A type 1 diabetes genetic risk score discriminates between type 1 diabetes and type 2 diabetes in a Chinese population
Jingyi Hu, Guozhi Jiang, Jiabi Qin, Shuoming Luo, Baoqi Fan, Zhiguo Xie, Raymond Wan, Xia Li, Claudia H. T. Tam, Zhenqian Wang, Jin Ding, Ying Xia, Yuanqin Yang, Jian Lin, Gechang Yu, Ping Jin, Cadmon K.P. Lim, Hong Kong Diabetes Biobank Study Group, Andrea O. Y. Luk, Wing Yee So, Juliana C.N. Chan, Cong‐Yi Wang, Jiaqi Huang, Michael N. Weedon, William Hagopian, Richard A. Oram, Ronald C.W., Yang Xiao, Zhiguang Zhou
Abstract
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 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.