Enhancing Genome-Wide Association Studies via Integrative Network Analysis

描述（由申请人提供）：许多常见疾病是由多种遗传和环境因素之间的复杂相互作用引起的。全基因组关联研究（GWAS）全面比较了受影响人群和对照人群中的常见遗传变异，以确定可能与疾病相关的变异。近年来，GWAS成功地鉴定了许多疾病的易感基因。然而，研究人员认识到GWAS在表征复杂疾病的遗传基础方面存在许多局限性，包括由于样本量小而导致的统计功效降低，在捕获多个因素之间的相互作用时单独考虑个体变异的不足，预测个体疾病风险的成功率不高，以及缺乏对将已识别的变异与疾病相关的生物学和功能机制的见解。 该项目旨在通过使用蛋白质-蛋白质相互作用（PPI）网络作为一个综合框架来解释GWAS在功能背景下的结果，以增强GWAS。PPI网络表征了功能蛋白质之间的物理和功能相互作用，因此它们有助于理解多种遗传因子之间的功能关系。本项目将促进有效利用生产者价格指数网络，以确定功能关系 通过开发有效的计算算法，将整合多个来源的“组学”数据，在GWAS中涉及的遗传因素之间进行研究。特别是，为了增强GWAS捕获的相对较弱的关联信号，我们将结合单个蛋白质的联合收割机关联评分，以识别当 一起考虑。我们还将通过将搜索空间限制在已知的物理和功能相互作用来搜索与疾病相关的多种遗传因素的组合。我们还将根据疾病中的集体差异表达对已识别的蛋白质组进行评分，以期深入了解遗传差异与基因表达失调之间的关系。我们将使用从公共数据库（Wellcome Trust Case-Control Consortium和The Database of Genotypes and Phenotypes）以及我们的合作者获得的大型病例对照数据集，对各种疾病进行广泛的测试。特别是，我们将扩展与候选基因关联资源（CARe）项目的现有合作，该项目包括40，000名个体，并通过功能基因与CARe项目中重要的心血管表型的关联来验证我们的算法开发。 这项研究将产生新的计算工具，这些工具将可靠地将基因组数据与功能和疾病表型联系起来，以推动对复杂疾病的集中和有效的机制研究。（包括临床研究和模式生物的研究）由我们的合作者和更广泛的生物医学科学界，最终提供诊断和预后生物标志物和机制见解，以更全面和有效地为临床研究提供信息，现有的GWAS。

DESCRIPTION (provided by applicant): Many common diseases arise from complex interactions among multiple genetic and environmental factors. Genome Wide Association Studies (GWAS) comprehensively compare common genetic variants in affected and control populations to identify variants that are potentially associated with disease. In recent years, GWAS successfully identified susceptible genes for many diseases. However, researchers recognize many limitations of GWAS in characterizing the genetic bases of complex diseases, including reduced statistical power due to small sample size, inadequacy of separate consideration of individual variants in capturing the interplay between multiple factors, modest success in predicting individual risk for disease, and lack of insights into the biological and functional mechanisms that relate identified variants to the disease. This project aims to enhance GWAS by using protein-protein interaction (PPI) networks as an integrative framework to interpret the outcome of GWAS within a functional context. PPI networks characterize the physical and functional interactions among functional proteins; thus they are useful in understanding the functional relationships between multiple genetic factors. This project will facilitate effective use of PPI networks to identify the functional relationships among genetic factors implicated in GWAS by developing efficient computational algorithms that will integrate multiple sources of "omic" data. In particular, to enhance the relatively weak association signals captured by GWAS, we will combine association scores of individual proteins to identify interacting groups of proteins that exhibit a stronger association signal when considered together. We will also search for combinations of multiple genetic factors that are associated with disease by confining the search space to known physical and functional interactions. We will also score identified groups of proteins in terms of their collective differential expression in the disease, with a view to gaining insights into the relationship between genetic differences and dysregulation of gene expression. We will extensively test the proposed algorithms on a variety of diseases, using large case-control datasets obtained from public databases (Wellcome Trust Case-Control Consortium and The database of Genotypes and Phenotypes), as well as our collaborators. In particular, we will extend our existing collaborations with the Candidate Gene Association Resource (CARe) project that includes 40,000 individuals and validate our algorithm development through functional gene association with cardiovascular phenotypes of importance in the CARe project. This research will result in novel computational tools that will reliably connect genomic data to function and disease phenotypes to drive focused and effective mechanistic studies of complex diseases (including clinical studies and studies in model organisms) by our collaborators and the wider biomedical science community, ultimately providing diagnostic and prognostic biomarkers and mechanistic insight to inform clinical studies more comprehensively and effectively than existing GWAS.