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

摘要 我们提交“西班牙裔/拉丁裔肥胖相关肝病发现的多组学：卡梅隆 县西班牙裔队列”（以下简称CCHC-Liver）作为疾病研究中心响应RFA-HG-22-008 (DSS)将参加健康和疾病多组学联盟（以下简称“联盟”）。 总的来说，该联盟将推进与使用多组学技术研究健康相关的科学 和疾病之间的关系。CCHC-Liver将利用CCHC现有的基础设施， 一个由西班牙裔/拉丁裔（HL）参与者组成的大型、随机确定的、精确表型分析的纵向队列 同意以后联系的人我们将实施肝病进展的纵向研究， 收集系列样本和心脏代谢危险因素（CMRF）的测量，健康的社会决定因素 (SDH)和代谢相关性脂肪肝（MAFLD），采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 批准的药物治疗。其患病率在HL人群中最高，然而，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）， 稀少， 特别 为了弥补这一差距，我们提出了两个目标。1)联合体的参与 疾病研究中心（DSS）。与来自RFA-HG-22-009 [组学生产中心]的团队一起 （OPC）]和RFA-HG-22-010 [数据分析和协调中心（DACC）]，我们将共同努力， 完成三个业务次级目标：制定收集、协调和整合的最佳做法 纵向多组学、表型和环境暴露数据;制定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”;并创建一个多 多维数据集，可用于研究社区。2)设计并实施肝脏研究 研究不足的高风险HL人群中的疾病进展。为此，我们将1）招募300名HL 参与者，200名患有MAFLD和100名没有疾病，同意收集数据，未来的研究使用， 和广泛的数据共享从现存的人口为基础的研究; 2）收集表型数据和生物标本 适当保存以供OPC在三个时间点生成组学数据; 3）将生物样本提交给 用于数据生成的OPC; 4）整合数据以识别与肝脏相关的WB和SAT多组学的变化 疾病进展; 5）在多组学中进行因果推断，以使用孟德尔遗传算法确定关联 6）将联合收割机MAFLD相关的遗传因素与多组学测量相结合，以评估 7）将联合收割机SDH和CMRF与多组学和MAFLD相结合， 因果调解; 8）通过结构方程建模表征新的多组学信号;以及9）确定 全球和当地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.