Hierarchical annotation of eQTLs by H-eQTL enables identification of genes with cell type-divergent regulation

While context-type-specific regulation of genes is largely determined by cis-regulatory regions, attempts to identify cell type-specific eQTLs are complicated by the nested nature of cell types. We present hierarchical eQTL (H-eQTL), a network-based model for hierarchical annotation of bulk-derived eQTLs to levels of a cell type tree using single-cell chromatin accessibility data and no clustering of cells into discrete cell types. Using our model, we annotate bulk-derived eQTLs from the developing brain with high specificity to levels of a cell type hierarchy, which allows sensitive detection of genes with multiple distinct non-coding elements regulating their expression in different cell types.

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Publication Details

Journal
Genome biology
Published
2024-11-25
DOI
https://doi.org/10.1186/s13059-024-03440-2
Citations
3
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
0.70

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article

Hierarchical annotation of eQTLs by H-eQTL enables identification of genes with cell type-divergent regulation

Katherine S. Pollard, Pawel F. Przytycki
3 citations
Genome biology
Gene expression and cancer classification
0.70
article

Hierarchical annotation of eQTLs by H-eQTL enables identification of genes with cell type-divergent regulation

Katherine S. Pollard, Pawel F. Przytycki
article en
3 citations

Abstract

While context-type-specific regulation of genes is largely determined by cis-regulatory regions, attempts to identify cell type-specific eQTLs are complicated by the nested nature of cell types. We present hierarchical eQTL (H-eQTL), a network-based model for hierarchical annotation of bulk-derived eQTLs to levels of a cell type tree using single-cell chromatin accessibility data and no clustering of cells into discrete cell types. Using our model, we annotate bulk-derived eQTLs from the developing brain with high specificity to levels of a cell type hierarchy, which allows sensitive detection of genes with multiple distinct non-coding elements regulating their expression in different cell types.

Genome biologyVol. 25(1)
Boston University (US), Gladstone Institutes (US), Chan Zuckerberg Initiative (United States) (US)
Additional Ventures, National Institutes of Health, National Institute of Mental Health
Partnerships for the goals
Openalex Percentile: Top 29%
Gene expression and cancer classification
0.70
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