参数加速失效模型定位生存性状位点

摘要

大多数现存的检测数量性状位点的统计方法,不适于去分析带有偏态分布和删失机制的生存性状。因此,将生存分析中参数和半参数模型镶嵌到数量性状区间定位的框架内,去定位生存性状。在生存性状分析领域里,加速失效时间模型被认为是用于分析数据的一个非常重要模型。基于加速失效时间模型,提出定位生存性状的参数模型,采用EM算法获得参数的极大似然估计。同时,视贝叶斯信息标准为模型选择标准。通过选取带有极大似然和寡参数的指定误差分布,构建了优化模型。为了证明方法的有效性,分析了一个实际数据集,结果表明,在生存分布的五个常用分布函数,对数Logistic分布是控制小鼠高氧性急性肺损伤的最优分布函数。%Most existing statistical methods for mapping quantitative trait loci（QTL） were 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 was considered as standard and fundamental model for data analysis.Based on AFT model,we proposed a parametric approach for mapping survival traits by 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 was constructed 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 the five commonly used survival distributions,Log-logistic distribution was the optimal survival function for mapping of hyperoxic acute lung injury（HALI）.