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 PALM’s 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 PALM’s utility, underscoring its potential to advance microbiome research. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵5 Twitter handle: @tangzz_lab Emails of other authors: zwei74{at}wisc.edu (ZW), qhong8{at}wisc.edu (QH), gchen25{at}wisc.edu (GC), tina.hartert{at}vumc.org (TVH), c.rosas.salazar{at}vumc.org (CRS), suman.r.das{at}vanderbilt.edu (SRD), meghan.h.shilts{at}vumc.org (MHS), and alevin1{at}hfhs.org (AML)