Stratification by a polygenic risk score of common variation aids in Alzheimer's disease rare variant discovery
We utilized an Alzheimer's disease (AD) polygenic risk score (PRS) to discover associations with novel rare variants (RVs).
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We utilized an Alzheimer's disease (AD) polygenic risk score (PRS) to discover associations with novel rare variants (RVs).
Inflammation is central to Alzheimer’s disease (AD) pathogenesis. Microglia, the resident innate immune cells of the brain, exhibit diverse inflammatory states and are enriched for AD-associated genetic variants within active cis-regulatory elements (CREs). However, the interplay among genetic variants, transcription factor (TF)–CRE– gene programs, and microglial responses across inflammatory and disease contexts remain poorly understood. Here, we develop context-dependent epigenomic networks (cEpiNets), integrating bulk and single-nucleus assay for transposase-accessible chromatin using sequencing (ATAC-seq) to reconstruct regulatory programs across inflammatory, genetic perturbation, and disease contexts. Leveraging TF footprinting and graph embedding, cEpiNets identifies shared and context-specific programs and predicts regulatory circuits in unseen biological contexts. In a SORL1-marked inflammatory microglial state that expands during AD progression, cEpiNets annotates AD risk variants at the SORL1 locus and identifies variants associated with cellular state abundance across donors. Cross-context analysis further identifies ZBTB14, whose inflammation-associated program connects AD risk variant–harboring CREs to target genes and widespread TF remodeling in AD. Donor-level ZBTB14 footprint activity is negatively associated with AD pathology, while combined IFNγ/TNFα stimulation represses ZBTB14 and activates a subset of inferred targets. Collectively, cEpiNets bridges genetic variation, regulatory programs, and disease-associated cellular phenotypes to facilitate mechanistic interpretation of complex disease genetics.
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