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Cox regression model for dissecting genetic architecture of survival time

文献类型： 外文期刊

作者： Jiang, Dan 1 ; Wang, Hongwei 2 ; Li, Jiahan 3 ; Wu, Yang 4 ; Fang, Ming 1 ; Yang, Runqing 5 ;

作者机构： 1.Heilongjiang Bayi Agr Univ, Life Sci Coll, Daqing 163319, Peoples R China

2.Beijing Daxing Anim Hlth Supervisory Commiss, Fishery Tech Extens Stn, Beijing 102600, Peoples R China

3.Univ Notre Dame, Notre Dame, IN 46637 USA

4.Chinese Acad Agr Sci, Inst Anim Sci, Beijing 100193, Peoples R China

5.Chinese Acad Fishery Sci, Res Ctr Aquat Biotechnol, Beijing 100141, Peoples R China

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

期刊名称：GENOMICS （ 2020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.

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