Parametric proportional hazards model for mapping genomic imprinting of survival traits
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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.
KeywordsCox proportional hazards model Imprinted quantitative trait loci Mapping Optimisation Survival distribution Survival trait
This work was supported by the 12th “Five-Year” National Science and Technology Support Project (2011BAD28B04), basic research fund programme 2010jc-2 of state-level public welfare scientific research institutions of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), and the National Natural Science Foundation of China (30972077 and 31172190).
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