Using the theory of added-variable plot for linear mixed models to decompose genetic effects in family data

Duarte Nubia E. (), Giolo Suely R., Pereira Alexandre C., Mariza de Andrade and Soler Júlia P.
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Duarte Nubia E.: Heart Institute, Medical School University of São Paulo, São Paulo, SP, Brazil Instituto do Coracão, São Paulo, SP CEP: 05403-000, Brazil
Giolo Suely R.: Department of Statistics, Federal University of Paraná, Curitiba, PR, Brazil
Pereira Alexandre C.: Heart Institute, Medical School University of São Paulo, São Paulo, SP, Brazil
Mariza de Andrade: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
Soler Júlia P.: Department of Statistics, University of São Paulo, São Paulo, SP, Brazil

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 3, 359-378

Abstract: Effective analytical tools are highly desirable for data analysis and for making the biological link between genotypic and phenotypic measures. In family data it is important to reconcile the methods that explain the phenotypic variability through fixed genetic effects and ones that estimate variance components using classical heritability methods. Thus, in this paper, we propose a method based on added-variable plot for polygenic linear mixed models applied to genome wide association studies in family-based designs. Our goal is to be able to discriminate genetic predictor variables in effects due to random polygenic and residual components. We also propose an index to detect influential families for each predictor variable identified with genetic effect. We assess the performance of our proposed method using our own family simulated data and the Genetic Analysis Workshop 17 family simulated data.

Keywords: association analysis; influential families; polygenic models; variance components (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/sagmb-2013-0057

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