Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-26T07:14:41.801Z Has data issue: false hasContentIssue false
  • English
  • Français

QTL mapping for seedling morphology under drought stress in wheat cross synthetic (W7984)/Opata

Published online by Cambridge University Press:  13 March 2018

Maria Khalid ,
Alvina Gul ,
Rabia Amir ,
Mohsin Ali ,
Fakiha Afzal ,
Umar Masood Quraishi ,
Zubair Ahmed  and
Awais Rasheed
Maria Khalid
Affiliation:
Atta-ur-Rehman School of Applied Biosciences (ASAB), National University of Science and Technology (NUST), Islamabad, Pakistan
Alvina Gul
Affiliation:
Atta-ur-Rehman School of Applied Biosciences (ASAB), National University of Science and Technology (NUST), Islamabad, Pakistan School of Integrative Plant Science, Cornell University, Ithaca, NY 14850, USA
Rabia Amir
Affiliation:
Atta-ur-Rehman School of Applied Biosciences (ASAB), National University of Science and Technology (NUST), Islamabad, Pakistan
Mohsin Ali
Affiliation:
Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing 100081, China
Fakiha Afzal
Affiliation:
Atta-ur-Rehman School of Applied Biosciences (ASAB), National University of Science and Technology (NUST), Islamabad, Pakistan
Umar Masood Quraishi
Affiliation:
Department of Plant Sciences, Quaid-i-Azam UniversityIslamabad 66000, Pakistan
Zubair Ahmed
Affiliation:
Crop Science Institute, National Agricultural Research Centre, Islamabad, Pakistan
Awais Rasheed*
Affiliation:
Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing 100081, China International Maize and Wheat Improvement Center (CIMMYT), c/o CAAS, 12 Zhongguancun South Street, Beijing 100081, China
*
*Corresponding author. E-mail: awais_rasheed@yahoo.com; a.rasheed@cgiar.org
Get access Rights & Permissions [Opens in a new window]

Abstract

Drought stress ‘particularly at seedling stage’ causes morpho-physiological differences in wheat which are crucial for its survival and adaptability. In the present study, 209 recombinant inbred lines (RILs) from synthetic wheat (W7984)× ‘Opata’ (also known as SynOpRIL) population were investigated under well-watered and water-limited conditions to identify quantitative trait loci (QTL) for morphological traits at seedling stage. Analysis of variance revealed significant differences (P < 0.01) among RILs, and water treatments for all traits with moderate to high broad sense heritability. Pearson's coefficient of correlation revealed positive correlation among all traits except dry root weight that showed poor correlation with fresh shoot weight (FSW) under water-limited conditions. A high-density linkage map was constructed with 2639 genotyping-by-sequencing markers and covering 5047 cM with an average marker density of 2 markers/cM. Composite interval mapping identified 16 QTL distributed over nine chromosomes, of which six were identified under well-watered and 10 in water-limited conditions. These QTL explained from 4 to 59% of the phenotypic variance. Six QTL were identified on chromosome 7B; three for shoot length under water-limited conditions (QSL.nust-7B) at 64, 104 and 221 cM, two for fresh root weight (QFRW.nust-7B) at 124 and 128 cM, and one for root length (QRL.nust-7B) at 122 cM positions. QFSW.nust-7B appeared to be the most significant QTL explaining 59% of the phenotypic variance and also associated with FSW at well-watered conditions. These QTL could serve as target regions for candidate gene discovery and marker-assisted selection in wheat breeding.

