Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM).

ABSTRACT: Microbiome association studies typically link host disease or other traits to summary statistics measured in metagenomics data, such as diversity or taxonomic composition. But identifying disease-associated species based on their relative abundance does not provide insight into why these microbes act as disease markers, and it overlooks cases where disease risk is related to specific strains with unique biological functions. To bridge this knowledge gap, we developed microSLAM, a mixed-effects model and an R package that performs association tests that connect host traits to the presence/absence of genes within each microbiome species, while accounting for strain genetic relatedness across hosts. Traits can be quantitative or binary (such as case/control). MicroSLAM is fit in three steps f

SUBMITTER: Goldman M 

PROVIDER: S-EPMC12136445 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature