A non-stationary model for functional mapping of complex traits

Wei Zhao, Ying Q. Chen, George Casella, James M. Cheverud, Rongling Wu

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

Abstract

Summary: Understanding the genetic control of growth is fundamental to agricultural, evolutionary and biomedical genetic research. In this article, we present a statistical model for mapping quantitative trait loci (QTL) that are responsible for genetic differences in growth trajectories during ontogenetic development. This model is derived within the maximum likelihood context, implemented with the expectation-maximization algorithm. We incorporate mathematical aspects of growth processes to model the mean vector and structured antedependence models to approximate time-dependent covariance matrices for longitudinal traits. Our model has been employed to map QTL that affect body mass growth trajectories in both male and female mice of an F2 population derived from the Large and Small mouse strains. The results from this model are compared with those from the autoregressive-based functional mapping approach. Based on results from computer simulation studies, we suggest that these two models are alternative to one another and should be used simultaneously for the same dataset.

Original languageEnglish (US)
Pages (from-to)2469-2477
Number of pages9
JournalBioinformatics
Volume21
Issue number10
DOIs
StatePublished - May 15 2005

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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