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A Tale of Two Genotypes: Consistency between Two High-Throughput Genotyping Centers

机译:两种基因型的故事:两个高通量基因分型中心之间的一致性

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摘要

Multiple genome-wide scans involving sib-pairs or limited pedigrees have been extensively used for a wide number of complex genetic conditions. Comparing data from two or more scans, as well as combining data, require an understanding of the sources of genotyping errors and data discrepancies. We have conducted two genome-wide scans for age-related maculopathy using the Center for Inherited Disease Research (CIDR) and the Mammalian Genotyping Service (MGS). Thirty individuals were typed in common, in order to allow for the alignment of alleles and comparison of the data sets. The analysis of these 8914 genotypes distributed over 321 markers in common demonstrated excellent agreement between these two laboratories, which have low rates of internal errors. Under the assumption that within each genotype, the smaller MGS allele should correspond to the smaller CIDR allele, the alleles align well between the two centers, with only a small fraction (less than 0.65%) of the aligned alleles showing large differences in sizes. However, since called allele sizes are integer “labels” which may not directly reflect the true underlying allele sizes, it is important to carefully prepare in advance if one wishes to merge data from different laboratories. In particular, it would not suffice to attempt to align alleles by typing only one or two controls in common. Fortunately, for the purposes of linkage analysis, one can avoid merging difficulties by simply carrying out linkage analyses using laboratory-specific allele labels and allele frequencies for each laboratory-specific subset of the data.
机译:涉及同胞对或有限血统的多重全基因组扫描已广泛用于多种复杂的遗传条件。比较来自两次或更多次扫描的数据以及合并数据,需要了解基因分型错误和数据差异的来源。我们使用遗传病研究中心(CIDR)和哺乳动物基因分型服务(MGS)对年龄相关性黄斑病进行了两次全基因组扫描。为了允许等位基因的比对和数据集的比较,共有30个个体被共同输入。对分布在321个标记上的这8914个基因型的分析显示,这两个实验室之间的一致性极好,内部错误率低。在每个基因型内,较小的MGS等位基因应对应较小的CIDR等位基因的假设下,等位基因在两个中心之间很好地对齐,只有一小部分(小于0.65%)的对齐等位基因显示出较大的大小差异。但是,由于所谓的等位基因大小是整数“标签”,可能无法直接反映真实的基础等位基因大小,因此,如果要合并来自不同实验室的数据,则需要事先仔细准备,这一点很重要。特别是,仅通过键入一个或两个共同的控件来尝试对齐等位基因是不够的。幸运的是,出于连锁分析的目的,只需使用实验室特定的等位基因标签和每个实验室特定数据子集的等位基因频率进行连锁分析,就可以避免合并困难。

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