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A modified generalized Fisher method for combining probabilities from dependent tests.

Hongying Dai1, J Steven Leeder2, Yuehua Cui3

  • 1Department of Pediatrics, Research Development and Clinical Investigation, Children's Mercy Hospital Kansas City, MO, USA ; Department of Pediatrics, University of Missouri-Kansas City Kansas City, MO, USA ; Department of Informatic Medicine and Personalized Health, University of Missouri-Kansas City Kansas City, MO, USA.

Frontiers in Genetics
|March 7, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a modified Lancaster procedure to accurately analyze complex genetic data by accounting for correlations between genetic variants. The new method effectively controls Type I error rates and enhances the power to detect genetic associations, improving complex trait analysis.

Keywords:
correlated p-valuesgeneralized Fisher method (Lancaster procedure)high dimensional genetic datamultiple hypothesis testingweight function

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Area of Science:

  • Genomics and Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • High-throughput molecular technologies generate vast genetic data for complex trait research.
  • Simultaneous statistical testing of numerous genetic variants challenges Type I error rate control.
  • Existing p-value combining methods often overlook correlations in genetic data, potentially inflating error rates.

Purpose of the Study:

  • To develop a statistical method that accounts for correlation structures in genetic data for improved p-value combination.
  • To modify the Lancaster procedure to incorporate biological information and handle dependent genetic data.
  • To reduce Type I error rates and maintain statistical power in high-dimensional genetic analyses.

Main Methods:

  • Modification of the Lancaster procedure to incorporate the correlation structure among p-values.
  • Development of a weight function to integrate biological information into statistical testing.
  • Extensive empirical assessments to evaluate the performance of the modified procedure.

Main Results:

  • The modified Lancaster procedure significantly reduces Type I error rates caused by correlated p-values.
  • The method retains considerable power for detecting genetic signals in complex datasets.
  • Application to renal transplant data identified a novel association between B cell pathways and allograft tolerance.

Conclusions:

  • The proposed modified Lancaster procedure offers a robust approach for analyzing high-dimensional, correlated genetic data.
  • Accounting for correlation structures is crucial for accurate statistical inference in genetic association studies.
  • This method has the potential to uncover new biological insights, as demonstrated in renal transplant research.