Effects of Experimental Design, Genetic Architecture and Threshold on Power and False Positive Rate of Genome-Wide Association Studies

Genome-Wide Association Studies are an important tool for identifying genetic markers associated with a trait, but it has been plagued by the multiple testing problem, which necessitates a multiple testing correction method. While many multiple testing methods have been suggested, e.g. Bonferroni and Benjamini-Hochbergs False Discovery Rate, the quality of the adjusted threshold based on these methods is not as well investigated. The aim of this study was to evaluate the balance between power and false positive rate of a Genome-Wide Association Studies experiment with Bonferroni and Benjamini-Hochbergs False Discovery Rate multiple testing correction methods and to test the effects of various experimental design and genetic architecture parameters on this balance. Our results suggest that when the markers are independent the threshold from Benjamini-Hochbergs False Discovery Rate provides a better balance between power and false positive rate in an experiment. However, with correlations between markers the threshold of Benjamini-Hochbergs False Discovery Rate becomes too lenient with an excessive number of false positives. Experimental design parameters such as sample size and number of markers used, as well as genetic architecture of a trait affect the balance between power and false positive rate. This experiment provided guidance in selecting an appropriate experimental design and multiple testing correction method when conducting an experiment.