Genome-Wide Significance Levels and Weighted Hypothesis Testing

Details Genome-Wide Significance Levels and Weighted Hypothesis Testing Genome-Wide Significance Levels and Weighted Hypothesis Testing

2010-10-22

arxiv.org/abs/1010.4637v1

Statistical Science 2009, Vol. 24, No. 4, 398-413

Statistics

Methodology

Published in at http://dx.doi.org/10.1214/09-STS289 the Statistical Science (http://www.imstat.org/sts/) by the Institute of M

Scientific paper

10.1214/09-STS289

Genetic investigations often involve the testing of vast numbers of related hypotheses simultaneously. To control the overall error rate, a substantial penalty is required, making it difficult to detect signals of moderate strength. To improve the power in this setting, a number of authors have considered using weighted $p$-values, with the motivation often based upon the scientific plausibility of the hypotheses. We review this literature, derive optimal weights and show that the power is remarkably robust to misspecification of these weights. We consider two methods for choosing weights in practice. The first, external weighting, is based on prior information. The second, estimated weighting, uses the data to choose weights.

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