Gladstone Institutes, San Francisco, CA, USA.
Present address: Faculty of Computing & Data Sciences, Boston University, Boston University, Boston, MA, USA.
Genome Biol. 2024 Nov 25;25(1):299. doi: 10.1186/s13059-024-03440-2.
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.
虽然基因的上下文类型特异性调节在很大程度上由顺式调控区域决定，但尝试识别细胞类型特异性 eQTL 会受到细胞类型嵌套性质的影响。我们提出了层次 eQTL（H-eQTL），这是一种基于网络的模型，用于使用单细胞染色质可及性数据和不对细胞进行离散细胞类型聚类，将批量衍生的 eQTL 分层注释到细胞类型树的级别。使用我们的模型，我们将发育中大脑的批量衍生 eQTL 以高特异性注释到细胞类型层次的级别，这允许敏感地检测到具有多个不同非编码元件调节其在不同细胞类型中表达的基因。
