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Activity Number: 41 - Statistical Analysis of Networks
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #324156
Title: Data integration using identification and analysis of a statistical causal network to hypothesize stable pathways through different biological levels
Author(s): Azam Yazdani* and Akram Yazdani
Companies: The University of Texas School of Public Health, Houston and Icahn School of Medicine at Mount Sinai
Keywords: data integration ; statistical causal network ; structural equation modeling ; intermediate level ; G-DAG algorithm ; CCRS method
Abstract:

The aim of modeling a biological system is to gain sufficient understanding such that the behavior of the system can be predicted. To understand how the components and their interactions give rise to emergent properties of a system, statistical causal networks are employed in a systems biology framework. Knowing the network, we can measure causal effects using structural equation modeling to make strong hypotheses for causal genes and stable pathways from the genome to disease via intermediate levels. We focused on loss-of-function variants across the whole genome, serum metabolites distributed across multiple functional classes, cardiovascular risk factors, and disease. Through, convex-concave rare variants selection (CCRS) method, we hypothesized causal genes. A causal network over the metabolites was identified using the genome directed acyclic graph (G-DAG) algorithm. Knowing the metabolomics network, we measured causal effects using structural equation modeling to assess the hypothesized genes and identify metabolites with direct effects on risk factors/disease. By integrating the results, we identified stable pathways from the genome to risk factors/disease via metabolomics.


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

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