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A logistic mixture model for characterizing genetic determinants causing differentiation in growth trajectories

Published online by Cambridge University Press:  26 July 2002

RONGLING WU ,
CHANG-XING MA ,
MYRON CHANG ,
RAMON C. LITTELL ,
SAMUEL S. WU ,
TONGMING YIN ,
MINREN HUANG ,
MINGXIU WANG  and
GEORGE CASELLA
RONGLING WU
Affiliation:
Department of Statistics, University of Florida, Gainesville, FL 32611, USA
CHANG-XING MA
Affiliation:
Department of Statistics, University of Florida, Gainesville, FL 32611, USA Department of Statistics, Nankai University, Tianjian 300071, China
MYRON CHANG
Affiliation:
Department of Statistics, University of Florida, Gainesville, FL 32611, USA
RAMON C. LITTELL
Affiliation:
Department of Statistics, University of Florida, Gainesville, FL 32611, USA
SAMUEL S. WU
Affiliation:
Department of Statistics, University of Florida, Gainesville, FL 32611, USA
TONGMING YIN
Affiliation:
The Key National Laboratory of Forest Genetics and Gene Engineering, Nanjing Forestry University, Nanjing, Jinagsu 210037, China
MINREN HUANG
Affiliation:
The Key National Laboratory of Forest Genetics and Gene Engineering, Nanjing Forestry University, Nanjing, Jinagsu 210037, China
MINGXIU WANG
Affiliation:
The Key National Laboratory of Forest Genetics and Gene Engineering, Nanjing Forestry University, Nanjing, Jinagsu 210037, China
GEORGE CASELLA
Affiliation:
Department of Statistics, University of Florida, Gainesville, FL 32611, USA
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Abstract

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The logistic or S-shaped curve of growth is one of the few universal laws in biology. It is certain that there exist specific genes affecting growth curves, but, due to a lack of statistical models, it is unclear how these genes cause phenotypic differentiation in growth and developmental trajectories. In this paper we present a statistical model for detecting major genes responsible for growth trajectories. This model is incorporated with pervasive logistic growth curves under the maximum likelihood framework and, thus, is expected to improve over previous models in both parameter estimation and inference. The power of this model is demonstrated by an example using forest tree data, in which evidence of major genes affecting stem growth processes is successfully detected. The implications for this model and its extensions are discussed.

Type
Research Article
Information
Genetics Research , Volume 79 , Issue 3 , June 2002 , pp. 235 - 245
Copyright
© 2002 Cambridge University Press