• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of immunogenetics >Human leucocyte antigen class I and II imputation in a multiracial population
【24h】

Human leucocyte antigen class I and II imputation in a multiracial population

机译：多种族人群中的人类白细胞抗原I和II类归因

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Human leucocyte antigen (HLA) genes play a central role in response to pathogens and in autoimmunity. Research to understand the effects of HLA genes on health has been limited because HLA genotyping protocols are labour intensive and expensive. Recently, algorithms to impute HLA genotype data using genome-wide association study (GWAS) data have been published. However, imputation accuracy for most of these algorithms was based primarily on training data sets of European ancestry individuals. We considered performance of two HLA-dedicated imputation algorithms - SNP2HLA and HIBAG - in a multiracial population of n = 1587 women with HLA genotyping data by gold standard methods. We first compared accuracy defined as the percentage of correctly predicted alleles of HLA-B and HLA-C imputation using SNP2HLA and HIBAG using a breakdown of the data set into an 80% training group and a 20% testing group. Estimates of accuracy for HIBAG were either the same or better than those for SNP2HLA. We then conducted a more thorough test of HIBAG imputation accuracy using five independent 10-fold cross-validation procedures with delineation of ancestry groups using ancestry informative markers. Overall accuracy for HIBAG was 89%. Accuracy by HLA gene was 93% for HLA-A, 84% for HLA-B, 94% for HLA-C, 83% for HLA-DQA1, 91% for HLA-DQB1 and 88% for HLA-DRB1. Accuracy was highest in the African ancestry group ( the largest group) and lowest in the Hispanic group ( the smallest group). Despite suboptimal imputation accuracy for some HLA gene/ancestry group combinations, the HIBAG algorithm has the advantage of providing posterior estimates of accuracy which enable the investigator to analyse subsets of the population with high predicted (e.g. > 95%) imputation accuracy.
机译：人类白细胞抗原（HLA）基因在对病原体的反应和自身免疫中起着核心作用。由于HLA基因分型方案劳动强度大且昂贵，因此了解HLA基因对健康的影响的研究受到了限制。最近，已经发布了使用全基因组关联研究（GWAS）数据估算HLA基因型数据的算法。但是，大多数这些算法的估算精度主要基于欧洲血统个体的训练数据集。我们通过金标准方法，在具有HLA基因分型数据的n = 1587名多种族女性中，考虑了两种HLA专用归因算法SNP2HLA和HIBAG的性能。我们首先比较准确度，定义为使用SNP2HLA和HIBAG正确预测的HLA-B和HLA-C推算等位基因的百分比，将数据集细分为80％训练组和20％测试组。 HIBAG的准确度估计值与SNP2HLA相同或更好。然后，我们使用五个独立的10倍交叉验证程序对HIBAG插补准确性进行了更彻底的测试，并使用祖先信息标记物描绘了祖先群体。 HIBAG的总体准确度为89％。 HLA基因的准确性对于HLA-A为93％，对于HLA-B为84％，对于HLA-C为94％，对于HLA-DQA1为83％，对于HLA-DQB1为91％，对于HLA-DRB1为88％。在非洲血统组中，准确性最高（最大），而在西班牙裔组中，准确性最低（最小）。尽管某些HLA基因/祖先群体组合的插补准确度欠佳，但HIBAG算法的优点是可以提供准确度的后验估计，这使研究人员能够以较高的预测插补准确度（例如> 95％）分析总体子集。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号