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bioRxiv · 10.64898/2026.04.09.717497

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

Wei, Z.·Hong, Q.·Chen, G.·Hartert, T. V.·Rosas-Salazar, C.·Das, S. R.·Shilts, M. H.·Levin, A. M.·Tang, Z.-Z.
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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.

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Wei, Z., Hong, Q., Chen, G., Hartert, T. V., Rosas-Salazar, C., Das, S. R., Shilts, M. H., Levin, A. M., Tang, Z.-Z.. 2026-04-10. Fast and reliable association discovery in large-scale microbiome studies and meta-analyses using PALM. https://doi.org/10.64898/2026.04.09.717497

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