Determining Effects of Genes, Environment, and Gene X Environment Interaction That Are Common to Breast and Ovarian Cancers Via Bivariate Logistic Regression

Abstract

A new method for the simultaneous genetic analysis of two or more discrete traits such as the presence of breast and ovarian cancers in twins was developed. A generalized estimation equations (GEE) logistic regression model was used for the modeling. A shared trait is defined for two discrete traits based upon explicit patterns of trait concordance and discordance within twin pairs; this shared trait is assessed for the influence of additive genetic and/or common environmental effects. Data are summarized in the form of 2 x 2 tables (for monozygotic and dizygotic twins) by combining appropriate cells form the 16-cell multinomial distribution to define the individual and shared trait. Hypothesis tests for additive genetic and common environmental influence are performed using repeated measures logistic regression via the GEE approach. The model specification is highly flexible accounts for the correlated structure of the parameter estimates and does not require multivariate normality assumption for the underlying liability distribution. The approach was applied to data sets form the Vietnam Era Twin Registry and the Mid Atlantic twin Registry. Currently, efforts are being taken to collect adequate data on cancer outcomes that will provide enough power to apply this methodology to twins with cancer.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2003
Accession Number
ADA418938

Entities

People

  • Viswanathan Ramakrishnan

Organizations

  • Virginia Commonwealth University

Tags

Communities of Interest

  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Breast Cancer
  • Cancer
  • Colon Cancer
  • Computer Programs
  • Data Analysis
  • Data Science
  • Department Of Veterans Affairs
  • Diseases And Disorders
  • Genetics
  • Health Services
  • Information Science
  • Neoplasms
  • Ovarian Cancer
  • Regression Analysis
  • Statistical Analysis
  • Statistics
  • Surveys

Readers

  • Molecular and genetic basis of cancer.
  • Statistical inference.
  • Women's Health and Cancer Risk Research: African American Women and Pregnancy Outcomes.

Technology Areas

  • Biotechnology