Discovery of Immunogenomic Associations with Disease and Differential Risk Across Diverse Populations

摘要 免疫相关基因中的遗传变异，如人类白细胞抗原（HLA）基因座中的遗传变异， 在器官系统中的作用。HLA变异，称为HLA等位基因，用于匹配器官捐赠者，并已被 与药物不良反应（ADR）、癌症、感染以及心血管和神经系统疾病相关。 然而，大多数研究都集中在HLA变异对特定免疫介导疾病的影响上; HLA变异对所有人类疾病影响尚未深入研究。拟议研究 该计划将解决识别免疫基因组学对广谱疾病影响的挑战 和ADR。以前关于HLA影响的研究几乎完全集中在欧洲人群体中， 因此，祖先群体之间的差异并没有得到很好的理解。我们所有人的可用性 研究计划（AoU），一个大型的，多样化的DNA生物库，加上电子健康记录（EHR）， 研究HLA等位基因如何同时影响多个不同人群的多种疾病。 我们建议进行系统的调查，HLA等位基因与疾病的关联，使用两个 基于全表型关联研究（PheWAS）的方法。PheWAS是一种疾病中性 这是一种确定广泛疾病中遗传变异之间关联的方法。在特定 目标1，将使用全基因组序列数据确定HLA等位基因，并将在 AllofUs以确定HLA等位基因在器官系统中的影响，并探索HLA等位基因的祖先差异。 HLA协会。我们将确定HLA-A、-B、-C、-DR和-DQ等位基因与一个全面的 在AoU中的主要祖先群体内和之间的一系列疾病。尽管功能强大，但PheWAS分析有限 识别单个等位基因与感兴趣的表型的联系， （HLA等位基因的组合，或HLA基因与一些其他基因组背景之间的组合）可能被遗漏。 具体目标2将解决这个缺点-我们将开发机器学习策略来探索效果 HLA等位基因相互作用对疾病的影响，并探索识别HLA多效性影响的潜力 等位基因这种创新的基于PheWAS的方法有可能发现许多新的机制， 疾病，确定可以预测疾病的生物标志物，并创建一个路线图，未来的研究人员 研究HLA变异对人类疾病的影响。正如我们以前的工作所表明的，PheWAS具有 将数十年的免疫基因组学发现浓缩为单一分析的潜力。当应用于- 研究，不同的人群，这项工作有可能加速这一领域的研究。这种方法可以 可以应用于许多其他基因组基因座，通过其他特征如性别和/或 性别，并确定疾病系统的多效性效应，创造了一些潜在的富有成效的 未来的研究方向。

ABSTRACT Genetic variation in immune-related genes, as in the human leukocyte antigen (HLA) locus, plays a pervasive role across organ systems. HLA variation, called HLA alleles, is used to match organ donors, and has been associated with adverse drug reactions (ADRs), cancer, infections, and cardiovascular and neurologic diseases. However, most studies focus on the impact of HLA variation on specific immune-mediated diseases; the broader influence of HLA variation across all human disease has not been investigated in depth. The proposed research program will address the challenge of identifying immunogenomic influence on a broad spectrum of diseases and ADRs. Previous studies of HLA influence have almost exclusively focused on populations of European descent, thus differences across ancestral groups are not well understood. The availability of the All of Us Research Program (AoU), a large, diverse DNA biobank coupled to electronic health records (EHR) enables investigation of how HLA alleles influence many diseases across multiple diverse populations simultaneously. We propose to perform systematic investigation of the association of HLA alleles with disease, using a two pronged approach based on the phenome-wide association study (PheWAS). PheWAS is a disease-neutral approach that identifies the association between genetic variation across a broad set of diseases. In Specific Aim 1, HLA alleles will be determined using whole genome sequence data, and PheWAS will be deployed in AllofUs to determine the influences of HLA alleles across organ systems, and to explore ancestral differences in HLA associations. We will determine association of HLA-A, -B, -C, -DR, and -DQ alleles with a comprehensive set of diseases within and across major ancestry groups in AoU. Despite its power, PheWAS analysis is limited to identifying single-allele connections to phenotypes of interest, so influences that result from HLA interactions (either combinations of HLA alleles, or between an HLA gene and some other genomic context) may be missed. Specific Aim 2 will address this shortcoming – we will develop Machine Learning strategies to explore the effect of HLA allele interactions on disease, and explore the potential for recognizing pleiotropic influences of HLA alleles. This innovative PheWAS-based approach has the potential to discover novel mechanisms of many diseases, identify biomarkers that may predict disease, and create a roadmap by which future researchers investigate the impact of HLA variation in human disease. As indicated by our previous work, PheWAS has the potential to condense decades of immunogenomic discoveries into a single analysis. When applied to under- studied, diverse populations, this work has the potential to accelerate this field of research. This approach can be applied to many other genomic loci, differential associations by other characteristics such as sex and/or gender, and identification of pleiotropic effects across disease systems, creating a number of potentially fruitful avenues of future research.