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Related Experiment Videos

Ascertainment adjustment in complex diseases.

David V Glidden1, Kung-Yee Liang

  • 1Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, California 94143-0560, USA. david@biostat.ucsf.edu

Genetic Epidemiology
|October 18, 2002
PubMed
Summary
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Genetic studies face challenges with oversampling families and residual association. While conceptually simple, parameter estimation can be problematic, especially with misspecified random effects distributions in ascertainment-adjusted models.

Area of Science:

  • Statistical genetics
  • Complex disease research

Background:

  • Genetic studies of complex diseases require simultaneous handling of family oversampling and residual association.
  • Residual association accounts for unmeasured genetic and environmental factors influencing disease risk.

Purpose of the Study:

  • To revisit and re-evaluate statistical challenges in genetic studies, specifically parameter estimation in ascertainment-adjusted models.
  • To present a more optimistic assessment of estimation challenges compared to previous work.

Main Methods:

  • Revisiting statistical issues and examples previously discussed by Burton et al. [2000].
  • Analyzing the impact of misspecification of random effects distribution in ascertainment-adjusted likelihood.

Main Results:

Related Experiment Videos

  • Parameter estimation in this context is conceptually straightforward but practically challenging.
  • Slight misspecification of the random effects distribution can lead to severely biased parameter estimates.

Conclusions:

  • Scientists should exercise caution when interpreting results from ascertainment-adjusted variance-component models.
  • The practical implementation of statistical models in genetic studies requires careful consideration of potential biases.