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Related Experiment Videos

Resampling-based multiple hypothesis testing procedures for genetic case-control association studies.

Bingshu E Chen1, Lori C Sakoda, Ann W Hsing

  • 1Biostatistics Branch, Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland 20852-7244, USA. cheneric@mail.nih.gov

Genetic Epidemiology
|June 7, 2006
PubMed
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New statistical tests for gene association studies improve disease prediction. A resampling-based omnibus test offers significant power gains over standard methods for identifying disease-associated genetic variations.

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Gene-based hypothesis tests assess associations between genetic variations (SNPs, haplotypes) and disease in case-control studies.
  • Standard statistical tests control false-positive rates but may lack power due to polymorphism correlations.
  • Permutation resampling offers a way to construct more powerful tests by accounting for these correlations.

Purpose of the Study:

  • To compare the power and type I error control of different gene-based statistical tests.
  • To evaluate the computational burden versus power gain for advanced statistical methods.
  • To introduce and validate a novel resampling-based omnibus test for genetic association studies.

Main Methods:

  • Comparison of the Simes Global Test and the min P test using simulation studies with empirical haplotype structures.

Related Experiment Videos

  • Evaluation of a resampling-based omnibus test combining min P and haplotype frequency tests.
  • Application of the proposed omnibus test to population-based data from the Shanghai Biliary Tract Cancer Study, examining PTGS2 gene polymorphisms.

    • Main Results:

    • The min P test controlled type I error and showed modest power gains (2.1% on average) over the Simes test.
    • The min P test sometimes under-performed haplotype analysis when a haplotype conferred disease susceptibility.
    • The proposed omnibus test controlled type I error and achieved consistent power gains (5.7% on average) compared to Bonferroni tests.

    Conclusions:

    • The resampling-based omnibus test provides a powerful and reliable method for gene-based association studies.
    • This novel test demonstrates advantages in detecting disease-associated genetic variations, as shown in the PTGS2 gene analysis.
    • The omnibus test offers a computationally justified approach for enhancing power in genetic association research.