identify case-control gene expression differences; detect SNP eQTLs for the selected genes using genome-wide SNP data in controls; and test the association of these SNPs to MDD in two independent GWA datasets. The prior selection of a small number of eQTLs in a separate sample greatly improves power by reducing the burden of multiple testing in GWA analysis. (2) We will extend a novel multivariate method that uses machine learning algorithms to detect gene regulatory networks, and then apply this model to the analysis of the association of co-regulated gene modules to MDD. These algorithms (a) use a background pathway graph that integrates a wide range of high-throughput and curated bioinformatic data to predict biological relationships between genes, to bias the algorithm toward biologically plausible hypotheses; and then (b) use eQTL data to construct a set of gene regulatory modules (co-expressed genes) and infer a sparse regulatory program for each module. The method will then be applied to the GWA data, and (using a novel transfer learning method) to brain data. These studies will identify genes and gene networks underlying susceptibility to major depression. PUBLIC HEALTH RELEVANCE: The proposed project will recruit individuals with and without histories of recurrent depression and then study both the DNA sequence and the activity levels of genes in blood cells. Using a new set of statistical methods, the study will attempt to identify genes and networks of genes that are altered in some people who suffer from depression. This information would be useful in finding new treatment or prevention strategies.

描述（由申请人提供）：重度抑郁症（MDD）是世界上导致残疾的主要原因（WHO）。严重的，经常性的形式往往在儿童或青春期发病，并成为慢性。遗传率（35-70%）与遗传学方法已经取得重大发现的其他常见疾病相似。然而，全基因组关联（GWA）研究尚未产生显著结果，成功可能需要比现有样本大得多的样本。迫切需要替代的发现策略。在这里，我们将整合来自新鲜血液的全基因组基因表达数据与GWAS数据，以检测表达数量性状基因座（eQTLs -影响基因表达的SNP基因座），目的是使用两种新的分析策略发现新的MDD易感基因座，包括将在本工作中大量开发的基因网络分析方法。我们还将寻求用脑组织的全基因组表达数据来完善和确认结果。我们将与一家调查研究公司合作，采用一种新的策略，招募一个新的、基于人群的样本，其中包括500例新的复发性MDD病例和500例无抑郁症对照。使用从RNA稳定的全血中提取的RNA，用Illumina HT-12阵列测定全基因组基因表达水平，并从DNA测定常见的SNP基因型（Illumina 660 W）。(We还将对来自39例病例和27例对照标本的两个区域的脑组织进行分析，以进行确证性研究。）将考虑潜在的非遗传变异性来源，并进行统计学控制。两种新的分析策略将用于发现MDD易感等位基因、基因和基因调控网络。(1)单变量发现策略将识别病例-对照基因表达差异;使用对照中的全基因组SNP数据检测所选基因的SNP eQTL;并在两个独立的GWA数据集中测试这些SNP与MDD的关联。在单独的样品中预先选择少量eQTL通过减少GWA分析中的多重测试的负担而大大提高了功效。(2)我们将扩展一种新的多变量方法，该方法使用机器学习算法来检测基因调控网络，然后将该模型应用于分析共调控基因模块与MDD的关联。这些算法（a）使用背景途径图，其整合了广泛的高通量和策划的生物信息学数据来预测基因之间的生物学关系，以使算法偏向生物学上合理的假设;然后（B）使用eQTL数据来构建一组基因调控模块（共表达基因）并推断每个模块的稀疏调控程序。然后将该方法应用于GWA数据，并（使用新的迁移学习方法）应用于大脑数据。这些研究将确定导致抑郁症易感性的基因和基因网络。 公共卫生关系：该项目将招募有和没有复发性抑郁症病史的个体，然后研究血细胞中基因的DNA序列和活性水平。使用一套新的统计方法，该研究将试图识别在一些患有抑郁症的人中发生改变的基因和基因网络。这些信息将有助于寻找新的治疗或预防策略。