Suppr超能文献

仅使用三个单核苷酸多态性定义高危 1 型糖尿病 HLA-DR 和 HLA-DQ 类型。

Definition of high-risk type 1 diabetes HLA-DR and HLA-DQ types using only three single nucleotide polymorphisms.

机构信息

Centre for Diabetes Research, The Western Australian Institute for Medical Research, Perth, Western Australia, Australia.

出版信息

Diabetes. 2013 Jun;62(6):2135-40. doi: 10.2337/db12-1398. Epub 2013 Feb 1.

Abstract

Evaluating risk of developing type 1 diabetes (T1D) depends on determining an individual's HLA type, especially of the HLA DRB1 and DQB1 alleles. Individuals positive for HLA-DRB103 (DR3) or HLA-DRB104 (DR4) with DQB103:02 (DQ8) have the highest risk of developing T1D. Currently, HLA typing methods are relatively expensive and time consuming. We sought to determine the minimum number of single nucleotide polymorphisms (SNPs) that could rapidly define the HLA-DR types relevant to T1D, namely, DR3/4, DR3/3, DR4/4, DR3/X, DR4/X, and DRX/X (where X is neither DR3 nor DR4), and could distinguish the highest-risk DR4 type (DR4-DQ8) as well as the non-T1D-associated DR4-DQB103:01 type. We analyzed 19,035 SNPs of 10,579 subjects (7,405 from a discovery set and 3,174 from a validation set) from the Type 1 Diabetes Genetics Consortium and developed a novel machine learning method to select as few as three SNPs that could define the HLA-DR and HLA-DQ types accurately. The overall accuracy was 99.3%, area under curve was 0.997, true-positive rates were >0.99, and false-positive rates were <0.001. We confirmed the reliability of these SNPs by 10-fold cross-validation. Our approach predicts HLA-DR/DQ types relevant to T1D more accurately than existing methods and is rapid and cost-effective.

摘要

评估 1 型糖尿病 (T1D) 的发病风险取决于确定个体的 HLA 类型,尤其是 HLA-DRB1 和 DQB1 等位基因。HLA-DRB103 (DR3) 或 HLA-DRB104 (DR4) 阳性且 DQB103:02 (DQ8) 的个体具有发生 T1D 的最高风险。目前,HLA 分型方法相对昂贵且耗时。我们旨在确定可快速定义与 T1D 相关的 HLA-DR 类型(即 DR3/4、DR3/3、DR4/4、DR3/X、DR4/X 和 DRX/X(其中 X 既不是 DR3 也不是 DR4))的最小数量的单核苷酸多态性 (SNP),并能够区分最高风险的 DR4 类型 (DR4-DQ8) 以及与非 T1D 相关的 DR4-DQB103:01 类型。我们分析了来自 1 型糖尿病遗传学联合会的 10579 名受试者(发现集 7405 名,验证集 3174 名)的 19035 个 SNP,并开发了一种新的机器学习方法来选择尽可能少的三个 SNP,以准确定义 HLA-DR 和 HLA-DQ 类型。总体准确率为 99.3%,曲线下面积为 0.997,真阳性率>0.99,假阳性率<0.001。我们通过 10 倍交叉验证证实了这些 SNP 的可靠性。与现有方法相比,我们的方法能更准确地预测与 T1D 相关的 HLA-DR/DQ 类型,并且快速且具有成本效益。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验