Nonparametric modeling of longitudinal covariance structure in functional mapping of quantitative trait loci

John Stephen Yap Jianqing Fan Rongling Wu

Statistics Theory and Methods mathscidoc:1912.43328

Biometrics, 65, (4), 1068-1077, 2009.12
<b></b> Estimation of the covariance structure of longitudinal processes is a fundamental prerequisite for the practical deployment of functional mapping designed to study the genetic regulation and network of quantitative variation in dynamic complex traits. We present a nonparametric approach for estimating the covariance structure of a quantitative trait measured repeatedly at a series of time points. Specifically, we adopt Huang et al.'s (2006,<i>Biometrika</i><b>93</b>, 8598) approach of invoking the modified Cholesky decomposition and converting the problem into modeling a sequence of regressions of responses. A regularized covariance estimator is obtained using a normal penalized likelihood with an<i>L</i><sub>2</sub> penalty. This approach, embedded within a mixture likelihood framework, leads to enhanced accuracy, precision, and flexibility of functional mapping while preserving its biological relevance. Simulation studies are
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@inproceedings{john2009nonparametric,
  title={Nonparametric modeling of longitudinal covariance structure in functional mapping of quantitative trait loci},
  author={John Stephen Yap, Jianqing Fan, and Rongling Wu},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113733559792888},
  booktitle={Biometrics},
  volume={65},
  number={4},
  pages={1068-1077},
  year={2009},
}
John Stephen Yap, Jianqing Fan, and Rongling Wu. Nonparametric modeling of longitudinal covariance structure in functional mapping of quantitative trait loci. 2009. Vol. 65. In Biometrics. pp.1068-1077. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113733559792888.
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