Software Application Profile: TriplotGUI, a molecular epidemiology toolbox for investigating associations between exposures, omics, and outcomes

Artikel i vetenskaplig tidskrift, 2025

Observational epidemiological research has evolved from single exposure–outcome studies to exposure-wide and outcome-wide investigations [1, 2]. Omics technologies have further enabled molecular epidemiology to mechanistically link exposures to outcomes via molecular data as potential mediators [3]. By providing comprehensive views of biological systems across genomics, transcriptomics, proteomics, and metabolomics, omics technologies capture the cumulative effects of environmental and endogenous factors over time [4], helping to elucidate the biochemical mechanisms underlying disease pathophysiology [5]. For example, metabolite profiling, representing a “snapshot” of the human metabolome, is frequently used to explore exposome-metabolome-disease linkages [6]. However, omics data typically contain a large number of molecular features [7] and mechanistic investigations using omics necessitate the selection of exposure- and outcome-relevant features. Common approaches include variable selection through supervised machine learning or performing univariate analysis, which allows confounder adjustment and more straightforward interpretation. However, univariate analysis struggles with collinearity, potentially misattributing effects among correlated variables and distorting their individual contributions, increasing false discovery rates [8]. Supervised machine learning can both select predictive features and manage collinearity [8]. However, it offers limited interpretability and confounder adjustment is often not possible [9]. Moreover, both methods rely on arbitrary thresholds (e.g. P values or importance rankings), introducing subjectivity in feature selection.