中国农业科学 ›› 2014, Vol. 47 ›› Issue (20): 3941-3952.doi: 10.3864/j.issn.0578-1752.2014.20.002

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

栽培大豆农艺性状的关联分析及优异等位变异挖掘

杨胜先1,2,牛远1,李梦1,魏世平1,刘晓芬1,吕海燕1,章元明1   

  1. 1南京农业大学作物遗传与种质创新国家重点实验室,南京210095
    2毕节市农业科学研究所,贵州毕节 551700
  • 收稿日期:2014-05-14 修回日期:2014-07-15 出版日期:2014-10-16 发布日期:2014-10-16
  • 通讯作者: 章元明,E-mail:soyzhang@ njau.edu.cn
  • 作者简介:杨胜先,E-mail:ysxnj@163.com
  • 基金资助:
    国家重点基础研究发展计划(“973”计划)(2011CB109300)、国家自然科学基金(30971848)、贵州省科学技术基金(黔科合J字[2013]2004号)

Association Mapping of Agronomic Traits in Soybean (Glycine max L. Merr.) and Mining of Novel Alleles

YANG Sheng-xian1,2, NIU Yuan1, LI Meng1, WEI Shi-ping1, LIU Xiao-fen1, LÜ Hai-yan1, ZHANG Yuan-ming1   

  1. 1State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095
    2Bijie Institute of Agricultural Sciences, Bijie 551700, Guizhou
  • Received:2014-05-14 Revised:2014-07-15 Online:2014-10-16 Published:2014-10-16

摘要: 【目的】发掘大豆农艺性状稳定表达的QTL、优异等位变异及其携带优异等位变异的载体材料,为大豆分子标记辅助选择和分子设计育种等后续研究提供重要依据。【方法】利用大豆基因组上均匀分布的135对SSR引物对通过分层随机抽取的6大生态区的257个栽培大豆品种进行全基因组扫描以获得分子标记信息,2009年和2010年在南京农业大学江浦农场对供试群体株高、分枝数、主茎节数、茎粗和单株荚数进行表型鉴定以获得数量性状表型观测值,用广义线性模型、优化压缩混合线性模型和上位性关联分析3种方法,实施农艺性状表型与SSR标记基因型间的关联分析。【结果】供试品种株高、分枝数、主茎节数、茎粗和单株荚数变异系数介于24.62%—52.34%,品种间和品种与环境互作差异极显著;广义线性模型、优化压缩混合线性模型和上位性关联分析3种方法分别检测到年份间稳定表达的上述5性状的47、11和58个QTL,其中,2种方法间和3种方法间分别有34和4个共同QTL,上位性关联分析检测到1对上位性QTL和32个环境互作QTL;检测得到的主效QTL中,有18个主效QTL与前人鉴定的QTL位置一致或紧密连锁;发掘了一批农艺性状的优异等位基因及携带优异等位基因的载体材料,其中,satt669-145bp、satt102-154bp、satt382-395 bp和satt534-161bp分别是主茎节数、分枝数、茎粗和株高的增效效应最大的等位基因,其载体材料分别为白花豆、合肥两塘焦双青豆、油葫芦和浦霞大豆。【结论】检测到年份间或方法间稳定表达的株高、分枝数、主茎节数、茎粗和单株荚数5个农艺性状的107个主效 QTL。

关键词: 大豆, 农艺性状, SSR, 关联分析, 数量性状基因座, 优异等位基因

Abstract: 【Objective】Mining stable quantitative trait loci (QTL), their novel alleles and the corresponding accessions for agronomic traits in soybean can provide important foundation for subsequent studies, such as molecular marker assisted selection and molecular breeding by design. 【Method】There were 135 simple-sequence repeat (SSR) markers uniformly distributed on the soybean genome which were used to scan 257 soybean cultivars obtained by stratified random sampling from six geographic ecotypes in China so that molecular marker information was gotten. Plant height, number of branches, number of nodes on main stem, stem diameter and number of pods per plant for each cultivar were measured at Jiangpu Experimental Station of Nanjing Agricultural University in 2009 and 2010 so that their phenotypic observations were obtained. The above phenotypic observations for each trait, along with the above molecular marker information, in the 257 soybean cultivars were used to carry out association analysis using generalized linear model (GLM), enriched compression mixed linear model (ECMLM) and epistatic association mapping (EAM) approaches. 【Result】Coefficients of variation for the above traits ranged from 24.62% to 52.34%, and significant differences at the 0.01 level among cultivars and for cultivar-by-environment interaction were observed, indicating abundant genetic variation in the mapping population. Forty-seven, eleven and fifty-eight commonly main-effect QTL across two years were detected by the above three approaches, respectively. Four (thirty-four) commonly main-effect QTL were identified across the above three (two) methods. One epistatic QTL and 32 QTL-by-environment interactions were identified by epistatic association mapping. Eighteen QTL identified in this study were consistent with (or linked to) those in previous studies. Some novel alleles for each agronomic trait and their corresponding accessions were mined. For example, satt669-145bp, satt102-154bp, satt382-395 bp and satt534-161bp were novel alleles for number of nodes on main stem, number of branches, stem diameter and plant height, respectively; and the corresponding accessions were Baihuadou, Hefeiliangtangjiaoshuangqingdou, Youhulu and Puxiadadou. 【Conclusion】One hundred and seven stable main-effect QTL for the above traits were detected across years or across methods.

Key words: soybean, agronomic trait, simple-sequence repeat, association mapping, quantitative trait locus, novel allele