Genetic Architecture of Parkinson's Disease in African-American and Latino Veterans

帕金森病(PD)是增长最快的神经系统疾病，预计其在全球范围内流行 到2040年翻一番，这一趋势被一些人称为“帕金森病大流行”。治未病 迫切需要在早期或症状前阶段进行治疗和更好的检测方法 来自人类基因研究的数据使我们更接近这些目标。不幸的是，这样的研究 基本上被排除在外的非欧洲血统的人，有可能进一步恶化现有的健康差距 少数族裔人口。该项目试图通过研究遗传结构来解决这一知识差距。 在参加百万退伍军人计划(MVP)和其他队列的非洲裔美国人和拉丁裔人中，帕金森病的发病率。 为这个项目组建的研究团队在临床运动障碍方面拥有丰富的专业知识， 生物信息学、分子遗传学和统计遗传学，具有绘制疾病图谱的专门知识 “混合”(混合血统)群体中的基因。同一组调查人员最近发表了第一份 只有帕金森病的混合作图分析和全基因组关联研究(GWAS)曾在 拉丁裔人口(根据拉丁美洲遗传学研究联盟的队列) 帕金森病[大帕金森病])。 基本的方法将是执行两种互补的技术(1)混合映射， 一种利用当地血统来识别基因组中来自特定祖先的区域的技术 在病例和对照中，祖先群体的遗传频率更高，以及(2)拖拉机GWAS，一种新的分析方法 这种方法不同于传统的GWAS方法，是为适应混合个体而设计的。一次发现 样本将使用来自MVP和退伍军人帕金森病遗传学倡议(VET-VET)的队列创建 Pd)和复制样本将由Large-Pd和几个公开可用的数据集组装而成。 首先将在Discovery样本上执行混合映射和拖拉机GWAS，分析每个样本 祖先群体分开，所有群体结合在一起。我们还将执行关联的变量集测试 使用注释信息“(STAAR)执行以基因为中心的关联测试 适用于单标记分析(如GWAS)。这些过程将在复制中重复 用于验证结果的样本。将使用组合来执行发现的候选区域的优先顺序 (1)在基因组上的物理位置(位置定位)，(2)表达数量性状基因座(EQTL) 以及(3)染色质相互作用图谱。 此外，还将计算多基因风险分数(PR)。PRS是对一个人基因的估计 对某一性状或疾病的易感性，根据他们的基因型谱和相关的GWAs数据进行计算。这些 评分已被应用于越来越多的疾病，最终目标是风险分层 随后进行临床干预。但基于GWAS的PRS模型的结果来自欧洲血统的个人 当应用于非欧洲人口时，往往不太准确。因此，PRS模型将是 从Discovery示例的欧洲、非洲和拉丁裔组件及其 将比较所有三个子组的表现(例如，拉丁裔到非裔美国人和拉丁裔到 欧裔美国人)。最后，此项目的结果将与任何其他项目的结果进行交叉验证 未来几年将会有合适的研究。 该项目的结果将为更好地了解非洲帕金森病的遗传结构提供帮助 美国人和拉美人这一点很重要，原因有两个。首先，它使这个领域更接近于一个更公平的 在未来几年将遗传信息应用于临床决策方面的平衡(例如，PRS模型)。 其次，它开始释放在非欧洲人群中发现新PD基因的潜力，这将 进一步加深我们对帕金森病病理生理学的理解。

Parkinson’s disease (PD) is the fastest growing neurological disorder and its worldwide prevalence is expected to double by the year 2040, a trend that some have labeled the “PD pandemic.” Disease disease-modifying therapies and better methods of detection in early or pre-symptomatic phases are desperately needed and data from human genetic studies have moved us much closer to those goals. Unfortunately, such studies have largely excluded individuals of non-European origin which risks further worsening existing health disparities for minority populations. The project seeks to address this gap in knowledge by studying the genetic architecture of PD in African American and Latino participants in the Million Veteran Program (MVP) and other cohorts. The research team assembled for this project has extensive expertise in clinical movement disorders, bioinformatics, molecular genetics, and statistical genetics with specialized knowledge in mapping disease genes in “admixed” (mixed ancestry) populations. This same group of investigators recently published the first and only admixture mapping analysis and genome-wide association study (GWAS) of PD ever conducted in a Latino population (based on a cohort from the Latin American Research Consortium on the Genetics of Parkinson’s Disease [LARGE-PD]). The fundamental approach will be to perform two complementary techniques (1) admixture mapping, a technique that leverages local ancestry to identify regions of the genome where ancestry from a particular ancestral population is inherited more frequently in cases vs controls, and (2) Tractor GWAS, a new analytical approach that unlike traditional GWAS methods is designed to accommodate admixed individuals. A Discovery sample will be created using cohorts from MVP and the Veterans Parkinson’s Disease Genetics Initiative (Vet- PD) and a Replication sample will be assembled from LARGE-PD and several publicly available datasets. Admixture mapping and Tractor GWAS will first be performed on the Discovery Sample, analyzing each ancestry group separately and all groups combined. We will also perform the “variant-set test for association using annotation information” (STAAR) to perform gene-centric association tests of rare variants that are not suitable for single-marker analyses (such as GWAS). These processes will be repeated in the Replication sample to validate results. Prioritization of candidate regions discovered will be performed using a combination of (1) physical position on the genome (positional mapping), (2) expression quantitative trait locus (eQTL) mapping, and (3) chromatin interaction mapping. In addition, polygenic risk scores (PRS) will be calculated. A PRS is an estimate of an individual’s genetic liability to a trait or disease, calculated according to their genotype profile and relevant GWAS data. These scores have been applied to an increasing number of diseases with the eventual goal of risk stratification followed by clinical interventions. But PRS models based on GWAS results from individuals of European origin are often less accurate when applied to non-European populations. Therefore, PRS models will be constructed from the European, African, and Latino components of the Discovery sample and their performance will be compared across all three subgroups (e.g., Latino to African American and Latino to European American). Finally, findings from this project will be cross-validated with results from any other suitable studies that become available in future years. Results from this project will provide a better understanding of the genetic architecture of PD in African Americans and Latinos which is important for two reasons. First it moves the field closer to a more equitable balance in the application of genetic information to clinical decisions in future years (e.g., PRS models). Second, it begins to unlock the potential for new PD gene discovery in non-European populations which will further our understanding of PD pathophysiology.