title: Generalized linear model for mapping discrete trait loci implemented with LASSO algorithm.
abstract: 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.
authors.affiliation: Department of Gastroenterology, Tumor Hospital of Harbin Medical University, Harbin, People's Republic of China.
authors.forename: Jun
authors.initials: J
authors.lastname: Xing
authors.affiliation: Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing, People's Republic of China.
authors.forename: Huijiang
authors.initials: H
authors.lastname: Gao
authors.affiliation: Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing, People's Republic of China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Wu
authors.affiliation: School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, People's Republic of China.
authors.forename: Yani
authors.initials: Y
authors.lastname: Wu
authors.affiliation: School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, People's Republic of China.
authors.forename: Hongwang
authors.initials: H
authors.lastname: Li
authors.affiliation: Research Centre for Fisheries Resource and Environment, Chinese Academy of Fishery Sciences, Beijing, People's Republic of China.
authors.forename: Runqing
authors.initials: R
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping; D003198:Computer Simulation; D008040:Genetic Linkage; D005828:Genetics, Population; D015894:Genome, Human; D006801:Humans; D016014:Linear Models; D010641:Phenotype; D040641:Quantitative Trait Loci / Q000235:genetics*