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; 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地识别了人口中与复杂疾病特征相关的1,000个遗传基因座。但是，将GWAS研究产生的大量数据解释为对疾病标志物的理解，因此，这对于将GWAS发现转化为基因组医学应用至关重要，从而改善了诊断，治疗和结果的改善，这仍然是一个核心挑战。通过提供生物学框架以优先考虑关联，并在生物网络和途径的背景下解释多个相关基因座，将先前的生物信息纳入GWAS分析中的最新努力大大增强了GWAS发现的解释。我们最近证明，可以将特定位置的进化先验纳入GWAS结果的分析中，以优先考虑在整个研究中更可重现的变体。我们建议开发，调查和应用进化知情的综合方法，以包含和利用普通疾病的遗传复杂性。我们假设可以将特定位置的进化特征纳入多尺度的生物途径和网络分析中，并且可以将进化的知情途径和网络分析应用于现有的GWAS和临床数据集，以识别导致种群和个人中复杂疾病表型的机制。我们建议通过追求以下特定目的来开发和评估这些假设：（1）开发新颖的进化信息途径和网络分析方法来解释GWAS发现。 （2）将新方法应用于已建立的GWAS和T2D的临床数据，以阐明跨种群遗传结构的疾病机制。 （3）开发一个公共数据库和软件工具，以实现对更广泛的研究社区GWAS发现的进化知情网络分析。