Heritability jointly explained by host genotype and microbiome: will improve traits prediction?

Area of Science:

• Statistical Genetics
• Human Phenotypes
• Microbiome Research

Background:

• The 70th anniversary of heritability highlights the 'missing heritability' problem, where genome-wide association studies (GWASs) underestimate heritability compared to twin studies.
• Existing explanations for this discrepancy, such as genetic variants, dominance, epistasis, and environmental factors, remain debated.
• Current heritability estimation models in GWASs do not account for the significant role of the host's microbiome.

Purpose of the Study:

• To address the missing heritability problem by incorporating microbiome data into GWAS models.
• To improve the accuracy of heritability estimates and enhance human trait prediction for clinical applications.
• To explore statistical methods for integrating microbiome information into host GWAS for heritability estimation.

Main Methods:

• Reviewing the concept of heritability in GWAS and microbiome-wide association studies (MWAS).
• Focusing on heritability estimation from a statistical genetics perspective.
• Discussing a statistical method to incorporate microbiome data into host GWAS.

Main Results:

• The study proposes that incorporating microbiome data can substantially solve or clarify the missing heritability issue.
• This integration is supported by observations of the microbiome's role in complex phenotypes and its heritable nature.
• Current twin-based heritability estimates do not differentiate the microbiome's contribution to phenotypic variance.

Conclusions:

• Integrating host microbiome data into GWAS models is a promising strategy to resolve the missing heritability problem.
• This approach has the potential to increase the predictive utility of human traits for clinical purposes.
• Further statistical methods are needed to effectively incorporate microbiome data in heritability estimation within GWAS frameworks.