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Inference with approximate local false discovery rates.

Rajesh Karmakar1, Ruth Heller1, Saharon Rosset1

  • 1Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.

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Summary
This summary is machine-generated.

This study introduces a new method for large-scale multiple testing using neighborhood local false discovery rates (locFDR_N) to improve power in dependent test statistics. The approach enhances statistical power by considering local dependencies, outperforming traditional methods in simulations and a genetic study.

Keywords:
dependent test statisticsfalse discovery rategenome-wide association studieslarge scale inferencemultiple testing

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genetics

Background:

  • Efron's 2-group model is standard for large-scale multiple testing, assuming independent test statistics.
  • Marginal local false discovery rate (locFDR) controls false discoveries but doesn't account for dependencies.
  • Dependent test statistics in realistic settings can increase power, but calculations are often computationally prohibitive.

Purpose of the Study:

  • To develop a computationally feasible method to increase power in large-scale multiple testing by accounting for dependent test statistics.
  • To introduce and validate the neighborhood local false discovery rate (locFDR_N) for improved statistical decision-making.
  • To demonstrate the practical utility of the proposed method in genetic association studies.

Main Methods:

  • Proposed using locFDR_N, the probability of a null hypothesis given test statistics in an N-neighborhood.
  • Proved optimality of rejecting small locFDR_N within N-neighborhood-guided decisions, showing power increases with N.
  • Evaluated computational complexity relative to N, suggesting selection of the largest feasible neighborhood.

Main Results:

  • The locFDR_N approach offers substantial power gains over existing practical methods, even with small N-neighborhoods.
  • Power increases with the size of the N-neighborhood, balancing computational feasibility.
  • Simulations confirmed the proposed method's superior performance.

Conclusions:

  • The locFDR_N method provides a powerful and practical approach for large-scale multiple testing with dependent data.
  • The method demonstrated significant utility in a real-world genome-wide association study for height.
  • This approach offers a valuable tool for researchers dealing with complex, dependent datasets.