Improved HLA-based prediction of coeliac disease identifies two novel genetic interactions.

ABSTRACT

Human Leucocyte Antigen (HLA) testing is useful in the clinical work-up of coeliac disease (CD) with high negative but low positive predictive value. We construct a genomic risk score (GRS) using HLA risk genotypes to improve CD prediction and guide exclusion criteria. Imputed HLA genotypes for five European CD case-control GWAS (n > 15,000) were used to construct and validate an interpretable HLA-based risk model (HDQ15), which shows statistically significant improvements in predictive performance upon all previous HLA-based risk models. Conditioning on this model, we find two novel associations, HLA-DQ6.2 and HLA-DQ7.3, that interact significantly with HLA-DQ2.5 (p = 2.51 × 10-9, 1.99 × 10-7, respectively). Integrating these novel alleles into a new risk model (HDQ17) leads to predictive performance equivalent or better than the strongest reported GRS (GRS228) using 228 single nucleotide polymorphisms (SNPs). We also demonstrate that our proposed HLA-based models can be implemented using only six HLA tagging SNPs with statistically equivalent predictive performance. Using insights from our model to guide exclusionary criteria, we find the positive predictive value of CD testing in high-risk populations can be increased by 55%, from 17.5 to 27.1%, while maintaining a negative predictive value above 99%. Our results suggest that HLA typing is currently undervalued in CD assessment.