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

Rongling Wu, Chang Xing Ma, Myron Chang, Ramon C. Littell, Samuel S. Wu, Tongming Yin, Minren Huang, Mingxiu Wang, George Casella

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)235-245
Number of pages11
JournalGenetical research
Volume79
Issue number3
DOIs
StatePublished - 2002

All Science Journal Classification (ASJC) codes

  • Genetics

Fingerprint Dive into the research topics of 'A logistic mixture model for characterizing genetic determinants causing differentiation in growth trajectories'. Together they form a unique fingerprint.

Cite this