Integration of Multiomic and Multi-phenotypic Data Identifies Biological Pathways Associated with Physical Fitness

Abstract

Unraveling the complex associations between human phenotypes and molecular pathways can pave the way to improved health and performance, but faces a fundamental challenge: the measurable genes, proteins, and metabolites vastly outnumber the participants in even the largest studies, yielding spurious correlations. To address this imbalance, we have developed a bioinformatic framework and computational approach ("PhenoMol") to discover biological drivers of phenotypic characteristics that integrates all available phenotypic data predictive of outcomes and reduces multi-omic data dimensionality by generating "expression circuits" via graph theory constrained by prior biological knowledge of molecular interactions. We applied PhenoMol to analyze causal patterns and predict elite physical performance in a healthy cohort with deep physiological, physical, behavioral, cognitive, and molecular characterization. PhenoMol outperforms regression models based on equivalent analytic methodologies that do not employ network biology for dimensionality reduction. The PhenoMol software is provided for future studies.