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Identification of soybean drought-tolerant genotypes and loci correlated with agronomic traits contributes new candidate genes for breeding

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Drought tolerance level of 136 soybean genotypes, the correlations among traits were evaluated, and several important drought-tolerant genotypes, traits, SNPs and genes were possibly useful for soybean genetic breeding.

Abstract

Drought is an adverse environmental factor affecting crops growth, development, and yield. Promising genotypes and genes with improved tolerance to drought are probably effective ways to alleviate the situation. In this study, our main task was to determine drought tolerance level of 136 soybean genotypes, the correlations among physiological and agronomic traits under drought, and drought-tolerant single nucleotide polymorphism (SNPs) and genes. In this study, twenty-six varieties were identified as excellent tolerant genotypes to stress among which S14, S93 and S135 with high drought-tolerant index (DTI > 1.3) and yield (Y > 300 kg). Fourteen varieties were identified as drought-sensitive genotypes, such as S25, S45 and S58, with low drought-tolerant index (DTI < 0.5). 422 SNPs and 302 genes correlated with seed number per plant (SNPP), maturity (M), number of seeds per pod (NSPP), node number of main stem (NNMS), Stem diameter (SD) and pull stem (PS) were detected under well-watered and drought conditions by genome-wide association study (GWAS). Among them, we found SNPs (Chr 3:1758920-1958934) between drought-tolerant and sensitive genotypes. 13 genes (Glyma.03G017800, Glyma.03G018000, Glyma.03G018200, Glyma.03G018400, Glyma.03G018500, Glyma.03G018600, Glyma.03G018700, Glyma.03G018800, Glyma.03G018900, Glyma.03G019000, Glyma.03G019100, Glyma.03G019200, Glyma.03G019300) correlated with NNMS were detected. By qRT-PCR, the expression level of Glyma.03G018000 and Glyma.03G018900 in drought-tolerant varieties was significantly increased, but low or no expression in sensitive varieties under drought stress. This study provides important drought-tolerant genotypes, traits, SNPs and potential genes, possibly useful for soybean genetic breeding.

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Abbreviations

BN:

Branch number

CC:

Chlorophyll content

EPN:

Effective pod number

DTI:

Drought-tolerant index

SNPP:

Seed number per plant

SWPP:

Seed weight per plant

GWAS:

Genome-wide association study

Y:

Yield

HLP:

Height of lower pods

M:

Maturity

NNMS:

Node number of main stem

NSPP:

Number of seeds per pod

RECL:

Relative electric conductivity of leave

RWCL:

Relative water content of leave

PH:

Plant height

PS:

Pull stem

SD:

Stem diameter

SNP:

Single nucleotide polymorphism

qRT-PCR:

Fluorescence quantitative PCR

References

  • Andersen EC, Gerke JP, Shapiro JA, Crissman JR, Ghosh R, Bloom JS, Félix MA, Kruglyak L (2012) Chromosome-scale selective sweeps shape Caenorhabditis elegans genomic diversity. Nat Genet 44:285–290

    Article  CAS  Google Scholar 

  • Baird SJ (2015) Exploring linkage disequilibrium. Mol Ecol Resour 15(5):1017–1019

    Article  CAS  Google Scholar 

  • Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker EU, Cresko WA, Johnson EA (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3(10):e3376

    Article  Google Scholar 

  • Battisti DS, Naylor RL (2009) Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323:240–244

    Article  CAS  Google Scholar 

  • Brisson N, Gate P, Gouache D, Charmet G, Oury F, Huard F (2010) Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Res 119:201–212

    Article  Google Scholar 

  • Chen LM, Zhou XA, Li WB et al (2013) Genome-wide transcriptional analysis of two soybean genotypes under dehydration and rehydration conditions. BMC Genom 14:687

    Article  CAS  Google Scholar 

  • Edae EA, Byrne PF, Haley SD, Lopes MS, Reynolds MP (2014) Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor Appl Genet 127(4):791–807

    Article  CAS  Google Scholar 

  • Fan TL, Balta M, Rudd J, Payne WA (2005) Canopy temperature depression as a potential selection criterion for drought resistance in wheat. Agric Sci China 4(10):793–800

    Google Scholar 

  • George D (2012) IBM SPSS statistics 19 step by step: a simple guide and reference, 12/E[J]

  • Hufstetler EV, Boerma HR, Carter TE, Earl HG (2007) Genotypic variation for three physiological traits affecting drought tolerance in soybean. Crop Sci 47:25–35

