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.

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doi.org/10.1038/ejhg.2011.231, hdl.handle.net/1765/74022
European Journal of Human Genetics
Erasmus MC: University Medical Center Rotterdam

Uh, H.-W, Deelen, J, Beekman, M, Helmer, Q, Rivadeneira Ramirez, F, Hottenga, J.J, … Houwing-Duistermaat, J.J. (2012). How to deal with the early GWAS data when imputing and combining different arrays is necessary. European Journal of Human Genetics, 20(5), 572–576. doi:10.1038/ejhg.2011.231