scQTLbase: an integrated human single-cell eQTL database.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) identify disease-associated genetic variants, but functional interpretation is challenging.
• Expression quantitative trait loci (eQTLs) link genetic variants to gene expression, yet explain only a portion of disease-related variants.
• Single-cell eQTLs (sc-eQTLs) offer higher resolution for identifying disease risk genes and understanding transcriptional regulation.

Purpose of the Study:

• To develop the first comprehensive, integrated human single-cell eQTLs (sc-eQTLs) portal.
• To provide a centralized resource for searching, analyzing, and visualizing sc-eQTL data.
• To facilitate the discovery of novel disease susceptibility genes through fine-resolution genetic and gene expression analysis.

Main Methods:

• Compiled 304 human datasets covering 57 cell types and 95 cell states.
• Integrated approximately 16 million single nucleotide polymorphisms (SNPs) associated with cell-type/state gene expression.
• Incorporated around 0.69 million disease-associated sc-eQTLs from 3,333 traits/diseases.

Main Results:

• Developed scQTLbase, the first human sc-eQTLs portal (http://bioinfo.szbl.ac.cn/scQTLbase).
• The database contains extensive sc-eQTL data, including millions of SNPs and hundreds of thousands of disease-associated sc-eQTLs.
• Implemented functionalities for sc-eQTL search, gene expression visualization (UMAP plots), genome browsing, and colocalization analysis with GWAS data.

Conclusions:

• scQTLbase serves as a valuable one-stop portal for single-cell eQTL research.
• The resource significantly advances the identification and understanding of disease susceptibility genes.
• Facilitates deeper insights into genetic regulation of gene expression at the single-cell level.