Methods for Evolutionary Informed Network Analysis to Discover Disease Variation

描述（申请人提供）：遗传关联研究已成功鉴定出与人类复杂疾病性状相关的> 1，000个遗传基因座。然而，解释GWAS研究产生的大量数据仍然是一个核心挑战，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发现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。最近的努力，将以前的生物信息纳入GWAS分析，大大提高了GWAS的研究结果的解释，提供生物学框架，优先协会，并解释多个相关的基因座的背景下，生物网络和途径。我们最近证明，可以将位置特异性进化先验纳入GWAS结果的分析中，以优先考虑在研究中更具重现性的变体。我们建议开发，调查和应用进化知情的综合方法，拥抱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被纳入多尺度生物学途径和网络分析，并且进化信息途径和网络分析可以应用于现有的GWAS和临床数据集，以确定引起人群和个体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）发展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的疾病机制。(3)开发一个公共数据库和软件工具，以便为更广泛的研究界提供全球WAS研究结果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.