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Reporting Correct p Values in VEGAS Analyses.

Julian Hecker1, Anna Maaser2, Dmitry Prokopenko1

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Summary
This summary is machine-generated.

The VEGAS2 tool for gene-based association studies has an error in its top-percentage test, leading to false positives. Corrected p-values can be computed using provided code for accurate genetic association analysis.

Keywords:
p valuesVEGASVEGAS2gene-based test

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • VEGAS (versatile gene-based association study) is a widely used framework for gene-based association tests using summary statistics.
  • It incorporates linkage disequilibrium from reference panels to handle correlated test statistics.
  • The improved VEGAS2 version (2015) offers user-friendly tools for genetic analysis.

Purpose of the Study:

  • To identify and address an error in the top-percentage test implementation within VEGAS and VEGAS2.
  • To demonstrate the impact of this error on the reliability of gene-based association study results.
  • To provide a solution for obtaining accurate p-values in gene-based association analyses.

Main Methods:

  • Analysis of the VEGAS and VEGAS2 software implementations, focusing on the top-percentage test.
  • Utilizing real data examples to evaluate the performance of the erroneous test.
  • Development and provision of corrected code for p-value computation.

Main Results:

  • The top-percentage test in both VEGAS and VEGAS2 is erroneously implemented, resulting in deflated/anti-conservative p-values.
  • This error significantly increases the rate of false-positive findings in genetic association studies.
  • Inconsistencies were observed between different test options within the VEGAS2 framework due to the error.

Conclusions:

  • The identified error in VEGAS2's top-percentage test compromises the accuracy of gene-based association studies.
  • Researchers using VEGAS or VEGAS2 should be aware of this limitation to avoid misinterpreting results.
  • The provided code enables users to compute accurate p-values, ensuring more reliable genetic association findings.