Expression estimation and eQTL mapping for HLA genes with a personalized pipeline

Article Article scientifique Article Expression estimation and eQTL mapping for HLA genes with a personalized pipeline eng 2019 author Aguiar Vitor 879308 author César Jônatas author Delaneau Olivier 810945 author Dermitzakis Emmanouil 542683 author Meyer Diogo e1008091 10.1371/journal.pgen.1008091 31009447 1553-7390 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. Alleles Chromosome Mapping Computational Biology/methods Gene Expression Gene Frequency Genotype HLA Antigens/genetics Haplotypes Humans Quantitative Trait Loci Transcriptome Dewey 576.5 135 Département de médecine génétique et développement 21D false 551 Population Genomics and Genetics of Complex Traits 892 PLOS Genetics 15 4 357ebf43-fbc6-4b3c-9320-d58b282bea67 Article (Published version) Public journal.pgen.1008091.pdf 2890284 application/pdf CC BY openAccess Faculté de médecine / Section de médecine fondamentale / Département de médecine génétique et développement

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Expression estimation and eQTL mapping for HLA genes with a personalized pipeline	PLOS Genetics			2019	245	168

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