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A conditional inference framework for extending the transmission/disequilibrium test

L C Lazzeroni1, K Lange

  • 1Department Genetics, Stanford University, Calif. 94305-5405, USA. laura@osiris.stanford.edu

Human Heredity
|April 4, 1998
PubMed
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Permutation extensions of the transmission/disequilibrium test (TDT) maintain its advantage in avoiding spurious associations. This conditional inference framework allows for more powerful genetic linkage and association analyses.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • The transmission/disequilibrium test (TDT) is a standard method for detecting genetic linkage and association.
  • TDT is designed to prevent spurious associations caused by population stratification.
  • Existing TDT methods have limitations in handling complex genetic scenarios.

Purpose of the Study:

  • To introduce permutation extensions of the TDT.
  • To develop a conditional inference framework for TDT.
  • To enhance TDT's applicability to complex genetic data.

Main Methods:

  • Permutation testing applied to the TDT framework.
  • Conditional inference for genetic association studies.
  • Extensions for multiple alleles, multiple loci, and unaffected siblings.

Related Experiment Videos

Main Results:

  • Permutation extensions of TDT successfully avoid population stratification biases.
  • The conditional inference framework allows for robust analysis of multiple alleles and loci.
  • The proposed method offers a more powerful correction for multiple testing compared to Bonferroni correction.

Conclusions:

  • Permutation extensions of TDT provide a robust and powerful tool for genetic association studies.
  • The conditional inference framework significantly expands the utility of TDT.
  • This approach enhances the accuracy and power of detecting genetic influences on diseases.