Methods for Evolutionary Informed Network Analysis to Discover Disease Variation

描述(由申请人提供)：遗传关联研究已经成功地在人类群体中确定了与复杂疾病特征相关的1000个遗传位点。然而，为了更好地理解疾病标记物和机制，解释GWAS研究产生的大量数据仍然是一个核心挑战，这些机制对于将Gwas研究结果转化为基因组医学应用，从而改善诊断、治疗和结果至关重要。最近为将先前的生物信息纳入地理信息系统分析而作出的努力，通过提供生物框架来确定联系的轻重缓急，并在生物网络和路径的背景下解释多个相关的位置，极大地加强了对地理信息系统调查结果的解释。我们最近证明，特定位置的进化先验可以结合到GWAS结果的分析中，以确定在不同研究中更具重复性的变异的优先顺序。我们建议开发、调查和应用进化知情的综合方法，这些方法包含并利用常见疾病的遗传复杂性。我们假设特定位置的进化特征可以结合到多尺度生物路径和网络分析中，并且进化信息路径和网络分析可以应用于现有的GWAS和临床数据集，以识别导致人群和个人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说：(1)开发新的进化信息途径和网络分析方法来解释GWAS的发现。(2)将新的方法应用于已建立的GWA和T2D的临床数据，以阐明跨群体遗传结构潜在的疾病机制。(3)开发一个公共数据库和软件工具，以便能够为更广泛的研究界对全球气候变化网络研究结果进行循序渐进的知情网络分析。

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.