Re-assessment of multiple testing strategies for more efficient genome-wide association studies

• Published in: European journal of human genetics : EJHG Vol. 26; no. 7; pp. 1038 - 1048
• Main Authors: Otani, Takahiro, Noma, Hisashi, Nishino, Jo, Matsui, Shigeyuki
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.07.2018; Springer International Publishing
• Subjects: Arthritis, Rheumatoid - genetics; Computer Simulation; Genetic diversity; Genetic factors; Genome; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Heritability; Humans; Mental disorders; Polymorphism, Single Nucleotide - genetics; Rheumatoid arthritis; Schizophrenia; Schizophrenia - genetics; Statistics
• ISSN: 1018-4813; 1476-5438; 1476-5438
• DOI: 10.1038/s41431-018-0125-3

Although enormous costs have been dedicated to discovering relevant disease-related genetic variants, especially in genome-wide association studies (GWASs), only a small fraction of estimated heritability can be explained by these results. This is the so-called missing heritability problem. The conventional use of overly conservative multiple testing strategies based on controlling the familywise error rate (FWER), in particular with a genome-wide significance threshold of P <5 × 10 , is one of the most important issues from a statistical perspective. To help resolve this problem, we performed comprehensive re-assessments of currently available strategies using recently published, extremely large-scale GWAS data sets of rheumatoid arthritis and schizophrenia (>50,000 subjects). The estimates of statistical power averaged for all disease-related genetic variants of the standard FWER-based strategy were only 0.09% for the rheumatoid arthritis data and 0.04% for the schizophrenia data. To design more efficient strategies, we also conducted an extensive comparison of multiple testing strategies by applying false discovery rate (FDR)-controlling procedures to these data sets and simulations, and found that the FDR-based procedures achieved higher power than the FWER-based strategy, even at a strict FDR level (e.g., FDR = 1%). We also discuss a useful alternative measure, namely "partial power," which is an averaged power for detecting the clinically and biologically meaningful genetic factors with the largest effects. Simulation results suggest that the FDR-based procedures can achieve sufficient partial power (>80%) for detecting these factors (odds ratios of >1.05) with 80,000 subjects, and thus this may be a useful measure for defining realistic objectives of future GWASs.