Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data.

ABSTRACT:

Background

Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals.

Results

We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72

SUBMITTER: Li S 

PROVIDER: S-EPMC10111756 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature