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The transition from genomics to phenomics in personalized population health

Nature Reviews Genetics volume 25, pages 286–302 (2024)Cite this article

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Abstract

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

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Fig. 1: Phenomic snapshots track health trajectories.
Fig. 2: Five fundamental principles of personalized population health.
Fig. 3: The multidimensional health trajectory of an individual.
Fig. 4: Personalized population health enabled by innovative technologies and their large-scale application.
Fig. 5: The integration of big data and knowledge graphs enables advanced analytics that surpass correlations and facilitate the identification of causal mechanisms.

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Acknowledgements

The authors would like to thank N. Levine for the assistance with drafting figures, B. Barry and M. Simmons for the thorough editing, and L. Pflieger and B. Yurkovich for the useful discussions regarding scientific content.

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Authors and Affiliations

  1. Phenome Health, Seattle, WA, USA

    James T. Yurkovich, Simon J. Evans, Jeffrey L. Boore, Jennifer C. Lovejoy & Leroy E. Hood

  2. Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA

    James T. Yurkovich, Simon J. Evans, Noa Rappaport, Jeffrey L. Boore, Jennifer C. Lovejoy & Leroy E. Hood

  3. Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA

    James T. Yurkovich

  4. Institute for Systems Biology, Seattle, WA, USA

    Noa Rappaport, Jennifer C. Lovejoy, Nathan D. Price & Leroy E. Hood

  5. Thorne HealthTech, New York, NY, USA

    Nathan D. Price

  6. Department of Bioengineering, University of Washington, Seattle, WA, USA

    Nathan D. Price & Leroy E. Hood

  7. Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA

    Nathan D. Price & Leroy E. Hood

  8. Department of Immunology, University of Washington, Seattle, WA, USA

    Leroy E. Hood

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Contributions

J.T.Y., S.J.E., N.R. and J.L.B. researched the literature. J.T.Y., S.J.E., N.R., J.L.B., J.C.L. & L.E.H. contributed substantially to the discussions of the content. J.T.Y., S.J.E., N.R., J.L.B., J.C.L. & L.E.H. wrote the article. All authors reviewed and/or edited the manuscript before submission.

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Correspondence to Leroy E. Hood.

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Glossary

Actionable recommendations

Behavioural or clinical intervention strategies driven by data that can be pursued to reach specific health outcome.

Aptamers

Short, single-stranded nucleic acid fragments (DNA or RNA) that selectively bind to specific target molecules such as proteins with variable affinities and off-target binding.

Deep learning

A specific machine learning approach inspired by the human brain that uses multi-layered (so-called deep) network architectures.

Deep phenotyping

A comprehensive analysis of phenotypic drivers and endpoints, from molecular assays and clinical data to social determinants of health and digital measures of environmental exposures and behaviours.

Digital biomarkers

Lifestyle and physiological factors measured via digital health devices that enable remote — and possibly non-invasive — surveillance of the health state.

Digital health

Digital tools such as mobile apps, wearables and telehealth that help provide a more holistic view of the health state of an individual.

Digital twins

Computable digital, dynamic representations of a human that continuously integrates physiological and biochemical data with mechanistic knowledge.

Explainable artificial intelligence

(XAI). Interpretable deep learning that provides explanations for black-box predictions.

Genetic variants

Differences in the DNA sequence — from single nucleotides to large regions — among individuals, populations or species that may be inherited (passed down from parents in the germline) or somatic (developed de novo) and can explain differences in physical appearance, disease susceptibility and how people react to pharmacological interventions.

Health outcomes

Labels derived from clinical metrics that reflect an individual’s dynamic state of physical, mental and social well-being, often used as ground truth (or expected values) to train computational models.

Health trajectory

The trend of the integrated health of an individual, which is multidimensional and may simultaneously direct away from one disease and towards another.

Incidental findings

Observations that were not the primary objective of the study but could have potential significance or implications for the health of the subjects.

Knowledge graphs

Network representations of entities (for example, molecules, diseases and drugs) and the relationships between them.

Multi-omics

Multiple omic data modalities, such as genomics, transcriptomics, epigenomics, metabolomics, microbiomics and proteomics.

Personalized population health

An approach aimed at optimizing the health trajectory of each individual at the population scale.

Phenome

The collection of dynamic and observable characteristics of an organism, ranging from the amount of markers in blood (such as cholesterol levels) to physiological signals (such as heart rate).

Rare diseases

One of approximately 7,000 diseases affecting fewer than 1 in 200,000 people (USA) or fewer than 1 in 2,000 people (Europe), of which about 80% are thought to be genetic (so-called Mendelian diseases).

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Yurkovich, J.T., Evans, S.J., Rappaport, N. et al. The transition from genomics to phenomics in personalized population health. Nat Rev Genet 25, 286–302 (2024). https://doi.org/10.1038/s41576-023-00674-x

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