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

Abstract

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.

In-depth genetic analysis of 6p21.3 reveals insights into associations between HLA types and complex traits and disease

“…For instance, we observed that in many samples the expression of HLA-C was low (<40 TPM) using reference alignments, but it increased more than four-fold using HLA-specific alignments; while HLA-DPB2 expression decreased in 98% of samples. Our analyses (here and in Figure S3) and those of others (Aguiar et al, 2019;Panousis et al, 2014) suggests that HLA gene expression level estimates are improved by aligning RNA-seq reads to HLA type-specific cDNA sequences. the differences between TPM calculated by aligning RNA-seq reads to reference cDNA sequences (X axis) and HLA typespecific cDNA sequences (Y axis).…”

“…There were 146 expressed genes in the MHC interval (TPM >2 in at least 10 samples), of which 24 were HLA genes, including six HLA class I genes , 3 HLA class I pseudogenes, 11 HLA class II genes, MICA, MICB, TAP1 and TAP2. For each individual, we estimated expression levels of the 24 HLA genes by aligning reads to cDNA sequences specific for the HLA types called by HLA-VBSeq(Nariai et al, 2015) to avoid alignment biases (Aguiar et al, 2019;Gensterblum-Miller et al, 2018;Lee et al, 2018;Panousis et al, 2014) Supplementary File 4). The HLA class I genes tended to be expressed at high levels, consistent with their ubiquitous expression in all cell types, and the HLA class II genes tended to be expressed at lower levels, as expected due to their primary role in immune cells (Matzaraki et al, 2017).…”

Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci

“…While amino acid changes causing differential antigen display may be the primary autoimmune mechanism at the HLA locus, our data underscores the possibility that expression levels of HLA class II may also play a crucial and unappreciated role (35,36). Over the past several decades, there has been literature suggesting variation in expression among different HLA alleles (26,(37)(38)(39) -but to date the idea that this regulation changes with cell-state has not been established. It is well known that positive selection has resulted in dramatic coding variation within most HLA genes.…”

“…After identifying the causal regulatory variant driving condition-dependent HLA-DQB1 expression, we considered whether this effect is T cell-specific. We observed that our Late-Spike regulatory rs71542466 SNP (rSNP) was not in LD with reported eQTL SNPs for HLA-DQB1 in B cell derived lymphoblastoid cell lines (LCLs), monocytes, and resting and infected macrophages (Table S2, r 2 ≤0.27) (5,8,(25)(26)(27). In fact, our rSNP had no association with expression of HLA-DQB1 in resting macrophages (P = 0.81), in macrophages infected with Listeria (P = 0.31) or with Salmonella (P = 0.86) (27).…”

Genome-wide study identifies association between HLA-B*55:01 and penicillin allergy

“…It is being increasingly recognized that the involvement of HLA variation in hypersensitivity reactions goes beyond peptide specificity. Other factors, such as effects on HLA expression that influence the strength of the immune response have also been described 48 The analysis of eQTLs based on the data of the eQTLGen Consortium 21 revealed that the T allele of the lead SNP rs114892859 identified in our GWAS of penicillin allergy appears to be associated with the expression of several nearby genes, including lower expression of both HLA-B and HLA-C , and an even stronger effect on RNA levels of PSORS1C3 and MICA ( Table S2 ). Interestingly, variants in the PSORS1C3 gene have been associated with the risk of allopurinol, carbamazepine and phenytoin induced SJS/TEN hypersensitivity reactions 49 .…”