Methods for Evolutionary Informed Network Analysis to Discover Disease Variation

用于发现疾病变异的进化知情网络分析方法

• 批准号: 8826738
• 负责人: Joel Thomas Dudley
• 金额: $ 41.46万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8826738
• 关键词: Acute; Architecture; Benchmarking; Bioinformatics; Biological; Biology; Clinical; Clinical Data; Communities; Complex; Computers; Data; Data Set; Databases; Detection; Development; Diagnostic; Disease; Disease Marker; Ethnic Origin; Functional disorder; Gene Frequency; Genes; Genetic; Genetic Variation; Genome; Genomics; Health; Human; Individual; Knowledge; Lead; Learning; Measures; Medicine; Methods; Mus; Network-based; Non-Insulin-Dependent Diabetes Mellitus; Operating System; Pathway Analysis; Pathway interactions; Patients; Performance; Population; Population Heterogeneity; Positioning Attribute; Research; Sensitivity and Specificity; Software Tools; Source Code; Technology; Training and Education; Translating; Variant; Work; base; cohort; diabetes mellitus genetics; disease phenotype; exome; functional genomics; genetic association; genetic variant; genome wide association study; high throughput analysis; human tissue; improved; novel; novel strategies; programs; therapy outcome; tool; trait

DESCRIPTION (provided by applicant): Genetic association studies have been successful in identifying >1,000 genetic loci associated with complex disease traits in human populations. However, it remains a central challenge to interpret the vast amounts of data generated by GWAS studies towards an improved understanding of disease markers and, thus, mechanisms, which are critical for translating GWAS findings into genomic medicine applications enabling improvements in diagnostics, therapies, and outcomes. Recent efforts to incorporate prior biological information into GWAS analysis has greatly enhanced the interpretation of GWAS findings by providing biological frameworks for prioritizing associations, and for interpreting multiple associated loci within the contexts of biological networks and pathways. We recently demonstrated that position-specific evolutionary priors could be incorporated into analysis of GWAS results to prioritize variants that were more reproducible across studies. We propose to develop, investigate, and apply evolutionary informed integrative methods that embrace and leverage the genetic complexity of common disease. We hypothesize that position-specific evolutionary features can be incorporated into multiscale biological pathway and network analysis, and that evolutionary informed pathway and network analysis can be applied to existing GWAS and clinical data sets to identify mechanisms giving rise to complex disease phenotypes in populations and individuals. We propose to develop and evaluate these hypotheses through pursuit of the following specific aims: (1) Develop novel evolutionary-informed pathway and network analysis method for interpreting GWAS findings. (2) Apply novel methods to established GWAS and clinical data for T2D to elucidate disease mechanisms underlying the genetic architecture across populations. (3) Develop a public database and software tool to enable evolutionary informed network analysis of GWAS findings for the broader research community.

描述（由申请人提供）：遗传关联研究已成功识别出超过 1,000 个与人群中复杂疾病特征相关的遗传位点。然而，解释 GWAS 研究生成的大量数据以提高对疾病标志物和机制的理解仍然是一个核心挑战，这对于将 GWAS 研究结果转化为基因组医学应用，从而改进诊断、治疗和治疗至关重要。结果。最近将先前的生物信息纳入 GWAS 分析的努力通过提供用于优先关联的生物框架以及解释生物网络和通路背景下的多个相关位点，极大地增强了 GWAS 结果的解释。我们最近证明，位置特定的进化先验可以纳入 GWAS 结果的分析中，以优先考虑在研究中更具可重复性的变异。我们建议开发、研究和应用进化信息综合方法，包含并利用常见疾病的遗传复杂性。我们假设位置特异性进化特征可以纳入多尺度生物途径和网络分析中，并且进化知情途径和网络分析可以应用于现有的 GWAS 和临床数据集，以识别在人群和个体中引起复杂疾病表型的机制。我们建议通过追求以下具体目标来发展和评估这些假设：（1）开发新的进化途径和网络分析方法来解释 GWAS 结果。 (2) 将新方法应用于已建立的 GWAS 和 T2D 临床数据，以阐明跨人群遗传结构背后的疾病机制。 (3) 开发公共数据库和软件工具，以便为更广泛的研究界对 GWAS 研究结果进行进化知情网络分析。