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 (1.7 MB)
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

Multi-environment QTL mapping of crown root traits in a maize RIL population

Pengcheng Lia,b, Yingying Fana, Shuangyi Yina, Yunyun Wanga, Houmiao Wanga,b, Yang Xua,b, Zefeng Yanga,b,c,( ), Chenwu Xua,b,c( )
Jiangsu Key Laboratory of Crop Genetics and Physiology/Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou 225009, Jiangsu, China
Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, Jiangsu, China
Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, Jiangsu, China

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

Show Author Information

Abstract

Crown root traits, including crown root angle (CRA), diameter (CRD), and number (CRN), are major determining factors of root system architecture, which influences crop production. In maize, the genetic mechanisms determining crown root traits in the field are largely unknown. CRA, CRD, and CRN were evaluated in a recombinant inbred line population in three field trials. High phenotypic variation was observed for crown root traits, and all measured traits showed significant genotype–environment interactions. Single-environment (SEA) and multi-environment (MEA) quantitative trait locus (QTL) analyses were conducted for CRA, CRD, and CRN. Of 46 QTL detected by SEA, most explained less than 10% of the phenotypic variation, indicating that a large number of minor-effect QTL contributed to the genetic component of these traits. MEA detected 25 QTL associated with CRA, CRD, and CRN, and 2 and 1 QTL were identified with significant QTL-by-environment interaction effects for CRA and CRD, respectively. A total of 26.1% (12/46) of the QTL identified by SEA were also detected by MEA, with many being detected in more than one environment. These findings contribute to our understanding of the phenotypic and genotypic patterns of crown root traits in different environments. The identified environment-specific QTL and stable QTL may be used to improve root traits in maize breeding.

References

[1]

T.R. Sinclair, Challenges in breeding for yield increase for drought, Trends Plant Sci. 16 (2011) 289–293.
Crossref Google Scholar
[2]

L. Comas, S. Becker, V.M.V. Cruz, P. Byrne, D. Dierig, Root traits contributing to plant productivity under drought, Front. Plant Sci. 4 (2013) 442.
Crossref Google Scholar
[3]

J.P. Lynch, Root phenes that reduce the metabolic costs of soil exploration: opportunities for 21st century agriculture, Plant Cell Environ. 38 (2015) 1775–1784.
Crossref Google Scholar
[4]

G. Mi, F. Chen, Q. Wu, N. Lai, L. Yuan, F. Zhang, Ideotype root architecture for efficient nitrogen acquisition by maize in intensive cropping systems, Sci. China Life Sci. 53 (2010) 1369–1373.
Crossref Google Scholar
[5]

J.P. Lynch, Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems, Ann. Bot. 112 (2013) 347–357.
Crossref Google Scholar
[6]

Y. Uga, K. Sugimoto, S. Ogawa, J. Rane, M. Ishitani, N. Hara, Y. Kitomi, Y. Inukai, K. Ono, N. Kanno, Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions, Nat. Genet. 45 (2013) 1097–1102.
Crossref Google Scholar
[7]

V. Gewin, Food: an underground revolution, Nat. News. 466 (2010) 552–553.
Crossref Google Scholar
[8]

J. Lynch, Root architecture and plant productivity, Plant Physiol. 109 (1995) 7–13.
Crossref Google Scholar
[9]

E.D. Rogers, P.N. Benfey, Regulation of plant root system architecture: implications for crop advancement, Curr. Opin. Biotechnol. 32 (2015) 93–98.
Crossref Google Scholar
[10]
L. Feldman, The maize root, in: M. Freeling, V. Walbot (Eds.), The Maize Handbook, Springer Lab Manuals, Springer, New York, NY 1994, pp. 29–37.
Crossref
[11]

P. Saengwilai, X. Tian, J.P. Lynch, Low crown root number enhances nitrogen acquisition from low-nitrogen soils in maize, Plant Physiol. 166 (2014) 581–589.
Crossref Google Scholar
[12]

