Literature DB >> 28445652

Identification of quantitative trait loci underlying seed protein content of soybean including main, epistatic, and QTL × environment effects in different regions of Northeast China.

Weili Teng1,1, Wen Li1,1, Qi Zhang1,1, Depeng Wu1,1, Xue Zhao1,1, Haiyan Li1,1, Yingpeng Han1,1, Wenbin Li1,1.   

Abstract

The objective here was to identify QTL underlying soybean protein content (PC), and to evaluate the additive and epistatic effects of the QTLs. A mapping population, consisting of 129 recombinant inbred lines (RILs), was created by crossing 'Dongnong 46' and 'L-100'. Phenotypic data of the parents and RILs were collected for 4 years in three locations of Heilongjiang Province of China. A total of 213 SSR markers were used to construct a genetic linkage map. Eight QTLs, located on seven chromosomes (Chr), were identified to be associated with PC among the 10 tested environments. Of the seven QTLs, five QTLs, qPR-2 (Satt710, on Chr9), qPR-3 (Sat_122, on Chr12), qPR-5 (Satt543, on Chr17), qPR-7 (Satt163, on Chr18), and qPR-8 (Satt614, on Chr20), were detected in six, seven, seven, six, and seven environments, respectively, implying relatively stable QTLs. qPR-3 could explain 3.33%-11.26% of the phenotypic variation across eight tested environments. qPR-5 and qPR-8 explained 3.64%-10.1% and 11.86%-18.40% of the phenotypic variation, respectively, across seven tested environments. Eight QTLs associated with PC exhibited additive and (or) additive × environment interaction effects. The results showed that environment-independent QTLs often had higher additive effects. Moreover, five epistatic pairwise QTLs were identified in the 10 environments.

Entities:  

Keywords:  QTL; additive effect; effet additif; effet épistatique; epistatic effect; protein content; soya; soybean; teneur en protéines

Mesh:

Substances:

Year:  2017        PMID: 28445652     DOI: 10.1139/gen-2016-0189

Source DB:  PubMed          Journal:  Genome        ISSN: 0831-2796            Impact factor:   2.166


  7 in total

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Journal:  BMC Genomics       Date:  2019-06-17       Impact factor: 3.969

2.  Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline.

Authors:  Jun Qin; Fengmin Wang; Qingsong Zhao; Ainong Shi; Tiantian Zhao; Qijian Song; Waltram Ravelombola; Hongzhou An; Long Yan; Chunyan Yang; Mengchen Zhang
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3.  Transgressive Potential Prediction and Optimal Cross Design of Seed Protein Content in the Northeast China Soybean Population Based on Full Exploration of the QTL-Allele System.

Authors:  Weidan Feng; Lianshun Fu; Mengmeng Fu; Ziqian Sang; Yanping Wang; Lei Wang; Haixiang Ren; Weiguang Du; Xiaoshuai Hao; Lei Sun; Jiaoping Zhang; Wubin Wang; Guangnan Xing; Jianbo He; Junyi Gai
Journal:  Front Plant Sci       Date:  2022-07-12       Impact factor: 6.627

4.  Identification and characterization of a fast-neutron-induced mutant with elevated seed protein content in soybean.

Authors:  Elizabeth M Prenger; Alexandra Ostezan; M A Rouf Mian; Robert M Stupar; Travis Glenn; Zenglu Li
Journal:  Theor Appl Genet       Date:  2019-07-19       Impact factor: 5.699

5.  Genotype imputation for soybean nested association mapping population to improve precision of QTL detection.

Authors:  Linfeng Chen; Shouping Yang; Susan Araya; Charles Quigley; Earl Taliercio; Rouf Mian; James E Specht; Brian W Diers; Qijian Song
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6.  Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network.

Authors:  Susu Zhu; Lei Zhou; Chu Zhang; Yidan Bao; Baohua Wu; Hangjian Chu; Yue Yu; Yong He; Lei Feng
Journal:  Sensors (Basel)       Date:  2019-09-20       Impact factor: 3.576

7.  Analysis of Soybean Somatic Embryogenesis Using Chromosome Segment Substitution Lines and Transcriptome Sequencing.

Authors:  Si-Nan Li; Peng Cheng; Yun-Qi Bai; Yan Shi; Jing-Yao Yu; Rui-Chao Li; Run-Nan Zhou; Zhan-Guo Zhang; Xiao-Xia Wu; Qing-Shan Chen
Journal:  Genes (Basel)       Date:  2019-11-19       Impact factor: 4.096

  7 in total

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