Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies.
Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies.
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DOI：
10.3390/metabo12060512
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发表时间：
2022-06-02
期刊：
影响因子：
4.1
通讯作者：
中科院分区：
文献类型：
--
作者：
遗传学和代谢组学数据的整合需要仔细计算复杂的依赖关系，特别是在对家族组学数据进行建模时，例如，研究相关母子表型的胎儿编程。使用经典的全基因组关联方法识别基因决定的代谢型的努力已被证明对描述复杂疾病的特征是有用的，但结论往往限于一系列变异-代谢物关联。我们采用贝叶斯网络模型将代谢型与母子遗传相关性和代谢谱相关性结合起来，以研究母子表型关联的机制。利用多民族高血糖和不良妊娠结局(HAPO)研究的数据，我们证明了有序依赖的战略性规范、候选代谢物类型的预过滤、代谢物依赖的纳入和惩罚网络估计方法阐明了胎儿规划新生儿肥胖和代谢结果的潜在机制。探索贝叶斯网络在一系列惩罚参数上的增长，再加上交互式绘图，有助于解释网络边缘。这些方法广泛适用于相关个体的不同组学数据的集成。
The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal–offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant–metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal–offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal–offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.
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影响因子：
30.8
作者：
Das, Sayantan;Forer, Lukas;Schoenherr, Sebastian;Sidore, Carlo;Locke, Adam E.;Kwong, Alan;Vrieze, Scott I.;Chew, Emily Y.;Levy, Shawn;McGue, Matt;Schlessinger, David;Stambolian, Dwight;Loh, Po-Ru;Iacono, William G.;Swaroop, Anand;Scott, Laura J.;Cucca, Francesco;Kronenberg, Florian;Boehnke, Michael;Abecasis, Goncalo R.;Fuchsberger, Christian
通讯作者：
Fuchsberger, Christian
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
8.2
作者：
Kadakia, Rachel;Nodzenski, Michael;Lowe, William L., Jr.
通讯作者：
Lowe, William L., Jr.
影响因子：
3.7
作者：
Jourdan C;Petersen AK;Gieger C;Döring A;Illig T;Wang-Sattler R;Meisinger C;Peters A;Adamski J;Prehn C;Suhre K;Altmaier E;Kastenmüller G;Römisch-Margl W;Theis FJ;Krumsiek J;Wichmann HE;Linseisen J
通讯作者：
Linseisen J
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y