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Bioinformatics Advance Access originally published online on September 28, 2004
Bioinformatics 2005 21(6):781-787; doi:10.1093/bioinformatics/bti053
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

An efficient Monte Carlo approach to assessing statistical significance in genomic studies

D. Y. Lin

Department of Biostatistics, University of North Carolina McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC 27599-7420, USA

Motivation: Multiple hypothesis testing is a common problem in genome research, particularly in microarray experiments and genomewide association studies. Failure to account for the effects of multiple comparisons would result in an abundance of false positive results. The Bonferroni correction and Holm's step-down procedure are overly conservative, whereas the permutation test is time-consuming and is restricted to simple problems.

Results: We developed an efficient Monte Carlo approach to approximating the joint distribution of the test statistics along the genome. We then used the Monte Carlo distribution to evaluate the commonly used criteria for error control, such as familywise error rates and positive false discovery rates. This approach is applicable to any data structures and test statistics. Applications to simulated and real data demonstrate that the proposed approach provides accurate error control, and can be substantially more powerful than the Bonferroni and Holm methods, especially when the test statistics are highly correlated.

Contact: lin{at}bios.unc.edu


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