Spoiling the Whole Bunch: Quality Control Aimed at Preserving the Integrity of High-Throughput Genotyping |
| |
Authors: | Anna Pluzhnikov Jennifer E. Below Anuar Konkashbaev Emily Kistner-Griffin Dan L. Nicolae Nancy J. Cox |
| |
Affiliation: | 1 Department of Medicine, The University of Chicago, Chicago, IL 60637, USA 2 Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA 3 Department of Statistics, The University of Chicago, Chicago, IL 60637, USA 4 Department of Medicine, Hollings Cancer Center, Medical University of South Carolina, Charleston, SC 29425, USA 5 Center for Research and Evaluation, SUNY Upstate Medical University, Syracuse, NY 13201, USA |
| |
Abstract: | False-positive or false-negative results attributable to undetected genotyping errors and confounding factors present a constant challenge for genome-wide association studies (GWAS) given the low signals associated with complex phenotypes and the noise associated with high-throughput genotyping. In the context of the genetics of kidneys in diabetes (GoKinD) study, we identify a source of error in genotype calling and demonstrate that a standard battery of quality-control (QC) measures is not sufficient to detect and/or correct it. We show that, if genotyping and calling are done by plate (batch), even a few DNA samples of marginally acceptable quality can profoundly alter the allele calls for other samples on the plate. In turn, this leads to significant differential bias in estimates of allele frequency between plates and, potentially, to false-positive associations, particularly when case and control samples are not sufficiently randomized to plates. This problem may become widespread as investigators tap into existing public databases for GWAS control samples. We describe how to detect and correct this bias by utilizing additional sources of information, including raw signal-intensity data. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|