Skip to main content

Advertisement

SpringerLink
Log in

Diversity array technology (DArT)-based mapping of phenotypic variations among recombinant inbred lines of WAB638-1/PRIMAVERA under drought stress

  • Published:
Euphytica Aims and scope Submit manuscript

Abstract

Genetic analysis of phenotypic variations of traits under drought stress to identify associated loci will facilitate crop improvement and performance in marginalized drought-prone areas. To this end, we identified genomic regions/loci underlying variations among recombinant inbred lines (RILs) of WAB638-1/BRS-PRIMAVERA rice under drought stress and control environments using diversity array technology (DArT)-based markers in linkage analysis. The linkage map of 94 RILs and 581 DArT-Based markers (295 SNP and 286 SilicoDArT) covers all the 12 rice chromosomes. The total length of the linkage map was 3057.85 cM, with an average of 254.82 cM per chromosome. The average interval between two adjacent markers was 5.82 cM and ranged from 0.56 cM to 39.8 cM, with 429 (73.8%) markers less than 5.0 cM from their nearest neighbours. A total of 28 additive effect QTLs associated with seven agronomic traits across the two environments were localized on chromosomes 1, 2, 3, 8, 10, and 11. The QTLs individually explained 0.96% to 41.6% of the phenotypic variance (PVE), with 11 (40%) major-effect QTLs, each explaining more than 10% PVE. Four major-effect QTLs were mapped to regions consistent with other studies, while seven are newly reported. The consistent QTLs can be deployed for marker-assisted breeding, and the new QTLs should be further characterized and validated. While the QTLs detected for various agronomic traits advances our understanding of phenotypic variability, those associated with drought treatments are significant for improving rice performance in drought-prone areas of the upland ecology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Adeboye KA, Oduwaye OA, Daniel IO, Fofana M, Semon M (2021) Characterization of flowering time response among recombinant inbred lines of WAB638-1/PRIMAVERA rice under reproductive stage drought stress. Plant Genetic Resour Characteriz Utiliz 19(1):1–8. https://doi.org/10.1017/S1479262121000010

    Article  CAS  Google Scholar 

  • Adeboye KA, Oyedeji OE, Alqudah AM, Börner A, Oduwaye OA, Adebambo AO, Daniel IO (2020) Genetic structure and diversity of upland rice germplasm using diversity array technology (DArT)-based single nucleotide polymorphism (SNP) markers. Plant Genetic Resour Characteriz Utiliz 18(5):343–350. https://doi.org/10.1017/S1479262120000362

    Article  CAS  Google Scholar 

  • Ambawat S, Senthilvel S, Hash CT et al (2016) QTL mapping of pearl millet rust resistance using an integrated DArT- and SSR-based linkage map. Euphytica 209:461–476. https://doi.org/10.1007/s10681-016-1671-9

    Article  Google Scholar 

  • Barik SR, Pandit E, Mohanty SP et al (2020) Genetic mapping of physiological traits associated with terminal stage drought tolerance in rice. BMC Genet 21:76. https://doi.org/10.1186/s12863-020-00883-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Beighley DH (2010) Growth and production of rice. Soils Plant Growth Crop Prod Encycl Life Support Syst (EOLSS) 2:349

    Google Scholar 

  • Bernier J, Kumar A, Ramaiah V, Spaner D, Atlin GN (2007) A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Sci 47:507–516. https://doi.org/10.2135/cropsci2006.07.0495

    Article  Google Scholar 

  • Brondani C, Rangel N, Brondani V, Ferreira E (2002) QTL mapping and introgression of yield-related traits from Oryza glumaepatula to cultivated rice (Oryza sativa) using microsatellite markers. Theor Appl Genet 104:1192–1203

    Article  CAS  PubMed  Google Scholar 

  • Brouwer C and Heibloem M (1986) irrigation water management: Training manual no. 3. Food and Agriculture Organization of the United Nations, Rome, Italy

  • Cairns J, Impa S, O’Toole J, Jagadish S, Price A (2011) Influence of the soil physical environment on rice (Oryza sativa L.) response to drought stress and its implications for drought research. Field Crop Res 121:303–310

    Article  Google Scholar 

  • Cartwright DA, Troggio M, Velasco R, Gutin A (2007) Genetic mapping in the presence of genotyping errors. Genetics 176:2521–2527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Champoux MC, Wang G, Sarkarung S, Mackill DJ, O’Toole JC, Huang N, McCouch SR (1995) Locating genes associated with root morphology and drought avoidance in rice via linkage to molecular markers. Theor Appl Genet 90:969–981

