An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer's disease

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 Alzheimers 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 SIGLEC10 and HUNK. Associations detected by parmigiano replicate more reliably in held-out data than those from the original RVATs and show higher overlap with known AD associations. parmigiano offers a unified, computationally efficient approach to variant prioritization, enabling scalable, interpretable rare variant analyses across coding and noncoding regions.