Original Paper

Score Test for Linkage in Generalized Linear Models
J.J.P. Lebrec^{a, b}, H.C. van Houwelingen<sup>a

a</sup>Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands;
^bDepartment of Biostatistics and Medical Informatics, Université de Bretagne Occidentale, Brest, France

Address of Corresponding Author

Hum Hered 2007;64:5-15 (DOI: 10.1159/000101418)

  Key Words

• Binary trait
• Breast cancer
• Covariate
• Gene-environment interaction
• Migraine

  Abstract

We derive a test for linkage in a Generalized Linear Mixed Model (GLMM) framework which provides a natural adjustment for marginal covariate effects. The method boils down to the score test of a quasi-likelihood derived from the GLMM, it is computationally inexpensive and can be applied to arbitrary pedigrees. In particular, for binary traits, relative pairs of different nature (affected and discordant) and individuals with different covariate values can be naturally combined in a single test. The model introduced could explain a number of situations usually described as gene by covariate interaction phenomena, and offers substantial gains in efficiency compared to methods classically used in those instances.

Copyright © 2007 S. Karger AG, Basel

  Author Contacts

Jérémie Lebrec
Laboratoire de Biostatistiques & Informatique Médicale
Faculté de médecine et des sciences médicales, Université de Bretagne Occidentale
22 Avenue Camille Desmoulins, FR–29200 Brest (France)
Tel. +33 2 98 01 79 35, Fax +33 2 98 01 64 74, E-Mail jeremie.lebrec@univ-brest.fr

  Article Information

Published online: April 27, 2007
Number of Print Pages : 11
Number of Figures : 2, Number of Tables : 3, Number of References : 22