Systems biology approaches to deciphering metabolic phenotypes in a population-based study
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Date
2025-10-16
Authors
Κακαριάρη, Άννα-Αλεξάνδρα
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Abstract
This thesis presents a network-based integrative analysis of genetic and metabolomic data from participants in the Epirus Health Study, a large-scale, prospective cohort study in Greece focused on uncovering the underlying causes of complex chronic diseases. The primary focus is on inborn errors of metabolism (IEMs), a heterogeneous group of genetic disorders that disrupt normal cellular metabolism. The goal is to identify genetic variants and metabolite profiles that may underly unexplained metabolic phenotypes in the general population. To achieve this, we employed advanced network analysis tools –such as weighted gene co-expression network analysis (WGCNA), its extension csuWGCNA, community detection algorithms such as Girvan-Newman and Louvain, and visualization packages, such as igraph and ggraph– to construct multi-layered biological networks that integrate genes, metabolites, and disease phenotypes. Within these networks, we identified topological modules and analyze their structure, connectivity, and biological significance. This modular approach helps reveal key regulatory nodes and molecular pathways with potential diagnostic or therapeutic value. Ultimately, this study aims to improve the diagnostic resolution of metabolic disorders at the population level and to support the development of personalized medicine approaches for disease prevention, early detection, and targeted intervention.
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Keywords
Genomics, Metabolomics, Epirus Health Study, Inborn errors of metabolism (IEM), Weighted gene correlation network analysis (WGCNA), Edge betweenness, Girvan-Newman, Gene co-expression, Systems biology