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Mapping eQTLs with RNA-seq reveals novel susceptibility genes, non-coding RNAs and alternative-splicing events in systemic lupus erythematosus

Lookup NU author(s): Dr Ana Viñuela

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© The Author 2017. Published by Oxford University Press. All rights reserved.Studies attempting to functionally interpret complex-disease susceptibility loci by GWAS and eQTL integration have predominantly employed microarrays to quantify gene-expression. RNA-Seq has the potential to discover a more comprehensive set of eQTLs and illuminate the underlying molecular consequence. We examine the functional outcome of 39 variants associated with Systemic Lupus Erythematosus (SLE) through the integration of GWAS and eQTL data from the TwinsUK microarray and RNA-Seq cohort in lymphoblastoid cell lines. We use conditional analysis and a Bayesian colocalisation method to provide evidence of a shared causal-variant, then compare the ability of each quantification type to detect disease relevant eQTLs and eGenes. We discovered the greatest frequency of candidate-causal eQTLs using exon-level RNA-Seq, and identified novel SLE susceptibility genes (e.g. NADSYN1 and TCF7) that were concealed using microarrays, including four non-coding RNAs. Many of these eQTLs were found to influence the expression of several genes, supporting the notion that risk haplotypes may harbour multiple functional effects. Novel SLE associated splicing events were identified in the T-reg restricted transcription factor, IKZF2, and other candidate genes (e.g. WDFY4) through asQTL mapping using the Geuvadis cohort. We have significantly increased our understanding of the genetic control of gene-expression in SLE by maximising the leverage of RNA-Seq and performing integrative GWAS-eQTL analysis against gene, exon, and splice-junction quantifications. We conclude that to better understand the true functional consequence of regulatory variants, quantification by RNA-Seq should be performed at the exon-level as a minimum, and run in parallel with gene and splice-junction level quantification.


Publication metadata

Author(s): Odhams CA, Cortini A, Chen L, Roberts AL, Vinuela A, Buil A, Small KS, Dermitzakis ET, Morris DL, Vyse TJ, Cunninghame Graham DS

Publication type: Article

Publication status: Published

Journal: Human Molecular Genetics

Year: 2017

Volume: 26

Issue: 5

Pages: 1003-1017

Print publication date: 01/03/2017

Online publication date: 05/01/2017

Acceptance date: 05/12/2016

Date deposited: 01/07/2020

ISSN (print): 0964-6906

ISSN (electronic): 1460-2083

Publisher: Oxford University Press

URL: https://doi.org/10.1093/hmg/ddw417

DOI: 10.1093/hmg/ddw417

PubMed id: 28062664


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