    Article  Google Scholar 

  • Jiang L, Zhang H, Cai ZA (2009) Novel Bayes model: hidden Naive Bayes. IEEE Trans Knowl Data Eng 21(10):1361–1371

    Article  Google Scholar 

  • Ku YS, Au-Yeung WK, Yung YL, Li MW, Wen CQ, Liu X, Lam HM (2013) Drought stress and tolerance in soybean [M]. In: A comprehensive survey of international soybean research-genetics, physiology, agronomy and nitrogen relationships. IntechOpen, London

    Google Scholar 

  • Lan JS, Hu FS, Zhang JR (1990) The concept and statistical method of drought resistance index in crops. Acta Agric Boreal Sin 5(2):20–25

    Google Scholar 

  • Larbi A, Mekliche A (2004) Relative water content (RWC) and leaf senescence as screening tools for drought tolerance in wheat. Options Méditerranéennes Série A Séminaires Méditerranéens 60:193–196

    Google Scholar 

  • Li H (2009) Durbin R: fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25(14):1754–1760

    Article  CAS  Google Scholar 

  • Li RH, Guo PG, Baum M, Grando S, Ceccarelli S (2006) Evaluation of chlorophyll content and fluorescence parameters as indicators of drought tolerance in barley. Agric Sci China 5(10):751–757

    Article  CAS  Google Scholar 

  • Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079

    Article  Google Scholar 

  • Liu FH, Guo Y, Gu DM, Xiao G, Chen ZH, Chen SY (1997) Salt tolerance of transgenic plants with BADH cDNA. Acta Genet Sin 24:54–58

    CAS  Google Scholar 

  • Ma FY, Zhou ZG, Zheng Z, Zheng XR, Li MS, Li ZS, Zhang WG, Xing ML, Xia DL (2004) The development and improvement of drip irrigation under plastic film on cotton. Agric Res Arid Areas 22(3):202–208

    Google Scholar 

  • Manavalan LP, Guttikonda SK, Tran LS, Nguyen HT (2009) Physiological and molecular approaches to improve drought resistance in soybean. Plant Cell Physiol 50:1260–1276

    Article  CAS  Google Scholar 

  • Mwadzingeni L, Shimelis H, Tsilo T, Tesfay S (2016) Screening of bread wheat genotypes for drought tolerance using phenotypic and proline analyses. Front Plant Sci 7(1276):1–12

    Google Scholar 

  • North GB, Nobel PS (2010) Drought-induced changes in hydraulic conductivity and structure in roots of Ferocactus acanthodes and Opuntia ficus indica. New Phytol 120(1):9–19

    Article  Google Scholar 

  • Qiu LJ, Chang RZ (2006) Descriptors and data standard for soybean (Glycine spp.). Chinese Agriculture Press, Beijing

    Google Scholar 

  • Rampino P, Pataleo S, Gerardi C, Mita G, Perrotta C (2006) Drought stress response in wheat: physiological and molecular analysis of resistant and sensitive genotypes. Plant, Cell Environ 29(12):2143–2152

    Article  CAS  Google Scholar 

  • Rebolledo MC, Dingkuhn M, Courtois B, Gibon Y, Clément-Vidal A, Cruz DF, Duitama J, Lorieux M, Luquet D (2015) Phenotypic and genetic dissection of component traits for early vigour in rice using plant growth modelling, sugar content analyses and association mapping. J Exp Bot 66:5555–5566

    Article  CAS  Google Scholar 

  • Scaglione D, Fornasiero A, Pinto C et al (2015) A RAD-based linkage map of kiwifruit (Actinidia chinensis Pl.) as a tool to improve the genome assembly and to scan the genomic region of the gender determinant for the marker-assisted breeding. Tree Genet Genomes 11:115

    Article  Google Scholar 

  • Schmutz J, Cannon SB, Schlueter J et al (2010) Genome sequence of the paleopolyploid soybean. Nature 463:178–183

    Article  CAS  Google Scholar 

  • Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (2007) Climate change 2007: the physical science basis. In: Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Summary for policymakers, vol 18(2), pp 95–123

  • Su QH (2018) Genetic dissection of variations in chlorophyll content of flag leaf at post-anthesis and yield-related traits regulated by drought stress (D). Gansu Agriculture University, Lanzhou, pp 1–72

