Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Imputation is an extremely valuable tool in conducting and synthesising genome-wide association studies (GWASs). Directly typed SNP quality control (QC) is thought to affect imputation quality. It is, therefore, common practise to use quality-controlled (QCed) data as an input for imputing genotypes. This study aims to determine the effect of commonly applied QC steps on imputation outcomes. We performed several iterations of imputing SNPs across chromosome 22 in a dataset consisting of 3177 samples with Illumina 610 k (Illumina, San Diego, CA, USA) GWAS data, applying different QC steps each time. The imputed genotypes were compared with the directly typed genotypes. In addition, we investigated the correlation between alternatively QCed data. We also applied a series of post-imputation QC steps balancing elimination of poorly imputed SNPs and information loss. We found that the difference between the unQCed data and the fully QCed data on imputation outcome was minimal. Our study shows that imputation of common variants is generally very accurate and robust to GWAS QC, which is not a major factor affecting imputation outcome. A minority of common-frequency SNPs with particular properties cannot be accurately imputed regardless of QC stringency. These findings may not generalise to the imputation of low frequency and rare variants.

Original publication

DOI

10.1038/ejhg.2010.242

Type

Journal article

Journal

Eur j hum genet

Publication Date

05/2011

Volume

19

Pages

610 - 614

Keywords

Chromosomes, Human, Pair 22, Gene Frequency, Genome-Wide Association Study, Humans, Osteoarthritis, Polymorphism, Single Nucleotide, Quality Control