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

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 use...

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Main Authors:	Uh, Hae-Won, Deelen, Joris, Beekman, Marian, Helmer, Quinta, Rivadeneira, Fernando, Hottenga, Jouke-Jan, Boomsma, Dorret I, Hofman, Albert, Uitterlinden, André G, Slagboom, P E, Böhringer, Stefan, Houwing-Duistermaat, Jeanine J
Format:	Online
Language:	English
Published:	Nature Publishing Group 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3330212/

id	pubmed-3330212
recordtype	oai_dc
spelling	pubmed-33302122012-05-01 How to deal with the early GWAS data when imputing and combining different arrays is necessary Uh, Hae-Won Deelen, Joris Beekman, Marian Helmer, Quinta Rivadeneira, Fernando Hottenga, Jouke-Jan Boomsma, Dorret I Hofman, Albert Uitterlinden, André G Slagboom, P E Böhringer, Stefan Houwing-Duistermaat, Jeanine J Article 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 (RT2) for the post-imputation QCs in publications. Nature Publishing Group 2012-05 2011-12-21 /pmc/articles/PMC3330212/ /pubmed/22189269 http://dx.doi.org/10.1038/ejhg.2011.231 Text en Copyright © 2012 Macmillan Publishers Limited http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under the Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Uh, Hae-Won Deelen, Joris Beekman, Marian Helmer, Quinta Rivadeneira, Fernando Hottenga, Jouke-Jan Boomsma, Dorret I Hofman, Albert Uitterlinden, André G Slagboom, P E Böhringer, Stefan Houwing-Duistermaat, Jeanine J
spellingShingle	Uh, Hae-Won Deelen, Joris Beekman, Marian Helmer, Quinta Rivadeneira, Fernando Hottenga, Jouke-Jan Boomsma, Dorret I Hofman, Albert Uitterlinden, André G Slagboom, P E Böhringer, Stefan Houwing-Duistermaat, Jeanine J How to deal with the early GWAS data when imputing and combining different arrays is necessary
author_facet	Uh, Hae-Won Deelen, Joris Beekman, Marian Helmer, Quinta Rivadeneira, Fernando Hottenga, Jouke-Jan Boomsma, Dorret I Hofman, Albert Uitterlinden, André G Slagboom, P E Böhringer, Stefan Houwing-Duistermaat, Jeanine J
author_sort	Uh, Hae-Won
title	How to deal with the early GWAS data when imputing and combining different arrays is necessary
title_short	How to deal with the early GWAS data when imputing and combining different arrays is necessary
title_full	How to deal with the early GWAS data when imputing and combining different arrays is necessary
title_fullStr	How to deal with the early GWAS data when imputing and combining different arrays is necessary
title_full_unstemmed	How to deal with the early GWAS data when imputing and combining different arrays is necessary
title_sort	how to deal with the early gwas data when imputing and combining different arrays is necessary
description	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 (RT2) for the post-imputation QCs in publications.
publisher	Nature Publishing Group
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3330212/
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How to deal with the early GWAS data when imputing and combining different arrays is necessary

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