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Genome-Wide Significance Levels and Weighted Hypothesis Testing
Kathryn Roeder1, Larry Wasserman
1Professor of Statistics, Department of Statistics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
Statistical Science : a Review Journal of the Institute of Mathematical Statistics
|August 17, 2010
Summary
This study introduces weighted p-values to enhance the power of genetic studies with multiple hypotheses. Optimal weighting improves signal detection, even with imperfect weight estimation.
Area of Science:
- Genetics
- Statistical genetics
- Bioinformatics
Background:
- Genetic research frequently tests numerous hypotheses concurrently.
- Controlling the overall error rate necessitates significant penalties, hindering the detection of moderate signals.
- Weighted p-values offer a potential solution to increase statistical power in hypothesis testing.
Purpose of the Study:
- To review the literature on weighted p-values for hypothesis testing in genetic studies.
- To derive optimal weights for improving statistical power.
- To assess the robustness of power to weight misspecification and explore practical weighting methods.
Main Methods:
- Literature review of weighted p-value methodologies.
- Derivation of optimal weighting schemes.
- Analysis of power robustness under weight misspecification.
- Evaluation of external (prior information) and estimated (data-driven) weighting strategies.
Main Results:
- Weighted p-values can significantly improve the power to detect genetic signals.
- Statistical power demonstrates remarkable robustness even when weights are not perfectly specified.
- Both external and estimated weighting methods offer practical approaches for applying weights.
Conclusions:
- Weighted p-values are a valuable tool for enhancing statistical power in hypothesis-rich genetic investigations.
- The robustness of power to weight misspecification simplifies practical application.
- The choice between external and estimated weighting depends on the availability of prior information and data characteristics.
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