AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home The Crop Journal Article
PDF (569.3 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research paper | Open Access

Detecting the QTL-allele system controlling seed-flooding tolerance in a nested association mapping population of soybean

Muhammad Jaffer Alia,b,c,d,1, Guangnan Xinga,b,c,d,e,1, Jianbo Hea,b,c,d,e, Tuanjie Zhaoa,b,c,d,e, Junyi Gaia,b,c,d,e( )
Soybean Research Institute, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
MARA National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
MARA Key Laboratory of Biology and Genetic Improvement of Soybean (General), Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China

1 These authors contributed equally to this study.

Peer review under responsibility of Crop Science Society of China and Institute of Crop Science, CAAS.

Show Author Information

Abstract

Soil flooding stress, including seed-flooding, is a key issue in soybean production in high-rainfall and poorly drained areas. A nested association mapping (NAM) population comprising 230 lines of two recombinant inbred line (RIL) populations with a common parent was established and tested for seed-flooding tolerance using relative seedling length as indicator in two environments. The population was genotyped using RAD-seq (restriction site-associated DNA sequencing) to generate 6137 SNPLDB (SNP linkage disequilibrium block) markers. Using RTM-GWAS (restricted two-stage multi-locus multi-allele genome-wide association study), 26 main-effect QTL with 63 alleles and 12 QEI (QTL × environment) QTL with 27 alleles in a total of 33 QTL with 78 alleles (12 dual-effect alleles) were identified, explaining respectively 50.95% and 14.79% of phenotypic variation. The QTL-alleles were organized into main-effect and QEI matrices to show the genetic architecture of seed-flooding tolerance of the three parents and the NAM population. From the main-effect matrix, the best genotype was predicted to have genotypic value 1.924, compared to the parental value range 0.652–1.069, and 33 candidate genes involved in six biological processes were identified and confirmed by χ2 test. The results may provide a way to match the breeding by design strategy.

References

[1]

V. Nguyen, T. Vuong, T. VanToai, J. Lee, X. Wu, M. Mian, A. Dorrance, J. Shannon, H. Nguyen, Mapping of quantitative trait loci associated with resistance to and flooding tolerance in soybean, Crop Sci. 52 (2012) 2481–2493.
Crossref Google Scholar
[2]

N. Reyna, B. Cornelious, J.G. Shannon, C.H. Sneller, Evaluation of a QTL for waterlogging tolerance in southern soybean germplasm, Crop Sci. 43 (2003) 2077–2082.
Crossref Google Scholar
[3]

S. Shimada, Strategy for improving soybean productivity in the paddy field under paddy-upland rotation, Jpn. J. Crop Sci. 75 (2006) 394–399.
Google Scholar
[4]

T.T. VanToai, S.K. Martin, K. Chase, G. Boru, V. Schnipke, A.F. Schmitthenner, K.G. Lark, Identification of a QTL associated with tolerance of soybean to soil waterlogging, Crop Sci. 41 (2001) 1247–1252.
Crossref Google Scholar
[5]

T. Maekawa, S. Shimamura, S. Shimada, Effects of short-term waterlogging on soybean nodule nitrogen fixation at different soil reductions and temperatures, Plant Prod. Sci. 14 (2011) 349–358.
Crossref Google Scholar
[6]

N. Garcia, C.J. da-Silva, K.L.T. Cocco, D. Pomagualli, F.K. de Oliveira, J.V.L. da Silva, A.C.B. de Oliveira, L. do Amarante, Waterlogging tolerance of five soybean genotypes through different physiological and biochemical mechanisms, Environ. Exp. Bot. (2020) 103975.
Crossref Google Scholar
[7]

S. Tewari, N.K. Arora, Soybean production under flooding stress and its mitigation using plant growthpromoting microbes, Environmental Stresses in Soybean Production, Academic Press, New York, USA 2016, pp. 23–40.
Crossref
[8]

C. Wu, A. Zeng, P. Chen, L. Florez-Palacios, W. Hummer, J. Mokua, M. Klepadlo, L. Yan, Q. Ma, Y. Cheng, An effective field screening method for flood tolerance in soybean, Plant Breed. 136 (2017) 710–719.
Crossref Google Scholar
[9]

B. Cornelious, P. Chen, Y. Chen, N. de Leon, J.G. Shannon, D. Wang, Identification of QTLs underlying water-logging tolerance in soybean, Mol. Breed. 16 (2005) 103–112.
Crossref Google Scholar
[10]

