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A Bayesian approach to the transmission/disequilibrium test for binary traits.

Varghese George1, Purushottam W Laud

  • 1Department of Biostatistics, University of Alabama, 327N Ryals Public Health Building, 1665 University Boulevard, Birmingham, AL 35294-0022, USA. VGeorge@ms.soph.uab.edu

Genetic Epidemiology
|January 5, 2002
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This study introduces a Bayesian transmission/disequilibrium test (TDT) for binary traits, offering a complementary approach to classical methods for linkage and association analysis. The Bayesian TDT provides a robust framework for genetic studies, enhancing the assessment of genetic associations.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • The transmission/disequilibrium test (TDT) is a standard method for detecting linkage between marker and trait loci in binary traits, accounting for allelic association.
  • Classical TDT methods may face challenges in accurately assessing association in the presence of linkage when multiple offspring per family are considered, due to unaddressed correlation structures.

Purpose of the Study:

  • To propose a novel Bayesian transmission/disequilibrium test (TDT) as an alternative to traditional frequentist approaches for analyzing genetic linkage and association.
  • To evaluate the performance of the Bayesian TDT in terms of statistical power and type I error rates compared to existing frequentist methods.

Main Methods:

  • Development of a Bayesian framework for the TDT, utilizing Bayes factors for hypothesis testing to weigh evidence between competing genetic models.
  • Application of Markov Chain Monte Carlo (MCMC) methods to derive joint and marginal posterior distributions for genetic parameters like recombination fraction (theta) and disequilibrium coefficient (delta), assuming known disease inheritance modes.
  • Generation of Bayesian credible intervals for estimated genetic parameters.

Main Results:

  • The proposed Bayesian TDT method provides a complementary approach to classical TDT for linkage and association analysis in binary traits.
  • Comparison with frequentist methods indicates the Bayesian TDT's validity regarding type I error rates and potential for robust power assessment.
  • The Bayesian framework naturally yields credible intervals for key genetic parameters, aiding in the interpretation of linkage and association findings.

Conclusions:

  • The Bayesian TDT offers a statistically sound and flexible alternative for genetic association studies, particularly when dealing with complex family structures or correlation among offspring.
  • This approach enhances the ability to detect and quantify linkage and allelic association, contributing to a more comprehensive understanding of genetic disease etiology.
  • The Bayesian framework facilitates robust parameter estimation and hypothesis testing in genetic linkage analysis.