Journal of Applied Genetics

, Volume 54, Issue 1, pp 79–88

Parametric proportional hazards model for mapping genomic imprinting of survival traits

  • Huijiang Gao
  • Yongxin Liu
  • Tingting Zhang
  • Runqing Yang
  • Daniel R. Prows
Animal Genetics • Original Paper

DOI: 10.1007/s13353-012-0120-2

Cite this article as:
Gao, H., Liu, Y., Zhang, T. et al. J Appl Genetics (2013) 54: 79. doi:10.1007/s13353-012-0120-2


A number of imprinted genes have been observed in plants, animals and humans. They not only control growth and developmental traits, but may also be responsible for survival traits. Based on the Cox proportional hazards (PH) model, we constructed a general parametric model for dissecting genomic imprinting, in which a baseline hazard function is selectable for fitting the effects of imprinted quantitative trait loci (iQTL) genotypes on the survival curve. The expectation–maximisation (EM) algorithm is derived for solving the maximum likelihood estimates of iQTL parameters. The imprinting patterns of the detected iQTL are statistically tested under a series of null hypotheses. The Bayesian information criterion (BIC) model selection criterion is employed to choose an optimal baseline hazard function with maximum likelihood and parsimonious parameterisation. We applied the proposed approach to analyse the published data in an F2 population of mice and concluded that, among five commonly used survival distributions, the log-logistic distribution is the optimal baseline hazard function for the survival time of hyperoxic acute lung injury (HALI). Under this optimal model, five QTL were detected, among which four are imprinted in different imprinting patterns.


Cox proportional hazards model Imprinted quantitative trait loci Mapping Optimisation Survival distribution Survival trait 

Copyright information

© Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2012

Authors and Affiliations

  • Huijiang Gao
    • 1
  • Yongxin Liu
    • 2
  • Tingting Zhang
    • 3
  • Runqing Yang
    • 4
    • 5
  • Daniel R. Prows
    • 6
  1. 1.Institute of Animal SciencesChinese Academy of Agricultural SciencesBeijingPeople’s Republic of China
  2. 2.Research Centre for Aquatic BiotechnologyChinese Academy of Fishery SciencesBeijingPeople’s Republic of China
  3. 3.Department of Applied and Computational Mathematics and StatisticsUniversity of Notre DameNotre DameUSA
  4. 4.Beidaihe Central Experiment StationChinese Academy of Fishery SciencesQinhuangdaoPeople’s Republic of China
  5. 5.School of Agriculture and BiologyShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  6. 6.Division of Human GeneticsCincinnati Children’s Hospital Medical Center and University of Cincinnati College of MedicineCincinnatiUSA