Statistical optimization of parametric accelerated failure time model for mapping survival trait loci
文献类型： 外文期刊
作者： Piao, Zhongze 2 ; Zhou, Xiaojing 3 ; Yan, Li 8 ; Guo, Ying 3 ; Yang, Runqing 1 ; Luo, Zhixiang 7 ; Prows, Daniel R. 5 ;
作者机构： 1.Shanghai Jiao Tong Univ, Sch Agr & Biol, Shanghai 200240, Peoples R China
2.Shanghai Acad Agr Sci, Crop Breeding & Cultivat Res Inst, Shanghai 201106, Peoples R China
3.Heilongjiang Bayi Agr Univ, Dept Math, Daqing 163319, Peoples R China
4.Heilongjiang Bayi Agr Univ, Coll Anim Sci & Vet Med, Daqing 163319, Peoples R China
5.Cincinnati Childrens Hosp, Div Human Genet, Med Ctr, Cincinnati, OH 45229 USA
6.Univ Cincinnati, Coll Med, Cincinnati, OH 45229 USA
7.Anhui Acad Agr Sci, Rice Res Inst, Hefei 230036, Peoples R China
8.Heilongjiang Bayi Agr Univ, Coll Informat Technol, Daqing 163319, Peoples R China
关键词： statistical optimization
期刊名称：THEORETICAL AND APPLIED GENETICS （ 影响因子：5.699； 五年影响因子：5.565 ）
ISSN：
年卷期：
页码：
收录情况： SCI
摘要： 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.
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