S. Githiri, S. Watanabe, K. Harada, R. Takahashi, QTL analysis of flooding tolerance in soybean at an early vegetative growth stage, Plant Breed. 125 (2006) 613–618.
Crossref Google Scholar
[11]

S.K. Dhungana, H.S. Kim, B.K. Kang, J.H. Seo, H.T. Kim, S.O. Shin, C.H. Park, D.Y. Kwak, Quantitative trait loci mapping for flooding tolerance at an early growth stage of soybean recombinant inbred line population, Plant Breed. 139 (2020) 626–638.
Crossref Google Scholar
[12]

C. Wu, L.A. Mozzoni, D. Moseley, W. Hummer, H. Ye, P. Chen, G. Shannon, H. Nguyen, Genome-wide association mapping of flooding tolerance in soybean, Mol. Breed. 40 (2020) 4.
Crossref Google Scholar
[13]

B. Valliyodan, T.T. Van Toai, J.D. Alves, P.G. de Fátima, J.D. Lee, F.B. Fritschi, M.A. Rahman, R. Islam, J.G. Shannon, H.T. Nguyen, Expression of root-related transcription factors associated with flooding tolerance of soybean (Glycine max), Int. J. Mol. Sci. 15 (2014) 17622–17643.
Crossref Google Scholar
[14]

X. Wang, S. Komatsu, Proteomic approaches to uncover the flooding and drought stress response mechanisms in soybean, J. Proteomics 172 (2018) 201–215.
Crossref Google Scholar
[15]

T. Nakajima, A. Seino, T. Nakamura, Y. Goto, M. Kokubun, Does pre-germination flooding-tolerant soybean cultivar germinate better under hypoxia conditions? Plant. Prod. Sci. 18 (2015) 146–153.
Crossref Google Scholar
[16]

S.H. Duke, G. Kakefuda, C.A. Henson, N.L. Loeffler, N.M. Van Hulle, Role of the testa epidermis in the leakage of intracellular substances from imbibing soybean seeds and its implications for seedling survival, Physiol. Plant. 68 (1986) 625–631.
Crossref Google Scholar
[17]

F.F. Hou, F.S. Thseng, Studies on the flooding tolerance of soybean seed: varietal differences, Euphytica 57 (1991) 169–173.
Crossref Google Scholar
[18]

T. Sayama, T. Nakazaki, G. Ishikawa, K. Yagasaki, N. Yamada, N. Hirota, K. Hirata, T. Yoshikawa, H. Saito, M. Teraishi, Y. Okumoto, QTL analysis of seed-flooding tolerance in soybean (Glycine max [L.] Merr.), Plant Sci. 176 (2009) 514–521.
Crossref Google Scholar
[19]

N. Muramatsu, M. Kokubun, A. Horigane, Relation of seed structures to soybean cultivar difference in pre-germination flooding tolerance, Plant Prod. Sci. 11 (2008) 434–439.
Crossref Google Scholar
[20]

M.J. Ali, Z.P. Yu, G.N. Xing, T.J. Zhao, J.Y. Gai, Establishment of evaluation procedure for soybean seed-flooding tolerance and its application to screening for tolerant germplasm sources, Legume Res. 41 (2017) 34–40.
Crossref Google Scholar
[21]

Z. Yu, F. Chang, W. Lyu, R.A. Sharmin, Z. Wang, J. Kong, J.A. Bhat, T. Zhao, Identification of QTN and candidate gene for seed-flooding tolerance in soybean [Glycine max (L.) Merr.] using genome-wide association study (GWAS), Genes 10 (2019) 957.
Crossref Google Scholar
[22]

R.A. Sharmin, M.R. Bhuiyan, W. Lv, Z. Yu, F. Chang, J. Kong, J.A. Bhat, T. Zhao, RNA-seq based transcriptomic analysis revealed genes associated with seed-flooding tolerance in wild soybean (Glycine soja Sieb. & Zucc.), Environ. Exp. Bot. 171 (2020) 103906.
Crossref Google Scholar
[23]

J.B. Holland, Genetic architecture of complex traits in plants, Curr. Opin. Plant Boil. 10 (2007) 156–161.
Crossref Google Scholar
[24]

C. Center, Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results, Nat. Genet. 11 (1995) 241–247.
Crossref Google Scholar
[25]

