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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\u2009=\u20095\u2009\u00d7\u200910\u22128 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\u2013Hochberg and Benjamini\u2013Yekutieli 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 \u223c20,000 and &amp;gt;120,000 that relaxing the P-value threshold to 5\u2009\u00d7\u200910\u22127 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.<\/jats:p>","DOI":"10.1093\/g3journal\/jkaa056","type":"journal-article","created":{"date-parts":[[2021,2,15]],"date-time":"2021-02-15T00:23:43Z","timestamp":1613348623000},"source":"Crossref","is-referenced-by-count":179,"title":["Revisiting the genome-wide significance threshold for common variant GWAS"],"prefix":"10.1093","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0828-2044","authenticated-orcid":false,"given":"Zhongsheng","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6442-7754","authenticated-orcid":false,"given":"Michael","family":"Boehnke","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8990-2737","authenticated-orcid":false,"given":"Xiaoquan","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhramar","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,1,11]]},"reference":[{"key":"2021041310543766500_jkaa056-B1","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1038\/nature04226","article-title":"The international HapMap consortium: a haplotype map of the human genome","volume":"437","author":"Altshuler","year":"2005","journal-title":". 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