    Article  CAS  PubMed  Google Scholar 

  • Chancerel E, Lamy JB, Lesur I, Noirot C, Klopp C, Ehrenmann F et al (2013) High-density linkage mapping in a pine tree reveals a genomic region associated with inbreeding depression and provides clues to the extent and distribution of meiotic recombination. BMC Biol 11:50. https://doi.org/10.1186/1741-7007-11-50

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen DW, Chen LQ (2010) The first intraspecific genetic linkage maps of wintersweet [Chimonanthus praecox (L.) Link] based on AFLP and ISSR markers. Sci Hortic 124:88–94. https://doi.org/10.1016/j.scienta.2009.12.008

    Article  CAS  Google Scholar 

  • Cooper M (1999) Concepts and strategies for plant adaptation research in rainfed lowland rice. Field Crop Res 64:13–34

    Article  Google Scholar 

  • Cui H, Peng B, Xing Z, Yu B, Xu G, Zhang Q (2003) Molecular dissection of the genetic relationships of source, sink and transport tissue with yield traits in rice. Theor Appl Genet 106:649–658

    Article  CAS  PubMed  Google Scholar 

  • Cui K, Peng S, Xing Y, Yu S, Xu C (2002) Molecular dissection of relationship between seedling characteristics and seed size in rice. Acta Botanica Sinica 44:702–707

    CAS  Google Scholar 

  • De Leon TB, Linscombe S, Subudhi PK (2016) Molecular dissection of seedling salinity tolerance in rice (Oryza sativa L). Rice 9(1):52

    Article  PubMed  PubMed Central  Google Scholar 

  • Diagne A, Amovin-Assagba E, Futakuchi K, Wopereis MCS (2013a) Estimating of cultivated area, number of farming households and yield for major rice-growing environments in Africa. In: Wopereis MCS, Johnson DE, Ahimadi N, Tollens E, Jalloh A (eds) Realizing Africa’s rice promise. CAB International, Wallingford, UK, pp 35–45

    Chapter  Google Scholar 

  • Diagne A, Alla DY, Amovin-Assagba E, Saito K, Nakelse T (2013b) Farmer perceptions of the biophysical constraints to rice production in sub-Saharan Africa, and potential impact of research. In: Wopereis MCS, Johnson DE, Ahimadi N, Tollens E, Jalloh A (eds) Realizing Africa’s rice promise. CAB International, Wallingford, UK, pp 35–45

    Chapter  Google Scholar 

  • Dorenbos J and Pruitt WO (1977) Guideline for predicting crop water requirement: Irrigation and drainage paper 24. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy

  • Dubock A (2017) An overview of agriculture, nutrition and fortification, supplementation and biofortification: Golden Rice as an example for enhancing micronutrient intake. Agric Food Secur 6(1):1–20. https://doi.org/10.1186/s40066-017-0135-3

    Article  Google Scholar 

  • Egea LA, Mérida-García R, Kilian A, Hernandez P, Dorado G (2017) Assessment of genetic diversity and structure of large garlic (Allium sativum) germplasm bank, by diversity arrays technology “genotyping-by-sequencing” platform (DArTseq). Front Genet 8:98. https://doi.org/10.3389/fgene.2017.00098

    Article  PubMed  PubMed Central  Google Scholar 

  • FAO – Food and Agriculture Organization of the United Nations: FAOSTAT (2016). http://faostat3.fao.org/download/Q/QC/E.

  • FAO – Food and Agriculture Organization of the United Nations: FAOSTAT (2017). http://faostat3.fao.org/download/Q/QC/E.

  • FAO – Food and Agriculture Organization of the United Nations: FAOSTAT (2019). http://faostat3.fao.org/download/Q/QC/E.

  • Fiust A, Rapacz M, Wójcik-Jagła M et al (2015) Development of DArT-based PCR markers for selecting drought-tolerant spring barley. J Appl Genetics 56:299–309. https://doi.org/10.1007/s13353-015-0273-x

    Article  CAS  Google Scholar 

  • Fukai S, Pantuwan G, Jongdee B, Cooper M (1999) Screening for drought resistance in rainfed lowland rice. Field Crop Res 64:61–74

    Article  Google Scholar 

  • Handmer JY, Honda ZW, Kundzewicz N, Arnell G, Benito J, Hatfield IF, Mohamed P, Peduzzi S, Wu B, Sherstyukov K, Takahashi and Yan Z (2012) Changes in impacts of climate extremes: human systems and ecosystems. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M and Midgley PM (eds.) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge: Cambridge University Press, pp. 231–290

