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Genome-wide association study identifies QTL for thousand grain weight in winter wheat under normal- and late-sown stressed environments

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GWAS identified stable loci for TGW and stress tolerance in winter wheat based on two sowing conditions, which will provide opportunities for developing new cultivars with high yield and yield stability.

Abstract

Wheat is an important food crop widely cultivated in the world. Breeding new varieties with high yields and superior adaptability is the main goal of modern wheat breeding program. In order to determine the marker–trait associations (MATs), a set of 688 diverse winter wheat accessions were subjected to genome-wide association study (GWAS) using the wheat 90K array. Field trials under normal-sown (NS) and late-sown (LS) conditions were conducted for thousand grain weight (TGW) and stress susceptibility index (SSI) at three different sites across two consecutive years. A total of 179 (NS) and 158 (LS) MATs corresponded with TGW; of these, 16 and 6 stable MATs for TGWNS and TGWLS were identified on chromosomes 1B, 2B, 3A, 3B, 5A, 5B, 5D, 6B, and 7D across at least three environments. Notably, a QTL hot spot controlling TGW under NS and LS conditions was found on chromosome 5A (140–142 cM). Moreover, 8 of 228 stable MATs on chromosomes 4B, 5A, and 5D for SSI were detected. A haplotype block associated with TGW and SSI was located on chromosome 5A at 91 cM, nearby the vernalization gene VRN-A1. Additionally, analysis of wheat varieties from the different eras revealed that the grain weight and stress tolerance are not improved concurrently. Overall, our results provide promising alleles controlling grain weight and stress tolerance (particularly for thermotolerance) for wheat breeders, which can be used in marker-assisted selection for improving grain yield and yield stability in wheat.

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References

  • Abdurakhmonov IY, Abdukarimov A (2008) Application of association mapping to understanding the genetic diversity of plant germplasm resources. Int J Plant Genom 2008:574927

    Google Scholar 

  • Ayeneh A, van Ginkel M, Reynolds MP, Ammar K (2002) Comparison of leaf, spike, peduncle and canopy temperature depression in wheat under heat stress. Field Crops Res 79:173–184

    Article  Google Scholar 

  • Bednarek J, Boulaflous A, Girousse C, Ravel C, Tassy C, Barret P, Bouzidi MF, Mouzeyar S (2012) Down-regulation of the TaGW2 gene by RNA interference results in decreased grain size and weight in wheat. J Exp Bot 63:5945–5955

    Article  CAS  PubMed  Google Scholar 

  • Blum A (1996) Crop responses to drought and the interpretation of adaptation. Plant Growth Regul 20:135–148

    Article  CAS  Google Scholar 

  • Bohnert HJ, Gong QQ, Li PH, Ma SS (2006) Unraveling abiotic stress tolerance mechanisms—getting genomics going. Curr Opin Plant Biol 9:180–188

    Article  CAS  PubMed  Google Scholar 

  • Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    Article  CAS  PubMed  Google Scholar 

  • Brinton J, Uauy C (2019) A reductionist approach to dissecting grain weight and yield in wheat. J Integr Plant Biol 61(3):337–358

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen F, Li XN, Cao YY, Sun JX, Zhang FY, Dong ZD, Cui DQ (2014) Analysis of association of puroindoline b-2 alleles with yield-related traits in bread wheat. Acta Agron Sin 19:17–21

    Article  CAS  Google Scholar 

  • Cheng X, Chai L, Chen Z, Xu L, Zhai H, Zhao A, Peng H, Yao Y, You M, Sun Q, Ni Z (2015) Identification and characterization of a high kernel weight mutant induced by gamma radiation in wheat (Triticum aestivum L.). BMC Genet 2015:16–127

    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 127:791–807

    Article  CAS  PubMed  Google Scholar 

  • Endelman JB, Jannink J-L (2012) Shrinkage estimation of the realized relationship matrix. Genes Genomes Genet 2:1405–1413

    Google Scholar 

  • Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620

    Article  CAS  PubMed  Google Scholar 

  • Fischer RA, Maurer R (1978) Drought resistance in spring wheat cultivars. 1. Grain-yield responses. Aust J Agric Res 29:897–912

