TY - JOUR
T1 - How to deal with the early GWAS data when imputing and combining different arrays is necessary
AU - Uh, HW
AU - Deelen, J
AU - Beekman, M
AU - Helmer, Q
AU - Rivadeneira, Fernando
AU - Hottenga, JJ (Jouke Jan)
AU - Boomsma, DI
AU - Hofman, Bert
AU - Uitterlinden, André
AU - Slagboom, PE (Eline)
AU - Bohringer, S
AU - Houwing-Duistermaat, JJ
PY - 2012
Y1 - 2012
N2 - 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. European Journal of Human Genetics (2012) 20, 572-576; doi:10.1038/ejhg.2011.231; published online 21 December 2011
AB - 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. European Journal of Human Genetics (2012) 20, 572-576; doi:10.1038/ejhg.2011.231; published online 21 December 2011
U2 - 10.1038/ejhg.2011.231
DO - 10.1038/ejhg.2011.231
M3 - Article
C2 - 22189269
SN - 1018-4813
VL - 20
SP - 572
EP - 576
JO - European Journal of Human Genetics
JF - European Journal of Human Genetics
IS - 5
ER -