Article
Score test for detecting linkage to complex traits in selected samples.
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, University of Leiden, PO Box 9604, Leiden, The Netherlands.
Genetic Epidemiology (Impact Factor: 4.02). 10/2004; 27(2):97108. DOI: 10.1002/gepi.20012 Source: PubMed

Article: Testing for genetic association in the presence of linkage and genecovariate interactions.
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ABSTRACT: In order to study familybased association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity 64, 515) by adding a genotypic effect to the mean. The corresponding score test is a weighted familybased association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of genecovariate interaction, we propose a linear regression method where the familyspecific score statistic is regressed on familyspecific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the withinfamily variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of genecovariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anticyclic citrullinated peptide increased the significance of the association with the DR locus.Biometrical Journal 02/2010; 52(1):2233. · 1.15 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: No Abstracts.Biometrical Journal 02/2010; 52(1):59. · 1.15 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Modelfree linkage analysis methods, based on identitybydescent allele sharing, are commonly used for complex trait analysis. The MaximumLikelihoodBinomial (MLB) approach, which is based on the hypothesis that parental alleles are binomially distributed among affected sibs, is particularly popular. An extension of this method to quantitative traits (QT) has been proposed (MLBQTL), based on the introduction of a latent binary variable capturing information about the linkage between the QT and the marker. Interestingly, the MLBQTL method does not require the decomposition of sibships into constituent sibpairs and requires no prior assumption about the distribution of the QT. We propose a new formulation of the MLB method for quantitative traits (nMLBQTL) that explicitly takes advantage of the independence of paternal and maternal allele transmission under the null hypothesis of no linkage. Simulation studies under H₀ showed that the nMLBQTL method generated very consistent type I errors. Furthermore, simulations under the alternative hypothesis showed that the nMLBQTL method was slightly, but systematically more powerful than the MLBQTL method, whatever the genetic model, residual correlation, ascertainment strategy and sibship size considered. Finally, the power of the nMLBQTL method is illustrated by a chromosomewide linkage scan for a quantitative endophenotype of leprosy infection. Overall, the nMLBQTL method is a robust, powerful, and flexible approach for detecting linkage with quantitative phenotypes, particularly in studies of non Gaussian phenotypes in large sibships.Genetic Epidemiology 01/2011; 35(1):4656. · 4.02 Impact Factor
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