Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples

Abstract

The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the specimen level. To further examine whether the differences from genotyping and genotype calling are possible sources of variation in GWAS results, an association analysis was applied to compare the associated SNPs. We observed that the inconsistency in genotypes not only propagated to the association analysis, but was amplified in the associated SNPs. Our studies show that inconsistencies between SNP arrays and between genotype calling algorithms are potential sources for the lack of reproducibility in GWAS results.

Pitfall of genome-wide association studies: Sources of inconsistency in genotypes and their effects

“…This type of inconsistency may cause both Type I errors and Type II errors in GWAS [46]. Evaluation of variations between platforms has been reported [47] and is expected to be thoroughly assessed in the future.…”

“…For example, the Affymetrix GeneChip Human Mapping 500K array set (called Affy500K hereafter) has been used in published GWAS [32,48,49], but later Affymetrix released the Affy6 to the market. As it is important to know whether genotypes determined with these two generations of SNP array are the same, we evaluated their consistency [47].…”

“…Thus, it is important to know how consistent are the genotypes determined by using the different genotypecalling algorithms, though each of the algorithms reported a high successful call rate and accuracy. We evaluated inconsistency in genotypes determined using three genotype calling algorithms (DM, BRLMM, and Birdseed) that were released along with three generations of SNP arrays from Affymetrix [47].…”

“…BRLMM (c)). The discordant successful genotype calls between two algorithms are depicted in the right panels of (a)-(c) (right y-axes) [47]. …”

Gene Markers Representing Stem Cells and Cancer Cells for Quality Control

“…Several methods are available to detect cell marker expression, such as surface protein marker detection, intracellular protein marker detection, and gene expression detection. The MAQC project, which is a collaborative effort conducted as part of the US Food and Drug Administration's Clinical Path Initiative for medical product development is useful to detect gene markers in cells (MAQC Consortium, 2006Fan et al, 2010;Oberthuer et al, 2010;Huan et al, 2010;Luo et al, 2008;Parry et al, 2010;Shi et al, 2010;Miclaus et al, 2010;Hong et al, 2010;Tillinghast, 2010). It began in February 2005 and aims to describe the reliability and evaluate the performance of microarrays on several platforms.…”