Integration of multiomic and multi-phenotypic data identifies biological pathways associated with physical fitness
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 network-based dimensionality reduction. Designed to be versatile and generalizable, PhenoMol enables studies across small and large populations to predict wellness, performance, and disease outcomes. The software is openly available to support future research in health, disease, and performance optimization.
Authors
- Matthew J. Misner
- Nhan Huynh (ORCID: https://orcid.org/0000-0001-5945-737X)
- J. Jobin
- Gregory M Freisinger (ORCID: https://orcid.org/0000-0002-2706-7016)
- Stephanie Stacey
- Ernest Fraenkel (ORCID: https://orcid.org/0000-0001-9249-8181)
- Evelina Loghin
- Kevin J. O’Donovan
- Gurvinder Singh Khinda (ORCID: https://orcid.org/0000-0001-6650-7742)
- N Barringer
- Ralf Lenigk
- Tyler C. Hammond (ORCID: https://orcid.org/0000-0002-6141-9150)
- Chrystal Chadwick (ORCID: https://orcid.org/0000-0001-6355-5677)
- John Graf (ORCID: https://orcid.org/0000-0002-0396-9867)
- Brian M. Davis (ORCID: https://orcid.org/0000-0002-4646-0569)
- Justin Welch
- Andrew Burns
- Sara Peterson
- Swapnil R. Chhabra
- Oliver Boomhower
- Luca Marinelli
- Ginty Fiona
- Peter Tu
- Rachel Sorell
- Leila Safa
- Alison Caron
- Christine Surrette
- Scott C. Evans
- Azar Alizadeh
- Taisha Joseh
- Neil Herm Hermansen
- J. Elizabeth McDonough
- J. Kenneth Wickiser
- Rui Xu
- Eric D. Williams
Institutions
- University of North Carolina at Chapel Hill (US)
- Deutz (Germany) (DE)
- United States Military Academy (US)
- GE Global Research (United States) (US)
- C4 Therapeutics (United States) (US)
- Massachusetts Institute of Technology (US)
- Columbia University (US)
Publication Details
- Journal
- Communications Biology
- Published
- 2026-02-17
- DOI
- https://doi.org/10.1038/s42003-026-09663-2
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Defense
- Massachusetts Institute of Technology
- Defense Advanced Research Projects Agency
- Advanced Research Projects Agency
- U.S. Military Academy
- Lawrence Livermore National Laboratory