  • Harushima Y, Yano M, Shomura A, Sato M, Shimano T, Kuboki Y, Yamamoto T, Lin SY, Antonio BA, Parco A (1998) A high-density rice genetic linkage map with 2275 markers using a single F2 population. Genetics 148(1):479–494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hittalmani S, Huang N, Courtious B, Venuprasad R, Shashidar HE, Bagali GG, Li ZK, Zhuang JY, Zheng KL, Liu GF, Wang GC, Singh VP, Sidhu JS, Srivantaneeyakul S, McLaren G, Khush GS (2003) Identification of QTL for growth and grain yield related traits in rice across nine locations in Asia. Theor Appl Genet 107:679–690. https://doi.org/10.1007/s00122-003-1269-1

    Article  PubMed  Google Scholar 

  • Hu J, Zhu L, Zeng DL, Gao ZY et al (2010) Identification and characterization of NARROW ANDROLLED LEAF 1, a novel gene regulating leaf morphology and plant architecture in rice. Plant Mol Biol 73:283–292

    Article  CAS  PubMed  Google Scholar 

  • Hussain W, Baenziger PS, Belamkar V, Guttieri MJ, Venegas JP, Easterly A, Sallam A, Poland P (2017) Genotyping-by-sequencing derived high-density linkage map and its application to QTL mapping of flag leaf traits in bread wheat. Sci Rep 7:16394. https://doi.org/10.1038/s41598-017-16006-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ibrahim FA (2014) Reproductive Stage Drought Screening of F3 Progenies from a Reciprocal Oryza sativa x Oryza sativa Cross. Master Dissertation, University of Ibadan, Nigeria

  • Ishimaru K, Yano M, Aoki N, Ono K, Hirose T, Lin SY, Monna L, Sasaki T, Ohsugi R (2001) Toward the mapping of physiological and agronomic characters on a rice function map: QTL analysis and comparison between QTLs and expressed sequence tags. Theor Appl Genet 102:793–800

    Article  CAS  Google Scholar 

  • Islam M, Ontoy J, Subudhi PK (2019) Meta-analysis of quantitative trait loci associated with seedling-stage salt tolerance in rice (Oryza sativa L). Plants 8(2):33

    Article  CAS  PubMed Central  Google Scholar 

  • Kaladhar K, Swamy BPM, Babu AP, Reddy C, Sarala N (2008) Mapping quantitative trait loci for yield traits in BC2F2 population derived from “Swarna” x O. nivara cross. Rice Genet Newslett 24:34–36

    Google Scholar 

  • Kazan K, Lyons R (2015) Link between flowering time and stress tolerance. J Exp Bot 67(1):47–60. https://doi.org/10.1093/jxb/erv441

    Article  CAS  PubMed  Google Scholar 

  • Kesseli RV, Paran I, Michelmore RW (1994) Analysis of a detailed genetic linkage map of Lactuca sativa (Lettuce) constructed from RFLP and RAPD markers. Genetics 136:1435–1446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lanceras JC, Pantuwan G, Jongdee B, Toojinda T (2004) Quantitative trait Loci associated with drought tolerance at reproductive stage in rice. Plant Physiol 135:384–399. https://doi.org/10.1104/pp.103.035527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lei L, Zheng H, Wang J, Liu H, Sun J, Zhao H, Yang L, Zou D (2018) Genetic dissection of rice (Oryza sativa L) tiller, plant height, and grain yield based on QTL mapping and metaanalysis. Euphytica 214(7):109

    Article  Google Scholar 

  • Lewis P, Monem MA, Impiglia A (2018) Impacts of climate change on farming systems and livelihoods in the near east and North Africa - With a special focus on small-scale family farming. FAO, Cairo, p 92

    Google Scholar 

  • Li C, Bai G, Chao S, Wang Z (2015) A high-density SNP and SSR consensus map reveals segregation distortion regions in wheat. Biomed Res Int 2015:1–10

    Google Scholar 

  • Li H, Vaillancourt R, Mendham N et al (2008) Comparative mapping of quantitative trait loci associated with waterlogging tolerance in barley (Hordeum vulgare L.). BMC Genom 9:401. https://doi.org/10.1186/1471-2164-9-401

