Health Disparities and Genetic Architecture of Lupus in African Americans

描述（由申请人提供）：系统性红斑狼疮（SLE或狼疮）是一种多系统、临床异质性自身免疫性疾病，具有实质性遗传基础。SLE不成比例地影响妇女（90%）和少数民族，如非洲裔美国人（AA）。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）。种族间的遗传变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AA，在很大程度上是未知的。虽然最近对欧洲和亚洲血统的全基因组关联研究（GWAS）确定了40多个易感基因座，但这些基因座都没有集中在AA中，以验证这些关联的稳健性或识别新信号。此外，由于大多数相关变异位于内含子或基因内区域，GWAS无法成功地精确定位实际的易感变异或提供这些相关信号背后的致病变异的完整等位基因谱。因此，很难预测功能性后果 基因关联。这种对潜在生物学机制的认识不足阻碍了SLE诊断和治疗的改善。我们的研究团队已经获得了超越GWAS所需的经验、专业知识、资源和基础设施，以加速发现和表征GWAS信号背后的因果变异。我们已经成功地确定了ITGAM，IFIH 1和NCF 2的功能性SLE易感变异，并建议将这一发现工作扩展到AA的其他候选基因。这是了解SLE疾病差异的必要前提。我们的实验设计结合了来自遗传学（包括测序）、临床亚表型和自身抗体、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，其次是生物信息学和分子建模，以了解预测功能SNP的机制。目的1是对>1500个AA样品进行靶向深度测序，以彻底评估25个强相关（10-24<p<10-6）信号。目的2是使用研究外对照（dbGaP）（> 18，000）进行基于插补的关联分析，以最大化检测相关变体的功效，并在>4000个AA样本中确认这些关联。我们提出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体与SLE临床亚表型（例如，狼疮性肾炎）和自身抗体。目的4是使用生物信息学分析和分子建模来预测SLE易感变体的机制作用。最终，该项目将产生一组与SLE相关的AA功能变体，为深入的生物学实验提供基础，以定义病理机制，并定义遗传结构以揭示潜在的健康差异。这可能会定义新的目标，并指导未来的治疗干预措施的选择。

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.