Keywords

analysis of variance (ANOVA)broad sense heritabilitydroughtInternational Triticeae Mapping Initiative (ITMI)Pearson's correlationQTL mappingwheat
Type
Research Article
Information
Plant Genetic Resources , Volume 16 , Issue 4 , August 2018 , pp. 359 - 366
Copyright
Copyright © NIAB 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Acuna-Galindo, MA, Mason, RE, Subrahmanyam, NK and Hays, D. (2015) Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress. Crop Science 55: 477–492.Google Scholar
Afzal, F, Reddy, B, Gul, A, Subhani, A, Shazadi, K, Quraishi, UM, Ibrahim, AMH and Rasheed, A. (2017) Physiological, biochemical and agronomic traits associated with drought tolerance in a synthetic-derived wheat diversity panel. Crop and Pasture Science 68: 213–224.Google Scholar
Ain, QU, Rasheed, A, Anwar, A, Mahmood, T, Imtiaz, M, Mahmood, T, Xia, X, He, Z and Quraishi, UM. (2015) Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. Frontiers in Plant Science 6: 743.Google Scholar
Ayalew, H, Liu, H and Yan, G. (2017) Identification and validation of root length QTLs for water stress resistance in hexaploid wheat (Triticum aestivum L.). Euphytica 213: 126.Google Scholar
Balota, B, Green, AJ, Griffey, CA, Pitman, R and Thomason, W. (2017) Genetic gains for physiological traits associated with yield in soft red winter wheat in the Eastern United States from 1919 to 2009. European Journal of Agronomy 84: 76–83.Google Scholar
Czyczyło-Mysza, I, Marcińska, I, Skrzypek, E, Cyganek, K, Juzoń, K and Karbarz, M. (2014) QTL mapping for germination of seeds obtained from previous wheat generation under drought. Central European Journal of Biology 9: 374–382.Google Scholar
Doyle, JJ and Doyle, JL. (1987) A rapid DNA isolation procedure for small quantities from fresh leaf tissue. Phytochemical Bulletin 19: 11–15.Google Scholar
Ehdaie, B, Mohammadi, SA and Nouraein, M (2016) QTLs for root traits at mid-tillering and for root and shoot traits at maturity in a RIL population of spring bread wheat grown under well-watered conditions. Euphytica 211: 17-38.Google Scholar
El-Rawy, MA and Hassan, MI. (2014) A diallel analysis of drought tolerance indices at seedling stage in bread wheat (Triticum aestivum L.). Plant Breeding and Biotechnology 2: 276–288.Google Scholar
Foito, A, Byrne, SL, Stewart, D and Barth, S (2009) Transcriptional and metabolic profiles of Lolium perenne L. genotypes in response to a PEG induced water stress. Plant Biotechnology Journal 7: 719–732.Google Scholar
Gupta, PK, Balyan, HS and Gahlaut, V (2017) QTL analysis for drought tolerance in wheat: present status and future possibilities. Agronomy 7: 5.Google Scholar
Iannucci, A, Marone, D, Russo, MA, Vita, PD, Miullo, V, Ferragonio, P, Blanco, A, Gadaleta, A and Mastrangelo, AM (2017) Mapping QTL for root and shoot morphological traits in a durum wheat × T. dicoccum segregating population at seedling stage. International Journal of Genomics 17: 6876393.Google Scholar
Kabir, MR, Liu, G, Guan, P, Wang, F, Khan, AA, Ni, Z, Yao, Y, Hu, Z, Xin, M, Peng, H and Sun, Q (2015) Mapping QTLs associated with root traits using two different populations in wheat (Triticum aestivum L.). Euphytica 206: 175–190.Google Scholar
Li, HH, Ye, GY and Wang, JK (2007) A modified algorithm for the improvement of composite interval mapping. Genetics 175: 361–374.Google Scholar
Liu, X, Li, R, Chang, X and Jing, R (2013) Mapping QTLs for seedling root traits in a doubled haploid wheat population under different water regimes. Euphytica 189: 51–66.Google Scholar
Liu, X, Zhang, H, Hu, X, Wang, H, Gao, J, Liang, S and Feng, J (2017) QTL mapping for drought tolerance coefficient of seedling related traits during wheat germination. Journal of Nuclear Agricultural Sciences 31: 209–217.Google Scholar
