Zhenwei Zhou1,2, Kathryn L Lunetta2, Alicia K Smith3,4, Erika J Wolf1,2, Annjanette Stone5, Steven A Schichman5, Regina E McGlinchey6,7, William P Milberg7,8, Mark W Miller1,8, Mark W Logue1,2,8,9. 1. National Center for PTSD, VA Boston Healthcare System, Boston, MA 02130, USA. 2. Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. 3. Department of Gynecology & Obstetrics, Emory University, Atlanta, GA 30322, USA. 4. Department of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA 30329, USA. 5. Pharmacogenomics Analysis Laboratory, Research Service, Central Arkansas Veterans Healthcare System, Little Rock, AR 72205, USA. 6. Geriatric Research Educational & Clinical Center & Translational Research Center for TBI & Stress Disorders, VA Boston Healthcare System, Boston, MA 02130, USA. 7. Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA. 8. Department of Psychiatry, Boston University, Boston, MA 02118, USA. 9. Biomedical Genetics, Boston University School of Medicine, Boston, MA 02118, USA.
Abstract
Aim: We compared the performance of multiple testing corrections for candidate gene methylation studies, namely Sidak (accurate Bonferroni), false-discovery rate and three adjustments that incorporate the correlation between CpGs: extreme tail theory (ETT), Gao et al. (GEA), and Li and Ji methods. Materials & methods: The experiment-wide type 1 error rate was examined in simulations based on Illumina EPIC and 450K data. Results: For high-correlation genes, Sidak and false-discovery rate corrections were conservative while the Li and Ji method was liberal. The GEA method tended to be conservative unless a threshold parameter was adjusted. The ETT yielded an appropriate type 1 error rate. Conclusion: For genes with substantial correlation across measured CpGs, GEA and ETT can appropriately correct for multiple testing in candidate gene methylation studies.
Aim: We compared the performance of multiple testing corrections for candidate gene methylation studies, namely Sidak (accurate Bonferroni), false-discovery rate and three adjustments that incorporate the correlation between CpGs: extreme tail theory (ETT), Gao et al. (GEA), and Li and Ji methods. Materials & methods: The experiment-wide type 1 error rate was examined in simulations based on Illumina EPIC and 450K data. Results: For high-correlation genes, Sidak and false-discovery rate corrections were conservative while the Li and Ji method was liberal. The GEA method tended to be conservative unless a threshold parameter was adjusted. The ETT yielded an appropriate type 1 error rate. Conclusion: For genes with substantial correlation across measured CpGs, GEA and ETT can appropriately correct for multiple testing in candidate gene methylation studies.
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