HLA-focused type 1 diabetes genetic risk prediction in populations of diverse ancestry.

IF 10.2 1区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Dominika A Michalek, Courtney Tern, Catherine C Robertson, Wei-Min Chen, Suna Onengut-Gumuscu, Stephen S Rich
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引用次数: 0

Abstract

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.

不同祖先人群中以hla为中心的1型糖尿病遗传风险预测
目的/假设:1型糖尿病的特征是胰腺细胞的破坏。遗传因素约占总风险的50%,HLA区域的变异占这种遗传风险的一半。历史上的研究主要集中在欧洲血统的人群上。我们使用来自四个祖先群体(混合非洲人[AFR; T1D GRSHLA-AFR],混合美国人[AMR; T1D GRSHLA-AMR],欧洲人[EUR; T1D GRSHLA-EUR]和芬兰人[FIN; T1D GRSHLA-FIN])的snp或HLA等位基因开发了以HLA为重点的1型糖尿病遗传风险评分(T1D GRSHLA)。我们还开发了一个跨祖先GRS (ALL; T1D GRSHLA-ALL)。我们评估了GRS在每个人群中的表现,以确定构建分数的可转移性。方法:采用HLA- tapas多民族参考面板,对HLA区41689份样本和13695个snp进行基因分型,并进行HLA等位基因的估算。在每个人群组中鉴定与1型糖尿病相关的条件独立snp和HLA等位基因,构建T1D GRSHLA模型。生成的T1D GRSHLA模型用于预测四个祖先群体中以hla为中心的1型糖尿病遗传风险。采用受试者工作特征(ROC) auc评估各T1D GRSHLA模型的性能,并进行统计学比较。结果:每个T1D GRSHLA模型包含不同数量的条件独立HLA区域snp (AFR, n=5; AMR, n=3; EUR, n=38; FIN, n=6; ALL, n=36)和HLA等位基因(AFR, n=6; AMR, n=5; EUR, n=40; FIN, n=8; ALL, n=41)。来自snp或HLA等位基因的T1D GRSHLA的ROC AUC值相似,范围从0.73 (T1D GRSHLA-allele- amr适用于FIN)到0.88 (T1D GRSHLA-allele-EUR适用于EUR)。使用条件独立snp (T1D GRSHLA-SNP-ALL)或HLA等位基因(T1D grshla -等位基因-all)组合的ROC AUC在所有祖先群体中表现一致,snp的值为0.82至0.88,HLA等位基因的值为0.80至0.87。结论/解释:来自snp的T1D GRSHLA模型的表现与来自不同祖先的HLA等位基因的模型相当。此外,T1D GRSHLA-SNP-ALL和grshla -等位基因-all模型在跨祖先群体应用时具有一致的高ROC AUC值。需要在更多样化的人群中进行更大规模的研究,以更好地评估T1D GRSHLA在不同祖先之间的可转移性。
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来源期刊
Diabetologia
Diabetologia 医学-内分泌学与代谢
CiteScore
18.10
自引率
2.40%
发文量
193
审稿时长
1 months
期刊介绍: Diabetologia, the authoritative journal dedicated to diabetes research, holds high visibility through society membership, libraries, and social media. As the official journal of the European Association for the Study of Diabetes, it is ranked in the top quartile of the 2019 JCR Impact Factors in the Endocrinology & Metabolism category. The journal boasts dedicated and expert editorial teams committed to supporting authors throughout the peer review process.
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