Leveraging the Microbiome, Local Admixture, and Machine Learning to Optimize Anticoagulant Pharmacogenomics in Medically Underserved Patients

利用微生物组、局部混合物和机器学习来优化医疗服务不足的患者的抗凝药物基因组学

• 批准号: 10656719
• 负责人: Jason Hansen Karnes
• 金额: $ 10.68万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-12 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10656719
• 关键词: Admixture; Adverse drug event; Adverse event; Affect; African American; African American population; Algorithms; Anticoagulants; Asian; Award; CYP2C9 gene; Cardiovascular Diseases; Characteristics; Clinical; Clinical Research; Complex; DNA; Data; Dose; Drug Prescriptions; Drug Receptors; Electronic Health Record; Enzymes; Epigenetic Process; Ethnic group; European; Fostering; Frequencies; Funding; Gene Frequency; Generations; Genes; Genetic; Genome; Genomics; Genotype; Guidelines; Haplotypes; Hispanic; Individual; Investigation; Latino; Latino Population; Machine Learning; Medical Research; Minority Groups; Mission; Native American Ancestry; Outcome; Participant; Patient Self-Report; Patients; Pattern; Performance; Pharmaceutical Preparations; Pharmacogenetics; Pharmacogenomics; Pharmacologic Substance; Pharmacotherapy; Population; Population Heterogeneity; Public Health; Race; Research; Research Project Grants; Safety; Source; Testing; Therapeutic; Toxic effect; Translating; United States; United States National Institutes of Health; Variant; Warfarin; Work; clinically relevant; cohort; disparity elimination; disparity reduction; drug efficacy; ethnic diversity; genetic association; genome wide association study; genomic data; improved; medically underserved; medication safety; microbiome; novel; racial and ethnic; racial disparity; response; social health determinants; trait; treatment disparity

ABSTRACT Currently available pharmacogenomic (PGx) algorithms have critical limitations, including a lack of generalizability to non-white populations. Under-representation in clinical studies, the propensity to cause adverse events, and a lack of consideration of admixed populations in clinical PGx guidelines are all factors that contribute to limited utility of PGx algorithms in diverse populations. Thus, our originally awarded proposal focused on improving warfarin stable dose prediction, as it continues to remain one of the most prescribed drugs in the United States and a leading cause of adverse drug events particularly in underserved patients such as African Americans (AAs) and Latinos. Preliminary results from this proposal demonstrate that generation of local ancestry (LA) estimates enables inclusion of admixed populations and improves power in genetic association studies on diverse and admixed populations. Thus, we seek to expand upon our original proposal to perform more inclusive pharmacogenetic studies by generating LA estimates in the large, racially/ethnically diverse AllofUs cohort. We will investigate the relationships between LA and PGx variants and showcase the utility of LA estimates and the AllofUs cohort by identifying novel PGx variants associated with warfarin stable dose. Our overarching hypothesis is that LA can be used to enable genomic association analyses that are more inclusive of admixed and diverse cohorts and to uncover novel findings that were previously overlooked in ancestrally European populations. We will pursue two Specific Aims (SAs) to test this hypothesis: (SA1) Characterize LA for major pharmacogenes and its correlation with global ancestry and PGx variants in diverse populations from AllofUs and; (SA2) Leverage LA to identify novel PGx variants related to warfarin stable dose in admixed AllofUs participants. In SA1, We will estimate LA using RFMix from genome array and sequencing data in the AllofUs Controlled Tier. We will test if LA at clinically relevant pharmacogenes correlates with patient-level global ancestry and presence of clinically relevant pharmacogenomic variants. In SA2, we will incorporate LA estimates from SA1 into genome-wide association analyses for warfarin stable dose using Tractor while controlling for clinical characteristics and clinically relevant PGx variants in admixed individuals from AllofUs, including Hispanic, AA, and multi-race individuals. The outcomes of this work will provide a framework for LA investigation with other PGx drug-gene pairs and enable the identification of novel PGx variants that affect drug response in medically underserved, diverse populations. This research has the potential to identify new sources of variability in warfarin dose, improve the safety and efficacy of warfarin treatment, and reduce disparities in PGx research for medically underserved patients.

