Diabetologia · 2025
HLA-focused type 1 diabetes genetic risk prediction in populations of diverse ancestry.
СтатьяПоддержка исследования: вне правительства СШАПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Michalek Dominika A, Tern Courtney, Robertson Catherine C, Chen Wei-Min, Onengut-Gumuscu Suna, Rich Stephen S
Аннотация
AIMS/HYPOTHESIS: Type 1 diabetes is characterised by the destruction of pancreatic beta cells. Genetic factors account for approximately 50% of the total risk, with variants in the HLA region contributing to half of this genetic risk. Research has historically focused on populations of European ancestry. We developed HLA-focused type 1 diabetes genetic risk scores (T1D GRSHLA) using SNPs or HLA alleles from four ancestry groups (admixed African [AFR; T1D GRSHLA-AFR], admixed American [AMR; T1D GRSHLA-AMR], European [EUR; T1D GRSHLA-EUR] and Finnish [FIN; T1D GRSHLA-FIN]). We also developed an across-ancestry GRS (ALL; T1D GRSHLA-ALL). We assessed the performance of the GRS in each population to determine the transferability of constructed scores.
METHODS: A total of 41,689 samples and 13,695 SNPs in the HLA region were genotyped, with HLA alleles imputed using the HLA-TAPAS multi-ethnic reference panel. Conditionally independent SNPs and HLA alleles associated with type 1 diabetes were identified in each population group to construct T1D GRSHLA models. Generated T1D GRSHLA models were used to predict HLA-focused type 1 diabetes genetic risk across four ancestry groups. The performance of each T1D GRSHLA model was assessed using receiver operating characteristic (ROC) AUCs, and compared statistically.
RESULTS: Each T1D GRSHLA model included a different number of conditionally independent HLA-region SNPs (AFR, n=5; AMR, n=3; EUR, n=38; FIN, n=6; ALL, n=36) and HLA alleles (AFR, n=6; AMR, n=5; EUR, n=40; FIN, n=8; ALL, n=41). The ROC AUC values for the T1D GRSHLA from SNPs or HLA alleles were similar, and ranged from 0.73 (T1D GRSHLA-allele-AMR applied to FIN) to 0.88 (T1D GRSHLA-allele-EUR applied to EUR). The ROC AUC using the combined set of conditionally independent SNPs (T1D GRSHLA-SNP-ALL) or HLA alleles (T1D GRSHLA-allele-ALL) performed uniformly well across all ancestry groups, with values ranging from 0.82 to 0.88 for SNPs and 0.80 to 0.87 for HLA alleles.
CONCLUSIONS/INTERPRETATION: T1D GRSHLA models derived from SNPs performed equivalently to those derived from HLA alleles across ancestries. In addition, T1D GRSHLA-SNP-ALL and GRSHLA-allele-ALL models had consistently high ROC AUC values when applied across ancestry groups. Larger studies in more diverse populations are needed to better assess the transferability of T1D GRSHLA across ancestries.
METHODS: A total of 41,689 samples and 13,695 SNPs in the HLA region were genotyped, with HLA alleles imputed using the HLA-TAPAS multi-ethnic reference panel. Conditionally independent SNPs and HLA alleles associated with type 1 diabetes were identified in each population group to construct T1D GRSHLA models. Generated T1D GRSHLA models were used to predict HLA-focused type 1 diabetes genetic risk across four ancestry groups. The performance of each T1D GRSHLA model was assessed using receiver operating characteristic (ROC) AUCs, and compared statistically.
RESULTS: Each T1D GRSHLA model included a different number of conditionally independent HLA-region SNPs (AFR, n=5; AMR, n=3; EUR, n=38; FIN, n=6; ALL, n=36) and HLA alleles (AFR, n=6; AMR, n=5; EUR, n=40; FIN, n=8; ALL, n=41). The ROC AUC values for the T1D GRSHLA from SNPs or HLA alleles were similar, and ranged from 0.73 (T1D GRSHLA-allele-AMR applied to FIN) to 0.88 (T1D GRSHLA-allele-EUR applied to EUR). The ROC AUC using the combined set of conditionally independent SNPs (T1D GRSHLA-SNP-ALL) or HLA alleles (T1D GRSHLA-allele-ALL) performed uniformly well across all ancestry groups, with values ranging from 0.82 to 0.88 for SNPs and 0.80 to 0.87 for HLA alleles.
CONCLUSIONS/INTERPRETATION: T1D GRSHLA models derived from SNPs performed equivalently to those derived from HLA alleles across ancestries. In addition, T1D GRSHLA-SNP-ALL and GRSHLA-allele-ALL models had consistently high ROC AUC values when applied across ancestry groups. Larger studies in more diverse populations are needed to better assess the transferability of T1D GRSHLA across ancestries.