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. 2024 Jan 5;52(D1):D1010-D1017.
doi: 10.1093/nar/gkad781.

scQTLbase: an integrated human single-cell eQTL database

Ruofan Ding  1 , Qixuan Wang  1 , Lihai Gong  1 , Ting Zhang  1 , Xudong Zou  1 , Kewei Xiong  1 , Qi Liao  2 , Mireya Plass  3   4 , Lei Li  1
Affiliations

scQTLbase: an integrated human single-cell eQTL database

Ruofan Ding et al. Nucleic Acids Res. .

Abstract

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with diseases and traits. However, the functional interpretation of these variants remains challenging. Expression quantitative trait loci (eQTLs) have been widely used to identify mutations linked to disease, yet they explain only 20-50% of disease-related variants. Single-cell eQTLs (sc-eQTLs) studies provide an immense opportunity to identify new disease risk genes with expanded eQTL scales and transcriptional regulation at a much finer resolution. However, there is no comprehensive database dedicated to single-cell eQTLs that users can use to search, analyse and visualize them. Therefore, we developed the scQTLbase (http://bioinfo.szbl.ac.cn/scQTLbase), the first integrated human sc-eQTLs portal, featuring 304 datasets spanning 57 cell types and 95 cell states. It contains ∼16 million SNPs significantly associated with cell-type/state gene expression and ∼0.69 million disease-associated sc-eQTLs from 3 333 traits/diseases. In addition, scQTLbase offers sc-eQTL search, gene expression visualization in UMAP plots, a genome browser, and colocalization visualization based on the GWAS dataset of interest. scQTLbase provides a one-stop portal for sc-eQTLs that will significantly advance the discovery of disease susceptibility genes.

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Figures

Graphical Abstract
Graphical Abstract
Figure 1.
Figure 1.
The data structure and general function of scQTLbase. The left panel is the sc-eQTL data summary and the right panel is the functionality of the database.
Figure 2.
Figure 2.
The web interface of scQTLbase. (A) Query interface and result visualization for ‘Gene/SNP Search’ in sc-eQTLs. (B) Example of UMAP view (top panel) and corresponding marker genes/egenes (bottom panel) from the study ‘Van der Wijst-2018-Nat. Genet.’ (C) Genome browser view showing sc-eQTLs across cell types in the study ‘Jerber-2021-Nat. Genet.’ (D) Interface for ‘Colocalization’ and an example of the LocusCompare plot at the gene ‘BIN1,’ displaying Alzheimer's disease GWAS P-values and sc-eQTLs P-values in Microglia (Micro) from the study ‘Bryois-2022-Nat. Neurosci.’ (E) Interface for ‘Traits/Diseases’ and an example of sc-eQTLs related to asthma.
See this image and copyright information in PMC

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