Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis

Abstract

Objective Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci and particularly at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions and recently discovered non-HLA loci into an improved T1D GRS2 to better discriminate diabetes subtypes and to predict T1D in newborn screening studies. Research Design and Methods We used 6481 cases and 9247 controls from the Type 1 Diabetes Genetics Consortium (T1DGC) to analyse variants associated with T1D in both the HLA region and across the genome. We modelled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create an improved T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening, diabetes classification, and to provide a framework for comparison to previous scores. Results The T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (AUC=0.92; p<0.0001 vs older scores) and even more discriminative of early onset T1D (AUC=0.96). In simulated newborn screening, The T1D GRS2 was nearly twice as efficient as HLA genotyping alone, and 50% better than current genetic scores in general population T1D prediction. Conclusion An improved T1D GRS2 is highly useful to classify adult incident diabetes type, and to improve newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.