When testing a large number of hypotheses, such as an association genome scan, the power to detect modest effects can be low, due to the penalty for multiple testing. This is especially true when traditional approaches such as the Bonferroni correction are employed.
The false discovery (FDR) approach increases power in a multiple testing scenario, but it is still challenging to obtain significant results when very large numbers of tests are performed. To enhance power further, a weighted FDR approach can be employed that involves weighting the hypotheses based on prior data, such as a linkage scan, knowledge about biological pathways, candidate genes and so on.
The wFDR procedure up-weights likely candidates and down-weights others, while maintaining control of the overall rate of false discoveries. The weights must average to 1 and be chosen independently of the association test statistics.
Simulations reveal that if an informative linkage study was used for the weights, the weighted FDR approach improves power considerably. Remarkably, the loss in power is small even if the linkage study was uninformative.
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weighted_FDR.R - Weighted false discovery rate for multiple testing 1.2 Beta
Software to apply weighted false discovery rate for multiple testing
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