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Testing for association between disease and linked marker loci: a log-linear-model analysis.

L Tiret1, P Amouyel, R Rakotovao

  • 1Institut National de la Santé et de la Recherche Médicale (INSERM), Unité 258, Hôpital Broussais, Paris, France.

American Journal of Human Genetics
|May 1, 1991
PubMed
Summary

This study introduces a log-linear model for analyzing genetic association studies, which are crucial for identifying complex disease factors. The model effectively analyzes genotypic data to assess allelic frequencies and genetic interactions, aiding in disease locus identification.

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Area of Science:

  • Genetics
  • Epidemiology
  • Statistical modeling

Background:

  • Complex disease research often compares genetic markers in patients versus controls.
  • Analyzing association studies is challenging due to the availability of genotypic, not gametic, data.

Purpose of the Study:

  • To present a valid log-linear model for analyzing genetic association studies with genotypic data.
  • To enable testing of allelic frequencies, Hardy-Weinberg equilibrium, and disease-marker interactions.

Main Methods:

  • Log-linear model analysis applied to genotypic data from patient and control groups.
  • Testing for differences in allelic frequencies and Hardy-Weinberg equilibrium.
  • Assessing interaction between marker alleles and disease status to infer dominance.

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Main Results:

  • The log-linear model allows testing for differences in allelic frequencies between affected and unaffected individuals.
  • It assesses Hardy-Weinberg equilibrium and interactions, providing insights into disease locus dominance via odds ratios.
  • The model can be extended to analyze multiple linked markers and populations.

Conclusions:

  • Log-linear models offer a robust statistical framework for genetic association studies, particularly with genotypic data.
  • This approach facilitates the identification of genetic factors contributing to complex diseases.
  • The model's ability to assess interactions and dominance is key for understanding disease susceptibility loci.