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Related Experiment Videos

Testing for linkage under robust genetic models.

R Guerra1, Y Wan, A Jia

  • 1Biostatistics Center, Department of Statistical Science, Southern Methodist University, Dallas, Tex. 75275-0332, USA. rguerra@mail.smu.edu

Human Heredity
|June 12, 1999
PubMed
Summary
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The robust variance components model is more powerful for genetic linkage analysis than the sibling pair approach. The Monte Carlo test offers the best power and validity for smaller sample sizes or low heritability.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Quantitative Trait Linkage Analysis

Background:

  • Assessing genetic linkage between quantitative traits and specific loci is crucial in genetic studies.
  • Robust genetic models are employed, but the comparative performance of significance tests remains underexplored.

Purpose of the Study:

  • To evaluate the relative performance, including significance level and statistical power, of various significance tests within robust variance components models.
  • To compare these tests against the robust sibling pair test.

Main Methods:

  • Simulations were conducted to assess the power and validity of different statistical tests.
  • The robust variance components model was utilized, incorporating standard Wald, likelihood ratio, quasilikelihood Wald, and Monte Carlo tests.

Related Experiment Videos

  • The robust sibling pair test was included for comparison.

    • Main Results:

    • Variance components models demonstrated higher power than the robust sibling pair approach for fixed sample sizes.
    • With approximately 100 nuclear families and moderate to high heritability (>0.20), all variance components model-based tests performed equally well.
    • For fewer than 100 families or low heritability (<0.20), the Monte Carlo test exhibited superior power and validity.

    Conclusions:

    • The choice of statistical test depends on sample size and heritability in genetic linkage analysis.
    • The robust variance components model, particularly the Monte Carlo test, offers advantages in specific scenarios.
    • Application to low-density lipoprotein cholesterol and apolipoprotein E suggests the sibling pair method may be more effective in practice than simulations indicate.