Interval Mapping Method Based On AFT Model To Map Surviva Trait Loci And Genomic Imprinting

Survival or failure time traits, sometimes named time to event traits, can be broadly defined as the lengthof time between two events, which have been widely observed in nature, such as herd life or heading time inplants. The main features of a survival trait are that it is the time until some event occurs, and some of theobservations are censored.Most existing statistical methods for mapping quantitative trait loci (QTL) are not suitable for analyzingsurvival traits with a skewed distribution and censoring mechanism. As a result, researchers incorporateparametric and semi-parametric models of survival analysis into the framework of the interval mapping forQTL controlling survival traits. In survival analysis, accelerated failure time (AFT) model is considered as ade facto standard and fundamental model for data analysis. This study made the following two aspects:1. Parametric accelerated failure time model for mapping survival trait lociBased on AFT model, we propose a parametric approach for mapping survival traits by using theExpectation-Maximization (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 isconstructed by choosing specific error distributions with maximum likelihood and parsimonious parameters.One real dataset was analyzed by our proposed method for illustration. The results showed that among thefive commonly used survival distributions, Log-logistic distribution is the optimal survival function formapping of hyperoxic acute lung injury (HALI).2. Characterization of genomic imprinting effects and patterns with parametric accelerated failure time modelGenomic imprinting, a non-equivalent genetic phenomenon of allele expression that depends on parentalorigins, has been ubiquitously observed in nature. It not only controls the traits of growth and development butalso may be responsible for survival traits. Based on the accelerated failure time (AFT) model, we construct ageneral parametric model for mapping the imprinted QTL (iQTL). Within the framework of interval mapping,maximum likelihood estimation of iQTL parameters is implemented via EM algorithm. The imprintingpatterns of the detected iQTL are statistically inferred according to a series of null hypotheses. BIC modelselection criterion is employed to choose an optimal baseline hazard function with maximum likelihood andparsimonious parameters. A published dataset from a mouse model system was used to illustrate the proposedapproach. Results show that among the five commonly used survival distributions, Log-logistic distribution isthe optimal baseline hazard function for mapping QTL of HALI survival; under Log-logistic distribution, fourQTLs were identified, in which one QTL was inherited in Mendelian fashion, whereas others were imprintedin different imprinting patterns.