How to deal with the early GWAS data when imputing and combining different arrays is necessary

HW Uh, Joris Deelen, Marian Beekman, Quinta Helmer, Fernando Rivadeneira, Jouke Jan Hottenga, Dorret I. Boomsma, Albert Hofman, André G. Uitterlinden, P. E. Slagboom, Stefan Böhringer, Jeanine J. Houwing-Duistermaat

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)572-576
Number of pages5
JournalEuropean Journal of Human Genetics
Issue number5
Publication statusPublished - 1 May 2012
Externally publishedYes


  • GWAS
  • imputation
  • quality control


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