A New genotype calling Method for Affymetrix SNP Arrays
A New genotype calling Method for Affymetrix SNP Arrays
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一种新的Affymetrix SNP阵列基因型识别方法
DOI：
10.1142/s0219720011005458
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发表时间：
2011
影响因子：
1
通讯作者：
Jin Xu
中科院分区：
文献类型：
--
作者：
Bilin Fu;Jin Xu
现有的基于马氏距离分类器的稳健线性模型(RLMM)和基于最大似然分类的修正稳健线性模型(CRLMM)等基因分型方法为Affymetrix单核苷酸多态(SNP)芯片提供了准确的分型结果。然而，这些方法的计算成本很高，因为它们使用了包括芯片数据归一化和其他复杂统计技术在内的预处理程序。在小样本情况下，准确率可能会显着下降。我们开发了一种新的Affymetrix 100k和500k SNP芯片的基因分型方法。提出了一种两阶段分类方案，以获得快速的基因分型算法。第一阶段使用非监督分类来快速识别大多数SNPs的基因类型，并且准确率很高。第二阶段使用监督分类方法来结合来自HapMap数据或来自自我训练方案的等位基因频率信息。为每个基因型通话提供置信度分数。经已知黄金标准HapMap数据验证，其总体性能与CRLMM相当，并且在小样本情况下优于CRLMM。新算法在计算上简单且独立，因为可以在不使用任何其他训练数据的情况下使用自训练方案。实现调用算法的包可以在http://www.sfs.ecnu.edu.cn/teachers/xuj_en.html.上免费获得
Current genotype-calling methods such as Robust Linear Model with Mahalanobis Distance Classifier (RLMM) and Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) provide accurate calling results for Affymetrix Single Nucleotide Polymorphisms (SNP) chips. However, these methods are computationally expensive as they employ preprocess procedures, including chip data normalization and other sophisticated statistical techniques. In the small sample case the accuracy rate may drop significantly. We develop a new genotype calling method for Affymetrix 100 k and 500 k SNP chips. A two-stage classification scheme is proposed to obtain a fast genotype calling algorithm. The first stage uses unsupervised classification to quickly discriminate genotypes with high accuracy for the majority of the SNPs. And the second stage employs a supervised classification method to incorporate allele frequency information either from the HapMap data or from a self-training scheme. Confidence score is provided for every genotype call. The overall performance is shown to be comparable to that of CRLMM as verified by the known gold standard HapMap data and is superior in small sample cases. The new algorithm is computationally simple and standalone in the sense that a self-training scheme can be used without employing any other training data. A package implementing the calling algorithm is freely available at http://www.sfs.ecnu.edu.cn/teachers/xuj_en.html.