    Article  CAS  Google Scholar 

  • Li ZK, Yu SB, Lafitte HR, Huang N, Courtois B, Hittalmani S, Vijayakumar CH, Liu GF, Wang GC, Shashidhar HE, Zhuang JY, Zheng KL, Singh VP, Sidhu JS, Srivantaneeyakul S, Khush GS (2003) QTL x environment interactions in rice. I. heading date and plant height. Theor Appl Genet 108:141–153

    Article  CAS  PubMed  Google Scholar 

  • Lu CF, Shen LH, Tan ZB, Xu YB, He P, Chen Y, Zhu LH (1997) Comparative mapping of QTLs for agronomic traits of rice across environments by using a doubled-haploid population. Theor Appl Genet 94:145–150

    Article  CAS  PubMed  Google Scholar 

  • Lu C, Shen L, Tan Z, Xu Y, He P, Chen Y, Zhu L, Xu YB (1996) Comparative mapping of QTLs for agronomic traits of rice across environments using a doubled haploid population. Theor Appl Genet 93:1211–1217

    Article  CAS  PubMed  Google Scholar 

  • Ma LL, Guo LB, Qian Q (2006) Germplasm resources and genetic analysis of large grain in rice. Chin Bull Bot 23:395–401 (in Chinese with an English abstract)

    Google Scholar 

  • Mackay TFC, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Genetics 10:565. https://doi.org/10.1038/nrg2612

    Article  CAS  PubMed  Google Scholar 

  • Maheswaran M, Huang N, Sreerangasamy S, McCouch SR (2000) Mapping quantitative trait loci associated with days to flowering and photoperiod sensitivity in rice (Oryza sativa L.). Mol Breed 6:145–155. https://doi.org/10.1023/A:1009618621720

    Article  CAS  Google Scholar 

  • Marino R, Volante A, Brandolini A, Heun M (2018) A high-resolution einkorn (Triticum monococcum L.) linkage map involving wild, domesticated and feral einkorn genotypes. Plant Breed 137(5):682–690. https://doi.org/10.1111/pbr.12637

    Article  CAS  Google Scholar 

  • Marri PR, Sarla N, Reddy LV, Siddiq EA (2005) Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon. BMC Genet 6:33

    Article  PubMed  PubMed Central  Google Scholar 

  • Meng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in bi-parental populations. Crop J 3:169–173

    Article  Google Scholar 

  • Moncada P, Martinez CP, Borrero J, Chatel M, Gauch H, Guimaraes E, Tohme J, McCouch SR (2001) Quantitative trait loci for yield and yield components in an Oryza sativa X Oryza rufipogon BC2F2 population evaluated in an upland environment. Theor Appl Genet 102(1):41–52

    Article  CAS  Google Scholar 

  • Myśków B, Góralska M, Lenarczyk N et al (2018) Putative candidate genes responsible for leaf rolling in rye (Secale cereale L.). BMC Genet 19:57. https://doi.org/10.1186/s12863-018-0665-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Purbajanti ED, Kusmiyati P, Fuskhah E (2017) Growth, yield and physiological characters of three types of indonesian rice under limited water supply. Asian J Plant Sci 16:101–108

    Article  CAS  Google Scholar 

  • Qu P, Shi J, Chen T, Chen K, Shen CC, Wang J, Zhao X, Ye G, Xu J, Zhang L (2020) Construction and integration of genetic linkage maps from three multi-parent advanced generation inter-cross populations in rice. Rice 13:13. https://doi.org/10.1186/s12284-020-0373-z

    Article  PubMed  PubMed Central  Google Scholar 

  • Ravindra-Babu V, Shreya K, Kuldeep SD, Usharani G, Siva SA (2012) Correlation and path analysis studies in popular rice hybrids of India. Int J Sci Res Publ 2(3):1–5

    Google Scholar 

  • Ren R, Ray R, Li P, Xu J, Zhang M, Liu G et al (2015) Construction of a high-density DArTseq SNP-based genetic map and identification of genomic regions with segregation distortion in a genetic population derived from a cross between feral and cultivated-type watermelon. Mol Genet Genom 290:1457–1470. https://doi.org/10.1007/s00438-015-0997-7

    Article  CAS  Google Scholar 

  • Saintenac C, Jiang D, Wang S, Akhunov E (2013) Sequence-based mapping of the polyploid wheat genome. G3 Genes Genomes Genetics 3:1105–1114

    PubMed  PubMed Central  Google Scholar 

  • Sandhu N, Jain S, Kumar A, Mehla BS, Jain R (2013) Genetic variation, linkage mapping of QTL and correlation studies for yield, root, and agronomic traits for aerobic adaptation. BMC Genet 14:104. https://doi.org/10.1186/1471-2156-14-104

