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Structured Antedependence Models for Functional Mapping of Multiple Longitudinal Traits

Author

Listed:
  • Zhao Wei

    (University of Florida)

  • Hou Wei

    (University of Florida)

  • Littell Ramon C.

    (University of Florida)

  • Wu Rongling

    (University of Florida)

Abstract

In this article, we present a statistical model for mapping quantitative trait loci (QTL) that determine growth trajectories of two correlated traits during ontogenetic development. This model is derived within the maximum likelihood context, incorporated by mathematical aspects of growth processes to model the mean vector and by structured antedependence (SAD) models to approximate time-dependent covariance matrices for longitudinal traits. It provides a quantitative framework for testing the relative importance of two mechanisms, pleiotropy and linkage, in contributing to genetic correlations during ontogeny. This model has been employed to map QTL affecting stem height and diameter growth trajectories in an interspecific hybrid progeny of Populus, leading to the successful discovery of three pleiotropic QTL on different linkage groups. The implications of this model for genetic mapping within a broader context are discussed.

Suggested Citation

  • Zhao Wei & Hou Wei & Littell Ramon C. & Wu Rongling, 2005. "Structured Antedependence Models for Functional Mapping of Multiple Longitudinal Traits," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-28, November.
    • Handle: RePEc:bpj:sagmbi:v:4:y:2005:i:1:n:33
    DOI: 10.2202/1544-6115.1136
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    Cited by:

    1. Kohli, Priya & Garcia, Tanya P. & Pourahmadi, Mohsen, 2016. "Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 87-100.
    2. Jiguo Cao & Liangliang Wang & Zhongwen Huang & Junyi Gai & Rongling Wu, 2017. "Functional Mapping of Multiple Dynamic Traits," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 60-75, March.
    3. Kim, Chulmin & Zimmerman, Dale L., 2012. "Unconstrained models for the covariance structure of multivariate longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 104-118.
    4. Feng, Sanying & Lian, Heng & Xue, Liugen, 2016. "A new nested Cholesky decomposition and estimation for the covariance matrix of bivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 102(C), pages 98-109.

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