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Originally published as Genetics Published Articles Ahead of Print on September 15, 2004.

Genetics, Vol. 168, 2383-2394, December 2004, Copyright © 2004
doi:10.1534/genetics.104.034447

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A Mechanistic Model for Genetic Machinery of Ontogenetic Growth

Rongling Wu*,1, Zuoheng Wang*, Wei Zhao* and James M. Cheverud{dagger}

* Department of Statistics, University of Florida, Gainesville, Florida 32611
{dagger} Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri 63110

1 Corresponding author: Department of Statistics, 533 McCarty Hall C, University of Florida, Gainesville, FL 32611.
E-mail: rwu{at}stat.ufl.edu

Two different genetic mechanisms can be proposed to explain variation in growth trajectories. The allelic sensitivity hypothesis states that growth trajectory is controlled by the time-dependent expression of alleles at the deterministic quantitative trait loci (dQTL) formed during embryogenesis. The gene regulation hypothesis states that the differentiation in growth process is due to the opportunistic quantitative trait loci (oQTL) through their mediation with new developmental signals. These two hypotheses of genetic control have been elucidated in the literature. Here, we propose a new statistical model for discerning these two mechanisms in the context of growth trajectories by integrating growth laws within a QTL-mapping framework. This model is developed within the maximum-likelihood context, implemented with a grid approach for estimating the genomic positions of the deterministic and opportunistic QTL and the simplex algorithm for estimating the growth curve parameters of the genotypes at these QTL and the parameters modeling the residual (co)variance matrix. Our model allows for extensive hypothesis tests for the genetic control of growth processes and developmental events by these two types of QTL. The application of this new model to an F2 progeny in mice leads to the detection of deterministic and opportunistic QTL on chromosome 1 for mouse body mass growth. The estimates of QTL positions and effects from our model are broadly in agreement with those by traditional interval-mapping approaches. The implications of this model for biological and biomedical research are discussed.




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