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TDT statistics for mapping quantitative trait loci.

M M Xiong1, J Krushkal, E Boerwinkle

  • 1Human Genetics Centre, University of Texas-Houston Health Science Center 77225, USA. mxiong@utsph.sph.uth.tmc.edu

Annals of Human Genetics
|March 24, 1999
PubMed
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This study introduces a generalized transmission disequilibrium test (TDT) for quantitative traits, offering increased power and flexibility over previous methods. The new TDT effectively maps quantitative trait loci, even with population substructure.

Area of Science:

  • Genetics
  • Statistical genetics
  • Quantitative trait analysis

Background:

  • The transmission disequilibrium test (TDT) is a powerful tool for detecting linkage between genetic markers and disease loci.
  • Previous extensions of TDT to quantitative traits have limitations, such as requiring specific family structures and marker-trait locus relationships.

Purpose of the Study:

  • To develop and validate a generalized TDT for quantitative traits that overcomes the limitations of prior methods.
  • To assess the power of the proposed TDT for mapping quantitative trait loci (QTLs) and provide study design guidelines.
  • To evaluate the TDT's validity in the presence of population substructure.

Main Methods:

  • Proposed a generalized TDT for quantitative traits accommodating multiple children per family and varying parental heterozygosity.

Related Experiment Videos

  • Developed analytic formulae to calculate the power of the TDT for QTL mapping.
  • Investigated the impact of various factors on TDT power.
  • Applied the generalized TDT to systolic blood pressure variation in the Rochester Family Heart Study.

    • Main Results:

    • The proposed TDT is more powerful than Allison's basic test and the extreme discordant sib pair linkage method.
    • The generalized TDT remains a valid test of linkage even with population substructure.
    • Analytic power calculations provide guidance for optimal study design.

    Conclusions:

    • The generalized TDT for quantitative traits offers a more flexible and powerful approach for genetic linkage analysis.
    • This method is robust to population substructure, enhancing its applicability.
    • The findings provide valuable insights for designing future genetic studies of quantitative traits.