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Adjusted P values for genome-wide scans.

Theodore C Lystig1

  • 1Department of Mathematical Statistics, Chalmers University of Technology, Eklandagatan 86, 412 96 Göteborg, Sweden. lystig@math.chalmers.se

Genetics
|August 22, 2003
PubMed
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Genome-wide scans for quantitative trait loci (QTL) can be improved by adding adjusted P-values to LOD score plots. This visual enhancement clarifies statistical significance across the genome, moving beyond simple thresholds.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Quantitative trait loci (QTL) mapping is crucial for understanding genetic contributions to complex traits.
  • Traditional LOD score plots offer a primary measure of evidence but lack nuanced significance indication.
  • Genome-wide significance thresholds are often binary, potentially oversimplifying complex genetic findings.

Purpose of the Study:

  • To propose and validate a method for enhancing QTL plots with adjusted P-values.
  • To provide a more intuitive visualization of genome-wide significance for LOD scores.
  • To improve the interpretation of QTL scan results by offering a graded significance scale.

Main Methods:

  • Supplementing traditional logarithm of odds (LOD) score plots with a secondary axis for adjusted P-value quantiles.

Related Experiment Videos

  • Labeling local LOD score maxima with their corresponding location-specific adjusted P-values.
  • Modifying existing algorithms for permutation-based thresholds to generate adjusted P-values.

    • Main Results:

    • The enhanced plots provide a visual gradation of genome-wide significance for LOD score curves.
    • Adjusted P-values offer a more informative measure of significance for individual LOD scores compared to single thresholds.
    • The method allows for a clearer distinction between suggestive and significant QTL findings.

    Conclusions:

    • Incorporating adjusted P-values into QTL plots enhances the interpretability of genome-wide scan results.
    • This approach offers a more sophisticated understanding of statistical significance in genetic mapping.
    • The proposed modification provides a valuable tool for researchers in genetics and related fields.