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

摘要 目前可用的药物基因组学(PGx)算法具有严重的局限性，包括缺乏 可推广到非白人人群。在临床研究中代表性不足，导致 不良事件，以及在临床PGx指南中缺乏对混合人群的考虑都是 导致PGx算法在不同群体中的实用性有限。因此，我们最初获奖的提案 专注于改进华法林稳定剂量预测，因为它仍然是最常开出处方的药物之一 在美国，并且是不良药物事件的主要原因，特别是在服务不足的患者中，例如 非洲裔美国人(AA)和拉丁裔美国人。这一提议的初步结果表明，产生的局部 祖先(LA)估计使混合种群得以纳入，并提高了遗传关联的能力 关于多样化和混杂种群的研究。因此，我们寻求扩展我们最初的建议，以执行 通过在种族/民族多样性的大范围内生成LA估计，进行更具包容性的药物遗传学研究 阿洛弗斯队列。我们将研究LA和PGx变体之间的关系，并展示LA的效用 通过识别与华法林稳定剂量相关的新的PGx变种，评估和allfus的队列。我们的 最重要的假设是LA可以用来实现更具包容性的基因组关联分析 混合和多样化的队列，并发现以前在祖先中被忽视的新发现 欧洲人口。我们将追求两个特定目标(SA)来检验这一假设：(SA1)表征LA 主要药源及其与全球血统和不同人群中PGx变异的相关性 AllfU和；(SA2)利用LA识别与混合AllfU中的华法林稳定剂量相关的新的PGx变种 参与者。在SA1中，我们将使用来自基因组阵列的RFMix和Alallfus中的测序数据来估计LA 控制层。我们将测试临床相关药源的LA是否与患者水平的全球相关 临床上相关的药物基因组变异的祖先和存在。在SA2中，我们将纳入LA估计 从SA1到全基因组的关联分析--使用拖拉机同时控制 来自异基因输尿系的混合个体的临床特征和临床相关的PGx变异，包括 西班牙裔、AA和多种族个体。这项工作的结果将为LA的调查提供一个框架 与其他PGx药物基因对进行比较，并能够识别影响药物反应的新的PGx变异体 医疗服务不足，多样化的人群。这项研究有可能发现新的变异性来源。 在华法林剂量方面，提高华法林治疗的安全性和有效性，并减少在PGx研究中的差距 对于医疗服务不足的患者。

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.