Multi-omics for obesity-associated liver disease discovery in Hispanics/Latinos: the Cameron County Hispanic Cohort

摘要 我们提交了《西班牙裔/拉丁裔肥胖相关肝病的多组学研究：卡梅伦家族》 响应RFA-HG-22-008的县西班牙裔队列“(以下简称CCHC-肝脏)，作为疾病研究站点 (D)将参加多组学促进健康和疾病联盟(下称“该联盟”)。 总的来说，该联盟将推动与使用多组学技术研究健康相关的科学 和疾病在祖先不同的人群中。CCHC-Liver将利用CCHC现有的基础设施， 西班牙裔/拉丁裔(HL)参与者的一个大型、随机确定、精致表型的纵向队列 已经同意将来联系的人。我们将实施肝病进展的纵向研究， 收集心脏代谢危险因素(CMRF)的系列标本和测量方法，这是健康的社会决定因素 (SDH)和代谢相关脂肪肝(MAFLD)，用连续瞬时弹性成像进行评估 (TE)和生物标志物(FIB-4，APRI)]总称“MAFLD”包括一系列慢性肝病， 包括非酒精性脂肪肝(NAFLD)、非酒精性脂肪性肝炎(NASH)和纤维化，并且没有 批准的药物疗法。它在HL人群中的患病率最高，但在可及组织中的纵向组学 对于肝病，即全血(WB)和腹部皮下脂肪(SAT)， 稀有的， 特地 为了解决这一差距，我们提出了两个目标。1)财团参与 作为疾病研究站点(DSS)。与来自RFA-HG-22-009[Omics生产中心]的团队一起 (OPCS)]和RFA-HG-22-010[数据分析和协调中心(DACC)]，我们将共同努力 完成三个业务子目标：制定收集、统一和整合的最佳做法 纵向多组型、表型和环境暴露数据；开发数据分析的最佳实践 检测和评估与健康和疾病状态相关的分子“概况”；并创建多个 研究社区可用的维度数据集。2)设计并实现了一个肝脏研究系统 未被充分研究的高危HL人群中的疾病进展。为了达到这个目标，我们将招收300名HL 200名患有MAFLD的参与者和100名未患疾病的参与者同意收集数据，未来研究使用， 从现有的基于人群的研究中广泛共享数据；2)收集表型数据和生物样本 适当保存以供OPC跨三个时间点生成组学数据；3)将生物样品提交给 OPC用于数据生产；4)整合数据以确定与肝脏相关的WB和SAT多组学的变化 疾病进展；5)在多重组学中进行因果推断，以使用孟德尔方法确定关联 随机化(MR)；6)将MAFLD相关遗传因素与多组学措施相结合进行评估 7)将SDH和CMRF与多组学和MAFLD相结合进行评估 因果中介；8)通过结构方程建模来表征新的多组学信号；以及9)确定 全球和地方血统对与肝病进展相关的多组学的影响。

ABSTRACT We submit “Multi-omics for obesity-associated liver disease discovery in Hispanics/Latinos: the Cameron County Hispanic Cohort” (hereafter CCHC-Liver) in response to RFA-HG-22-008, as a disease study site (DSS) that will participate in the Multi-omics for Health and Disease Consortium (hereafter, “the Consortium”). Collectively, this Consortium will advance the science related to use of multi-omics technologies to study health and disease in ancestrally diverse populations. CCHC-Liver will leverage the extant infrastructure of the CCHC, a large, randomly ascertained, exquisitely phenotyped, longitudinal cohort of Hispanic/Latino (HL) participants that have been consented for future contact. We will implement a longitudinal study of liver disease progression, collecting serial specimens and measures of cardiometabolic risk factors (CMRF), social determinants of health (SDH), and of metabolic-associated fatty liver disease (MAFLD), assessed with serial transient elastography (TE) and biomarkers (FIB-4, APRI)] .The umbrella term “MAFLD” encompasses a range of chronic liver diseases, including non-alcoholic fatty liver (NAFLD), non-alcoholic steatohepatitis (NASH), and fibrosis, and has no approved drug therapy. Its prevalence is highest in HL populations, yet, longitudinal omics in accessible tissues for liver disease, namely, whole blood (WB) and abdominal subcutaneous adipose (SAT), are scarce, particularly inthe most vulnerable populations.To address this gap, we propose two Aims. 1) Consortium participation as a disease study site (DSS). Together with the teams from RFA-HG-22-009 [Omics Production Centers (OPCs)] and RFA-HG-22-010 [Data Analysis and Coordination Center (DACC)], we will work collaboratively to complete three operational subaims to: develop best practices for the collection, harmonization, and integration of longitudinal multi-omic, phenotypic, and environmental exposure data; develop best practices for data analysis to detect and assess molecular “profiles” associated with healthy and disease states; and create a multi- dimensional dataset that is available to the research community. 2) Design and implement a study of liver disease progression in an understudied, high-risk HL population. In this aim we will 1) enroll 300 HL participants, 200 with MAFLD and 100 without disease, consented for collection of data, future research use, and broad data sharing from an extant population-based study; 2) collect phenotypic data and biospecimens suitably preserved for omics data generation by OPCs across three time points; 3) submit biospecimens to the OPCs for data production; 4) integrate data to identify changes in WB and SAT multi-omics associated with liver disease progression; 5) perform causal inference in multi-omics to determine associations using Mendelian randomization (MR); 6) combine MAFLD-associated genetic factors with multi-omics measures to evaluate mechanistic frameworks via colocalization; 7) combine SDH and CMRF with multi-omics and MAFLD to assess causal mediation; 8) characterize novel multi-omics signals via structural equation modeling; and 9) determine global and local ancestry effects on multi-omics associated with liver disease progression.