    Google Scholar 

  • Tebaldi C, Lobell DB (2008) Towards probabilistic projections of climate change impacts on global crop yields. Geophys Res Lett 35:L08705

    Article  Google Scholar 

  • Torres-Martínez L, Emery NC (2016) Genome-wide SNP discovery in the annual herb, Lasthenia fremontii (Asteraceae): genetic resources for the conservation and restoration of a California vernal pool endemic. Conserv Genet Resour 8:145–158

    Article  Google Scholar 

  • Tran LS, Mochida K (2010) Functional genomics of soybean for improvement of productivity in adverse conditions. Funct Integr Genom 10:447–462

    Article  CAS  Google Scholar 

  • Wang XD (2016) Identification transgenic wheat with drought resistance through physiological and biochemical characters and agronomic traits (D). Northwest A&F University, Shanxi, pp 1–77

    Google Scholar 

  • Willing EM, Hoffmann M, Klein JD, Weigel D, Dreyer C (2011) Paired-end RAD-seq for de novo assembly and marker design without available reference. Bioinformatics 16:2187–2193

    Article  Google Scholar 

  • Yang DL, Jing RJ, Chen XP, Li W (2007) Quantitative trait loci mapping for chlorophyll fluorescence and associated traits in wheat (Triticum aestivum L.). J Integr Plant Biol 49(5):646–654

    Article  CAS  Google Scholar 

  • Yang SX, Niu Y, Li M, Wei SP, Liu XF, Lü HY, Zhang YM (2014) Association mapping of agronomic traits in soybean (Glycine max L. Merr.) and mining of novel alleles. Sci Agric Sin 47(20):3941–3952

    CAS  Google Scholar 

  • Zhou R, Chen HF, Wang XZ, Zhang XJ, Shan ZH, Wu XJ, Cai SP, Qiu DZ, Zhou XA, Wu JS (2009) QTL analysis of yield, yield components, and lodging in soybean. Acta Agron Sin 35(5):821–830

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS) for supplying soybean core germplasm resources. The work was funded by Molecular Assisted Breeding System for Drought Resistance and Lodging Resistance in Soybean (2012BB043); National Genetically Modified Organisms Breeding Major Projects (2013ZX08004-002) and (2013ZX08004-005); The Science and technology innovation project of Chinese Academy of Agricultural Sciences, Molecular Mechanism Analysis and Germplasm Innovation of Soybean Stress Resistance to Tolerance and Nutrient Efficient Utilization (CAAS-ASTIP-2011-OCRI, 2011–2015).

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Authors and Affiliations

  1. Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan, 430062, China

    Limiao Chen, Yisheng Fang, Haifeng Chen, Hongli Yang, Dong Cao, Qingnan Hao, Songli Yuan, Chanjuan Zhang, Wei Guo, Shuilian Chen, Zhonglu Yang, Zhihui Shan, Xiaojuan Zhang, Dezhen Qiu & Xin-An Zhou

  2. Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Xudong 2nd Road, Wuhan, 430062, China

    Limiao Chen, Yisheng Fang, Haifeng Chen, Hongli Yang, Dong Cao, Qingnan Hao, Songli Yuan, Chanjuan Zhang, Wei Guo, Shuilian Chen, Zhonglu Yang, Zhihui Shan, Xiaojuan Zhang, Dezhen Qiu & Xin-An Zhou

  3. Crop Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Key Lab of Cereal Quality Research and Genetic Improvement, Xinjiang Production and Construction Crops, Shihezi, 832000, China

    Kai Zeng, Hengbin Zhang & Yong Zhan

  4. College of Life Sciences, Xinyang Normal University, Xinyang, 464000, China

    Xiangyong Li

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  1. Limiao Chen
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  18. Yong Zhan
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  19. Xin-An Zhou
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Contributions

XAZ and YZ: experiments design; LMC: writing original draft; LMC, YSF, HBZ and KZ: experiments operation; LMC and XYL: Data analysis; HFC, QNH, HLY, DC, CJZ, SLY, SLC, WG, ZLY, ZHS, XJZ, and DZQ: supplied reagents, materials, and analytical tools.

Corresponding authors

Correspondence to Yong Zhan or Xin-An Zhou.

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Chen, L., Fang, Y., Li, X. et al. Identification of soybean drought-tolerant genotypes and loci correlated with agronomic traits contributes new candidate genes for breeding. Plant Mol Biol 102, 109–122 (2020). https://doi.org/10.1007/s11103-019-00934-7

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