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 n…

Discussion The rapid development and diversification of technologies for molecular measurement create an opportunity to identify molecular features associated with complex traits of human performance. One complicating factor is the relatively small cohort sizes of most human subject studies, which limits applicability of purely data-driven statistical methods to discover significant associations among tens of thousands of analytes. To address this issue, we have developed a computational approach (PhenoMol) to tackle feature-rich behavioral, cognitive and molecular datasets collected from cohorts where the number of data points vastly outnumbers the cohort size. There are a variety of algorithms that exist for the integration of multi-omics data to identify specific groups, types, or states of either individual cells or subjects. These algorithms aim at integrating different molecular layers that include epigenetics, transcriptomics, proteomics, and metabolomics and relate them to the observed phenotypes of health and disease status at the individual level. Many of the computational approaches that have been explored are linear algorithms, which are often more robust due to small n…

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