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当前位置: 首页 > 文章 > 玉米株高性状QTL的贝叶斯定位分析 扬州大学学报(农业与生命科学版) 2013,34 (2) 41-46
Position: Home > Articles > Bayesian mapping of quantitative trait loci controlling plant height in maize(Zea mays L.) Journal of Yangzhou University(Agricultural and Life Science Edition) 2013,34 (2) 41-46

玉米株高性状QTL的贝叶斯定位分析

作  者：
胡文明;汤在祥;张恩盈;徐辰武
单  位：
青岛农业大学农学与植物保护学院青岛市主要农作物种质资源创新与应用重点实验室;扬州大学农学院;扬州大学江苏省作物遗传生理重点实验室教育部植物功能基因组学重点实验室;扬州大学江苏省作物遗传生理重点实验室
关键词：
玉米;株高;数量性状基因座位;贝叶斯统计;R/qtlbim
摘  要：
为探索控制玉米株高的主效QTL、互作QTL及其遗传结构,通过基于贝叶斯理论的R/qtlbim QTL定位分析软件包,对一个玉米F2:3群体株高性状进行QTL分析。通过一维扫描,初步检测到8个主效QTL,分别位于第3～7染色体上,单个QTL的贝叶斯因子(以2logBF衡量)为2.237～6.196。通过二维后验概率扫描发现大量较弱的互作信号,多数集中在3～7染色体上。通过贝叶斯因子分析,获得控制玉米株高的最优遗传结构,该遗传结构包含6个主效应QTL,QTL间不存在互作效应。逐步回归拟合分析表明,该遗传结构达到极显著水平,可解释37.383%表型变异,6个QTL均达到极显著水平,单个QTL的表型变异贡献为3.924%～10.776%。这表明,株高的遗传结构相对较为简单,QTL互作对株高的影响较小,可忽略不计。
译  名：
Bayesian mapping of quantitative trait loci controlling plant height in maize(Zea mays L.)
作  者：
HU Wenming1,TANG Zaixiang1,2,ZHANG Enying1,XU Chenwu1(1.Key Lab of Crop Gen and Physio of Jiangsu Prov/Key Lab of Plant Funct Genomics of MOE,Yangzhou Univ,Yangzhou 225009,China;2.Med Coll of Pub Health,Soochow Univ,Suzhou 215123,China)
关键词：
maize;plant height;quantitative trait loci;Bayesian statistics;R/qtlbim
摘  要：
In order to explore the main effect,interactive effect and genetic architecture of plant height in maize,an F2:3population was used to map QTLs controlling plant height of maize by QTL mapping software package R/qtlbim based on Bayesian statistics.With the one-dimension scan,a total of eight QTLs were mapped on chromosome 3to 7,and the 2logBF of each QTL ranged from 2.237to 6.196.Two-dimension scan showed that there existed a large number of interactive signals with weaker effects.These interaction signals were also concentrated on chromosome 3to 7.With the Bayes factor analysis,an optimal genetic architecture of plant height in this population was obtained,and it was composed of six main effects QTLs without interaction.The stepwise regression analysis further indicated that the genetic architecture was significant at 0.001levels and could explain 37.33%of phenotype variation.Each of six QTLs was very significant and could explain 3.924%to 10.776%of phenotype variation.This result suggested that the genetic architecture for plant height was relative simple and the interaction among QTLs might be ignored.

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