基于加速失效时间模型生存性状遗传构架的Bayes定位分析
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摘要
在生存分析领域,除比例风险回归模型外,还有一类模型与之并列,叫做加速失效时间模型(accelerated failure time model,简称为AFT模型)。AFT模型研究协变量与对数生存时间之间的回归关系,模型的形式与对回归系数的解释与一般的线性回归方程相似,而对分析结果的解释则较比例风险回归模型简单、直观,更易于理解。
生存时间是一种特殊的数量性状,它不服从于正态分布,在以往的研究中,有许多专门的统计方法对其进行分析,如参数,半参数模型等,都适用于对生存性状的孟德尔遗传学的QTL进行作图分析,但是这些方法总是包含着非线性因素的解决办法,因此不适于对生存性状多个互作QTL的遗传构架分析。而AFT模型适用的特殊性就在于它的线性。
本文对生存分析及QTL定位相关概念方法进行了详细介绍,并将生存分析中的加速失效时间模型应用于生物遗传领域的QTL定位分析中,与Bayes作图方法相结合,探讨了基于加速失效时间模型生存性状遗传结构的Bayes定位分析原理。模拟研究与实际数据分析证明了基于加速失效时间模型的Bayes QTL定位方法比直接分析生存数据具有更高的检测效率和参数估计精度。为以后研究生物生存性状遗传结构提供了新的方法依据,同时也具有重要的生物学意义。
In the field of survival analysis, there is a model besides proportional hazard function model, called the accelerated failure time model (AFT). This model can analyze the regression relation between covariate and log survival time, and its form and explanation of regression coefficient is close to the general linear regression equation, the explanation of analysis results is more simple, more direct and easier to understand than that of proportional hazard function model.
Survival time is a special quantitative trait, which does not follow normal distribution. There are some specific statistical approaches, such as parametric and semi-parametric models, available to map QTL for survival traits. These approaches always involve the solution of nonlinear equations, therefore, they are not suitable to analyse genomic architecture of multiple epistatic QTLs of survival traits. However, accelerated failure time model is an exception due to its linearity.
This paper introduces the related concepts and methods of survival analysis and QTL mapping in detail. This study uses accelerated failure time model for Bayesian QTL mapping, combining the survival analysis and biometrical genetics, and studies on the principle of Bayesian mapping analysis for Genomic Architecture of Survival Traits based on the accelerated failure time model. The simulation studies and real data analysis proves that this method is higher efficient in detection and more precise in estimate of parameters than analysing survival trait directly. This study provides a new method for analysis of genetic structure in biotic survival traits, and has important significance in the field of Biology.
生存时间是一种特殊的数量性状,它不服从于正态分布,在以往的研究中,有许多专门的统计方法对其进行分析,如参数,半参数模型等,都适用于对生存性状的孟德尔遗传学的QTL进行作图分析,但是这些方法总是包含着非线性因素的解决办法,因此不适于对生存性状多个互作QTL的遗传构架分析。而AFT模型适用的特殊性就在于它的线性。
本文对生存分析及QTL定位相关概念方法进行了详细介绍,并将生存分析中的加速失效时间模型应用于生物遗传领域的QTL定位分析中,与Bayes作图方法相结合,探讨了基于加速失效时间模型生存性状遗传结构的Bayes定位分析原理。模拟研究与实际数据分析证明了基于加速失效时间模型的Bayes QTL定位方法比直接分析生存数据具有更高的检测效率和参数估计精度。为以后研究生物生存性状遗传结构提供了新的方法依据,同时也具有重要的生物学意义。
In the field of survival analysis, there is a model besides proportional hazard function model, called the accelerated failure time model (AFT). This model can analyze the regression relation between covariate and log survival time, and its form and explanation of regression coefficient is close to the general linear regression equation, the explanation of analysis results is more simple, more direct and easier to understand than that of proportional hazard function model.
Survival time is a special quantitative trait, which does not follow normal distribution. There are some specific statistical approaches, such as parametric and semi-parametric models, available to map QTL for survival traits. These approaches always involve the solution of nonlinear equations, therefore, they are not suitable to analyse genomic architecture of multiple epistatic QTLs of survival traits. However, accelerated failure time model is an exception due to its linearity.
This paper introduces the related concepts and methods of survival analysis and QTL mapping in detail. This study uses accelerated failure time model for Bayesian QTL mapping, combining the survival analysis and biometrical genetics, and studies on the principle of Bayesian mapping analysis for Genomic Architecture of Survival Traits based on the accelerated failure time model. The simulation studies and real data analysis proves that this method is higher efficient in detection and more precise in estimate of parameters than analysing survival trait directly. This study provides a new method for analysis of genetic structure in biotic survival traits, and has important significance in the field of Biology.
引文
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