Bayesian Network Models for Genetics and Metabolomics Studies of Fetal Programming

项目摘要 遗传学和代谢组学数据的集成建模要求对复合体进行仔细的规范 依赖性，特别是在研究相关临床胎儿程序化的潜在机制时 母亲及其新生儿的表型。最近为确定“基因决定的代谢型”所做的努力 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，为了准确地表征遗传和代谢对胎儿的贡献 发展，需要更复杂的模型。本研究的目标是开发贝叶斯网络 与母亲和母亲的临床表型相关的遗传学和代谢组学数据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 其中就有。贝叶斯网络构建了节点之间的一系列有向关系，其中变量 以其‘父’节点为条件来描述由‘子’节点表示的。我们的假设是 母亲和新生儿的代谢分型将提供一套候选基因-代谢物关系，其 联合对临床表型的贡献(S)可以在贝叶斯网络中简约地建模，该网络还 纳入不同基因类型和代谢物之间的依赖关系。为了开发网络模型，我们将使用现有的 高血糖和不良妊娠母婴配对的遗传学和代谢组学数据 结果(HAPO)研究。Hapo是一项涉及23,000名孕妇的国际观察性研究 和他们在2000-2006年间出生的新生儿的血糖水平之间存在正相关 孕期和新生儿出生体重和肥胖症。Hapo代谢组学是由美国国立卫生研究院资助的正在进行的 北欧、非洲加勒比、墨西哥裔美国人和泰国人母亲/新生儿配对的调查 涉及母体和母体的靶向和非靶向气相色谱/质谱学分析的祖先 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 在相同的四个祖先组中配对。大约1400对母子对在两个HAPO代谢组学中都有代表 和HAPO遗传学研究。使用这些数据，我们将应用一个全面的代谢分类管道来 确定集成模型的候选基因-代谢物关系。然后我们将开发和应用 结合复杂的贝叶斯网络模型统一母亲和新生儿的代谢型 孕妇和新生儿的基因型别和代谢谱之间的依赖关系。这些建议的分析将 通过将新陈代谢类型集成到贝叶斯网络模型中来增强现有的新陈代谢方法 已知临床表型的母婴遗传和代谢基础的特征 联想。所提出的方法也将更广泛地适用于不同组学数据的整合 适用于相关个人。

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.