Luo, W, Ma, J, Zhou, XH, Sun, M, Kong, XC, Wei, YM, Jiang, YF, Qi, PF, Jiang, QT, Liu, YX, Peng, YY, Chen, GY, Zheng, YL, Liu, C and Lan, XJ (2016) Identification of quantitative trait loci controlling agronomic traits indicates breeding potential of Tibetan semi wild wheat (ssp.). Crop Science 56: 2410–2420.Google Scholar
Ma, J, Luo, W, Zhang, H, Zhou, XH, Qin, NN, Wei, YM, Liu, Y, Jiang, QT, Chen, GY, Zheng, YL and Lan, XJ (2017) Identification of quantitative trait loci for seedling root traits from Tibetan semi-wild wheat (Triticum aestivum ssp. Tibetanum). Genome 60: 1068–1075.Google Scholar
Maccaferri, M, El-Feki, W, Nazemi, G, Salvi, S, Cane, MA, Colalongo, MC, Stefanelli, S and Tuberosa, R (2016) Prioritizing quantitative trait loci for root system architecture in tetraploid wheat. Journal of Experimental Botany 67: 1161–1178.Google Scholar
Michelbart, MV, Hasegawa, PM and Bailey-Serres, J. (2015) Genetic mechanisms of abiotic stress tolerance that translate to crop yield stability. Nature Reviews Genetics 16: 237–251.Google Scholar
Placido, DF, Campbell, MT, Folsom, JJ, Cui, X, Kruger, GR, Baenziger, PS and Walia, H. (2013) Introgression of novel traits from wild wheat relatives improves drought adaptation in wheat. Plant Physiology 16: 1806–1819.Google Scholar
Poland, JA, Brown, PJ, Sorrells, ME and Jannink, J-L. (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7, e32253.Google Scholar
Rasheed, A, Hao, Y, Xia, X, Khan, A, Xu, Y, Varshney, RK and He, Z. (2017) Crop breeding chips and genotyping platforms: progress, challenges, and perspectives. Molecular Plant 10: 1047–1064.Google Scholar
Rasheed, A, Ogbonnaya, F, Lagudah, E, Appels, R and He, Z. (2018) Goat grass genome's role in wheat improvement. Nature Plants, 4: 56–58.Google Scholar
Röder, MS, Korzun, V, Wendehake, K, Plaschke, J, Tixier, MH, Leroy, P and Ganal, MW (1998) A microsatellite map of wheat. Genetics, 149: 2007–2023.Google Scholar
Sorrells, ME, Gustafson, JP, Somers, D, Chao, S, Benscher, D, Guedira-Brown, G, Huttner, E, Kilian, A, McGuire, PE, Ross, K, Tanaka, J, Wenzl, P, Williams, K and Qualset, CO (2011) Reconstruction of the synthetic W7984 x Opata M85 wheat reference population. Genome 54: 875–882.Google Scholar
Stam, P (1993) Construction of integrated genetic linkage maps by means of a new computer package: Join Map. The Plant Journal 3: 739–744.Google Scholar
Takele, A (2000) Seedling emergence and of growth of sorghum genotypes under variable soil moisture deficit. Acta Agronomica Hungarica 48: 95–102.Google Scholar
van Deynze, AE, Dubcovsky, J, Gill, KS, Nelson, JC, Sorrells, ME, DvoÍák, J, Gill, BS, Lagudah, ES, McCouch, S R and Appels, R (1995) Molecular-genetic maps for group 1 chromosomes of Triticeae species and their relation to chromosomes in rice and oat. Genome 38: 45–59.Google Scholar
Wimmer, V, Albert, T, Auinger, H-J and Schn, CC (2012) Synbreed: a framework for the analysis of genomic prediction data using R. Bioinformatics 28: 2086–2087.Google Scholar
Zhang, H, Cui, FA, Wang, LI, Li, JU, Ding, A, Zhao, C, Bao, Y, Yang, Q and Wang, H. (2013) Conditional and unconditional QTL mapping of drought-tolerance-related traits of wheat seedling using two related RIL populations. Journal of Genetics 92: 213–231.Google Scholar
Zhu, J, Kaeppler, SM and Lynch, JP. (2005) Mapping of QTLs for lateral root branching and length in maize (Zea mays L.) under differential phosphorus supply. Theoretical and Applied Genetics 111: 688–695.Google Scholar
Zhu, J, Mickelson, SM, Kaeppler, SM and Lynch, JP. (2006) Detection of quantitative trait loci for seminal root traits in maize (Zea mays L.) seedlings grown under differential phosphorus levels. Theoretical Applied Genetics 113: 1–10.Google Scholar
Supplementary material: File

Khalid et al. supplementary material 1

Khalid et al. supplementary material

Download Khalid et al. supplementary material 1(File)
File 234.9 KB