A type 1 diabetes genetic risk score discriminates between type 1 diabetes and type 2 diabetes in a Chinese population

Aims/hypothesis: We aimed to generate a population-specific type 1 diabetes genetic risk score (GRS) and assess whether it could improve discrimination between type 1 diabetes and type 2 diabetes in a Chinese population. Methods: We performed a genome-wide association analysis on 1303 individuals with type 1 diabetes and 2236 control individuals. An independent replication cohort of 501 individuals with type 1 diabetes and 853 control individuals was used to validate the top common variant associations. HLA typing data were used to identify tag SNPs for DQA1-DQB1 haplotypes. We integrated significant signals to construct a Chinese type 1 diabetes GRS (C-GRS). The accuracy of the C-GRS was tested in an independent validation cohort consisting of 262 individuals with type 1 diabetes, 1080 individuals with type 2 diabetes and 208 control individuals. Results: We identified a variant, rs10232170, in BMPER as a possible novel type 1 diabetes locus (p=9.897×10⁻⁹). We identified tag SNPs for 13 DQA1-DQB1 haplotypes and 12 non-DQA1-DQB1 loci. Integrating 33 significant SNPs from HLA and non-HLA regions, C-GRS demonstrated high discriminative power for type 1 diabetes (AUC=0.876). It was tested in an independent validation cohort and showed high discrimination (AUC 0.871 for type 1 diabetes vs control group, 0.869 for type 1 diabetes vs type 2 diabetes). The C-GRS outperformed a European-derived GRS (0.871 vs 0.773, and 0.869 vs 0.793, respectively). Conclusions/interpretation: A type 1 diabetes C-GRS comprising 33 SNPs was highly discriminative of type 1 diabetes risk in the Chinese population and could aid in discriminating between type 1 diabetes and type 2 diabetes. This study highlights the potential of genetic information in improving prediction and precision diagnosis of type 1 diabetes in the Chinese population. Data availability: The raw sequencing data and summary statistics of genomic DNA derived from human samples have been deposited at the China National Center for Bioinformation (https://ngdc.cncb.ac.cn/omix) under accession number PRJCA023730.