HIBAG--HLA genotype imputation with attribute bagging.
HIBAG--HLA genotype imputation with attribute bagging.
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DOI：
10.1038/tpj.2013.18
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发表时间：
2014-04
期刊：
影响因子：
--
通讯作者：
Weir BS
中科院分区：
文献类型：
--
作者：
Zheng X;Shen J;Cox C;Wakefield JC;Ehm MG;Nelson MR;Weir BS
经典人类白细胞抗原（HLA）等位基因的基因分型是分析疾病和药物不良反应的重要工具，与主要组织相容性复合体（MHC）相关。然而，在全基因组单核苷酸多态性（SNP）分型或测序之后获得高分辨率HLA分型对于大样本来说通常成本过高。另一种方法利用MHC内的扩展单倍型结构，使用密集SNP基因型预测HLA等位基因，例如可从全基因组SNP组获得的那些。目前的HLA插补方法难以应用，或者可能需要用户访问具有SNP和HLA类型的大型训练数据集。我们提出了HIBAG，HLA插补使用属性BAGging，使预测平均HLA型后验概率在一个合奏的分类建立在自举样本。我们使用我们的研究数据（n=2668例欧洲血统的受试者）作为训练集和来自英国1958年出生队列研究（n = 1000例受试者）的HLA数据作为独立验证样本来评估HIBAG的性能。使用Illumina 1 M Duo、OmniQuad、OmniExpress、660 K和550 K平台共有的一组SNP标记，HLA-A、B、C、DRB 1和DQB 1的预测准确度范围为92.2%至98.1%。与其他两种主要方法HLA*IMP和BEAGLE相比，HIBAG表现良好。该方法在免费提供的HIBAG R软件包中实现，该软件包包括欧洲，亚洲，西班牙裔和非洲裔的预拟合分类器，提供了一种现成的插补方法，而无需访问大型训练数据集。
Genotyping of classical human leukocyte antigen (HLA) alleles is an essential tool in the analysis of diseases and adverse drug reactions with associations mapping to the major histocompatibility complex (MHC). However, deriving high-resolution HLA types subsequent to whole-genome single-nucleotide polymorphism (SNP) typing or sequencing is often cost prohibitive for large samples. An alternative approach takes advantage of the extended haplotype structure within the MHC to predict HLA alleles using dense SNP genotypes, such as those available from genome-wide SNP panels. Current methods for HLA imputation are difficult to apply or may require the user to have access to large training data sets with SNP and HLA types. We propose HIBAG, HLA Imputation using attribute BAGging, that makes predictions by averaging HLA-type posterior probabilities over an ensemble of classifiers built on bootstrap samples. We assess the performance of HIBAG using our study data (n=2668 subjects of European ancestry) as a training set and HLA data from the British 1958 birth cohort study (n≈1000 subjects) as independent validation samples. Prediction accuracies for HLA-A, B, C, DRB1 and DQB1 range from 92.2% to 98.1% using a set of SNP markers common to the Illumina 1M Duo, OmniQuad, OmniExpress, 660K and 550K platforms. HIBAG performed well compared with the other two leading methods, HLA*IMP and BEAGLE. This method is implemented in a freely available HIBAG R package that includes pre-fit classifiers for European, Asian, Hispanic and African ancestries, providing a readily available imputation approach without the need to have access to large training data sets.
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DOI：
10.1073/pnas.0409500102
发表时间：
2005-03-15
影响因子：
11.1
作者：
Hung, SL;Chung, WH;Chen, YT
通讯作者：
Chen, YT
影响因子：
14.9
作者：
Robinson J;Halliwell JA;McWilliam H;Lopez R;Parham P;Marsh SG
通讯作者：
Marsh SG
影响因子：
8
作者：
Bryll, R;Gutierrez-Osuna, R;Quek, F
通讯作者：
Quek, F
影响因子：
2.1
作者：
Li, Yun;Willer, Cristen J.;Ding, Jun;Scheet, Paul;Abecasis, Goncalo R.
通讯作者：
Abecasis, Goncalo R.
影响因子：
9.8
作者：
Leslie, Stephen;Donnelly, Peter;McVean, Gil
通讯作者：
McVean, Gil