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Related Experiment Videos

A simple correction for multiple comparisons in interval mapping genome scans.

J M Cheverud1

  • 1Department of Anatomy & Neurobiology, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO, 63110, USA. cheverud@pcg.wustl.edu

Heredity
|October 27, 2001
PubMed
Summary
This summary is machine-generated.

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This study presents a Bonferroni-based method to correct significance thresholds in genome scans. It accurately estimates the effective number of independent tests, improving multiple comparison corrections in genetic analyses.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Interval-mapping genome scans require accurate significance thresholds to identify genetic loci.
  • Existing methods for correcting point-wise significance thresholds can be complex or lack precision.

Purpose of the Study:

  • To introduce and evaluate a novel method for significance threshold correction in genome scans.
  • To assess the effectiveness of a Bonferroni test-based approach using the effective number of independent comparisons.

Main Methods:

  • Calculated the effective number of independent comparisons from the variance of eigenvalues of the marker correlation matrix.
  • Simulated 1000 normally distributed phenotypes across chromosomes with varying lengths and marker densities in a population of 500.

Related Experiment Videos

  • Compared experiment-wise significance thresholds from simulation with Bonferroni criterion and the new measure.

    • Main Results:

    • The Bonferroni calculation closely matched simulation-derived significance thresholds.
    • Threshold levels were significantly influenced by chromosome length and marker density.
    • A minor bias of approximately 1% was observed at 5% and 10% significance levels.

    Conclusions:

    • The proposed Bonferroni-based method provides a simple and effective correction for multiple comparisons in genome scans.
    • This approach accurately accounts for marker correlation, reducing the effective number of independent tests.
    • The method is easily implementable using standard statistical software packages.