GWAtoolbox: An R Package for Time Efficient Quality Control of Multiple GWAS Data Files

In the recent years, Genome-Wide Association Studies (GWASs) have been proven to be a very powerful approach to uncover common genetic variants affecting human disease risk and quantitative outcome levels. To date, 1212 genetic loci were reported to be significantly associated with at least one of 210 traits (Hin-dorff et al. (2011)). To allow sufficient power to identify variants with small effects, GWAS sample size has been augmented by pooling results from dozens of individual GWASs into large meta-analyses efforts. However, combining results from a large number of GWASs, which differ in terms of study design, popula-tion structure, data management, and statistical analysis, poses several challenges regarding the consistency and quality of data which are usually difficult to be addressed systematically. This is mainly due to the GWAS file size, which typically includes 2.5 to 7 million rows (corresponding to genetic variants) and> 9 columns (attributes). Consequently, the data harmonization across studies usually takes several weeks or months. While working in the CKDGen Consortium, aim to detect renal function genes (Köttgen et al. (2010)), we have been performing meta-analyses of several GWASs. To standardize and speed up the quality control (QC) process we developed the GWAtoolbox, an R package which lightens and accelerates the handling of huge amounts of data from GWASs. GWAtoolbox provides time efficient QC of data stored in dozens o