Bayesian Network Models for Genetics and Metabolomics Studies of Fetal Programming

用于胎儿编程遗传学和代谢组学研究的贝叶斯网络模型

• 批准号: 9221751
• 负责人: Denise M Scholtens
• 金额: $ 15.65万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-13 至 2017-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9221751
• 关键词: Affect; Alleles; Bayesian Modeling; Birth; Birth Weight; Candidate Disease Gene; Caribbean region; Child; Complex; Data; Dependency; Disease; European; Fetal Development; Funding; Genetic; Genetic Models; Genetic study; Genotype; Glucose; Goals; Hyperglycemia; Individual; Informatics; International; Investigation; Joints; Light; Link; Mass Fragmentography; Metabolic; Methods; Mexican Americans; Modeling; Mothers; Newborn Infant; Obesity; Observational Study; Outcome; Outcome Study; Parents; Phenotype; Pregnancy; Research Design; Resource Informatics; Resources; Series; Serum; United States National Institutes of Health; Work; adverse pregnancy outcome; clinical phenotype; computer based statistical methods; fetal; fetal programming; genome wide association study; genome-wide; interest; metabolic profile; metabolomics; model development; network models; offspring; pregnant; tool

Project Summary Integrated modeling of genetics and metabolomics data demands careful specification of complex dependencies, particularly when investigating mechanisms underlying fetal programming of related clinical phenotypes in mothers and their newborns. Recent efforts to identify `genetically determined metabotypes' using classic genome wide association study approaches have provided initial links between genetics and metabolomics data; however, to accurately characterize genetic and metabolic contributions to fetal development, more sophisticated models are required. The goal of this study is to develop Bayesian network models for cogent synthesis of genetics and metabolomics data related to clinical phenotypes for mothers and their newborns. In networks, nodes represent omics features of interest and edges represent relationships among them. Bayesian networks construct a series of directed relationships among nodes in which a variable represented by a `child' node is described conditional on its `parent' nodes. It is our hypothesis that metabotyping in mothers and newborns will provide a set of candidate gene-metabolite relationships whose joint contribution to clinical phenotype(s) can be parsimoniously modeled in Bayesian networks that also incorporate dependencies among genotypes and metabolites. To develop network models, we will use existing genetics and metabolomics data for mother/newborn pairs from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. HAPO was an international, observational study involving >23,000 pregnant mothers and their newborns from 2000-2006 that demonstrated a positive association between maternal glucose levels during pregnancy and newborn birth weight and adiposity. HAPO Metabolomics is an ongoing NIH-funded investigation of mother/newborn pairs of Northern European, Afro-Caribbean, Mexican-American and Thai ancestry involving targeted and non-targeted gas-chromatography/mass-spectrometry profiling of maternal and newborn cord serum. HAPO Genetics studies involved genome-wide genotyping for HAPO mother/newborn pairs in the same four ancestry groups. ~1400 mother/child pairs are represented in both HAPO Metabolomics and HAPO Genetics studies. Using these data, we will apply a comprehensive metabotyping pipeline to identify candidate gene-metabolite relationships for integrated models. We will then develop and apply Bayesian network models to unify maternal and newborn metabotypes in conjunction with complex dependencies among maternal and newborn genotypes and metabolic profiles. These proposed analyses will augment existing metabotyping approaches by integrating metabotypes into Bayesian network models for fuller characterization of maternal and fetal genetic and metabolic underpinnings of known clinical phenotype associations. The proposed methods will also be more broadly applicable to integration of diverse omics data for related individuals.

项目概要 遗传学和代谢组学数据的集成建模需要对复杂的数据进行仔细规范 依赖性，特别是在研究相关临床胎儿编程的机制时 母亲及其新生儿的表型。最近鉴定“基因决定的代谢型”的努力 使用经典的全基因组关联研究方法提供了遗传学和 代谢组学数据；然而，为了准确表征遗传和代谢对胎儿的贡献 的发展，需要更复杂的模型。本研究的目标是开发贝叶斯网络 与母亲和患者的临床表型相关的遗传学和代谢组学数据的令人信服的综合模型 他们的新生儿。在网络中，节点代表感兴趣的组学特征，边代表关系 他们之中。贝叶斯网络在节点之间构建一系列有向关系，其中变量 由“子”节点表示的描述是以其“父”节点为条件的。我们的假设是 母亲和新生儿的代谢分型将提供一组候选基因-代谢物关系，其 对临床表型的共同贡献可以在贝叶斯网络中简单地建模，该网络也 纳入基因型和代谢物之间的依赖性。为了开发网络模型，我们将使用现有的 来自高血糖和不良妊娠的母亲/新生儿对的遗传学和代谢组学数据 结果（HAPO）研究。 HAPO 是一项国际观察性研究，涉及超过 23,000 名怀孕母亲 及其 2000 年至 2006 年的新生儿，证明母亲的血糖水平之间存在正相关 怀孕期间和新生儿出生时的体重和肥胖情况。 HAPO 代谢组学是一项由 NIH 资助的持续项目 对北欧人、加勒比非洲人、墨西哥裔美国人和泰国人的母亲/新生儿进行调查 涉及母体和非目标气相色谱/质谱分析的血统分析 新生儿脐带血清。 HAPO 遗传学研究涉及 HAPO 母亲/新生儿的全基因组基因分型 在相同的四个祖先群体中配对。 HAPO 代谢组学中有约 1400 对母子对 和 HAPO 遗传学研究。使用这些数据，我们将应用全面的元分型管道 确定集成模型的候选基因-代谢物关系。然后我们将开发并应用 贝叶斯网络模型将孕产妇和新生儿代谢型与复杂的代谢型相结合 孕产妇和新生儿基因型和代谢特征之间的依赖性。这些拟议的分析将 通过将元类型集成到贝叶斯网络模型中来增强现有的元分型方法，以实现更全面的 已知临床表型的母体和胎儿遗传和代谢基础的表征 协会。所提出的方法也将更广泛地适用于不同组学数据的整合 对于相关个人。