抽象的 目前可用的药物基因组 (PGx) 算法具有严重的局限性，包括缺乏 对非白人群体的普遍适用性。临床研究中代表性不足，导致 不良事件以及临床 PGx 指南中缺乏对混合人群的考虑都是导致 导致 PGx 算法在不同人群中的实用性有限。因此，我们最初获奖的提案 专注于改善华法林稳定剂量预测，因为它仍然是最常用的药物之一 在美国，它是药物不良事件的主要原因，特别是在服务不足的患者中，例如 非裔美国人 (AA) 和拉丁裔美国人。该提案的初步结果表明，本地 血统 (LA) 估计能够纳入混合群体并提高遗传关联的能力 对多样化和混合人群的研究。因此，我们寻求扩展我们最初的提议来执行 通过在大型、种族/民族多样化的群体中生成 LA 估计值，进行更具包容性的药物遗传学研究 我们所有人的队列。我们将研究 LA 和 PGx 变体之间的关系并展示 LA 的实用性 通过识别与华法林稳定剂量相关的新的 PGx 变异来估计和 AllofUs 队列。我们的 总体假设是 LA 可用于实现更具包容性的基因组关联分析 混合和多样化的群体，并发现以前在祖先中被忽视的新发现 欧洲人口。我们将追求两个具体目标 (SA) 来检验这一假设： (SA1) 描述洛杉矶的特征 主要药物基因及其与全球血统和 PGx 变异在不同人群中的相关性 我们所有人以及； (SA2) 利用 LA 识别与混合 AllofUs 中华法林稳定剂量相关的新型 PGx 变体 参与者。在 SA1 中，我们将使用 RFMix 根据 AllofUs 中的基因组阵列和测序数据估计 LA 受控层。我们将测试临床相关药物基因的 LA 是否与患者水平的整体相关 临床相关药物基因组变异的血统和存在。在 SA2 中，我们将纳入 LA 估算 使用 Tractor 从 SA1 进行华法林稳定剂量的全基因组关联分析，同时控制 AllofUs 混合个体的临床特征和临床相关 PGx 变异，包括 西班牙裔、AA 和多种族人士。这项工作的成果将为洛杉矶调查提供一个框架 与其他 PGx 药物基因对，并能够识别影响药物反应的新型 PGx 变体 医疗服务不足，人口多样化。这项研究有可能识别新的变异来源 华法林剂量，提高华法林治疗的安全性和有效性，并减少 PGx 研究的差异 对于医疗服务不足的患者。

项目成果

PharmGKB summary: heparin-induced thrombocytopenia pathway, adverse drug reaction.

PharmGKB总结：肝素诱导的血小板减少途径、药物不良反应。

• 发表时间: 2022-04-01
• 作者: Miller, Elise;Norwood, Charles;Giles, Jason B;Huddart, Rachel;Karnes, Jason H;Whirl;Klein, Teri E
• 通讯作者: Klein, Teri E

Elucidation of Cellular Contributions to Heparin-Induced Thrombocytopenia Using Omic Approaches.

使用组学方法阐明细胞对肝素诱导的血小板减少症的贡献。

• 发表时间: 2021
• 作者: Giles, Jason B;Miller, Elise C;Steiner, Heidi E;Karnes, Jason H
• 通讯作者: Karnes, Jason H

Mining the Plasma Proteome for Insights into the Molecular Pathology of Pulmonary Arterial Hypertension.

挖掘血浆蛋白质组以深入了解肺动脉高压的分子病理学。

• 发表时间: 2022-06-15
• 影响因子: 24.7
• 作者: Harbaum, Lars;Rhodes, Christopher J;Wharton, John;Lawrie, Allan;Karnes, Jason H;Desai, Ankit A;Nichols, William C;Humbert, Marc;Montani, David;Girerd, Barbara;Sitbon, Olivier;Boehm, Mario;Novoyatleva, Tatyana;Schermuly, Ralph T;Ghofrani, H A
• 通讯作者: Ghofrani, H A

Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans.

机器学习预测美国拉丁裔和拉丁美洲人的稳定华法林剂量。

• 发表时间: 2021
• 作者: Steiner, Heidi E;Giles, Jason B;Patterson, Hayley Knight;Feng, Jianglin;El Rouby, Nihal;Claudio, Karla;Marcatto, Leiliane Rodrigues;Tavares, Leticia Camargo;Galvez, Jubby Marcela;Calderon;Sun, Xiaoxiao;Hutz, Mara H;Scott
• 通讯作者: Scott

Mendelian randomisation and experimental medicine approaches to interleukin-6 as a drug target in pulmonary arterial hypertension.

孟德尔随机化和实验医学方法将白细胞介素 6 作为肺动脉高压的药物靶点。

• 发表时间: 2022
• 作者: Toshner, Mark;Church, Colin;Harbaum, Lars;Rhodes, Christopher;Villar Moreschi, Sofia S;Liley, James;Jones, Rowena;Arora, Amit;Batai, Ken;Desai, Ankit A;Coghlan, John G;Gibbs, J Simon R;Gor, Dee;Gräf, Stefan;Harlow, Louise;Hernandez
• 通讯作者: Hernandez