E.S. Buckler, J.B. Holland, P.J. Bradbury, C.B. Acharya, P.J. Brown, C. Browne, E. Ersoz, S. Flint-Garcia, A. Garcia, J.C. Glaubitz, The genetic architecture of maize flowering time, Science 325 (2009) 714–718.
Crossref Google Scholar
[26]

K. Chandler, A.E. Lipka, B.F. Owens, H. Li, E.S. Buckler, T. Rocheford, M.A. Gore, Genetic analysis of visually scored orange kernel color in maize, Crop Sci. 53 (2013) 189–200.
Crossref Google Scholar
[27]

J.P. Cook, M.D. McMullen, J.B. Holland, F. Tian, P. Bradbury, J. Ross-Ibarra, E.S. Buckler, S.A. Flint-Garcia, Genetic architecture of maize kernel composition in the nested association mapping and inbred association panels, Plant Physiol. 158 (2012) 824–834.
Crossref Google Scholar
[28]

F. Tian, P.J. Bradbury, P.J. Brown, H. Hung, Q. Sun, S. Flint-Garcia, T.R. Rocheford, M.D. McMullen, J.B. Holland, E.S. Buckler, Genome-wide association study of leaf architecture in the maize nested association mapping population, Nat. Genet. 43 (2011) 159–162.
Crossref Google Scholar
[29]

H. Li, P. Bradbury, E. Ersoz, E.S. Buckler, J. Wang, Joint QTL linkage mapping for multiple-cross mating design sharing one common parent, PLoS One 6 (2011) e17573.
Crossref Google Scholar
[30]

J.B. He, S. Meng, T.J. Zhao, G.N. Xing, S.P. Yang, Y. Li, R.Z. Guan, J.J. Lu, Y.F. Wang, Q.J. Xia, B. Yang, J.Y. Gai, An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding, Theor. Appl. Genet. 130 (2017) 2327–2343.
Crossref Google Scholar
[31]

J. Gai, L. Chen, Y. Zhang, T. Zhao, G. Xing, H. Xing, Genome-wide genetic dissection of germplasm resources and implications for breeding by design in soybean, Breed. Sci. 61 (2012) 495–510.
Crossref Google Scholar
[32]

Y.H. Zhang, J.B. He, Y.F. Wang, G.N. Xing, J.M. Zhao, Y. Li, S.P. Yang, R.G. Palmer, T.J. Zhao, J.Y. Gai, Establishment of a 100-seed weight quantitative trait locus-allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes, J. Exp. Bot. 66 (2015) 6311–6325.
Crossref Google Scholar
[33]

S. Meng, J. He, T. Zhao, G. Xing, Y. Li, S. Yang, J. Lu, Y. Wang, J. Gai, Detecting the QTL-allele system of seed isoflavone content in Chinese soybean landrace population for optimal cross design and gene system exploration, Theor. Appl. Genet. 129 (2016) 1557–1576.
Crossref Google Scholar
[34]

S. Li, Y. Cao, J. He, T. Zhao, J. Gai, Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure, Theor. Appl. Genet. 130 (2017) 2297–2314.
Crossref Google Scholar
[35]

M.A. Khan, F. Tong, W. Wang, J. He, T. Zhao, J. Gai, 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, Planta 4 (2018) 947–962.
Crossref Google Scholar
[36]

SAS Institute Inc., SAS/STAT 9.1 User’s Guide, SAS Institute Inc., Cary, NC, USA, 2004.
[37]

C.H. Hanson, H.F. Robinson, R.E. Comstock, Biometrical studies of yield in segregating populations of Korean Lespedeza, Agron. J. 48 (1956) 268–272.
Crossref Google Scholar
[38]

M. Murray, W.F. Thompson, Rapid isolation of high molecular weight plant DNA, Nucleic Acids Res. 8 (1980) 4321–4326.
Crossref Google Scholar
[39]

P. Andolfatto, D. Davison, D. Erezyilmaz, T.T. Hu, J. Mast, T. Sunayama-Morita, D.L. Stern, Multiplexed shotgun genotyping for rapid and efficient genetic mapping, Genome Res. 21 (2011) 610–617.
Crossref Google Scholar
[40]

J. Schmutz, S.B. Cannon, J. Schlueter, J. Ma, T. Mitros, W. Nelson, D.L. Hyten, Q. Song, J.J. Thelen, J. Cheng, Genome sequence of the palaeopolyploid soybean, Nature 463 (2010) 178–183.
Crossref Google Scholar
[41]

