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确定了人类群体中与复杂疾病特征相关的1000个遗传位点。然而，解释由GWAS研究产生的大量数据，以提高对疾病标志物的理解，从而提高对机制的理解，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、治疗和结果至关重要，仍然是一个核心挑战。最近将先前的生物学信息纳入GWAS分析的努力，通过提供优先关联的生物学框架，以及在生物网络和途径背景下解释多个相关位点，极大地增强了对GWAS结果的解释。我们最近证明，位置特异性进化先验可以纳入GWAS结果的分析，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，包括和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学途径和网络分析，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，以确定群体和个体中产生复杂疾病表型的机制。我们建议通过以下具体目标来发展和评估这些假设：(1)开发新的进化信息通路和网络分析方法来解释GWAS的发现。(2)将新方法应用于已建立的GWAS和T2D的临床数据，以阐明跨人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，为更广泛的研究界提供GWAS发现的进化知情网络分析。

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.