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Bioinformatics Advance Access published online on September 28, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti053
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received July 22, 2004
Revised August 30, 2004
Accepted August 31, 2004

Article

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

D. Y. Lin 1*

1 Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina

* To whom correspondence should be addressed. E-mail: lin{at}bios.unc.edu.


  Abstract

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, while the permutation test is time-consuming and is restricted to simple problems.

Results: We develop an efficient Monte Carlo approach to approximating the joint distribution of the test statistics along the genome. We then use the Monte Carlo distribution to evaluate the commonly used criteria for error control, such as family-wise 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.


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