The influence of HLA genetic variation on plasma protein expression

Abstract

Genetic variation in the human leukocyte antigen (HLA) loci is associated with risk of immune-mediated diseases, but the molecular effects of HLA polymorphism are unclear. Here we examined the effects of HLA genetic variation on the expression of 2940 plasma proteins across 45,330 Europeans in the UK Biobank, with replication analyses across multiple ancestry groups. We detected 504 proteins affected by HLA variants (HLA-pQTL), including widespread trans effects by autoimmune disease risk alleles. More than 80% of the HLA-pQTL fine-mapped to amino acid positions in the peptide binding groove. HLA-I and II affected proteins expressed in similar cell types but in different pathways of both adaptive and innate immunity. Finally, we investigated potential HLA-pQTL effects on disease by integrating HLA-pQTL with fine-mapped HLA-disease signals in the UK Biobank. Our data reveal the diverse effects of HLA genetic variation and aid the interpretation of associations between HLA alleles and immune-mediated diseases.

Fig. 1. HLA-pQTL in the UK Biobank.

a Schematic of the study. We used imputation of one and two-field HLA alleles and amino acid polymorphisms across 45,330 Europeans from the UKB Pharma Proteomics Project (UKB-PPP) together with plasma protein levels quantified using the OLINK platform as input to pQTL mapping for 2940 proteins. For each protein, we tested each HLA variant (allele or amino acid) independently in a multivariable linear model incorporating covariates as specified by the UKB-PPP. We performed replication studies in subgroups of non-European ancestry African (AFR); Central/South Asian (CSA), Middle Eastern (MID), East Asian (EAS), and admixed American (AMR). Following systematic pQTL mapping, conditional, and fine-mapping analyses, we examined the expression of HLA-pGenes on two independent single-cell RNA-sequencing datasets, and integrated our HLA-pQTL fine mapping with summary statistics of HLA-disease fine mapping from Sakaue et al.. b Number and location of proteins (pGenes) in the discovery cohort affected by lead HLA variants corresponding to each HLA-I and HLA-II locus. c For each ranked pGene in the discovery cohort, the proportion of protein expression variance explained by all lead and conditional HLA-pQTL. d Proportion of all fine-mapped lead and conditional HLA-pQTL in the discovery cohort within and outside the peptide binding groove (exons 2 and 3 for HLA-I; exon 2 for HLA-II). e Top three significant (P < 5.0 × 10⁻⁸) amino acid positions identified via conditional haplotype analysis for HLA-B. The protein with the lowest P value across all proteins tested is labeled. P value determined via two-sided ANOVA and omnibus test; the threshold of P < 5.0 ×10⁻⁸ is the commonly used conservative GWAS threshold after Bonferroni correction for the number of genome-wide SNPs. f Top three significant (P < 5.0 × 10⁻⁸) amino acid positions identified via conditional haplotype analysis for HLA-DRB1. P value determined via two-sided ANOVA and omnibus test; the threshold of P < 5.0 × 10⁻⁸ is the commonly used conservative GWAS threshold after Bonferroni correction for the number of genome-wide SNPs. The protein with the lowest P value across all proteins tested is labeled together with its top three significant conditional positions.

Fig. 2. Trans HLA-pQTL networks and gene families affected by HLA-I and HLA-II genetic variation.

a Network depicting proteins and gene ontology terms affected (at FDR P < 0.0001) by only HLA-I lead variants after adjusting for all HLA-II two-field alleles. Edges connect proteins to gene ontology terms. b Network depicting proteins affected (at FDR P < 0.0001) by only HLA-II lead variants from after adjusting for all HLA-I two-field alleles. Edges connect proteins to gene ontology terms.

Fig. 3. Expression of HLA-pGenes on immune cells from the Yazar et al. atlas.

a UMAP depicting cell types from the Yazar et al. immune cell atlas b Selected marker genes for the clusters named in (a). c (a). d Selected pGenes from among the top 50 lead HLA-pQTL. These proteins were selected due to their strong associations with HLA genetic variation and unclear or undefined roles in the immune response. Boxplots show minimum, maximum, interquartile range, and outliers. P values are unadjusted, two-sided, and determined via t test and linear regression analysis. The p value threshold is after Bonferroni correction for the total number of proteins test (5 × 10⁻⁸/2940 proteins tested = P ≤ 1.70 × 10⁻¹¹). For all boxplots, the sample size used for analysis was N = 34,490 individuals, e.g., the number of individuals in the UKB-PPP discovery cohort.

Fig. 4. Integration of fine-mapped HLA-pQTL with fine-mapped HLA-trait data from Sakaue et al..

Abbreviations for diseases and biomarkers are provided in Supplementary Data 20.