    Article  Google Scholar 

  • Flint-Garcia SA, Thornsberry JM, Buckler ES IV (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374

    Article  CAS  PubMed  Google Scholar 

  • Fu DL, Szucs P, Yan LL, Helguera M, Skinner JS, von Zitzewitz J, Hayes PM, Dubcovsky J (2005) Large deletions within the first intron in VRN-1 are associated with spring growth habit in barley and wheat (vol 273, pg 54, 2005). Mol Genet Genom 274:442–443

    Article  CAS  Google Scholar 

  • Gao L, Zhao G, Huang D, Jia J (2017) Candidate loci involved in domestication and improvement detected by a published 90K wheat SNP array. Sci Rep 7:44530

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Githiri SM, Watanabe S, Harada K, Takahashi R (2006) QTL analysis of flooding tolerance in soybean at an early vegetative growth stage. Plant Breed 125:613–618

    Article  CAS  Google Scholar 

  • Guan PF, Lu LH, Jia LJ, Kabir MR, Zhang JB, Lan TY, Zhao Y, Xin MM, Hu ZR, Yao YY, Ni ZF, Sun QX, Peng HR (2018) Global QTL analysis identifies genomic regions on chromosomes 4A and 4B harboring stable loci for yield-related traits across different environments in wheat (Triticum aestivum L.). Front Plant Sci 9:529

    Article  PubMed  PubMed Central  Google Scholar 

  • Habtemariam Z, Awais R, Farid M, Ayele B, Ogbonnaya FC (2014) Genome-wide association mapping for seedling and adult plant resistance to stripe rust in synthetic hexaploid wheat. PLoS ONE 9:e105593–e105593

    Article  CAS  Google Scholar 

  • Hanif M, Gao F, Liu J, Wen W, Zhang Y, Rasheed A, Xia X, He Z, Cao S (2016) TaTGW6-A1, an ortholog of rice TGW6, is associated with grain weight and yield in bread wheat. Mol Breed 36:1

    Article  CAS  Google Scholar 

  • Hansen J, Sato M, Ruedy R (2012) Perception of climate change. Proc Natl Acad Sci USA 109:E2415–E2423

    CAS  PubMed  PubMed Central  Google Scholar 

  • Hays D, Mason E, Do JH, Menz M, Reynolds M (2007) Expression quantitative trait loci mapping heat tolerance during reproductive development in wheat (Triticum aestivum ). In: Buck HT, Nisi JE, Salomon N (eds) Developments in plant breeding. Springer, Dordrecht, pp 373–382

    Google Scholar 

  • Howarth CJ (2005) Abiotic stresses: plant resistance through breeding and molecular approaches. In: Ashraf MA, Harris PJC (eds) Genetic improvements of tolerance to high temperature. Howarth Press Inc, New York

    Google Scholar 

  • Hunter MC, Smith RG, Schipanski ME, Atwood LW, Mortensen DA (2017) Agriculture in 2050: recalibrating targets for sustainable intensification. Bioscience 67:385–390

    Article  Google Scholar 

  • Ishimaru K, Hirotsu N, Madoka Y, Murakami N, Hara N, Onodera H, Kashiwagi T, Ujiie K, Shimizu B, Onishi A, Miyagawa H, Katoh E (2013) Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield. Nat Genet 45:707–711

    Article  CAS  PubMed  Google Scholar 

  • IWGSC (2018) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361:6403

    Google Scholar 

  • Jamil M, Ali A, Gul A, Ghafoor A, Napar AA, Ibrahim AMH, Naveed NH, Yasin NA, Mujeeb-Kazi A (2019) Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. BMC Plant Biol 19:149

    Article  PubMed  PubMed Central  Google Scholar 

  • Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723

    Article  PubMed  PubMed Central  Google Scholar 

  • Kumar U, Joshi AK, Kumari M, Paliwal R, Kumar S, Roeder MS (2010) Identification of QTLs for stay green trait in wheat (Triticum aestivum L.) in the 'Chirya 3' x 'Sonalika' population. Euphytica 174:437–445

