title: Cox regression model for dissecting genetic architecture of survival time.
abstract: 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.
authors.affiliation: Life Science College Heilongjiang Bayi Agricultural University, Daqing 163319, People's Republic of China.
authors.forename: Dan
authors.initials: D
authors.lastname: Jiang
authors.affiliation: Fishery Technical Extension Station, Beijing Daxing Animal Health Supervisory Commission, Beijing 102600, People's Republic of China.
authors.forename: Hongwei
authors.initials: H
authors.lastname: Wang
authors.affiliation: Applied and Computational Mathematics and Statistics, University of Notre Dame, IN 46637, USA.
authors.forename: Jiahan
authors.initials: J
authors.lastname: Li
authors.affiliation: Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, People's Republic of China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Life Science College Heilongjiang Bayi Agricultural University, Daqing 163319, People's Republic of China.
authors.forename: Ming
authors.initials: M
authors.lastname: Fang
authors.affiliation: Research Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, People's Republic of China. Electronic address: runqingyang@sjtu.edu.cn.
authors.forename: Runqing
authors.initials: R
authors.lastname: Yang
mesh_terms: D002874:Chromosome Mapping / Q000379:methods*; D018745:Genome, Plant; D001467:Hordeum / Q000235:genetics / Q000254:growth & development; D016013:Likelihood Functions; D016014:Linear Models; D008957:Models, Genetic; D063245:Plant Development / Q000235:genetics*; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D012044:Regression Analysis
keywords: Cox regression model; LASSO; Partial likelihood algorithm; QTL; Survival time