Statistical optimization of parametric accelerated failure time model for mapping survival trait loci
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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.
KeywordsQuantitative Trait Locus Bayesian Information Criterion Weibull Distribution Quantitative Trait Locus Effect Survival Distribution
This preparation of work is partially supported by the National Natural Science Foundation of China (30972077) and Key Basic Research Project in Shanghai (10JC1413900). We would like to thank Dr. Annie Lin for her suggestions and helps.
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