    Article  Google Scholar 

  • Kumar S, Kumari P, Kumar U, Grover M, Singh AK, Singh R, Sengar RS (2013) Molecular approaches for designing heat tolerant wheat. J Plant Biochem Biotechnol 22:359–371

    Article  CAS  Google Scholar 

  • Liu Y, Lin Y, Gao S, Li Z, Ma J, Deng M, Chen G, Wei Y, Zheng Y (2017) A genome-wide association study of 23 agronomic traits in Chinese wheat landraces. Plant J 91:861–873

    Article  CAS  PubMed  Google Scholar 

  • Lozada DN, Mason RE, Babar MA, Carver BF, Guedira GB, Merrill K, Arguello MN, Acuna A, Vieira L, Holder A, Addison C, Moon DE, Miller RG, Dreisigacker S (2017) Association mapping reveals loci associated with multiple traits that affect grain yield and adaptation in soft winter wheat. Euphytica 213:222

    Article  Google Scholar 

  • Lv Y, Guo ZL, Li XK, Ye HY, Li XH, Xiong LZ (2016) New insights into the genetic basis of natural chilling and cold shock tolerance in rice by genome-wide association analysis. Plant Cell Environ 39:556–570

    Article  CAS  PubMed  Google Scholar 

  • Mason R, Mondal S, Beecher FW, Pacheco A, Jampala B, Ibrahim AMH, Hays DB (2010) QTL associated with heat susceptibility index in wheat (Triticum aestivum L.) under short-term reproductive stage heat stress. Euphytica 174:423–436

    Article  Google Scholar 

  • Mason RE, Mondal S, Beecher FW, Hays DB (2011) Genetic loci linking improved heat tolerance in wheat (Triticum aestivum L.) to lower leaf and spike temperatures under controlled conditions. Euphytica 180:181–194

    Article  Google Scholar 

  • Mohler V, Albrecht T, Castell A, Diethelm M, Schweizer G, Hartl L (2016) Considering causal genes in the genetic dissection of kernel traits in common wheat. J Appl Genet 57:467–476

    Article  CAS  PubMed  Google Scholar 

  • Nadolska-Orczyk A, Rajchel IK, Orczyk W, Gasparis S (2017) Major genes determining yield-related traits in wheat and barley. Theor Appl Genet 130:1081–1098

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ogbonnaya FC, Rasheed A, Okechukwu EC, Jighly A, Makdis F, Wuletaw T, Hagras A, Uguru MI, Agbo CU (2017) Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments. Theor Appl Genet 130:1819–1835

    Article  PubMed  Google Scholar 

  • Oyiga BC, Sharma RC, Baum M, Ogbonnaya FC, Leon J, Ballvora A (2018) Allelic variations and differential expressions detected at quantitative trait loci for salt stress tolerance in wheat. Plant Cell Environ 41:919–935

    Article  CAS  PubMed  Google Scholar 

  • Paliwal R, Röder MS, Kumar U, Srivastava JP, Joshi AK (2012) QTL mapping of terminal heat tolerance in hexaploid wheat (T. aestivum L.). Theor Appl Genet 125:561–575

    Article  PubMed  Google Scholar 

  • Peng JH, Ronin Y, Fahima T, Roder MS, Li YC, Nevo E, Korol A (2003) Domestication quantitative trait loci in Triticum dicoccoides, the progenitor of wheat. Proc Natl Acad Sci USA 100:2489–2494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pinto SR, Reynolds MP, Mathews KL, McIntyre CL, Olivares-Villegas J-J, Chapman SC (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor Appl Genet 121:1001–1021

    Article  PubMed  PubMed Central  Google Scholar 

  • Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Reynolds MP, Balota M, Delgado MIB, Amani I, Fischer RA (1994) Physiological and morphological traits associated with spring wheat yield under hot, irrigated conditions. Aust J Plant Physiol 21:717–730

    Google Scholar 

  • Sehgal D, Skot L, Singh R, Srivastava RK, Das SP, Taunk J, Sharma PC, Pal R, Raj B, Hash CT, Yadav RS (2015) Exploring potential of pearl millet germplasm association panel for association mapping of drought tolerance traits. PLoS ONE 5:e0122165

