Fast and reliable association discovery in large-scale microbiome studies and meta-analyses using PALM

Music City Center 

Identifying key microbial features associated with clinical outcomes, host factors, and other covariates is central to advancing microbiome research. Recent microbiome association studies have scaled up significantly, incorporating a greater number of microbial features, covariates, and datasets from diverse populations and cohorts. The unique characteristics of microbiome data pose significant challenges for association analysis, particularly in large-scale and meta-analytic contexts, often leading to low replicability of findings. To address these challenges, we introduce PALM, a semi-parametric statistical framework designed for robust, scalable, and generalizable microbiome association discovery in large-scale studies and meta-analyses. Extensive realistic simulations demonstrate PALM's advantages in false discovery rate control, statistical power, computational efficiency, and cross-study signal harmonization. PALM's utility is illustrated through real data applications.

microbiome

association analysis

meta-analysis