S. Trachsel, S.M. Kaeppler, K.M. Brown, J.P. Lynch, Maize root growth angles become steeper under low N conditions, Field Crops Res. 140 (2013) 18–31.
Crossref Google Scholar
[13]

A.P. Wasson, G.J. Rebetzke, J.A. Kirkegaard, J. Christopher, R.A. Richards, M. Watt, Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding, J. Exp. Bot. 65 (2014) 6231–6249.
Crossref Google Scholar
[14]

M.A. Miguel, J.A. Postma, J.P. Lynch, Phene synergism between root hair length and basal root growth angle for phosphorus acquisition, Plant Physiol. 167 (2015) 1430–1439.
Crossref Google Scholar
[15]

E.S. Mace, V. Singh, E.J. Van Oosterom, G.L. Hammer, C.H. Hunt, D.R. Jordan, QTL for nodal root angle in sorghum (Sorghum bicolor L. Moench) co-locate with QTL for traits associated with drought adaptation, Theor. Appl. Genet. 124 (2012) 97–109.
Crossref Google Scholar
[16]

J.G. Chimungu, K.W. Loades, J.P. Lynch, Root anatomical phenes predict root penetration ability and biomechanical properties in maize (Zea mays), J. Exp. Bot. 66 (2015) 3151–3162.
Crossref Google Scholar
[17]

L.J. Clark, W.R. Whalley, P.B. Barraclough, How do roots penetrate strong soil? Plant Soil 255 (2003) 93–104.
Crossref Google Scholar
[18]

A.R. Ennos, M.J. Crook, C. Grimshaw, The anchorage mechanics of maize, Zea mays, J. Exp. Bot. 44 (1993) 147–153.
Crossref Google Scholar
[19]

H. Cai, F. Chen, G. Mi, F. Zhang, H.P. Maurer, W. Liu, J.C. Reif, L. Yuan, Mapping QTLs for root system architecture of maize (Zea mays L.) in the field at different developmental stages, Theor. Appl. Genet. 125 (2012) 1313–1324.
Crossref Google Scholar
[20]

L.X. Ku, Z.H. Sun, C.L. Wang, J. Zhang, R.F. Zhao, H.Y. Liu, G.Q. Tai, Y.H. Chen, QTL mapping and epistasis analysis of brace root traits in maize, Mol. Breed. 30 (2012) 697–708.
Crossref Google Scholar
[21]

F. Omori, Y. Mano, QTL mapping of root angle in F2 populations from maize ‘B73’× teosinte ‘Zea luxurians,’, Plant Root. 1 (2007) 57–65.
Crossref Google Scholar
[22]

Y. Uga, Y. Kitomi, S. Ishikawa, M. Yano, Genetic improvement for root growth angle to enhance crop production, Breed. Sci. 65 (2015) 111–119.
Crossref Google Scholar
[23]

B. Courtois, N. Ahmadi, F. Khowaja, A.H. Price, J.-F. Rami, J. Frouin, C. Hamelin, M. Ruiz, Rice root genetic architecture: meta-analysis from a drought QTL database, Rice 2 (2009) 115.
Crossref Google Scholar
[24]

J. Li, Y. Han, L. Liu, Y. Chen, Y. Du, J. Zhang, H. Sun, Q. Zhao, qRT9, a quantitative trait locus controlling root thickness and root length in upland rice, J. Exp. Bot. 66 (2015) 2723–2732.
Crossref Google Scholar
[25]

M.C.F.R. Redillas, J.S. Jeong, Y.S. Kim, H. Jung, S.W. Bang, Y.D. Choi, S. Ha, C. Reuzeau, J. Kim, The overexpression of OsNAC9 alters the root architecture of rice plants enhancing drought resistance and grain yield under field conditions, Plant Biotechnol. J. 10 (2012) 792–805.
Crossref Google Scholar
[26]

