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Score test for linkage in generalized linear models.

作者信息

Lebrec J J P, van Houwelingen H C

机构信息

Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.

出版信息

Hum Hered. 2007;64(1):5-15. doi: 10.1159/000101418. Epub 2007 Apr 27.

DOI:10.1159/000101418
PMID:17483592
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.

摘要

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