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Related Experiment Videos

Maximizing association statistics over genetic models.

Juan R González1, Josep L Carrasco, Frank Dudbridge

  • 1Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. jrgonzalez@imim.es

Genetic Epidemiology
|January 30, 2008
PubMed
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When the genetic model is unknown, the max-statistic effectively assesses disease association by selecting the best model. This method offers a powerful safeguard against genetic model uncertainty in large studies.

Area of Science:

  • Genetics
  • Statistical genetics
  • Disease association studies

Background:

  • Assessing genetic association with disease often requires assuming an inheritance model, commonly the additive model tested with the Cochran-Armitage trend test.
  • Alternative models like dominant or recessive inheritance can be tested by grouping genotypes, but require prior expectation.
  • When the genetic model is unknown, the max-statistic combines results from additive, dominant, and recessive models to identify associations.

Purpose of the Study:

  • To develop simpler and more feasible alternatives to the max-statistic for large-scale genetic association studies.
  • To provide methods for calculating statistical significance and power for the max-statistic, overcoming limitations of permutation or Monte Carlo simulations.

Main Methods:

Related Experiment Videos

  • Simulations were used to determine the effective number of tests for the max-statistic procedure.
  • The asymptotic distribution of the max-statistic was derived.
  • A formula for power calculations in study designs utilizing the max-statistic was developed.

    • Main Results:

    • The max-statistic procedure has an effective number of tests of 2.2, useful for P-value correction.
    • The derived asymptotic distribution simplifies significance level calculations.
    • The max-statistic approach demonstrated power in simulations, safeguarding against model uncertainty.

    Conclusions:

    • The max-statistic is a powerful tool for genetic association studies when the inheritance model is uncertain.
    • The developed methods enhance the feasibility and application of the max-statistic in large-scale genetic research.
    • This approach provides robust disease association assessment irrespective of the underlying genetic model.