gms | German Medical Science

Background: Genome-wide association studies (GWAs) investigating the relationship between millions of genetic markers and a clinically relevant phenotype were originally based on the common disease – common variant assumption [1], thus aiming at identifying a small number of common genetic loci as cause for common diseases. In recent years, the focus of GWAs has shifted, and the task is no longer only the discovery of common genetic loci, but the discovery of loci with small effects by (mega-)meta-analyses or the aggregation of genomic information into genetic risk prediction scores. Since even low error frequencies can distort association results, extensive and accurate quality control of the given data is mandatory. However, after extensive discussions about standards for quality control in GWAS in the early years [2], further work on how to control data quality and adapt data cleaning to new GWAS aims and sizes is rare.

Methods: The aim of this study was to perform an extensive literature review to evaluate currently applied quality control criteria and their justification. Building on the findings from the literature search, a workflow was developed to include justified quality control steps, such as the proportion of missing data or the mean heterozygosity of the data, which are applied iteratively to the data. This workflow is subsequently illustrated using a real data set.

Results: Our results show that in most published GWAs, no scientific reasons for the applied quality steps are given. Cutoffs for the most common quality measures are mostly not explained. Especially the principal component analysis and the test for deviation from Hardy-Weinberg equilibrium are frequently used as quality criteria in many GWAs without analyzing the existing conditions exactly and adjusting the quality control accordingly.

However, if these quality measures are applied without being aware of the threshold values that are necessary depending on the data, the user risks losing relevant data and thus reduces the chance of finding an association. On the other hand, a thoughtless use of different quality criteria may prevent the identification of erroneous data, which then unnecessarily biases the results.

Conclusion: It is pointed out that researchers still have to decide between universal and individual parameters and therefore between optimal comparability to other analyses and optimal conditions within the specific study, keeping in mind that a strict quality control, which removes all data with a high risk of bias, always carries the risk that the remaining data is too homogeneous to make small effects visible. The developed workflow can be used for a uniform execution of GWAs and provides a suitable basis for customizing the quality control if required. To be able to better evaluate the results of GWAs in the future, the scientific necessities of the individual steps should always be mentioned.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.