Over the last decade, GWAS meta-analyses have used a strict P-value threshold of 5 × 10-8 to classify associations as significant. Here, we use our current understanding of frequently studied traits including lipid levels, height, and BMI to revisit this genome-wide significance threshold. We compare the performance of studies using the P = 5 × 10-8 threshold in terms of true and false positive rate to other multiple testing strategies: (1) less stringent P-value thresholds, (2) controlling the FDR with the Benjamini-Hochberg and Benjamini-Yekutieli procedure, and (3) controlling the Bayesian FDR with posterior probabilities. We applied these procedures to re-analyze results from the Global Lipids and GIANT GWAS meta-analysis consortia and supported them with extensive simulation that mimics the empirical data. We observe in simulated studies with sample sizes ∼20,000 and >120,000 that relaxing the P-value threshold to 5 × 10-7 increased discovery at the cost of 18% and 8% of additional loci being false positive results, respectively. FDR and Bayesian FDR are well controlled for both sample sizes with a few exceptions that disappear under a less stringent definition of true positives and the two approaches yield similar results. Our work quantifies the value of using a relaxed P-value threshold in large studies to increase their true positive discovery but also show the excess false positive rates due to such actions in modest-sized studies. These results may guide investigators considering different thresholds in replication studies and downstream work such as gene-set enrichment or pathway analysis. Finally, we demonstrate the viability of FDR-controlling procedures in GWAS.