Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.

Dongyuan Wu; Susmita Datta

doi:10.3390/genes17060625

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Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.

Dongyuan Wu·Susmita Datta

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Abstract

BACKGROUND: Expression quantitative trait loci (eQTL) analysis links genetic variants to gene expression levels, helping to uncover how genetic variation contributes to gene regulation. While traditional eQTL analyses rely on bulk RNA-seq data, recent advances in single-cell RNA sequencing (scRNA-seq) have made it possible to detect cell-type-specific eQTLs. However, the inherent sparsity and heterogeneity of scRNA-seq data present major challenges for standard modeling approaches. METHODS: In this paper, we propose a novel statistical framework, Bootstrap Penalized Hurdle regression model (BPHurdle), designed specifically for scRNA-seq data. BPHurdle employs a hurdle modeling framework, where a logistic component accounts for the excess zeros in single-cell expression data, and a Poisson component jointly evaluates the effects of multiple SNPs on positive gene expression levels. RESULTS: Through simulation studies, we show that BPHurdle achieves high accuracy and robustness in identifying regulatory variants. We further demonstrate its utility on a real dataset through a case study focusing on a subset of differentially expressed genes, where it successfully identifies reliable cell-type-specific eQTLs. CONCLUSIONS: Overall, BPHurdle offers an advanced and flexible approach for single-cell eQTL mapping, providing deeper insight into the genetic regulation of gene expression at cellular resolution.

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Quantitative Trait Loci Single-Cell Gene Expression Analysis Single-Cell Analysis Polymorphism, Single Nucleotide Humans Models, Genetic Gene Expression Profiling Models, Statistical bootstrap eQTL analysis hurdle model penalized regression single-cell Journal Article Research Support, N.I.H., Extramural

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Dongyuan Wu, Susmita Datta. 2026-05-29. Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.. https://doi.org/10.3390/genes17060625

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