R. Li, C. Yu, Y. Li, T.W. Lam, S.M. Yiu, K. Kristiansen, J. Wang, SOAP2: an improved ultrafast tool for short read alignment, Bioinformatics 25 (2009) 1966–1967.
Crossref Google Scholar
[42]

X. Yi, Y. Liang, E. Huerta-Sanchez, X. Jin, Z.X.P. Cuo, J.E. Pool, X. Xu, H. Jiang, N. Vinckenbosch, T.S. Korneliussen, Sequencing of 50 human exomes reveals adaptation to high altitude, Science 329 (2010) 75–78.
Crossref Google Scholar
[43]

P. Scheet, M.A. Stephens, Fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase, Am. J. Hum. Genet. 78 (2006) 629–644.
Crossref Google Scholar
[44]

S.B. Gabriel, S.F. Schaffner, H. Nguyen, J.M. Moore, J. Roy, B. Blumenstiel, J. Higgins, M. DeFelice, A. Lochner, M. Faggart, The structure of haplotype blocks in the human genome, Science 296 (2002) 2225–2229.
Crossref Google Scholar
[45]

S.R. McCouch, X. Chen, O. Panaud, S. Temnykh, Y. Xu, Y.G. Cho, N. Huang, M. Blair, Microsatellite marker development, mapping and applications in rice genetics and breeding, Plant Mol. Bio. 35 (1997) 89–99.
Crossref Google Scholar
[46]

F. Ahmed, M. Rafii, M.R. Ismail, A.S. Juraimi, H. Rahim, R. Asfaliza, M.A. Latif, Waterlogging tolerance of crops: breeding, mechanism of tolerance, molecular approaches, and future prospects, BioMed. Res. Int. 2013 (2013) 963525.
Crossref Google Scholar
[47]

L.N. Lukens, J. Doebley, Epistatic and environmental interactions for quantitative trait loci involved in maize evolution, Genet. Res. 74 (1999) 291–302.
Crossref Google Scholar
[48]

M.A. Rahman, I.K. Bimpong, J.B. Bizimana, E.D. Pascual, M. Arceta, B.M. Swamy, R.K. Singh, Mapping QTLs using a novel source of salinity tolerance from Hasawi and their interaction with environments in rice, Rice 10 (2017) 47.
Crossref Google Scholar
[49]

Z.K. Li, S.B. Yu, H.R. Lafitte, N. Huang, B. Courtois, S. Hittalmani, J.Y. Zhuang, QTL × environment interactions in rice. I. Heading date and plant height, Theor. Appl. Genet. 108 (2003) 141–153.
Crossref Google Scholar
[50]

R. Messmer, Y. Fracheboud, M. Bänziger, M. Vargas, P. Stamp, J.M. Ribaut, Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits, Theor. Appl. Genet. 119 (2009) 913–930.
Crossref Google Scholar
[51]

M.N. Khan, K. Sakata, S. Komatsu, Proteomic analysis of soybean hypocotyl during recovery after flooding stress, J. Proteomics 121 (2015) 15–27.
Crossref Google Scholar
[52]

A.H.M. Kamal, S. Komatsu, Proteins involved in biophoton emission and flooding-stress responses in soybean under light and dark conditions, Mol. Biol. Rep. 43 (2016) 73–89.
Crossref Google Scholar
[53]

Y. Wang, J. Lu, S. Chen, L. Shu, R.G. Palmer, G. Xing, Y. Li, S. Yang, D. Yu, T. Zhao, Exploration of presence/absence variation and corresponding polymorphic markers in soybean genome, J. Integr. Plant Biol. 56 (2014) 1009–1019.
Crossref Google Scholar
The Crop Journal
Volume 8 Issue 5,
October 2020
Pages 781-792
DOI: 10.1016/j.cj.2020.06.008
Cite this article:
Ali MJ, Xing G, He J, et al. Detecting the QTL-allele system controlling seed-flooding tolerance in a nested association mapping population of soybean. The Crop Journal, 2020, 8(5): 781-792. https://doi.org/10.1016/j.cj.2020.06.008

231

Views

1

Downloads

20

Crossref

N/A

Web of Science

18

Scopus

0

CSCD

Altmetrics
Received: 30 December 2019
Revised: 19 May 2020
Accepted: 22 July 2020
Published: 10 August 2020
© 2020 Crop Science Society of China and Institute of Crop Science, CAAS.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Return