Abstract: Genome-Wide Association Studies (GWAS) can reveal the genetic basis of the behavior. However, the association analysis embodies a massive multiple testing problem, where millions of SNPs (Single Nucleotide Polymorphisms) are tested. It is vital to reduce the risk of false positive in multiple testing with an appropriate corrections method. Firstly, Family-Wise Error Rate (FWER) and False Discovery Rate (FDR), the two standard measures of Type I errors in multiple testing were introduced. Secondly, three FWER (i.e., Bonferroni, Holm Step–Down and Permutation) and one FDR (i.e., BH) multiple testing corrections method were discussed from the concept to implementation. Finally, a method to simulate GWAS data was proposed, and the four multiple testing corrections methods were evaluated on the simulated GWAS data. Results showed that SNPs reported without multiple testing corrections had both the highest average hit and the average false alarm. FWER methods reported fewer false alarms, but their average hits were also fewer than that from uncorrected or BH method. In contrast, BH method did well in balance between the false alarm and hit. Furthermore, a comprehensive index, called explained rate, was introduced to evaluate the different methods quantitatively. Results showed BH method had the highest explained rate. In the future GWAS study, researchers would better do multiple testing corrections with BH method.

Key words: Genome-Wide Association Studies, multiple testing corrections, FWER, FDR, simulation