RT Journal Article
SR Electronic
T1 Co-expression analysis reveals interpretable gene modules controlled by <em>trans</em>-acting genetic variants
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.22.055335
DO 10.1101/2020.04.22.055335
A1 Liis Kolberg
A1 Nurlan Kerimov
A1 Hedi Peterson
A1 Kaur Alasoo
YR 2020
UL http://biorxiv.org/content/early/2020/04/24/2020.04.22.055335.abstract
AB Background Developing novel therapies for complex disease requires better understanding of the causal processes that contribute to disease onset and progression. Although trans-acting gene expression quantitative trait loci (trans-eQTLs) can be a powerful approach to directly reveal cellular processes modulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect sizes and large number of genes tested. However, if a single trans-eQTL controls a group of co-regulated genes, then multiple testing burden can be greatly reduced by summarising gene expression at the level of co-expression modules prior to trans-eQTL analysis.Results We analysed gene expression and genotype data from six blood cell types from 226 to 710 individuals. We inferred gene co-expression modules with five methods on the full dataset, as well as in each cell type separately. We detected a number of established co-expression module trans-eQTLs, such as the monocyte-specific associations at the IFNB1 and LYZ loci, as well as a platelet-specific ARHGEF3 locus associated with mean platelet volume. We also discovered a novel trans association near the SLC39A8 gene in LPS-stimulated monocytes. Here, we linked an early-response cis-eQTL of the SLC39A8 gene to a module of co-expressed metallothionein genes upregulated more than 20 hours later and used motif analysis to identify zinc-induced activation of the MTF1 transcription factor as a likely mediator of this effect.Conclusions Our analysis provides a rare detailed characterisation of a trans-eQTL effect cascade from a proximal cis effect to the affected signalling pathway, transcription factor, and target genes. This highlights how co-expression analysis combined with functional enrichment analysis can greatly improve the identification and prioritisation of trans-eQTLs when applied to emerging cell-type specific datasets.Competing Interest StatementThe authors have declared no competing interest.