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

发现免疫基因组与不同人群的疾病和差异风险的关联

• 批准号: 10796657
• 负责人: Jason Hansen Karnes
• 金额: $ 22.36万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-10 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10796657
• 关键词: Acceleration; Address; African; African American; All of Us Research Program; Alleles; American; Antigen Presentation; Antigenic Variation; Attention; Binding; Bioinformatics; Biological; Biology; Cardiovascular Diseases; Catalogs; Characteristics; Classification; Coupled; DNA; Data; Data Set; Development; Diagnosis; Digit structure; Disease; Disease stratification; Disease susceptibility; Disparate; Disparity; Electronic Health Record; Ethnic Population; European; European ancestry; Future; Gender; Genes; Genetic; Genetic Variation; Genome; Genomics; HLA Antigens; Hispanic; Human; Immune; Immune response; Immunogenomics; Individual; Infection; Investigation; Knowledge; Latino; Literature; Machine Learning; Major Histocompatibility Complex; Malignant Neoplasms; Mediating; Methods; Mission; Modeling; Neural Network Simulation; Organ Donor; Participant; Pathogenesis; Patients; Peptides; Phenotype; Play; Population; Population Heterogeneity; Prevalence; Public Health; Research; Research Personnel; Risk; Risk Factors; Role; System; Techniques; Testing; Underrepresented Populations; United States National Institutes of Health; Variant; Work; adverse drug reaction; biobank; biomarker identification; body system; combat; combinatorial; data acquisition; disorder risk; diverse data; ethnic disparity; genome sequencing; genome wide association study; genomic locus; health inequalities; human disease; immunoregulation; improved; innovation; interest; learning strategy; machine learning method; nervous system disorder; novel; novel strategies; phenome; pleiotropism; programs; protein complex; racial disparity; racial population; sex; social health determinants; tool; whole genome

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.

抽象的 免疫相关基因的遗传变异，如人类白细胞抗原 (HLA) 基因座，起着普遍的作用。 HLA 变异（称为 HLA 等位基因）用于匹配器官捐献者，并且已被用于跨器官系统的作用。 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，大多数研究关注的是 HLA 变异对特定免疫介导疾病的影响。 HLA 变异对所有人类疾病的影响尚未得到深入研究。 该计划将解决识别免疫基因组对广泛疾病的影响的挑战 之前关于 HLA 影响的研究几乎全部集中在欧洲人群。 血统，因此我们所有人的可用性还没有得到很好的理解。 研究计划 (AoU) 是一个大型、多样化的 DNA 生物库，与电子健康记录 (EHR) 相结合，使 研究 HLA 等位基因如何同时影响多个不同人群的许多疾病。 我们建议使用两种方法对 HLA 等位基因与疾病的关联进行系统研究 基于全表组关联研究（PheWAS）的多管齐下的方法是一种疾病中性的方法。 确定多种疾病遗传变异之间关联的方法。 目标 1，将使用全基因组序列数据确定 HLA 等位基因，并将 PheWAS 部署在 AllofUs 确定 HLA 等位基因对器官系统的影响，并探索祖先的差异 我们将通过全面的分析来确定 HLA-A、-B、-C、-DR 和 -DQ 等位基因的关联。 尽管 PheWAS 分析功能强大，但其分析能力有限。 识别与感兴趣的表型的单等位基因连接，以及 HLA 相互作用产生的影响 （HLA 等位基因的组合，或 HLA 基因与其他基因组背景之间的组合）可能会被遗漏。 具体目标 2 将解决这个缺点——我们将制定机器学习策略来探索其效果 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 这种基于 PheWAS 的创新方法有可能发现许多新的机制。 疾病，识别可以预测疾病的生物标志物，并创建一个路线图，未来的研究人员可以通过该路线图 研究 HLA 变异对人类疾病的影响 正如我们之前的工作所示，PheWAS 具有以下特点： 当应用于不足时，有可能将数十年的免疫基因组发现浓缩为单一分析。 研究的不同人群，这项工作有可能加速这一领域的研究。 可应用于许多其他基因组位点，通过其他特征（例如性别和/或）进行差异关联 性别，以及跨疾病系统多效性的识别，创造了许多潜在的富有成效的成果 未来的研究途径。