Cox regression model for dissecting genetic architecture of survival time

文献类型： 外文期刊

第一作者： Jiang, Dan

作者： Jiang, Dan;Fang, Ming;Wang, Hongwei;Li, Jiahan;Wu, Yang;Yang, Runqing

作者机构：

关键词： Survival time;QTL;LASSO;Cox regression model;Partial likelihood algorithm

期刊名称：GENOMICS （ 影响因子：5.736； 五年影响因子：4.939 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Common quantitative trait locus (QTL) mapping methods fail to analyze survival traits of skewed normal distributions. As a result, some mapping methods for survival traits have been proposed based on survival analysis. Under a single QTL model, however, those methods perform poorly in detecting multiple QTLs and provide biased estimates of QTL parameters. For sparse oversaturated model used to map survival time loci, the least absolute shrinkage and selection operator (LASSO) for Cox regression model can be employed to efficiently shrink most of genetic effects to zero. Then, a few non-zero genetic effects are re-estimated and statistically tested using the standard maximum Cox partial likelihood method. Simulation shows that the proposed method has higher statistic power for QTL detection than that of the LASSO for logarithmic linear model or the interval mapping based on Cox model, although it somewhat underestimates QTL effects. Especially, computational speed of the method is very fast. An application of this method illustrates mapping main effect and interacting QTLs for heading time in the North American Barley Genome Mapping Project. (C) 2014 Elsevier Inc. All rights reserved.

分类号： R394

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