Literature DB >> 26163701

Establishment of a 100-seed weight quantitative trait locus-allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes.

Yinghu Zhang1, Jianbo He2, Yufeng Wang2, Guangnan Xing2, Jinming Zhao2, Yan Li2, Shouping Yang2, R G Palmer3, Tuanjie Zhao4, Junyi Gai4.   

Abstract

A representative sample comprising 366 accessions from the Chinese soybean landrace population (CSLRP) was tested under four growth environments for determination of the whole-genome quantitative trait loci (QTLs) system of the 100-seed weight trait (ranging from 4.59g to 40.35g) through genome-wide association study (GWAS). A total of 116 769 single nucleotide polymorphisms (SNPs) were identified and organized into 29 121 SNP linkage disequilibrium blocks (SNPLDBs) to fit the property of multiple alleles/haplotypes per locus in germplasm. An innovative two-stage GWAS was conducted using a single locus model for shrinking the marker number followed by a multiple loci model utilizing a stepwise regression for the whole-genome QTL identification. In total, 98.45% of the phenotypic variance (PV) was accounted for by four large-contribution major QTLs (36.33%), 51 small-contribution major QTLs (43.24%), and a number of unmapped minor QTLs (18.88%), with the QTL×environment variance representing only 1.01% of the PV. The allele numbers of each QTL ranged from two to 10. A total of 263 alleles along with the respective allele effects were estimated and organized into a 263×366 matrix, giving the compact genetic constitution of the CSLRP. Differentiations among the ecoregion matrices were found. No landrace had alleles which were all positive or all negative, indicating a hidden potential for recombination. The optimal crosses within and among ecoregions were predicted, and showed great transgressive potential. From the QTL system, 39 candidate genes were annotated, of which 26 were involved with the gene ontology categories of biological process, cellular component, and molecular function, indicating that diverse genes are involved in directing the 100-seed weight.
© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  100-seed weight; Chinese soybean landrace population (CSLRP); QTL–allele matrix; SNP linkage disequilibrium block (SNPLDB); gene ontology (GO) analysis; genome-wide association study (GWAS); soybean [Glycine max (L.) Merr.].

Mesh:

Year:  2015        PMID: 26163701     DOI: 10.1093/jxb/erv342

Source DB:  PubMed          Journal:  J Exp Bot        ISSN: 0022-0957            Impact factor:   6.992


  22 in total

1.  Genome mapping of quantitative trait loci (QTL) controlling domestication traits of intermediate wheatgrass (Thinopyrum intermedium).

Authors:  Steve Larson; Lee DeHaan; Jesse Poland; Xiaofei Zhang; Kevin Dorn; Traci Kantarski; James Anderson; Jeremy Schmutz; Jane Grimwood; Jerry Jenkins; Shengqiang Shu; Jared Crain; Matthew Robbins; Kevin Jensen
Journal:  Theor Appl Genet       Date:  2019-06-06       Impact factor: 5.699

2.  Analysis of QTL-allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure.

Authors:  Mueen Alam Khan; Fei Tong; Wubin Wang; Jianbo He; Tuanjie Zhao; Junyi Gai
Journal:  Planta       Date:  2018-07-06       Impact factor: 4.116

3.  Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure.

Authors:  Liyuan Pan; Jianbo He; Tuanjie Zhao; Guangnan Xing; Yufeng Wang; Deyue Yu; Shouyi Chen; Junyi Gai
Journal:  Theor Appl Genet       Date:  2018-08-30       Impact factor: 5.699

4.  An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding.

Authors:  Jianbo He; Shan Meng; Tuanjie Zhao; Guangnan Xing; Shouping Yang; Yan Li; Rongzhan Guan; Jiangjie Lu; Yufeng Wang; Qiuju Xia; Bing Yang; Junyi Gai
Journal:  Theor Appl Genet       Date:  2017-08-21       Impact factor: 5.699

5.  Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure.

Authors:  Shuguang Li; Yongce Cao; Jianbo He; Tuanjie Zhao; Junyi Gai
Journal:  Theor Appl Genet       Date:  2017-08-10       Impact factor: 5.699

6.  Identification of QTL with large effect on seed weight in a selective population of soybean with genome-wide association and fixation index analyses.

Authors:  Long Yan; Nicolle Hofmann; Shuxian Li; Marcio Elias Ferreira; Baohua Song; Guoliang Jiang; Shuxin Ren; Charles Quigley; Edward Fickus; Perry Cregan; Qijian Song
Journal:  BMC Genomics       Date:  2017-07-12       Impact factor: 3.969

Review 7.  Genome-Wide Association Study Reveals Natural Variations Contributing to Drought Resistance in Crops.

Authors:  Hongwei Wang; Feng Qin
Journal:  Front Plant Sci       Date:  2017-06-30       Impact factor: 5.753

8.  Identification of QTNs and Their Candidate Genes for 100-Seed Weight in Soybean (Glycine max L.) Using Multi-Locus Genome-Wide Association Studies.

Authors:  Muhammad Ikram; Xu Han; Jian-Fang Zuo; Jian Song; Chun-Yu Han; Ya-Wen Zhang; Yuan-Ming Zhang
Journal:  Genes (Basel)       Date:  2020-06-27       Impact factor: 4.096

9.  Genetic dissection of yield-related traits via genome-wide association analysis across multiple environments in wild soybean (Glycine soja Sieb. and Zucc.).

Authors:  Dezhou Hu; Huairen Zhang; Qing Du; Zhenbin Hu; Zhongyi Yang; Xiao Li; Jiao Wang; Fang Huang; Deyue Yu; Hui Wang; Guizhen Kan
Journal:  Planta       Date:  2020-01-06       Impact factor: 4.116

10.  QTL Location and Epistatic Effect Analysis of 100-Seed Weight Using Wild Soybean (Glycine soja Sieb. & Zucc.) Chromosome Segment Substitution Lines.

Authors:  Dawei Xin; Zhaoming Qi; Hongwei Jiang; Zhenbang Hu; Rongsheng Zhu; Jiahui Hu; Heyu Han; Guohua Hu; Chunyan Liu; Qingshan Chen
Journal:  PLoS One       Date:  2016-03-02       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.