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Activity Number: 142 - Metabolomics Data Analytics - the New Frontier in Precision Medicine
Type: Invited
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract #326866 Presentation
Title: Bayesian Network Models for Integrating Genetics and Metabolomics Data
Author(s): Denise Marie Scholtens* and Alan Kuang
Companies: Northwestern University and Northwestern University
Keywords: genetics; metabolomics; Bayesian networks

Integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modeling familial omics data, for example, to study fetal programming of related maternal-offspring phenotypes. Efforts to find 'genetically determined metabotypes' using classic GWAS approaches have proven useful for characterizing complex disease, but conclusions are often limited to a disjointed series variant-metabolite associations. We adapted Bayesian network models to integrate metabotypes with maternal-fetal genetic dependencies and metabolic profile correlations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study, we demonstrate that strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, spinglass clustering of metabolites and conditional linear Gaussian methods clarify fetal programming of newborn adiposity related to maternal glycemia. Exploration of network growth over a range of penalty parameters, coupled with interactive plotting, facilitate interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.

Authors who are presenting talks have a * after their name.

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