A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver

Tobias Strunz, Felix Grassmann, Javier Gayán, Satu Nahkuri, Debora Souza-Costa, Cyrille Maugeais, Sascha Fauser, Everson Nogoceke, Bernhard H.F. Weber*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)
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Abstract

Genome-wide association studies (GWAS) have identified numerous genetic variants in the human genome associated with diseases and traits. Nevertheless, for most loci the causative variant is still unknown. Expression quantitative trait loci (eQTL) in disease relevant tissues is an excellent approach to correlate genetic association with gene expression. While liver is the primary site of gene transcription for two pathways relevant to age-related macular degeneration (AMD), namely the complement system and cholesterol metabolism, we explored the contribution of AMD associated variants to modulate liver gene expression. We extracted publicly available data and computed the largest eQTL data set for liver tissue to date. Genotypes and expression data from all studies underwent rigorous quality control. Subsequently, Matrix eQTL was used to identify significant local eQTL. In total, liver samples from 588 individuals revealed 202,489 significant eQTL variants affecting 1,959 genes (Q-Value < 0.001). In addition, a further 101 independent eQTL signals were identified in 93 of the 1,959 eQTL genes. Importantly, our results independently reinforce the notion that high density lipoprotein metabolism plays a role in AMD pathogenesis. Taken together, our study generated a first comprehensive map reflecting the genetic regulatory landscape of gene expression in liver.

Original languageEnglish
Article number5865
Number of pages11
JournalScientific Reports
Volume8
DOIs
Publication statusPublished - 12 Apr 2018

Bibliographical note

TS was an awardee of the Roche Internships for Scientific Exchange (RiSE) Programme. The work has been supported in part by institutional funds (TG77) of the Institute of Human Genetics Regensburg and by a grant from the Helmut Ecker Foundation (Ingolstadt, Germany) to BHFW (No. 05/17).

Keywords

  • data mining
  • Gene expression

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