    Article  CAS  Google Scholar 

  • Sehgal D, Autrique E, Singh R, Ellis M, Singh S, Dreisigacker S (2017) Identification of genomic regions for grain yield and yield stability and their epistatic interactions. Sci Rep 7:41578

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sharma RC, Tiwary AK, Ortiz-Ferrara G (2008) Reduction in kernel weight as a potential indirect selection criterion for wheat grain yield under terminal heat stress. Plant Breed 127:241–248

    Article  Google Scholar 

  • Shiferaw B, Smale M, Braun H-J, Duveiller E, Reynolds M, Muricho G (2013) Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Secur 5:291–317

    Article  Google Scholar 

  • Stone PJ, Nicolas ME (1995) Effect of timing of heat stress during grain filling on two wheat varieties differing in heat tolerance. 1. Grain growth. Aust J Plant Physiol 22:927–934

    Google Scholar 

  • Storey JD (2002) A direct approach to false discovery rates. J Roy Stat Soc: Ser B (Stat Methodol) 64(3):479–498

    Article  Google Scholar 

  • Su QN, Zhang XL, Zhang W, Zhang N, Song LQ, Liu L, Xue X, Liu GT, Liu JJ, Meng DY, Zhi LY, Ji J, Zhao XQ, Yang CL, Tong YP, Liu ZY, Li JM (2018) QTL detection for kernel size and weight in bread wheat (Triticum aestivum L.) using a high-density SNP and SSR-based linkage map. Front Plant Sci 9:1484

    Article  PubMed  PubMed Central  Google Scholar 

  • Sukumaran S, Xiang W, Bean SR, Pedersen JF, Kresovich S, Tuinstra MR, Tesso TT, Hamblin MT, Yu J (2012) Association mapping for grain quality in a diverse sorghum collection. Plant Genome 5:126–135

    Article  CAS  Google Scholar 

  • Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP (2015) Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor Appl Genet 128:353–363

    Article  CAS  PubMed  Google Scholar 

  • Sukumaran S, Reynolds MP, Sansaloni C (2018) Genome-wide association analyses identify QTL hotspots for yield and component traits in durum wheat grown under yield potential, drought, and heat stress environments. Front Plant Sci 9:81

    Article  PubMed  PubMed Central  Google Scholar 

  • Sun C, Zhang F, Yan X, Zhang X, Dong Z, Cui D, Chen F (2017) Genome-wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China. Plant Biotechnol J 15:953–969

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Talukder SK, Babar MA, Vijayalakshmi K, Poland J, Prasad PVV, Bowden R, Fritz A (2014) Mapping QTL for the traits associated with heat tolerance in wheat (Triticum aestivum L.). BMC Genet 15:97

    Article  PubMed  PubMed Central  Google Scholar 

  • Tricker PJ, ElHabti A, Schmidt J, Fleury D (2018) The physiological and genetic basis of combined drought and heat tolerance in wheat. J Exp Bot 69(13):3195–3210

    Article  CAS  PubMed  Google Scholar 

  • United Nations (2017) World population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100. Department of Economic and Social Affairs

  • Valluru R, Reynolds MP, Davies WJ, Sukumaran S (2017) Phenotypic and genome-wide association analysis of spike ethylene in diverse wheat genotypes under heat stress. New Phytol 214:271–283

    Article  CAS  PubMed  Google Scholar 

  • Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L, Mastrangelo AM, Whan A, Stephen S, Barker G, Wieseke R, Plieske J, Lillemo M, Mather D, Appels R, Dolferus R, Brown-Guedira G, Korol A, Akhunova AR, Feuillet C, Salse J, Morgante M, Pozniak C, Luo M-C, Dvorak J, Morell M, Dubcovsky J, Ganal M, Tuberosa R, Lawley C, Mikoulitch I, Cavanagh C, Edwards KJ, Hayden M, Akhunov E (2014) Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array. Plant Biotechnol J 12:787–796

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Weir BS (1996) Methods for discrete population genetic data. In: Genetic data analysis II, pp.xii + 377. Sinauer Associates, Sunderland, Massachusetts

