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Related Experiment Videos

Circumventing multiple testing: a multilocus Monte Carlo approach to testing for association.

L M McIntyre1, E R Martin, K L Simonsen

  • 1Institute for Clinical and Epidemiological Research, Veterans Affairs Medical Center, Durham, North Carolina, USA.

Genetic Epidemiology
|June 22, 2000
PubMed
Summary

A new Monte Carlo procedure offers a more powerful way to assess genetic association with diseases using family data. This method is particularly effective when analyzing tightly linked markers, outperforming the traditional Bonferroni correction.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Dense marker maps are now feasible due to technological advancements.
  • Performing individual association tests for each marker leads to multiple testing issues.
  • The Bonferroni correction is a common but potentially underpowered solution for multiple testing in dense marker scenarios.

Purpose of the Study:

  • To propose a Monte Carlo procedure for global significance assessment in family-based association studies.
  • To evaluate the power of the Monte Carlo procedure compared to the Bonferroni correction, especially with linked markers.

Main Methods:

  • Computer simulations were used to study the transmission/disequilibrium test (TDT).
  • The study examined scenarios with tightly linked markers and varying degrees of association between them.

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  • The power of the TDT was compared using both Bonferroni and Monte Carlo significance assessment procedures.

    • Main Results:

    • Both Bonferroni and Monte Carlo procedures showed similar power when marker alleles were not associated.
    • The Monte Carlo procedure demonstrated significantly higher power than the Bonferroni correction when alleles at linked markers were associated.
    • The proposed Monte Carlo method provides a more powerful alternative for genetic association studies with linked markers.

    Conclusions:

    • The Monte Carlo procedure is recommended for association studies involving potentially associated linked markers.
    • This method ensures appropriate significance levels and optimizes statistical power.
    • It serves as a robust approach for genetic association analysis in complex marker datasets.