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Microbiome epidemiology and association studies in human health

Nature Reviews Genetics volume 24, pages 109–124 (2023)Cite this article

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Abstract

Studies of the human microbiome share both technical and conceptual similarities with genome-wide association studies and genetic epidemiology. However, the microbiome has many features that differ from genomes, such as its temporal and spatial variability, highly distinct genetic architecture and person-to-person variation. Moreover, there are various potential mechanisms by which distinct aspects of the human microbiome can relate to health outcomes. Recent advances, including next-generation sequencing and the proliferation of multi-omic data types, have enabled the exploration of the mechanisms that connect microbial communities to human health. Here, we review the ways in which features of the microbiome at various body sites can influence health outcomes, and we describe emerging opportunities and future directions for advanced microbiome epidemiology.

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Fig. 1: Similarities between microbiome epidemiology and genetic epidemiology.
Fig. 2: Differences between microbiome epidemiology and genetic epidemiology.
Fig. 3: Different features of the microbiome can influence human health outcomes.
Fig. 4: The microbiome has been associated with diverse body-wide health outcomes.
Fig. 5: Future directions for microbiome epidemiology.

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Acknowledgements

E.A.F. drafted the figures. The authors’ work was supported in part by the NIH NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) grant T32DK007703 (H.V.), NIH K23DK125838 (L.H.N.), R24DK110499 (C.H.), American Gastroenterological Association Research Scholars Award (L.H.N.), the Crohn’s and Colitis Foundation Career Development Award and Research Fellowship Award (L.H.N.), and Takeda Pharmaceuticals research agreement 4100215484 (C.H.).

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Author notes

  1. These authors jointly supervised this work: Long H. Nguyen, Curtis Huttenhower.

Authors and Affiliations

  1. Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Hannah VanEvery, Eric A. Franzosa, Long H. Nguyen & Curtis Huttenhower

  2. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Hannah VanEvery & Long H. Nguyen

  3. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Hannah VanEvery, Eric A. Franzosa, Long H. Nguyen & Curtis Huttenhower

  4. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Hannah VanEvery & Long H. Nguyen

  5. Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Eric A. Franzosa & Curtis Huttenhower

  6. Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Curtis Huttenhower

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  1. Hannah VanEvery
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The authors contributed equally to all aspects of the manuscript.

Corresponding authors

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C.H. is a member of the scientific advisory boards of Seres Therapeutics and Empress Therapeutics. The other authors declare no competing interests.

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Glossary

Gnotobiotic

A term that denotes a condition of having a defined community of known microorganisms present, incuding germ-free.

Non-independence

In which observations are in some way related, such that the value of one observation affects the value of others.

Compositionality

Data in which measurements are proportions, that is, they must sum to a fixed constant such as 100%.

Beta-diversity

Ecological measure that captures similarity between communities.

Alpha-diversity

Ecological measure that captures aspects of the richness and/or evenness of features within a community.

Genetic risk score

A summary statistic of the effect of overall genetic variants on a phenotype of interest.

Foodomics

The application of multi-omic technologies to investigate and exploit food science, typically as used to improve host health.

Random forests

Machine learning algorithms that use multiple decision trees to solve classification or regression problems (for example, were samples derived from cases or controls?).

Support vector machines

(SVMs). Machine learning algorithms that classify data by maximizing class separation in a (possibly transformed) high-dimensional space.

Logistic regression

A predictive analysis that estimates the probability of a categorical, often binary, dependent variable given one or more independent variables.

Heteroscedasticity

A variable characteristic for which the variability is unequal across the range of another predictor.

Zero-inflation

Data (or a corresponding probability distribution) in which identically zero observations are frequent.

Omnibus test

A class of statistical tests that quantify the variance explained in a (typically high-dimensional) measurement by a single (continuous or categorical) variable, as well as its significance relative to overall (unexplained) variance.

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VanEvery, H., Franzosa, E.A., Nguyen, L.H. et al. Microbiome epidemiology and association studies in human health. Nat Rev Genet 24, 109–124 (2023). https://doi.org/10.1038/s41576-022-00529-x

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