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Logistic regression model for analyzing extended haplotype data

S Wallenstein1, S E Hodge, A Weston

  • 1Department of Biomathematical Sciences, Mount Sinai School of Medicine, New York, New York 10029, USA. wallenst@msvax.mssm.edu

Genetic Epidemiology
|April 29, 1998
PubMed
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This study introduces a new statistical method for analyzing extended haplotypes, like those in the p53 gene, as cancer risk factors. The approach uses logistic regression to account for multiple genetic factors and other variables, improving cancer risk assessment.

Area of Science:

  • Genetics
  • Cancer Research
  • Statistical Genetics

Background:

  • Increased interest in extended haplotypes, particularly in the p53 gene, as potential cancer risk factors.
  • Existing statistical methods for haplotype analysis have limitations in power and accounting for covariates.

Purpose of the Study:

  • To develop and describe a novel statistical analysis for comparing cases and controls regarding extended haplotypes.
  • To enable the inclusion of covariates in haplotype-based cancer risk analyses.

Main Methods:

  • Developed a logistic regression model assuming additivity for haplotype analysis.
  • Predictor variables include haplotype copy number (0, 1, or 2) and other explanatory variables.
  • Applied the method to p53 and breast cancer data.

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Main Results:

  • The proposed logistic regression model effectively incorporates haplotype information and covariates.
  • The model allows for the estimation of log odds ratios for disease risk associated with specific haplotypes.
  • Demonstrated utility with published p53 and breast cancer data.

Conclusions:

  • The additive logistic regression model provides a powerful and flexible approach for haplotype-based association studies.
  • This method can be applied to various polymorphic systems for disease risk evaluation.
  • Enhances the ability to identify genetic risk factors for cancer and other diseases.