Unraveling the Influence of Genetic Variants on Gene Regulation

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Genetics and Genomics".

Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 1064

Special Issue Editor


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Guest Editor
School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA
Interests: genetics; gene expression; genomics; T2D genetics and Alzheimer’s disease genetics

Special Issue Information

Dear Colleagues,

The genetics field is reaching a new stage. Over the last few years, multiple projects have increased our understanding of species-specific genetic variability. Large-scale sequencing projects identified millions of genetic variants and the subset of variants associated with relevant phenotypes. Most associated genetic variants identified have modest effect, low replicability, and are unable to explain the observed heritability of a phenotype. Global gene expression profiles provide a genomic functional landscape that led to the identification of functional networks altered in the presence of a pathology, pollutants, or condition of interest. The integration of gene expression and genomic variation will provide the identification of highly functional genetic variants with higher potential to affect any phenotype. The challenging integration demands new statistical associative frameworks and methods relying on machine learning and artificial intelligence to reduce the computation burden. We are pleased to invite you to submit your scientific work on this Special Issue entitled “Unraveling the Influence of Genetic Variants on Gene Regulation”. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: EQTL identification, methodological advances, machine learning methods applied to the identification of EQTLs, and the identification of EQTL associated with phenotypes of interest. We look forward to receiving your contributions.

Dr. Marcio A.A. Almeida
Guest Editor

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Keywords

  • genetics
  • genomics
  • gene expression analysis
  • EQTL identification
  • machine learning and artificial intelligence

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Published Papers (1 paper)

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Research

11 pages, 1524 KB  
Article
scQTLtools: An R/Bioconductor Package for Comprehensive Identification and Visualization of Single-Cell eQTLs
by Xiaofeng Wu, Xin Huang, Pinjing Chen, Jingtong Kang, Jin Yang, Zhanpeng Huang and Siwen Xu
Biology 2025, 14(7), 743; https://doi.org/10.3390/biology14070743 - 23 Jun 2025
Viewed by 561
Abstract
Single-cell RNA sequencing (scRNA-seq) enables expression quantitative trait locus (eQTL) analysis at cellular resolution, offering new opportunities to uncover regulatory variants with cell-type-specific effects. However, existing tools are often limited in functionality, input compatibility, or scalability for sparse single-cell data. To address these [...] Read more.
Single-cell RNA sequencing (scRNA-seq) enables expression quantitative trait locus (eQTL) analysis at cellular resolution, offering new opportunities to uncover regulatory variants with cell-type-specific effects. However, existing tools are often limited in functionality, input compatibility, or scalability for sparse single-cell data. To address these challenges, we developed scQTLtools, a comprehensive R/Bioconductor package that facilitates end-to-end single-cell eQTL analysis, from preprocessing to visualization. The toolkit supports flexible input formats, including Seurat and SingleCellExperiment objects, handles both binary and three-class genotype encodings, and provides dedicated functions for gene expression normalization, SNP and gene filtering, eQTL mapping, and versatile result visualization. To accommodate diverse data characteristics, scQTLtools implements three statistical models—linear regression, Poisson regression, and zero-inflated negative binomial regression. We applied scQTLtools to scRNA-seq data from human acute myeloid leukemia and identified eQTLs with regulatory effects that varied across cell types. Visualization of SNP–gene pairs revealed both positive and negative associations between genotype and gene expression. These results demonstrate the ability of scQTLtools to uncover cell-type-specific regulatory variation that is often missed by bulk eQTL analyses. Currently, scQTLtools supports cis-eQTL mapping; future development will extend to include trans-eQTL detection. Overall, scQTLtools offers a robust, flexible, and user-friendly framework for dissecting genotype–expression relationships in heterogeneous cellular populations. Full article
(This article belongs to the Special Issue Unraveling the Influence of Genetic Variants on Gene Regulation)
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