J.S. Jeong, Y.S. Kim, M.C.F.R. Redillas, G. Jang, H. Jung, S.W. Bang, Y. Do Choi, S. Ha, C. Reuzeau, J. Kim, OsNAC5 overexpression enlarges root diameter in rice plants leading to enhanced drought tolerance and increased grain yield in the field, Plant Biotechnol. J. 11 (2013) 101–114.
Crossref Google Scholar
[27]

P. Yu, P.J. White, F. Hochholdinger, C. Li, Phenotypic plasticity of the maize root system in response to heterogeneous nitrogen availability, Planta 240 (2014) 667–678.
Crossref Google Scholar
[28]

J. Sebastian, M.C. Yee, W.G. Viana, R. Rellán-Álvarez, M. Feldman, H.D. Priest, C. Trontin, T. Lee, H. Jiang, I. Baxter, Grasses suppress shoot-borne roots to conserve water during drought, Proc. Natl. Acad. Sci. U. S. A. 113 (2016) 8861–8866.
Crossref Google Scholar
[29]

P. Li, Z. Zhuang, H. Cai, S. Cheng, A.A. Soomro, Z. Liu, R. Gu, G. Mi, L. Yuan, F. Chen, Use of genotype-environment interactions to elucidate the pattern of maize root plasticity to nitrogen deficiency, J. Integr. Plant Biol. 58 (2016) 242–253.
Crossref Google Scholar
[30]

P. Li, Y. Zhang, S. Yin, P. Zhu, T. Pan, Y. Xu, J. Wang, D. Hao, H. Fang, C. Xu, QTL-by-environment interaction in the response of maize root and shoot traits to different water regimes, Front. Plant Sci. 9 (2018) 229.
Crossref Google Scholar
[31]

S. Yin, P. Li, Y. Xu, J. Liu, T. Yang, J. Wei, S. Xu, J. Yu, H. Fang, L. Xue, Genetic and genomic analysis of the seed-filling process in maize based on a logistic model, Heredity 124 (2020) 122–134.
Crossref Google Scholar
[32]

S. Trachsel, S.M. Kaeppler, K.M. Brown, J.P. Lynch, Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field, Plant Soil 341 (2011) 75–87.
Crossref Google Scholar
[33]

Z. Zhang, X. Zhang, Z. Lin, J. Wang, M. Xu, J. Lai, J. Yu, Z. Lin, The genetic architecture of nodal root number in maize, Plant J. 93 (2018) 1032–1044.
Crossref Google Scholar
[34]

K.W. Broman, H. Wu, Ś. Sen, G.A. Churchill, R/qtl: QTL mapping in experimental crosses, Bioinformatics 19 (2003) 889–890.
Crossref Google Scholar
[35]
D.D. Kosambi, The estimation of map distances from recombination values, in: R. Ramaswamy (Ed.), D.D, Kosambi, Springer, New Delhi, India 2016, pp. 125–130.
Crossref
[36]

A.R. Hallauer, M.J. Carena, J.B. Miranda, Quantitative Genetics in Maize Breeding, Springer, New York, NY, USA, 2010 81–172.
Crossref
[37]

S. Li, J. Wang, L. Zhang, Inclusive composite interval mapping of QTL by environment interactions in biparental populations, PLoS One 10 (2015) e0132414.
Crossref Google Scholar
[38]

L. Meng, H. Li, L. Zhang, J.K. Wang, QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations, Crop J. 3 (2015) 269–283.
Crossref Google Scholar
[39]

P. Li, F. Chen, H. Cai, J. Liu, Q. Pan, Z. Liu, R. Gu, G. Mi, F. Zhang, L. Yuan, A genetic relationship between nitrogen use efficiency and seedling root traits in maize as revealed by QTL analysis, J. Exp. Bot. 66 (2015) 3175–3188.
Crossref Google Scholar
[40]
F. Hochholdinger, The maize root system: morphology, anatomy, and genetics, in: J.L. Bennetzen, S.C. Hake (Eds.), Handbook of Maize: Its Biology, Springer, New York, NY, USA 2009, pp. 145–160.
Crossref
[41]

