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Relative-risk regression models using cases and their parents

D J Schaid1

  • 1Department of Health Sciences Research, Mayo Clinic/Mayo Foundation, Rochester, MN 55905, USA.

Genetic Epidemiology
|January 1, 1995
PubMed
Summary
This summary is machine-generated.

New regression models analyze genetic markers and disease risk using family data. These models improve upon existing methods by comparing case genotypes to Mendelian expectations, not just sibling controls.

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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Family-based studies are crucial for identifying genetic associations with diseases.
  • Existing methods like the haplotype relative-risk model have limitations in handling complex family structures and censored data.
  • There is a need for robust statistical models to analyze genetic marker associations in case-parental designs.

Purpose of the Study:

  • To present novel relative-risk regression models for genetic association studies using case-parental data.
  • To generalize the haplotype relative-risk method and incorporate features of proportional hazards models.
  • To enable the modeling of allele-specific disease risks and gene-environment interactions.

Main Methods:

  • Development of relative-risk regression models tailored for case-parental studies.
  • Generalization of the haplotype relative-risk approach to accommodate censored unaffected sibs.
  • Comparison of case genotype frequencies against Mendelian expectations for robust control.

Main Results:

  • The models were applied to the Problem 2 data set, analyzing binary affection status.
  • Four candidate-gene loci showed significant associations with affection status after accounting for age-related risk.
  • Two significant associations correctly identified major gene loci, while two were identified as false positives.

Conclusions:

  • The developed relative-risk regression models offer a powerful tool for genetic association studies in family designs.
  • These models provide a more accurate assessment of genetic contributions to disease risk compared to traditional methods.
  • The application demonstrated the models' capability in identifying true genetic associations and potential false positives.