    Article  PubMed  PubMed Central  Google Scholar 

  • Sansaloni C, Petroli C, Jaccoud D, Carling J, Detering F, Grattapaglia D et al (2011) Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus. BMC Proc 5:P54. https://doi.org/10.1186/1753-6561-5-S7-P54

    Article  PubMed Central  Google Scholar 

  • Sasaki T, Burr B (2000) International Rice Genome Sequencing Project: the effort to completely sequence the rice genome. Curr Opin Plant Biol 3(2):138–141. https://doi.org/10.1016/s1369-5266(99)00047-3

    Article  CAS  PubMed  Google Scholar 

  • Schouten HJ, van de Weg WE, Khan SA, van Kaauwen MPW, Koehorst-van HJJ, Putten YN (2012) Diversity arrays technology (DArT) markers in apple for genetic linkage maps. Mol Breeding 29:645–660. https://doi.org/10.1007/s11032-011-9579-5

    Article  CAS  Google Scholar 

  • Semagn K, Bjørnstad A, Xu Y (2010) The genetic dissection of quantitative traits in crops. Electron J Biotechnol 13(5):14. https://doi.org/10.2225/vol13-issue5-fulltext-14

    Article  Google Scholar 

  • Shahin A, Arens P, Van Heusden AW, Van Der Linden G, Van Kaauwen M, Khan N et al (2011) Genetic mapping in Lilium: mapping of major genes and quantitative trait loci for several ornamental traits and disease resistances. Plant Breed 130:372–382. https://doi.org/10.1111/j.1439-0523.2010.01812.x

    Article  CAS  Google Scholar 

  • Simmonds J, Scott P, Leverington-Waite M et al (2014) Identification and independent validation of a stable yield and thousand grain weight QTL on chromosome 6A of hexaploid wheat (Triticum aestivum L.). BMC Plant Biol 14:191. https://doi.org/10.1186/s12870-014-0191-9

    Article  PubMed  PubMed Central  Google Scholar 

  • Solis J, Gutierrez A, Mangu V, Sanchez E, Bedre R, Linscombe S, Baisakh N (2018) Genetic mapping of quantitative trait loci for grain yield under drought in rice under controlled greenhouse conditions. Front Chem 5:129. https://doi.org/10.3389/fchem.2017.00129

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Statista (2016). http://www.statista.com/topics/1443/rice/

  • Suenaga K, Khairallah M, William HM, Hoisington DA (2005) A new intervarietal linkage map and its application for quantitative trait locus analysis of ‘gigas’ features in bread wheat. Genome 48:65–75

    Article  CAS  PubMed  Google Scholar 

  • Swamy BP, Vikram P, Dixit S, Ahmed HU, Kumar A (2011) Metaanalysis of grain yield QTL identified during agricultural drought in grasses showed consensus. BMC Genom 12:319. https://doi.org/10.1186/1471-2164-12-319

    Article  Google Scholar 

  • Teng S, Qian Q, Zeng DL, Kunihiro Y, Fujimoto K, Huang DN, Zhu LH (2004) QTL analysis of leaf photosynthetic rate and related physiological traits in rice (Oryza sativa L.). Euphtyca 135:1–7

    Article  CAS  Google Scholar 

  • Thorat BS, Kunkerkar RL, Raut SM, Desai SS, Gavai MP, Keluskar MH, Dhekale JS (2019) Correlation studies in hybrid rice (Oryza sativa L.). Int J Current Microbiol Appl Sci 8(4):1158–1164

    Article  Google Scholar 

  • Truong SK, McCormick RF, Morishige DT, Mullet JE (2014) Resolution of genetic map expansion caused by excess heterozygosity in plant recombinant inbred populations. G3 Genes Genomes Genetics 4:1963–1969

    PubMed  PubMed Central  Google Scholar 

  • Venuprasad R, Bool ME, Quiatchon L, Atlin GN (2012) A QTL for rice grain yield in aerobic environments with large effects in three genetic backgrounds. Theor Appl Genet 124:323–332. https://doi.org/10.1007/s00122-011-1707-4

    Article  CAS  Google Scholar 

  • Venuprasad R, Shashidhar HE, Hittalmani S, Hemamalini GS (2002) Tagging quantitative trait loci associated with grain yield and root morphological traits in rice (Oryza sativa L.) under contrasting moisture regimes. Euphytica 128:293–300

