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Related Experiment Videos

Controlling the proportion of false positives in multiple dependent tests.

R L Fernando1, D Nettleton, B R Southey

  • 1Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA. rohan@istate.edu

Genetics
|March 17, 2004
PubMed
Summary

Controlling false positives in genome scan mapping is crucial. This study introduces a proportion of false positives (PFP) approach, offering improved experimental power compared to traditional genomewise error rate (GWER) methods.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome scan mapping involves numerous statistical tests, necessitating robust error rate control.
  • Traditional methods like controlling the genomewise error rate (GWER) can lead to overly stringent thresholds, reducing experimental power.

Purpose of the Study:

  • To introduce and evaluate a novel statistical approach for controlling false positives in genome scan mapping.
  • To compare the proposed proportion of false positives (PFP) method with existing error rate control strategies.

Main Methods:

  • Developed a method to control the proportion of false positives (PFP) among all significant results.
  • Estimated the proportion of true null hypotheses directly from experimental data.
  • Conducted simulations to compare PFP with GWER, false discovery rate (FDR), and positive false discovery rate (pFDR) under various models.

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Main Results:

  • The proposed PFP approach allows for a desired PFP level without being dependent on test correlation or the number of tests.
  • The PFP method provides a more powerful alternative to GWER by allowing less stringent comparisonwise error rates (CWER).
  • Simulations demonstrated practical and philosophical similarities and differences between PFP, FDR, pFDR, and GWER.

Conclusions:

  • The proportion of false positives (PFP) approach offers a viable and potentially more powerful alternative for error control in genome scan mapping.
  • Estimating the proportion of true null hypotheses directly from data is feasible and essential for the PFP method.
  • This work highlights the importance of selecting appropriate error control strategies for maximizing discovery power in complex genetic studies.