microbiomeGWAS

microbiomeGWAS identifies host genetic variants associated with microbiome β-diversity by testing SNP main effects and SNP-environment interactions using pairwise distance matrices derived from 16S rRNA gene sequencing for genome-wide association analyses.

Key Features:

• Association Analysis: Analyzes microbiome β-diversity represented as pairwise distance matrices to capture community-level composition rather than univariate measures.
• SNP and Environmental Interactions: Tests single nucleotide polymorphism (SNP) main effects and SNP-environment interactions on microbiome composition.
• Statistical Framework: Accounts for the dependent nature of pairwise distance data and the positive skewness and kurtosis observed in score statistics.
• P-Value Correction: Adjusts for skewness and kurtosis to produce corrected p-value approximations, with corrections validated through simulations.
• Distance Metrics: Supports analysis of unweighted and weighted UniFrac distance matrices derived from 16S rRNA gene sequencing.
• Large-Scale GWAS Support: Enables genome-wide association studies of the human microbiome with emphasis on computational efficiency and statistical accuracy.

Scientific Applications:

• Elucidating Biological Mechanisms: Identifies genetic variants associated with microbiome composition to help elucidate underlying biological mechanisms.
• Prioritizing Genetic Variants: Highlights variants that significantly impact microbiome diversity for follow-up functional or genetic studies.
• Improving Genetic Risk Prediction: Provides microbiome-associated genetic insights that can enhance prediction of genetic risk in diseases influenced by the microbiome.

Methodology:

Analyzes β-diversity using pairwise distance matrices (including unweighted and weighted UniFrac) derived from 16S rRNA gene sequencing; computes score statistics for SNP main effects and SNP-environment interactions; and adjusts score statistics for skewness and kurtosis to obtain corrected p-value approximations, with corrections validated by simulations.