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 

机构地区：[1]School of Public Health,Medical College of Soochow University,Suzhou 215123,China [2]Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology,Key Laboratory of Plant Functional Genomics of Ministry of Education,Yangzhou University,Yangzhou 225009,China [3]Department of Epidemiology and Biostatistics,School of Public Health,Nantong University,Nantong 226019,China

出　　处：《Chinese Science Bulletin》2012年第21期2666-2674,共9页

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

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 traits.The 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 effect.However,current methods cannot uncover the entire genetic structure of complex traits.For example,current methods cannot distinguish between main effects and epistatic effects.In this paper,a linear epistatic model was constructed to dissect complex traits.First,all the long substituted segments were divided into overlapping small bins,and each small bin was considered a unique independent variable.The genetic model for complex traits was then constructed.When considering all the possible main effects and epistatic effects,the dimensions of the linear model can become extremely high.Therefore,variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study.Furthermore,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 QTLs.Simulation studies suggested that all of the above methods,excluding the LASSO and PENAL approaches,performed satisfactorily.The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.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 traits.The 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 effect.However,current methods cannot uncover the entire genetic structure of complex traits.For example,current methods cannot distinguish between main effects and epistatic effects.In this paper,a linear epistatic model was constructed to dissect complex traits.First,all the long substituted segments were divided into overlapping small bins,and each small bin was considered a unique independent variable.The genetic model for complex traits was then constructed.When considering all the possible main effects and epistatic effects,the dimensions of the linear model can become extremely high.Therefore,variable selection via stepwise regression （Bin-REG） was proposed for the epistatic QTL analysis in the present study.Furthermore,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 QTLs.Simulation studies suggested that all of the above methods,excluding the LASSO and PENAL approaches,performed satisfactorily.The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.

关 键 词：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 

分 类 号：Q343[生物学—遗传学]