Titre
Expression estimation and eQTL mapping for HLA genes with a personalized pipeline.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Aguiar, VRC
Auteure/Auteur
César, J.
Auteure/Auteur
Delaneau, O.
Auteure/Auteur
Dermitzakis, E.T.
Auteure/Auteur
Meyer, D.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1553-7404
Statut éditorial
Publié
Date de publication
2019-04
Volume
15
Numéro
4
Première page
e1008091
Peer-reviewed
Oui
Langue
anglais
Résumé
The HLA (Human Leukocyte Antigens) genes are well-documented targets of balancing selection, and variation at these loci is associated with many disease phenotypes. Variation in expression levels also influences disease susceptibility and resistance, but little information exists about the regulation and population-level patterns of expression. This results from the difficulty in mapping short reads originated from these highly polymorphic loci, and in accounting for the existence of several paralogues. We developed a computational pipeline to accurately estimate expression for HLA genes based on RNA-seq, improving both locus-level and allele-level estimates. First, reads are aligned to all known HLA sequences in order to infer HLA genotypes, then quantification of expression is carried out using a personalized index. We use simulations to show that expression estimates obtained in this way are not biased due to divergence from the reference genome. We applied our pipeline to the GEUVADIS dataset, and compared the quantifications to those obtained with reference transcriptome. Although the personalized pipeline recovers more reads, we found that using the reference transcriptome produces estimates similar to the personalized pipeline (r ≥ 0.87) with the exception of HLA-DQA1. We describe the impact of the HLA-personalized approach on downstream analyses for nine classical HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRA, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1). Although the influence of the HLA-personalized approach is modest for eQTL mapping, the p-values and the causality of the eQTLs obtained are better than when the reference transcriptome is used. We investigate how the eQTLs we identified explain variation in expression among lineages of HLA alleles. Finally, we discuss possible causes underlying differences between expression estimates obtained using RNA-seq, antibody-based approaches and qPCR.
PID Serval
serval:BIB_7C131D63033D
PMID
Open Access
Oui
Date de création
2019-06-03T08:06:57.012Z
Date de création dans IRIS
2025-05-21T03:17:21Z
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En cours de chargement...
Nom
journal.pgen.1008091-1.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
2.64 MB
Format
Adobe PDF
PID Serval
serval:BIB_7C131D63033D.P001
URN
urn:nbn:ch:serval-BIB_7C131D63033D5
Somme de contrôle
(MD5):5f143e4a8bc75629f8d8e2c1554527d4