Network Models for Metabolomics

代谢组学网络模型

• 批准号: 10656396
• 负责人: RAJI BALASUBRAMANIAN
• 金额: $ 33.47万
• 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/10656396
• 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; 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; Pregnancy; Productivity; 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; adverse pregnancy outcome; age group; age related; biomarker discovery; boys; case control; clinical investigation; clinically relevant; 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.

摘要 我们的建议描述了基于网络的方法来分析来自代谢组学研究的数据。该物种fic 这项建议的目的包括： 目标1：代谢组学研究中的变量选择方法，包括代谢物依赖和外部 路径信息。我们提出了一种贝叶斯变量选择方法来结合部分观察到的、 外部路径网络和数据驱动的偏相关网络。 目标2：确定研究中不同群体特征的不同代谢网络的模型，并添加 可选择性地检测子组件与组-特定fic与结果的关联。当新陈代谢网络 根据群体、暴露水平(例如治疗)或其他因素的不同，我们拟议的框架将提供 在可能的情况下，识别特定于组的fic网络以及与结果相关的子组件的方法 群体特有的fi方式。 目的3：确定共同调节前-代谢物之间关系的代谢物亚网络的方法 姿态和结果。我们提出了一个两阶段分析框架，包括(1)代谢物子网的检测- 与结果相联系而丰富的工作；以及(2)估计间接影响的程度 通过代谢物子网络。 应用方法检验WHI、NHS和HAPO代谢组学研究中的临床假说 在AIMS 1中开发，我们将在NHS和孕产妇中确定与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 由于激素治疗(雌激素、孕激素加雌激素、安慰剂)的启动而改变的代谢亚网络 在年龄组内，具有与随后的CHD风险相关的治疗/年龄相关模块；在HAPO中 研究、检测男婴母亲与女婴母亲之间的母体代谢网络差异以及性别差异fic 与母体血糖有关的新生儿身体成分中性别差异的亚成分 怀孕了。AIM 3方法将被应用于检测可能介导 暴露在WHI中的饮食评分和CHD风险；以及孕期和新生儿期间的母体血糖 HAPO中的肥胖。 影响：重要的是，fi不能对复合体代谢基础的研究进行联邦投资 例如通过美国国立卫生研究院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来有效地挖掘快速增长的代谢组学数据源，以阐明 与新生儿相关的复杂疾病的病因学，如冠心病、中风和孕期母体血糖 出生时的大小。

项目成果

Path-level interpretation of Gaussian graphical models using the pair-path subscore.

• DOI: 10.1186/s12859-021-04542-5
• 发表时间: 2022-01-05
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Gill NP;Balasubramanian R;Bain JR;Muehlbauer MJ;Lowe WL Jr;Scholtens DM
• 通讯作者: Scholtens DM

Gaussian graphical models with applications to omics analyses.

• DOI: 10.1002/sim.9546
• 发表时间: 2022-11-10
• 期刊: STATISTICS IN MEDICINE
• 影响因子: 2
• 作者: Shutta, Katherine H.;De Vito, Roberta;Scholtens, Denise M.;Balasubramanian, Raji
• 通讯作者: Balasubramanian, Raji

Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies.

• DOI: 10.3390/metabo12060512
• 发表时间: 2022-06-02
• 期刊: Metabolites
• 影响因子: 4.1