scQTLbase: an integrated human single-cell eQTL database

Author:

Ding Ruofan1ORCID,Wang Qixuan1ORCID,Gong Lihai1ORCID,Zhang Ting1ORCID,Zou Xudong1ORCID,Xiong Kewei1ORCID,Liao Qi2,Plass Mireya34ORCID,Li Lei1ORCID

Affiliation:

1. Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518055, China

2. School of Public Health, Health Science Center, Ningbo University , Ningbo  315211, China

3. Gene Regulation of Cell Identity Group, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), and Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P-CMR[C], L′Hospitalet de Llobregat , Barcelona , Spain

4. Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) , Madrid , Spain

Abstract

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.

Funder

National Natural Science Foundation of China

Shenzhen Bay Laboratory

Spanish Ministry of Science and Innovation

Publisher

Oxford University Press (OUP)

Subject

Genetics

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