Decoding personal and population variability in the human multiome
Abstract
Background: With recent advances in technology, multiomic data have proliferated rapidly. Biobanks are now developing genomic, proteomic, lipidomic, and metabolomic data sets on the order of hundreds of thousands of people. However, bridging the gap between population-level data and the individual—a major challenge in precision medicine—remains a novel and challenging task.
Methods: We analyzed the landscape of contemporary multiomics data and the drivers of population and personal molecular variability using a longitudinal cohort uniquely suited to precision health applications. Using healthy routine visit data (94 participants, median five visits per participant), we profiled the human multiome across eight molecular omic types and 1.2 million measurements, with replication in a sister multiomic cohort (322 participants).
Results: Molecular data tended to cluster by omes, with the metabolome and proteome showing less intraomic correlation than other data types. While most blood-based molecular measurements followed log-normal distributions, between 2.0% and 8.1% of molecules showed evidence of bimodality, with cases driven by genetic polymorphisms or dietary differences (e.g., SERPINA5 [P = 1.4e-6], 2-piperidinone [P = 5.7e-5]). Molecular variation across the population was generally higher than that within an individual over time (e.g., xenobiotic metabolites, progestin steroids). Analyzing personal molecular variation identified several participants with notable temporal shifts (intraindividual CV > 4 SDs higher than population CV) in free fatty acids (FFA 20:1 and FFA 20:2) or cytokines (IL-17F and IFN-beta), which were unlikely to be detected cross-sectionally. Body mass index (BMI) had a much larger multiomic footprint (23.6% of molecules associated) than demographic factors like sex or age (5.7% and 1.9%, respectively), although machine-learning predictive models of sex (top ROC AUC of 0.98) were more successful than those for BMI (top R 2 of 0.59 by cytokines) or age (top R 2 of 0.34 by metabolites). Purely multiomic predictive models explained modest to high proportions of variation (R 2 = 0.22 to 0.94) in 20 common clinical tests, highlighting the potential clinical utility of these data even within a modestly sized cohort.
Conclusion: Analysis of the molecular distributions, variation, and covariation of longitudinal multiomics data revealed clinically relevant differences at both the population and personal levels. Based on these findings, we conclude with six key considerations for the next generation of population omics as the field advances toward the goals of precision medicine.
Multiomic data have proliferated rapidly in biomedical research, but many aspects of molecular variability remain poorly understood. We investigated in detail the drivers of personal and population variability in a longitudinal, multiomic cohort uniquely suited to precision health applications (94 participants, eight omic types, 1.2 million molecular measurements), with replication in a sister multiomic cohort (322 participants). While most molecules followed log-normal distributions, between 2.0% and 8.1% of molecules were bimodal, with cases driven by genetic polymorphisms or dietary differences (e.g., SERPINA5 [P = 1.4e-6], 2-piperidinone [P = 5.7e-5]). The metabolome and proteome showing less intraomic correlation than the lipidome or cytokines, and molecular variation across the population was generally higher than that within an individual over time with. Analyzing personal molecular variation identified several participants with notable temporal shifts (intraindividual CV > 4 SDs higher than population CV) in free fatty acids (FFA 20:1 and FFA 20:2) or cytokines (IL-17F and IFN-beta), which were unlikely to be detected cross-sectionally. Body mass index (BMI) was more broadly associated with molecular levels (23.6% of molecules associated) than were demographic factors like sex or age (5.7% and 1.9%, respectively), although machine-learning predictive models of sex (top ROC AUC of 0.98) were more successful than those for BMI (top R 2 of 0.59 by cytokines) or age (top R 2 of 0.34 by metabolites). Purely multiomic predictive models were modest to highly predictive (R 2 = 0.22 to 0.94) of 20 common clinical tests, highlighting the potential clinical utility of these data even within a modestly sized cohort. Our findings reveal six key considerations (missing variability, temporality, feasible clinicomics, complex modeling, aging complexity, and molecular matching) for precision medicine.
Related
- A comparative transcriptomic dataset of hepatocytes derived from a panel of human induced pluripotent stem cells with different ethnicities as an in vitro population variability toxicity model.
- From Mortality Salience to Existential Reflection: A Four-Level Model of Reflecting Upon Personal, Human, Biotic, and Cosmic Finitude
- Heterogeneity in pre-vaccination population immunity can contribute to variability in vaccine effectiveness estimates
- Beyond Blood and Urine: Decoding the Human Chemical Exposome across Matrices and Time.
- Genetic Variability of IFNAR2 Gene and Risk of COVID-19 Critical Illness Among the Tunisian Population.
