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Integration of Genetic Familial Dependence Structure in Latent Class Models

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  • Labbe Aurelie

    (University McGill and Douglas Mental Health University Institute)

  • Bureau Alexandre

    (Université Laval)

  • Merette Chantal

    (Université Laval)

Abstract

One of the main reasons for the slow progress in detecting susceptibility genes in complex diseases may be that the clinical diagnoses used as phenotypes are genetically heterogeneous. The general objective of this paper is to develop a latent class model to identify homogeneous disease sub-types based on multivariate disease measurements in pedigrees from genetic studies. Our hypothesis is that the resulting disease sub-types will be influenced by a small number of genes, that will thus be more easily detectable. Specifically, we extended latent class analysis to allow dependence between the latent disease class status of relatives within nuclear families as a function of their kinship. Such a dependence model is expected to capture the underlying Mendelian transmission of alleles within families. An EM algorithm maximizes the likelihood and a cross-validation approach selects the optimal model. Through a simulation study under a genetic disease class model, we show that taking into account familial dependence improves the classification of the individuals in their true classes, compared to a traditional model assuming independence. An application of our approach to a dataset from the Autism Genetics Research Exchange is also presented.

Suggested Citation

  • Labbe Aurelie & Bureau Alexandre & Merette Chantal, 2009. "Integration of Genetic Familial Dependence Structure in Latent Class Models," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-30, January.
    • Handle: RePEc:bpj:ijbist:v:5:y:2009:i:1:n:6
    DOI: 10.2202/1557-4679.1126
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    References listed on IDEAS

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    1. Beth A. Reboussin & Kung-Yee Liang & David M. Reboussin, 1999. "Estimating Equations for a Latent Transit ion Model with Multiple Discrete Indicators," Biometrics, The International Biometric Society, vol. 55(3), pages 839-845, September.
    2. Elizabeth S. Garrett & Scott L. Zeger, 2000. "Latent Class Model Diagnosis," Biometrics, The International Biometric Society, vol. 56(4), pages 1055-1067, December.
    3. van der Laan Mark J. & Dudoit Sandrine & Keles Sunduz, 2004. "Asymptotic Optimality of Likelihood-Based Cross-Validation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-25, March.
    4. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    5. Zhang H. & Feng R. & Zhu H., 2003. "A Latent Variable Model of Segregation Analysis for Ordinal Traits," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1023-1034, January.
    6. Schmidt Mike & Hauser Elizabeth R & Martin Eden R. & Schmidt Silke, 2005. "Extension of the SIMLA Package for Generating Pedigrees with Complex Inheritance Patterns: Environmental Covariates, Gene-Gene and Gene-Environment Interaction," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-22, June.
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    Cited by:

    1. Oualkacha Karim & Labbe Aurelie & Ciampi Antonio & Roy Marc-Andre & Maziade Michel, 2012. "Principal Components of Heritability for High Dimension Quantitative Traits and General Pedigrees," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-27, January.
    2. Arafat Tayeb & Aurélie Labbe & Alexandre Bureau & Chantal Mérette, 2011. "Solving genetic heterogeneity in extended families by identifying sub-types of complex diseases," Computational Statistics, Springer, vol. 26(3), pages 539-560, September.

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