Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
Eur J Hum Genet. 2012 May;20(5):572-6. doi: 10.1038/ejhg.2011.231. Epub 2011 Dec 21.
Genotype imputation has become an essential tool in the analysis of genome-wide association scans. This technique allows investigators to test association at ungenotyped genetic markers, and to combine results across studies that rely on different genotyping platforms. In addition, imputation is used within long-running studies to reuse genotypes produced across generations of platforms. Typically, genotypes of controls are reused and cases are genotyped on more novel platforms yielding a case-control study that is not matched for genotyping platforms. In this study, we scrutinize such a situation and validate GWAS results by actually retyping top-ranking SNPs with the Sequenom MassArray platform. We discuss the needed quality controls (QCs). In doing so, we report a considerable discrepancy between the results from imputed and retyped data when applying recommended QCs from the literature. These discrepancies appear to be caused by extrapolating differences between arrays by the process of imputation. To avoid false positive results, we recommend that more stringent QCs should be applied. We also advocate reporting the imputation quality measure (R(T)(2)) for the post-imputation QCs in publications.
基因分型已成为全基因组关联扫描分析的重要工具。该技术允许研究人员在未基因分型的遗传标记上进行关联测试，并整合依赖于不同基因分型平台的研究结果。此外，在长期研究中，还可以重复使用跨代平台生成的基因型。通常情况下，会重复使用对照者的基因型，而在更新型的平台上对病例进行基因分型，从而生成未匹配基因分型平台的病例对照研究。在本研究中，我们详细研究了这种情况，并通过实际使用 Sequenom MassArray 平台重新对排名最高的 SNP 进行基因分型来验证 GWAS 结果。我们讨论了所需的质量控制 (QC)。在这样做的过程中，我们报告了当应用文献中推荐的 QC 时，从已推断和重新分型的数据中得到的结果之间存在相当大的差异。这些差异似乎是由推断过程中数组之间的外推差异引起的。为了避免假阳性结果，我们建议应应用更严格的 QC。我们还主张在出版物中报告基因分型后 QC 的推断质量度量 (R(T)(2))。
