IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v5y2009i1n6.html
   My bibliography  Save this article

Integration of Genetic Familial Dependence Structure in Latent Class Models

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1126
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1126?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Beth A. Reboussin & Nicholas S. Ialongo, 2010. "Latent transition models with latent class predictors: attention deficit hyperactivity disorder subtypes and high school marijuana use," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 145-164, January.
    3. Diana L. Miglioretti, 2003. "Latent Transition Regression for Mixed Outcomes," Biometrics, The International Biometric Society, vol. 59(3), pages 710-720, September.
    4. Julia Y. Lin & Thomas R. Ten Have & Michael R. Elliott, 2009. "Nested Markov Compliance Class Model in the Presence of Time-Varying Noncompliance," Biometrics, The International Biometric Society, vol. 65(2), pages 505-513, June.
    5. Beth A. Reboussin & Edward H. Ip & Mark Wolfson, 2008. "Locally dependent latent class models with covariates: an application to under‐age drinking in the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 877-897, October.
    6. Michael Prendergast & David Huang & Yih-Ing Hser, 2008. "Patterns of Crime and Drug Use Trajectories in Relation to Treatment Initiation and 5-Year Outcomes," Evaluation Review, , vol. 32(1), pages 59-82, February.
    7. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 235-260, June.
    8. Getachew A. Dagne, 2016. "A growth mixture Tobit model: application to AIDS studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1174-1185, July.
    9. Bruce Desmarais, 2012. "Lessons in disguise: multivariate predictive mistakes in collective choice models," Public Choice, Springer, vol. 151(3), pages 719-737, June.
    10. Bacci, Silvia & Bartolucci, Francesco & Pigini, Claudia & Signorelli, Marcello, 2014. "A finite mixture latent trajectory model for hirings and separations in the labor market," MPRA Paper 59730, University Library of Munich, Germany.
    11. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.
    12. Zhu, Hongtu & Zhang, Heping, 2006. "Asymptotics for estimation and testing procedures under loss of identifiability," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 19-45, January.
    13. Yuan Liu & Hongyun Liu, 2019. "Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 659-677, October.
    14. Benjamin Agbo & Hussain Al-Aqrabi & Richard Hill & Tariq Alsboui, 2022. "Missing Data Imputation in the Internet of Things Sensor Networks," Future Internet, MDPI, vol. 14(5), pages 1-16, May.
    15. Pietro Lovaglio & Mario Mezzanzanica, 2013. "Classification of longitudinal career paths," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 989-1008, February.
    16. Yan Feng & Erpeng Liu & Zhang Yue & Qilin Zhang & Tiankuo Han, 2019. "The Evolutionary Trends of Health Behaviors in Chinese Elderly and the Influencing Factors of These Trends: 2005–2014," IJERPH, MDPI, vol. 16(10), pages 1-17, May.
    17. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.
    18. Joanna F. Dipnall & Belinda J. Gabbe & Warwick J. Teague & Ben Beck, 2020. "Identifying Homogeneous Patterns of Injury in Paediatric Trauma Patients to Improve Risk-Adjusted Models of Mortality and Functional Outcomes," IJERPH, MDPI, vol. 17(3), pages 1-20, January.
    19. Lu, Xiaosun & Huang, Yangxin & Zhu, Yiliang, 2016. "Finite mixture of nonlinear mixed-effects joint models in the presence of missing and mismeasured covariate, with application to AIDS studies," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 119-130.
    20. Yangxin Huang & Xiaosun Lu & Jiaqing Chen & Juan Liang & Miriam Zangmeister, 2018. "Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 699-718, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:ijbist:v:5:y:2009:i:1:n:6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.