Genetic Prediction for Treatment Resistance in Kawasaki Disease

项目摘要/摘要 川崎病(KD)是导致儿童心血管疾病的主要原因。对静脉注射免疫球蛋白反应差 仍然是KD患者冠状动脉风险的关键决定因素之一。无法预测这种反应 而发生持续性冠状动脉动脉瘤的可能性是导致 强化治疗的进展和发展。目前可用的数据表明KD易感性和 治疗反应，以及冠状动脉疾病的倾向，取决于单个患者的 遗传背景。旨在确定合适的遗传生物标记物的研究已受到以下方面的影响：1) 表型缺乏严密性，2)使用全基因组关联研究，通常使用芯片或阵列 检测常见的变异而不是低频或罕见的变异，3)机制不清楚 KD患者IVIG抗炎作用的研究(对指导大多数药物基因组学研究是必要的)4)关注基因 候选人，这是不切实际的临床测试，以及5)模糊的种族分配方法 混淆的药物基因组学。此外，外显子组测序和分析可能会遗漏潜在的 作为IVIG抗炎机制的重要变体包括基因间转录调控 地区。我们假设，通过使用改进的和严格的表型技术结合 全基因组测序(WGS)和分析，我们将能够识别精选的生物标记物 川崎病治疗反应和冠状动脉动脉瘤发展的预测。太平洋西北部 川崎病数据-生物库，主要通过Pi Portman，R21HL090558， Thrasher研究基金会；以及位于东南部什雷斯塔的Pi AHA积累了DNA和临床 来自800多名KD患者的数据，有资格进行药物基因组学分析。我们将利用这笔财富 DNA和临床数据以及最近更新的AHA临床KD标准，以识别罕见和 决定IVIG治疗反应的常见变种。WGS还将允许)识别 个体私人SNPs(稀有变种)，b)识别特定人群的私人SNPs，c)建立 完整的遗传变异情况，包括结构变异(CNV和插入/缺失)，d)基因- 基于对常见和罕见变异的分析，以及e)识别实际的功能SNP 连锁不平衡(LD)中的普通归因型或SNPs。此外，我们将考虑种族因素，以及 KD中的重要变异，通过使用血统信息标记和主体进行严格的种族分配 成分分析。我们将使用严格的方法来实现以下具体目标1)执行 全基因组测序鉴定基因变异可作为IVIG的临床生物标志物 川崎病患者耐药情况分析2)确定与巨大冠状动脉相关的新的基因组变异 KD患儿中的动脉瘤(GCA)。3)准备评估非洲人对IVIG的抵抗力是否更大 如果这种反应取决于基于种族的基因变异频率的差异。

PROJECT SUMMARY/ABSTRACT Kawasaki Disease (KD) is a major contributor to cardiovascular morbidity in children. Poor response to IVIG remains one of the critical determinants of coronary artery risk in KD. The inability to predict this response and the potential for developing persistent coronary artery aneurysms serves as a major impediment to progress and development of intensified therapy. Currently available data indicate that KD susceptibility and treatment response, as well as the propensity for coronary artery disease, depend on an individual patient's genetic background. Studies directed at identifying appropriate genetic biomarkers have been impaired by: 1) phenotyping lacking rigor, 2) use of genome wide association studies often employing chips or arrays for detection of common variants rather than low frequency or rare variants, 3) lack of clarity for the mechanisms of IVIG anti-inflammation in KD (necessary for guiding most pharmacogenomics studies) 4) focus on gene candidates, which are impractical for clinical testing, and 5) vague racial assignment methodology confounding pharmacogenomics. Furthermore, exome sequencing and analyses likely would miss potential important variants as IVIG anti-inflammatory mechanism includes transcriptional regulation at intergenic regions. We hypothesize that, by using improved and rigorous phenotyping techniques in combination with whole genome sequencing (WGS) and analyses, we will be able to identify select biomarkers for accurate prediction of KD treatment response and development of coronary aneurysms. The Pacific Northwest Kawasaki Disease Data-Biobank, established mainly through funding via PI Portman, R21HL090558, Thrasher Research Foundation; and PI, Shrestha, Southeastern AHA has accumulated DNA and clinical data from over 800 KD patients, eligible for pharmacogenomics analyses. We will leverage this wealth of DNA and clinical data along with recently updated AHA clinical KD criteria in order to identify rare and common variants, which determine IVIG treatment response. WGS will also allow a) identification of individual private SNPs (rare variants), b) identification of population-specific private SNPs, c) building a complete picture of genetic variations including structural variants (CNVs and insertion/deletions), d) gene- based analysis of both common and rare variants, and e) identification of actual functional SNPs as opposed to common imputed or SNPs in linkage disequilibrium (LD). Additionally, we will account for race, an important variant in KD, by rigorous racial assignment using ancestry information markers and principal component analyses. We will use rigorous methodology to achieve the following specific aims 1) Perform whole genome sequencing to identify genetic variations, which could serve as clinical biomarkers for IVIG resistance in KD patients. 2) Determine novel genomic variants associated with giant coronary artery aneurysms (GCA) among children with KD. 3) Prepare to assess if IVIG resistance is greater among African Americans and if this response depends on racial based differences in the frequency of genetic variations.