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

Author

Listed:
  • 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)

  • de Andrade Mariza

    (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)

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.

Suggested Citation

  • Duarte Nubia E. & Giolo Suely R. & Pereira Alexandre C. & de Andrade Mariza & Soler Júlia P., 2014. "Using the theory of added-variable plot for linear mixed models to decompose genetic effects in family data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(3), pages 359-378, June.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:3:p:20:n:6
    DOI: 10.1515/sagmb-2013-0057
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    References listed on IDEAS

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    1. Li-Chu Chien, 2011. "A robust diagnostic plot for explanatory variables under model mis-specification," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(1), pages 113-126.
    2. J. S. Hodges, 1998. "Some algebra and geometry for hierarchical models, applied to diagnostics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(3), pages 497-536.
    3. Najaf Amin & Cornelia M van Duijn & Yurii S Aulchenko, 2007. "A Genomic Background Based Method for Association Analysis in Related Individuals," PLOS ONE, Public Library of Science, vol. 2(12), pages 1-7, December.
    4. Juvêncio S. Nobre & Julio M. Singer, 2011. "Leverage analysis for linear mixed models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 1063-1072, February.
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