Nature CommunicationsGenomicsPeer-reviewed journal article
Genetic integration with cell-specific nucleosome positioning resolves causal relationships underlying chromatin accessibility profiles
Xiaoou WangiD, Catherine C. RobertsoniD, Arushi VarshneyiD, Nandini ManickamiD, Peter OrchardiD, Markku LaaksoiD, Jaakko TuomilehtoiD, Timo Antero LakkaiD, Karen L. MohlkeiD, Michael BoehnkeiD, Laura J. ScottiD, Heikki A. KoistineniD, Francis S. CollinsiD, Stephen C.J. ParkeriD
Abstract
Cell type-specific chromatin accessibility QTL (caQTL) mapping is a promising approach to understand genetic control of chromatin landscapes and identify regulatory mechanisms underlying GWAS associations. However, current caQTL studies lack resolution and do not distinguish nucleosome-free regions (NFR) from positioned nucleosomes. Here, we leverage statistical modeling of fragment position and length to decompose ATAC-seq profiles into NFRs and phased nucleosomes. With single nucleus (sn)ATAC-seq from 281 human muscle biopsies, we map cell type-specific genetic effects on NFRs (76,027 nfrQTLs) and nucleosome occupancy (24,623 nucQTLs) across skeletal muscle cell types. Colocalization and causal inference between nucQTLs and nearby nfrQTLs and show that nfrQTLs are substantially more likely to causally influence nucQTLs and phase adjacent nucleosomes, indicating a causal relationship in shaping chromatin profiles. Hundreds of nfrQTLs colocalize with GWAS signals for muscle-related traits, including grip strength, atrial fibrillation, and fasting insulin, and the majority of colocalizing signals mapped to credible sets overlapping the corresponding nfrPeak. This approach adds mechanistic insights for how variants underlying caQTLs and GWAS signals exert their cis regulatory effects by initially modifying NFR accessibility and subsequently shaping broader chromatin landscapes, nucleosome positioning, gene expression, and ultimately higher-level traits and disease.
- organism: human
- assay: scATAC-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
- FWCI
- 2.04
- Field percentile
- top 22%
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