Health Disparities and Genetic Architecture of Lupus in African Americans

描述(申请人提供)：系统性红斑狼疮(SLE或狼疮)是一种具有坚实遗传基础的多系统、临床异质性的自身免疫性疾病。系统性红斑狼疮对女性(90%)和非洲裔美国人(AA)等少数民族的影响不成比例。与欧美人(EA)相比，AA的患病率高3-5倍，临床表现和器官损害更严重，尤其是肾脏(狼疮性肾炎)。种族间的遗传差异可以解释疾病严重程度和临床表现的潜在差异。然而，狼疮的遗传结构，特别是再生障碍性贫血，在很大程度上是未知的。虽然最近对欧洲和亚洲祖先的全基因组关联研究发现了40多个易感基因座，但这些研究都没有集中在AA中来验证这些关联的稳健性或识别新的信号。此外，由于大多数相关变异位于内含子或基因内区，GWAS不能准确定位实际的易感变异或提供这些关联信号背后的因果变异的全等位基因谱。因此，很难预测功能后果。 基因关联的结果。这种对潜在生物学机制的缺乏理解阻碍了对SLE诊断和治疗的改进。我们的研究团队已经获得了必要的经验、专业知识、资源和基础设施，以超越GWAS，加快发现和表征GWAS信号背后的因果变量。我们已经成功地在ITGAM、IFIH1和NCF2中发现了功能性SLE易感变异，并建议将这一发现努力扩展到AA的其他候选基因。这是了解系统性红斑狼疮疾病差异的基本前提。我们的实验设计结合了来自遗传学(包括测序)、临床亚型和自身抗体、eQTL和ENCODE(增强子、染色质状态、DNA和组蛋白甲基化等的注释)的数据，然后是生物信息学和分子建模，以了解预测功能SNPs的机制效应。目标1是对&gt；1500个AA样本进行有针对性的深度测序，以彻底评估25个强相关(10-24；p&lt；10-6)信号。目标2是使用研究外对照(DBGaP)(&gt；18,000)进行基于归因的关联分析，以最大限度地提高检测相关变异的能力，并在&gt；4000 AA样本中确认这些关联。我们建议的队列有足够的能力检测罕见和常见的变异。目的3是通过评估易感变异与SLE临床亚型(如狼疮性肾炎)和自身抗体之间的关系，阐明SLE的遗传和临床异质性。目标4是利用生物信息学分析和分子模拟来预测SLE易感变异的机制效应。最终，该项目将产生一组与系统性红斑狼疮相关的再生障碍性贫血的功能变异，为深入的生物学实验提供基础，以确定病理机制，并定义遗传结构以揭示潜在的健康差异。这可能会为未来的治疗干预定义新的靶点和指导选择。

DESCRIPTION (provided by applicant): Systemic lupus erythematosus (SLE or lupus) is a multi-system, clinically heterogeneous autoimmune disease with substantial genetic basis. SLE disproportionately affects women (90%) and ethnic minorities like African- Americans (AA). Compared to European-Americans (EA), AA show 3-5 fold higher prevalence and have more severe clinical manifestations and organ damage, especially kidneys (lupus nephritis). Genetic variation between ethnicities could account for underlying differences in disease severity and clinical manifestations. However, the genetic architecture of lupus, especially in AA, is largely unknown. While recent genome-wide association studies (GWAS) on European and Asian ancestries identified over 40 susceptibility loci, none of these were focused in AA to verify the robustness of these association or identify novel signals. Additionally, since the majority of associated variants are located in introns or intragenic regions, GWAS is not successful for pinpointing actual predisposing variants or providing the full allelic spectrum of causal variants underlying these association signals. Therefore, it is difficult to predict functional consequences of genetic association. This poor understanding of underlying biological mechanisms hinders improvements in the diagnosis and treatment for SLE. Our research team has acquired experience, expertise, resources, and infrastructure necessary to move beyond GWAS to accelerate the discovery and characterization of causal variants underlying GWAS signals. We have successfully identified functional SLE predisposing variants in ITGAM, IFIH1 and NCF2, and propose extending this discovery effort to other candidate genes in AA. This is an essential prerequisite to understanding disease disparities in SLE. Our experimental design incorporates data from genetics (including sequencing), clinical sub-phenotypes and autoantibodies, eQTLs, and ENCODE (annotation of enhancers, chromatin states, DNA and histone methylation, etc.), followed by bioinformatics and molecular modeling for understanding the mechanistic effects to predict functional SNPs. Aim 1 is to perform targeted deep-sequencing on >1500 AA samples to thoroughly assess 25 strongly associated (10-24<p<10-6) signals. Aim 2 is to conduct imputation-based association analysis using out-of-study controls (dbGaP) (>18,000) in order to maximize power to detect associated variants, and confirm these associations in >4000 AA samples. Our proposed cohort has adequate power to detect both rare and common variants. Aim 3 is to elucidate genetic and clinical heterogeneity of SLE by assessing association between predisposing variants and SLE clinical sub-phenotypes (e.g., lupus nephritis) and autoantibodies. Aim 4 is to predict mechanistic effects of SLE-predisposing variants using bioinformatics analysis and molecular modeling. Ultimately, this project will yield a set of SLE associated functional variants in AA, providing the basis for in-depth biological experiments to define the pathological mechanisms, and define genetic architecture to uncover underlying health disparities. This may define novel targets and guide options for future therapeutic interventions.