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, we developed PhenoMol, a bioinformatic framework that integrates comprehensive phenotypic data predictive of outcomes and reduces multi-omic dimensionality using graph theory constrained by prior biological knowledge. This approach generates biologically informed \"expression circuits\" to identify causal patterns. Applied to a deeply characterized healthy cohort, PhenoMol successfully predicted elite physical performance and outperformed regression models lacking

ORGANISM(S): Homo sapiens

SUBMITTER: Taisha Victoria Joseph-Risch 

PROVIDER: E-MTAB-16584 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress