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Re-evaluating the relationship between missing heritability and the microbiome.

Gavin M Douglas1, Joseph P Bielawski2,3, Morgan G I Langille4,5

  • 1Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada. gavin.douglas@dal.ca.

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|June 10, 2020
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Summary
This summary is machine-generated.

Human genome-wide association studies (GWASs) show lower heritability than familial studies. Integrating human microbiome data may help control for environmental factors in GWASs, offering new insights.

Keywords:
EnvironmentGWASGeneticsGxEHeritabilityHolobiontHologenomeHumanMicrobiome

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Area of Science:

  • Genetics
  • Microbiology
  • Bioinformatics

Background:

  • Human genome-wide association studies (GWASs) consistently report lower heritability estimates compared to familial studies.
  • Unmeasured genetic variation and gene-by-environment interactions are proposed explanations for this discrepancy.
  • The human microbiome is increasingly recognized as a potential factor influencing these estimates, often viewed as a source of unmeasured genetic variation.

Discussion:

  • This study critically examines the argument for including human microbiome genetic variation in heritability estimates, particularly concerning the holobiont model.
  • An alternative perspective is proposed: leveraging the microbiome to account for environmental variation within a population.
  • The potential utility of integrating microbiome sequencing data into GWAS frameworks is explored.

Key Insights:

  • The strict inclusion of microbiome genetic variation in heritability calculations faces theoretical challenges.
  • The human microbiome may serve as a valuable covariate for environmental factors in genetic association studies.
  • Further research is needed to validate the role of the microbiome in refining heritability estimates.

Outlook:

  • Investigating novel methodologies to integrate microbiome data into GWAS pipelines.
  • Developing strategies to quantify and control for environmental influences using microbiome profiles.
  • Assessing the impact of microbiome-informed GWAS on understanding complex human traits and diseases.