Development of an MHC imputation panel highlights independent contributions of HLA amino acid residues and C4 copy number variations to SLE risk.

OBJECTIVES

Systemic lupus erythematosus (SLE) is a complex autoimmune disease strongly associated with the major histocompatibility complex (MHC) region, but precisely pinpointing the risk variants remains challenging. This study aimed to comprehensively profile SLE-driving variants using a newly developed East Asian MHC imputation reference panel, capable of simultaneously imputing diverse MHC variants, including multilevel human leukocyte antigen (HLA) variants and copy number variations (CNVs) of C4 elements, such as C4A, C4B, and human endogenous retrovirus (HERV).

METHODS

Using the whole-genome-sequencing (WGS) data from ∼2000 Korean samples, we genotyped and phased MHC variants, including HLA variants and C4-related CNVs, to construct an MHC reference panel. Imputation performance of the panel was assessed through leave-one-out cross-validation and validated using WGS and droplet digital polymerase chain reaction methodology. The panel was applied to 2 independent SLE genome-wide association study datasets, followed by stepwise conditional analyses, fine-mapping, and model comparisons.

RESULTS

The MHC panel achieved high imputation accuracies of 95% for HLA and 94% for C4 at the haploid-level. Independent contributions to SLE risk were identified from 6 amino acid positions altering the epitope-binding surfaces of HLA-DRB1 and HLA-C. Reduced C4A copy numbers and increased HERV copy numbers, collectively lowering C4 protein levels, were associated with increased SLE risk, independent of HLA variants. Our refined MHC-SLE association model provided superior explanations for SLE risk over previous association models in an independent Korean population.

CONCLUSIONS

This study enhanced the understanding of HLA and C4 in SLE pathogenesis and holds promise for advancing MHC association studies for immune-mediated inflammatory disorders in East Asians using our MHC panel, accessible via https://coda.nih.go.kr/usab/kis/intro.do.