Abstract/Details

Testing genetic hypothesis on bivariate dichotomous twin data using repeated measures logistic regression


2004 2004

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Abstract (summary)

The purpose of this dissertation is to develop methods for testing if specific phenotypes are a result of genes and/or environmental influences using an analysis of bivariate categorical twin data. In the literature, several methods are available for analyzing twin data. These include: Logistic Regression method and Structural Equation Modeling (SEM). Recently the Generalized Estimating Equations (GEE) under the logit link has been suggested for modeling bivariate twin data, where data on two traits are simultaneously considered. A new measure for the effects shared by the two traits was proposed as an alternative to the cross-twin cross-trait odds ratio.

This work proposes and evaluates new measures for the two traits, which is termed, the Linked Trait Effect (LTE). Using this measure the bivariate logistic regression model is extended to include individual level covariates. A theoretical justification for the proposed covariate model is provided for an individual level dichotomous trait. The methodology is then compared to the existing bivariate regression approaches, both theoretically and in applications. The proposed methodology is then applied to data from two sources. Bivariate twin data on drinking and smoking with individual level covariates such as marital status and church attendance from the Mid-Atlantic Twin Registry (MATR) is analyzed.

Indexing (details)


Subject
Statistics
Classification
0463: Statistics
Identifier / keyword
Pure sciences; Bivariate; Dichotomous; Logistic regression; Repeated measures
Title
Testing genetic hypothesis on bivariate dichotomous twin data using repeated measures logistic regression
Author
Massie, Tammy Jeanne Parliment
Number of pages
246
Publication year
2004
Degree date
2004
School code
2383
Source
DAI-B 66/02, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780542019586, 0542019582
Advisor
Ramakrishnan, Viswanathan
University/institution
Virginia Commonwealth University
University location
United States -- Virginia
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3166830
ProQuest document ID
305055458
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305055458
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