Network Models for Metabolomics

代谢组学网络模型

• 批准号: 10225528
• 负责人: RAJI BALASUBRAMANIAN
• 金额: $ 33.78万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10225528
• 关键词: Address; African Caribbean; Area; Biochemical Pathway; Biochemical Process; Birth; Birth Weight; Blood specimen; Body Composition; Cardiovascular Diseases; Clinical; Clinical Data; Collaborations; Complex; Computing Methodologies; Coronary heart disease; Data; Data Analyses; Data Set; Data Sources; Dependence; Detection; Development; Disease; Estrogens; Etiology; European; Exhibits; Funding; Gender; Genetic; Gestational Diabetes; Glucose; Health; Hyperglycemia; Investigation; Investments; Link; Measures; Mediating; Metabolic; Methodology; Methods; Mexican Americans; Mining; Modeling; Mothers; Network-based; Newborn Infant; Nurses' Health Study; Outcome; Outcome Study; Pathway Analysis; Pathway interactions; Pattern; Phase; Placebos; Plant Roots; Pregnancy; Progestins; Research; Research Design; Research Priority; Risk; Sampling; Sampling Studies; Scientist; Sex Differences; Statistical Methods; Statistical Models; Stroke; Technology; Testing; United States National Institutes of Health; Woman; Women' s Health; Work; adverse pregnancy outcome; age group; age related; biomarker discovery; boys; case control; clinical investigation; clinically relevant; cohesion; data mining; data structure; design; dietary; experience; fetal programming; flexibility; girls; health data; hormone therapy; insight; interest; metabolomics; network models; newborn adiposity; novel; programs; sex; small molecule; stroke risk

Summary Our proposal describes network based approaches for the analysis of data from metabolomics studies. The specific aims of this proposal include: Aim 1: Variable selection methods in metabolomics studies, incorporating metabolite dependence and external pathway information. We propose a Bayesian variable selection approach to incorporate both a partially observed, external pathway network and a data-driven partial correlation network. Aim 2: Models to identify differential metabolic networks that characterize groups within a study, and addi- tionally detect subcomponents with group-specific associations with an outcome. When metabolic networks differ according to groups, exposure levels (e.g. treatment) or other factors, our proposed framework will provide an approach to identify group-specific networks as well as subcomponents that are associated with outcome, in a possibly group-specific manner. Aim 3: Methods to identify metabolite subnetworks that collectively mediate the relationship between an ex- posure and an outcome. We propose a two-phase analysis framework involving (1) Detection of metabolite subnet- works enriched for association with the outcome; and (2) Estimation of the magnitude of the indirect effects mediated by metabolite subnetworks. Application to testing clinical hypotheses in the WHI, NHS and HAPO metabolomics studies: Using methods developed in Aims 1, we will identify metabolites and modules associated with risk of stroke in the NHS and maternal metabolomic markers of newborn adiposity in the HAPO study. Using methods in Aim 2, in the WHI, we will identify metabolic subnetworks that change due to initiation of hormone therapy (estrogen, progestin plus estrogen, placebo) within age groups, with treatment/age dependent modules associated with subsequent risk of CHD; in the HAPO study, detect maternal metabolite networks that differ between mothers of boys versus mothers of girls and sex-specific subcomponents that inform sex-related differences in newborn body composition related to maternal glycemia during pregnancy. Aim 3 methods will be applied to detect metabolite subnetworks that potentially mediate the association of exposures such as dietary score and risk of CHD in the WHI; and maternal glucose during pregnancy and newborn adiposity in HAPO. IMPACT: Significant federal investment has been made into research of the metabolomic underpinnings of complex disorders, such as through the NIH's Common Fund Metabolomics program. Our interdisciplinary team proposes to develop and apply new statistical models to effectively mine rapidly growing metabolomics data sources to elucidate the etiology of complex disorders such as CHD, stroke and maternal glycemia during pregnancy as it relates to newborn size at birth.

总结 我们的建议描述了基于网络的方法，从代谢组学研究的数据分析。规格 这项建议的目的包括： 目的1：代谢组学研究中的变量选择方法，包括代谢物依赖性和外部 路径信息。我们提出了一种贝叶斯变量选择方法， 外部通路网络和数据驱动的部分相关网络。 目的2：模型，以确定差异代谢网络的特点组内的研究，并添加- 检测与结果具有特定于组的关联的子成分。当代谢网络 不同的群体，暴露水平（例如治疗）或其他因素，我们建议的框架将提供一个 一种识别特定群体网络以及与结果相关的子组件的方法， 组特定的方式。 目的3：确定共同介导前-后代谢产物之间关系的代谢物子网络的方法。 一个结果，一个结果。我们提出了一个两阶段的分析框架，包括（1）检测代谢物子网- 作品因与结果的关联而丰富;以及（2）估计介导的间接影响的大小 通过代谢物子网络。 在WHI、NHS和HAPO代谢组学研究中检验临床假设的应用：使用方法 在目标1中开发的，我们将识别与NHS和孕产妇中风风险相关的代谢物和模块 HAPO研究中新生儿肥胖的代谢组学标志物。使用目标2中的方法，在WHI中，我们将确定 因开始激素治疗（雌激素、炔雌醇加雌激素、安慰剂）而发生变化的代谢子网络 在年龄组内，治疗/年龄依赖性模块与随后的CHD风险相关;在HAPO中 研究，检测男孩母亲与女孩母亲之间不同的母体代谢物网络，以及性别特异性 子成分，告知与产妇分娩期间新生儿身体组成的性别相关差异 怀孕目的3方法将被应用于检测可能介导以下关联的代谢物子网络： 暴露，如饮食评分和WHI中的CHD风险;以及妊娠期和新生儿期间的母体葡萄糖 HAPO中的肥胖症。 影响：大量的联邦投资已经投入到复杂的代谢组学基础的研究中。 疾病，如通过NIH的共同基金代谢组学计划。我们的跨学科团队建议， 开发和应用新的统计模型，有效地挖掘快速增长的代谢组学数据源，以阐明 复杂疾病的病因学，如CHD、中风和妊娠期间的母亲流产，因为它与新生儿有关 出生时的大小