Generalized Linear Model for Mapping Discrete Trait Loci Implemented with LASSO Algorithm
文献类型： 外文期刊
作者： Xing, Jun 1 ; Gao, Huijiang 2 ; Wu, Yang 2 ; Wu, Yani 3 ; Li, Hongwang 3 ; Yang, Runqing 4 ;
作者机构： 1.Harbin Med Univ, Tumor Hosp, Dept Gastroenterol, Harbin, Peoples R China
2.Chinese Acad Agr Sci, Inst Anim Sci, Beijing 100193, Peoples R China
3.Shanghai Jiao Tong Univ, Sch Agr & Biol, Shanghai 200030, Peoples R China
4.Chinese Acad Fishery Sci, Res Ctr Fisheries Resource & Environm, Beijing, Peoples R China
期刊名称：PLOS ONE （ 影响因子：3.24； 五年影响因子：3.788 ）
ISSN： 1932-6203
年卷期： 2014 年 9 卷 9 期
页码：
收录情况： SCI
摘要： Generalized estimating equation (GEE) algorithm under a heterogeneous residual variance model is an extension of the iteratively reweighted least squares (IRLS) method for continuous traits to discrete traits. In contrast to mixture model-based expectation-maximization (EM) algorithm, the GEE algorithm can well detect quantitative trait locus (QTL), especially large effect QTLs located in large marker intervals in the manner of high computing speed. Based on a single QTL model, however, the GEE algorithm has very limited statistical power to detect multiple QTLs because of ignoring other linked QTLs. In this study, the fast least absolute shrinkage and selection operator (LASSO) is derived for generalized linear model (GLM) with all possible link functions. Under a heterogeneous residual variance model, the LASSO for GLM is used to iteratively estimate the non-zero genetic effects of those loci over entire genome. The iteratively reweighted LASSO is therefore extended to mapping QTL for discrete traits, such as ordinal, binary, and Poisson traits. The simulated and real data analyses are conducted to demonstrate the efficiency of the proposed method to simultaneously identify multiple QTLs for binary and Poisson traits as examples.
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