Abstract
The simultaneous testing of a large number of hypotheses in a genome scan, using individual thresholds for significance, inherently leads to inflated genome-wide false positive rates. There exist various approaches to approximating the correct genomewide p-values under various assumptions, either by way of asymptotics or simulations. We explore a philosophically different criterion, recently proposed in the literature, which controls the false discovery rate. The test statistics are assumed to arise from a mixture of distributions under the null and non-null hypotheses. We fit the mixture distribution using both a nonparametric approach and commingling analysis, and then apply the local false discovery rate to select cut-off points for regions to be declared interesting. Another criterion, the minimum total error, is also explored. Both criteria seem to be sensible alternatives to controlling the classical type I and type II error rates.
Department(s)
Mathematics
Document Type
Article
DOI
https://doi.org/10.1186/1471-2156-6-s1-s23
Rights Information
© 2005 The author(s). This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Publication Date
2005
Recommended Citation
Sinha, Ritwik, Moumita Sinha, George Mathew, Robert C. Elston, and Yuqun Luo. "Local false discovery rate and minimum total error rate approaches to identifying interesting chromosomal regions." In BMC genetics, vol. 6, no. S1, p. S23. BioMed Central, 2005.
Journal Title
In BMC genetics