  • Xia H, Luo Z, Xiong J, Ma XS, Lou QJ, Wei HB, Qiu J, Yang H, Liu GL, Fan LJ, Chen L, Luo LJ (2019) Bi-directional selection in upland rice leads to its adaptive differentiation from lowland rice in drought resistance and productivity. Mol Plant 12:170–184

    Article  CAS  PubMed  Google Scholar 

  • Yan L, Loukoianov A, Tranquilli G, Helguera M, Fahima T, Dubcovsky J (2003) Positional cloning of the wheat vernalization gene VRN1. Proc Natl Acad Sci USA 100:6263–6268

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yan L, Helguera M, Kato K, Fukuyama S, Sherman J, Dubcovsky J (2004) Allelic variation at the VRN-1 promoter region in polyploid wheat. Theor Appl Genet 109:1677–1686

    Article  CAS  PubMed  Google Scholar 

  • Yang X, Tian Z, Sun L, Chen B, Tubiello FN, Xu Y (2017) The impacts of increased heat stress events on wheat yield under climate change in China. Clim Change 140:605–620

    Article  CAS  Google Scholar 

  • Zanke CD, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Hinze M, Neumann F, Eichhorn A, Polley A, Jaenecke C, Ganal MW, Roeder MS (2015) Analysis of main effect QTL for thousand grain weight in European winter wheat (Triticum aestivum L.) by genome-wide association mapping. Front Plant Sci 6:644

    Article  PubMed  PubMed Central  Google Scholar 

  • Zeng DL, Tian ZX, Rao YC, Dong GJ, Yang YL, Huang LC, Leng YJ, Xu J, Sun C, Zhang GH, Hu J, Zhu L, Gao ZY, Hu XM, Guo LB, Xiong GS, Wang YH, Li JY, Qian Q (2017) Rational design of high-yield and superior-quality rice. Nat Plants 3:4

    Article  Google Scholar 

  • Zhang XJ, Wang JF, Huang J, Lan HX, Wang CL, Yin CF, Wu YY, Tang HJ, Qian Q, Li JY, Zhang HS (2012) Rare allele of OsPPKL1 associated with grain length causes extra-large grain and a significant yield increase in rice. Proc Natl Acad Sci USA 109:21534–21539

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was funded by the NSFC Projects of International Cooperation and Exchanges (Grant No. 31561143013), the National Key Research and Development Program of China (Grant Nos. 2016YFD0100102-9 and 2016YFD0100600), and the National Natural Science Foundation of China (Grant No. 91935303).

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  1. Xiaobo Wang, Panfeng Guan, and Mingming Xin have contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China

    Xiaobo Wang, Panfeng Guan, Mingming Xin, Yongfa Wang, Jinbo Zhang, Zhongfu Ni, Yingyin Yao, Zhaorong Hu, Huiru Peng & Qixin Sun

  2. Hebei Crop Genetic Breeding Laboratory, Institute of Cereal and Oil Crops of Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, 050035, China

    Xiyong Chen & Aiju Zhao

  3. Agronomy College, Northwest A&F University, Yangling, 712100, Shaanxi, China

    Manshuang Liu & Hongxia Li

  4. Institute of Wheat, Shanxi Academy of Agricultural Sciences, Linfen, 041000, China

    Mingyi Zhang & Lahu Lu

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  1. Xiaobo Wang
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Contributions

QS and HP planned and designed the research. XW, PG, and YW carried out experiments. XC, AZ, ML, HL, MZ, LL, and JZ participated in field trials. XW analyzed experimental results. XW, PG, MX, and HP wrote the manuscript. ZN, YY, ZH, and QS helped to revise the manuscript and all authors reviewed and commented on the manuscript. XW, PG, and MX contributed equally to this work.

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Correspondence to Huiru Peng or Qixin Sun.

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Wang, X., Guan, P., Xin, M. et al. Genome-wide association study identifies QTL for thousand grain weight in winter wheat under normal- and late-sown stressed environments. Theor Appl Genet 134, 143–157 (2021). https://doi.org/10.1007/s00122-020-03687-w

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