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Nonparametric modeling of longitudinal covariance structure in functional mapping of quantitative trait loci. (English) Zbl 1181.62186

Summary: 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 J. Z. Huang et al.’s [Biometrika 93, No. 1, 85 – 98 (2006; Zbl 1152.62346)] 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 \(L_2\) 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 performed to reveal the statistical properties and advantages of the proposed method. A real example from a mouse genome project is analyzed to illustrate the utilization of the methodology. The new method will provide a useful tool for genome-wide scanning for the existence and distribution of quantitative trait loci underlying a dynamic trait important to agriculture, biology, and health sciences.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62G05 Nonparametric estimation
92D10 Genetics and epigenetics
62N02 Estimation in survival analysis and censored data
65C60 Computational problems in statistics (MSC2010)

Citations:

Zbl 1152.62346

Software:

fda (R); SemiPar
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References:

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