Theoretical and Applied Genetics

, Volume 122, Issue 5, pp 855–863

Statistical optimization of parametric accelerated failure time model for mapping survival trait loci


  • Zhongze Piao
    • Crop Breeding and Cultivation Research InstituteShanghai Academy of Agricultural Sciences
  • Xiaojing Zhou
    • Department of MathematicsHeilongjiang Bayi Agricultural University
  • Li Yan
    • College of Information TechnologyHeilongjiang Bayi Agricultural University
  • Ying Guo
    • Department of MathematicsHeilongjiang Bayi Agricultural University
    • School of Agriculture and biologyShanghai Jiaotong University
    • College of Animal Science and Veterinary MedicineHeilongjiang Bayi Agricultural University
  • Zhixiang Luo
    • Rice Research InstituteAnhui Academy of Agricultural Sciences
  • Daniel R. Prows
    • Division of Human GeneticsCincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine
Original Paper

DOI: 10.1007/s00122-010-1491-6

Cite this article as:
Piao, Z., Zhou, X., Yan, L. et al. Theor Appl Genet (2011) 122: 855. doi:10.1007/s00122-010-1491-6


Most existing statistical methods for mapping quantitative trait loci (QTL) are not suitable for analyzing survival traits with a skewed distribution and censoring mechanism. As a result, researchers incorporate parametric and semi-parametric models of survival analysis into the framework of the interval mapping for QTL controlling survival traits. In survival analysis, accelerated failure time (AFT) model is considered as a de facto standard and fundamental model for data analysis. Based on AFT model, we propose a parametric approach for mapping survival traits using the EM algorithm to obtain the maximum likelihood estimates of the parameters. Also, with Bayesian information criterion (BIC) as a model selection criterion, an optimal mapping model is constructed by choosing specific error distributions with maximum likelihood and parsimonious parameters. Two real datasets were analyzed by our proposed method for illustration. The results show that among the five commonly used survival distributions, Weibull distribution is the optimal survival function for mapping of heading time in rice, while Log-logistic distribution is the optimal one for hyperoxic acute lung injury.

Copyright information

© Springer-Verlag 2010