Anderson Carl A, McRae Allan F, Visscher Peter M
Institute of Evolutionary Biology, University of Edinburgh, Scotland.
Genetics. 2006 Jul;173(3):1735-45. doi: 10.1534/genetics.106.055921. Epub 2006 Apr 19.
Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.
标准的数量性状基因座（QTL）定位技术通常假定该性状是完全可观测的且呈正态分布。在考虑生存或发病年龄性状时，这些假设往往是不正确的。已经开发出了用于定位生存性状QTL的方法；然而，它们计算量都很大，并且在标准的基因组分析软件包中无法使用。我们提出了一种用于分析连续生存数据的分组线性回归方法。通过模拟，我们将该方法与Cox和Weibull比例风险模型以及一种忽略删失的标准线性回归方法进行了比较。分组线性回归方法与Cox和Weibull比例风险方法具有同等效力，并且在存在删失观测值时明显优于标准线性回归方法。该方法对于删失个体的比例和性状的潜在分布也具有稳健性。基于线性回归方法，分组线性回归模型计算简单且快速，并且可以很容易地在免费的统计软件中实现。
