ArticlemedRxiv : the preprint server for health sciences2026
An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer's disease.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Rare genetic variants provide critical insight into the mechanisms underlying complex diseases, yet their study is limited by inherent statistical challenges, particularly in the noncoding genome where functional prioritization remains difficult. Here, we introduce parmigiano, an empirical Bayesian framework that systematically integrates functional annotations into existing rare variant association tests (RVATs), jointly learning annotation weights and a global variant filter threshold to enable trait-informed variant prioritization. We apply parmigiano to Alzheimer's disease (AD) whole-genome sequencing data (12,900 cases and 23,846 controls) and perform both coding and noncoding RVATs, leveraging AD-relevant cell-type-specific predictions of variant regulatory effect. Integrating parmigiano significantly increases association yield across five existing RVATs, uncovering 23 candidate AD genes - 19 uniquely detected by our framework - including
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