Animal Genetics • Original Paper

Journal of Applied Genetics

, Volume 54, Issue 1, pp 79-88

Parametric proportional hazards model for mapping genomic imprinting of survival traits

  • Huijiang GaoAffiliated withInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences
  • , Yongxin LiuAffiliated withResearch Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences
  • , Tingting ZhangAffiliated withDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame
  • , Runqing YangAffiliated withBeidaihe Central Experiment Station, Chinese Academy of Fishery SciencesSchool of Agriculture and Biology, Shanghai Jiao Tong University Email author 
  • , Daniel R. ProwsAffiliated withDivision of Human Genetics, Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine

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Abstract

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.

Keywords

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