BibTeX
@misc{panyard2026decoding,
title = {{Decoding personal and population variability in the human multiome}},
author = {Daniel J. Panyard and Martín Acosta Parra and Aubrey K. Roberts and Hirotaka Ieki and Tejaswini Mishra and Nasim Bararpour and Anisha Jaiswal and Sophia M. Schüssler-Fiorenza Rose and Kévin Contrepois and Daniel Hornburg and Sara Ahadi and Xiaotao Shen and Themistocles L. Assimes and Michael P. Snyder},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.28.26366658},
url = {https://www.medrxiv.org/content/10.64898/2026.10.28.26366658v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Decoding personal and population variability in the human multiome
AU - Daniel J. Panyard
AU - Martín Acosta Parra
AU - Aubrey K. Roberts
AU - Hirotaka Ieki
AU - Tejaswini Mishra
AU - Nasim Bararpour
AU - Anisha Jaiswal
AU - Sophia M. Schüssler-Fiorenza Rose
AU - Kévin Contrepois
AU - Daniel Hornburg
AU - Sara Ahadi
AU - Xiaotao Shen
AU - Themistocles L. Assimes
AU - Michael P. Snyder
PY - 2026
DA - 2026/10/28
PB - medRxiv
DO - 10.64898/2026.10.28.26366658
UR - https://www.medrxiv.org/content/10.64898/2026.10.28.26366658v1
AB - Background: With recent advances in technology, multiomic data have proliferated rapidly. Biobanks are now developing genomic, proteomic, lipidomic, and metabolomic data sets on the order of hundreds of thousands of people. However, bridging the gap between population-level data and the individual—a major challenge in precision medicine—remains a novel and challenging task. Methods: We analyzed the landscape of contemporary multiomics data and the drivers of population and personal molecular variability using a longitudinal cohort uniquely suited to precision health applications. Using healthy routine visit data (94 participants, median five visits per participant), we profiled the human multiome across eight molecular omic types and 1.2 million measurements, with replication in a sister multiomic cohort (322 participants). Results: Molecular data tended to cluster by omes, with the metabolome and proteome showing less intraomic correlation than other data types. While most blood-based molecular measurements followed log-normal distributions, between 2.0% and 8.1% of molecules showed evidence of bimodality, with cases driven by genetic polymorphisms or dietary differences (e.g., SERPINA5 [P = 1.4e-6], 2-piperidinone [P = 5.7e-5]). Molecular variation across the population was generally higher than that within an individual over time (e.g., xenobiotic metabolites, progestin steroids). Analyzing personal molecular variation identified several participants with notable temporal shifts (intraindividual CV > 4 SDs higher than population CV) in free fatty acids (FFA 20:1 and FFA 20:2) or cytokines (IL-17F and IFN-beta), which were unlikely to be detected cross-sectionally. Body mass index (BMI) had a much larger multiomic footprint (23.6% of molecules associated) than demographic factors like sex or age (5.7% and 1.9%, respectively), although machine-learning predictive models of sex (top ROC AUC of 0.98) were more successful than those for BMI (top R 2 of 0.59 by cytokines) or age (top R 2 of 0.34 by metabolites). Purely multiomic predictive models explained modest to high proportions of variation (R 2 = 0.22 to 0.94) in 20 common clinical tests, highlighting the potential clinical utility of these data even within a modestly sized cohort. Conclusion: Analysis of the molecular distributions, variation, and covariation of longitudinal multiomics data revealed clinically relevant differences at both the population and personal levels. Based on these findings, we conclude with six key considerations for the next generation of population omics as the field advances toward the goals of precision medicine. Multiomic data have proliferated rapidly in biomedical research, but many aspects of molecular variability remain poorly understood. We investigated in detail the drivers of personal and population variability in a longitudinal, multiomic cohort uniquely suited to precision health applications (94 participants, eight omic types, 1.2 million molecular measurements), with replication in a sister multiomic cohort (322 participants). While most molecules followed log-normal distributions, between 2.0% and 8.1% of molecules were bimodal, with cases driven by genetic polymorphisms or dietary differences (e.g., SERPINA5 [P = 1.4e-6], 2-piperidinone [P = 5.7e-5]). The metabolome and proteome showing less intraomic correlation than the lipidome or cytokines, and molecular variation across the population was generally higher than that within an individual over time with. Analyzing personal molecular variation identified several participants with notable temporal shifts (intraindividual CV > 4 SDs higher than population CV) in free fatty acids (FFA 20:1 and FFA 20:2) or cytokines (IL-17F and IFN-beta), which were unlikely to be detected cross-sectionally. Body mass index (BMI) was more broadly associated with molecular levels (23.6% of molecules associated) than were demographic factors like sex or age (5.7% and 1.9%, respectively), although machine-learning predictive models of sex (top ROC AUC of 0.98) were more successful than those for BMI (top R 2 of 0.59 by cytokines) or age (top R 2 of 0.34 by metabolites). Purely multiomic predictive models were modest to highly predictive (R 2 = 0.22 to 0.94) of 20 common clinical tests, highlighting the potential clinical utility of these data even within a modestly sized cohort. Our findings reveal six key considerations (missing variability, temporality, feasible clinicomics, complex modeling, aging complexity, and molecular matching) for precision medicine.
ER -