A.L. Burton, J.M. Johnson, J.M. Foerster, C.N. Hirsch, C.R. Buell, M.T. Hanlon, S.M. Kaeppler, K.M. Brown, J.P. Lynch, QTL mapping and phenotypic variation for root architectural traits in maize (Zea mays L.), Theor. Appl. Genet. 127 (2014) 2293–2311.
Crossref Google Scholar
[42]

J. Pace, C. Gardner, C. Romay, B. Ganapathysubramanian, T. Lübberstedt, Genome-wide association analysis of seedling root development in maize (Zea mays L.), BMC Genomics 16 (2015) 47.
Crossref Google Scholar
[43]

M.A. Piñeros, B.G. Larson, J.E. Shaff, D.J. Schneider, A.X. Falcão, L. Yuan, R.T. Clark, E.J. Craft, T.W. Davis, P. Pradier, Evolving technologies for growing, imaging and analyzing 3D root system architecture of crop plants, J. Integr. Plant Biol. 58 (2016) 230–241.
Crossref Google Scholar
[44]

J. Zhu, P.A. Ingram, P.N. Benfey, T. Elich, From lab to field, new approaches to phenotyping root system architecture, Curr. Opin. Plant Biol. 14 (2011) 310–317.
Crossref Google Scholar
[45]

J.A. Atkinson, M.P. Pound, M.J. Bennett, D.M. Wells, Uncovering the hidden half of plants using new advances in root phenotyping, Curr. Opin. Biotechnol. 55 (2019) 1–8.
Crossref Google Scholar
[46]

F. Hochholdinger, W.J. Park, M. Sauer, K. Woll, From weeds to crops: genetic analysis of root development in cereals, Trends Plant Sci. 9 (2004) 42–48.
Crossref Google Scholar
[47]

L.M. York, J.P. Lynch, Intensive field phenotyping of maize (Zea mays L.) root crowns identifies phenes and phene integration associated with plant growth and nitrogen acquisition, J. Exp. Bot. 66 (2015) 5493–5505.
Crossref Google Scholar
[48]

J. Liu, H. Cai, Q. Chu, X. Chen, F. Chen, L. Yuan, G. Mi, F. Zhang, Genetic analysis of vertical root pulling resistance (VRPR) in maize using two genetic populations, Mol. Breed. 28 (2011) 463–474.
Crossref Google Scholar
[49]

F.A. Van Eeuwijk, M.C.A.M. Bink, K. Chenu, S.C. Chapman, Detection and use of QTL for complex traits in multiple environments, Curr. Opin. Plant Biol. 13 (2010) 193–205.
Crossref Google Scholar
[50]

Y. Arai-Sanoh, T. Takai, S. Yoshinaga, H. Nakano, M. Kojima, H. Sakakibara, M. Kondo, Y. Uga, Deep rooting conferred by DEEPER ROOTING 1 enhances rice yield in paddy fields, Sci. Rep. 4 (2014) 5563.
Crossref Google Scholar
[51]

X. Mu, F. Chen, Q. Wu, Q. Chen, J. Wang, L. Yuan, G. Mi, Genetic improvement of root growth increases maize yield via enhanced post-silking nitrogen uptake, Eur. J. Agron. 63 (2015) 55–61.
Crossref Google Scholar
The Crop Journal
Volume 8 Issue 4,
August 2020
Pages 645-654
DOI: 10.1016/j.cj.2019.12.006
Cite this article:
Li P, Fan Y, Yin S, et al. Multi-environment QTL mapping of crown root traits in a maize RIL population. The Crop Journal, 2020, 8(4): 645-654. https://doi.org/10.1016/j.cj.2019.12.006

237

Views

4

Downloads

11

Crossref

N/A

Web of Science

12

Scopus

0

CSCD

Altmetrics
Received: 23 September 2019
Revised: 17 November 2019
Accepted: 20 February 2020
Published: 20 March 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