Algorithms for large-scale genotyping microarrays
Algorithms for large-scale genotyping microarrays
复制标题
DOI：
10.1093/bioinformatics/btg332
复制
发表时间：
2003-12-12
期刊：
影响因子：
5.8
通讯作者：
Kulp, D
中科院分区：
文献类型：
--
作者：
Liu, WM;Di, XJ;Kulp, D
动机：分析整个基因组中数千种单核苷酸多态性（SNP）至关重要，对于有效地绘制疾病基因并了解对疾病的易感性，药物疗效和对不同人群和个人的副作用至关重要。高密度寡核苷酸微阵列为这种分析提供了合理的成本。这样的分析需要精确，可靠的方法来提取特征提取，分类，统计建模和过滤。分析：我们提出了围绕MEDOIDS作为相对等位基因信号的分类方法的修改后的分区。我们使用平均轮廓宽度，分离和其他数量作为基因分类分类的质量度量。我们根据分类结果形成强大的统计模型，并使用这些模型进行基因型调用并计算呼叫的质量度量。我们将算法应用于几个不同的基因分型微阵列。我们使用参考类型，家庭中的Mendelian关系以及一对一的交叉验证来验证我们的结果。在常染色体上SNP的SNP的一致性率为99.36％，性别染色体的SNPs为99.64％。 AA，AB和BB细胞的一致性测试的一致性超过99.5％，高99.9％。我们还提供了一种基于X染色体上SNP的杂合呼叫率来确定样本性别的方法。有关更多信息，请参见http://www.affymetrix.com。微阵列数据也将从Affymetrix网站上获得。
Motivation: Analysis of many thousands of single nucleotide polymorphisms (SNPs) across whole genome is crucial to efficiently map disease genes and understanding susceptibility to diseases, drug efficacy and side effects for different populations and individuals. High density oligonucleotide microarrays provide the possibility for such analysis with reasonable cost. Such analysis requires accurate, reliable methods for feature extraction, classification, statistical modeling and filtering.Results: We propose the modified partitioning around medoids as a classification method for relative allele signals. We use the average silhouette width, separation and other quantities as quality measures for genotyping classification. We form robust statistical models based on the classification results and use these models to make genotype calls and calculate quality measures of calls. We apply our algorithms to several different genotyping microarrays. We use reference types, informative Mendelian relationship in families, and leave-one-out cross validation to verify our results. The concordance rates with the single base extension reference types are 99.36% for the SNPs on autosomes and 99.64% for the SNPs on sex chromosomes. The concordance of the leave-one-out test is over 99.5% and is 99.9% higher for AA, AB and BB cells. We also provide a method to determine the gender of a sample based on the heterozygous call rate of SNPs on the X chromosome. See http://www.affymetrix.com for further information. The microarray data will also be available from the Affymetrix web site.