High Precision Binary Trait Association on Phylogenetic Trees.

Area of Science:

• Microbial Genomics
• Computational Biology
• Population Genetics

Background:

• Traditional methods for identifying associations in bacterial genomes are hindered by genome-wide linkage causing evolutionary-induced associations.
• Existing microbial GWAS (mGWAS) methods often suffer from high false discovery rates, low statistical power, poor performance on negative interactions, and computational limitations for pangenome-wide studies.

Purpose of the Study:

• To present SimPhyNI, a computationally optimized framework for efficient and rigorous microbial genome-wide association studies (mGWAS).
• To enable robust identification of both positive and negative genetic associations in bacterial genomes at scale.

Main Methods:

• SimPhyNI constructs null co-occurrence distributions by simulating traits using phylogenetically-informed parameters, including time to first event.
• Log odds ratio scoring is employed to compare traits, enabling robust identification of positive and negative associations.
• The framework utilizes constrained variation in simulations to enhance precision and recall.

Main Results:

• SimPhyNI demonstrates high precision and recall for both positive and negative interactions on synthetic datasets.
• The framework successfully identified interactions between phage defense systems in E. coli and gene-gene interactions across the entire E. coli pangenome (>9 million tests).
• SimPhyNI achieves near-zero false positive rates and maintains computational efficiency for large-scale analyses.

Conclusions:

• SimPhyNI provides an efficient and scalable solution for mGWAS, overcoming limitations of previous methods.
• Its performance enables genome-wide discovery of genetic interactions driving microbial function, ecology, and disease.
• The framework's design supports extension to various trait types, facilitating broader applications in microbial research.