title: Score test for linkage in generalized linear models.
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.
authors.affiliation: Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands. jeremie.lebrec@univ-brest.fr
authors.forename: J J P
authors.initials: JJ
authors.lastname: Lebrec
authors.forename: H C
authors.initials: HC
authors.lastname: van Houwelingen
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008297:Male; D008881:Migraine Disorders / Q000235:genetics; D008957:Models, Genetic; D012044:Regression Analysis; D012107:Research Design