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

Abstract

Identifying microbial features associated with various covariates is a long-standing goal in microbiome research. Modern association studies incorporate an ever-increasing number of microbial features, covariates, and datasets from diverse cohorts. However, the complexity of microbiome data challenges analysis, often leading to poor replication of findings. We introduce PALM, a quasi-Poisson regression framework that enables fast and reliable association discovery in large-scale studies and meta-analyses. Extensive, realistic simulations demonstrate PALMs advantages in controlling false discovery rates, boosting power, improving computational efficiency, and preserving cross-study homogeneity of association effects. Three real-world applications at different scales illustrate PALMs utility, underscoring its potential to advance microbiome research.