HOMINID: a framework for identifying associations between host genetic variation and microbiome composition.

Area of Science:

• Microbiome research
• Host-microbe interactions
• Genetics

Background:

• Host genetic variation significantly influences the composition of the host-associated microbiota.
• Understanding these host-genetics-microbiome links is crucial for deciphering complex biological interactions.

Purpose of the Study:

• To introduce HOMINID, a novel computational approach for identifying host single nucleotide polymorphisms (SNPs) correlated with microbial taxa abundances.
• To evaluate HOMINID's accuracy and performance against existing methods using simulated and real-world data.

Main Methods:

• HOMINID utilizes Lasso linear regression to analyze host genetic variation and microbiome taxonomic composition data.
• The method identifies specific SNPs associated with variations in microbial abundances.
• Performance was validated using simulated datasets and applied to human genetic and microbiome data.

Main Results:

• HOMINID demonstrated high accuracy in identifying associated SNPs and outperformed existing computational methods in simulations.
• The approach successfully identified microbial taxa correlated with specific SNPs.
• Application to human data revealed 13 human SNPs whose genetic variation correlates with microbiome composition across different body sites.

Conclusions:

• HOMINID is a robust computational tool for detecting host genetic variants associated with microbiome composition.
• This method facilitates the discovery of underlying mechanisms governing host-microbiome interactions.
• The identified human SNPs provide a foundation for further research into host-genetics-microbiome relationships.