Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines

作     者：TANG ZaiXiang XIAO Jing HU WenMing YU Bo XU ChenWu 

作者机构：School of Public HealthMedical College of Soochow UniversitySuzhou 215123China Jiangsu Provincial Key Laboratory of Crop Genetics and PhysiologyKey Laboratory of Plant Functional Genomics of Ministry of EducationYangzhou UniversityYangzhou 225009China Department of Epidemiology and BiostatisticsSchool of Public HealthNantong UniversityNantong 226019China 

出 版 物：《Chinese Science Bulletin》 (Chinese Science Bulletin)

年 卷 期：2012年第57卷第21期

页      面：2666-2674页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 071007[理学-遗传学] 0901[农学-作物学] 090102[农学-作物遗传育种] 

基　　金：supported by the National Basic Research Program of China(2011CB100106) the National Natural Science Foundation of China(30971846and31171187) the Vital Project of Natural Science of Universities in Jiangsu Province(09KJA210002) to C.Xu the National Natural Science Foundation of China(31100882) to Z.Tang National Natural Science Foundation of China(31000539) to J.Xiao 

主　　题：染色体片段置换系 数量性状位点 基于模型 解剖 上位性效应 QTL分析 线性模型 变量选择 

摘      要：Chromosome segment substitution lines have been created in several experimental models,including many plant and animal species,and are useful tools for the genetic analysis and mapping of complex *** traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL s ***,current methods cannot uncover the entire genetic structure of complex *** example,current methods cannot distinguish between main effects and epistatic *** this paper,a linear epistatic model was constructed to dissect complex ***,all the long substituted segments were divided into overlapping small bins,and each small bin was considered a unique independent *** genetic model for complex traits was then *** considering all the possible main effects and epistatic effects,the dimensions of the linear model can become extremely ***,variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present ***,we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects,examined the fully Bayesian SSVS (stochastic search variable selection) approach,tested the empirical Bayes (E-BAYES) method,and evaluated the penalized likelihood (PENAL) method for mapping epistatic *** studies suggested that all of the above methods,excluding the LASSO and PENAL approaches,performed *** Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.