    Article  CAS  Google Scholar 

  • Vikram P, Swamy BPM, Dixit S, Ahmed H, Cruz MT, Singh AK et al (2012) Bulk segregant analysis: “an effective approach for mapping consistent-effect drought grain yield QTLs in rice.” Field Crops Res 134:185–192. https://doi.org/10.1016/j.fcr.2012.05.012

    Article  Google Scholar 

  • Wang J, Li H, Zhang L and Meng L (2016) Users’ Manual of QTL IciMapping. The Quantitative Genetics Group, Institute of Crop Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China, and Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6–641, 06600 Mexico, D.F., Mexico

  • Wang P, Zhou G, Cui K, Li Z, Yu S (2012) Clustered QTL for source leaf size and yield traits in rice (Oryza sativa L.). Mol Breed 29:99–113

    Article  CAS  Google Scholar 

  • Wang X, Pang Y, Zhang J, Zhang Q, Tao Y, Feng B, Zheng T, Xu J, Li Z (2014) Genetic background effects on QTL and QTL×environment interaction for yield and its component traits as revealed by reciprocal introgression lines in rice. Crop J 2(6):345–357. https://doi.org/10.1016/j.cj.2014.06.004

    Article  Google Scholar 

  • Wittenberg AHJ (2007) Genetic mapping using the diversity array technology (DArT). Application and validation using the whole-genome sequences of Arabidopsis thaliana and the fungal wheat pathogen Mycosphaerella graminicola [Doctoral thesis]. Wageningen University Press

  • Wójcik-Jagła M, Rapacz M, Tyrka M, Kościelniak J, Crissy K, Żmuda K (2013) Comparative QTL analysis of early short-time drought tolerance in Polish fodder and malting spring barleys. Theor Appl Genet 126:3021–3034

    Article  PubMed  PubMed Central  Google Scholar 

  • World Bank (2014). Transforming irrigation management in Nigeria. [Online] www.wds.worldbank.org/external/default/WDScontentserver/WDSp11B/2014/06/12/0035008120140612103504/Rendered/PDF/PAD1001 0REVISE020Box 385226B000OuO090.pdf

  • Xiao JH, Li J, Grandillo S, Ahn SN, Yuan LP, Tanksley SD (1998) Identification of trait-improving quantitative trait loci alleles from a wild rice relative, Oryza rufipogon. Genetics 150:899–909

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Q, Ma X, Lv T, Bai M, Wang Z, Niu J (2020) Effects of water stress on fluorescence parameters and photosynthetic characteristics of drip irrigation in rice. Water 12:289. https://doi.org/10.3390/w12010289

    Article  CAS  Google Scholar 

  • Xue D, Huang Y, Zhang X et al (2009) Identification of QTLs associated with salinity tolerance at late growth stage in barley. Euphytica 169:187–196. https://doi.org/10.1007/s10681-009-9919-2

    Article  Google Scholar 

  • Yang L, Wang J, Lei L, Wang J, Subhani MJ, Liu H, Sun J, Zheng H, Zhao H, Zou D (2018) QTL mapping for heading date, leaf area and chlorophyll content under cold and drought stress in two related recombinant inbred line populations (Japonica rice) and meta-analysis. Plant Breed 137(4):527–545

    Article  CAS  Google Scholar 

  • Yang QH, Lu W, Hu ML, Wang CM, Zhang RX, Yano M, Wan JM (2003) QTL and epistatic interaction underlying leaf chlorophyll and H2O2 content variation in rice (Oryza sativa L.). Acta Genet Sin 30:245–250

    CAS  PubMed  Google Scholar 

  • Yue B, Xue WY, Luo LJ, Xing YZ (2006) QTL analysis for flag leaf characteristics and their relationships with yield and yield traits in rice. Acta Genet Sin 33:824–832

    Article  CAS  PubMed  Google Scholar 

  • Zhang GH, Li SY, Wang L, Ye WJ, Zeng DL, Rao YC, Peng YL, Hu J, Yang YL, Xu J, Ren DY, Gao ZY, Zhu L, Dong GJ, Hu XM, Yan MX, Guo LB, Li CY, Qian Q (2014) LSCHL4 from japonica cultivar, which is allelic to NAL1, increases yield of indica super rice 93–11. Mol Plant 7:1350–1364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu C, Kobayashi K, Loladze I, Zhu J, Jiang Q, Xu X, Liu G, Seneweera S, Ebi KL, Drewnowski A, Fukagawa NK, Ziska LH (2018) Carbon dioxide (CO2) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. Sci Adv 4(5):eaaq1012. https://doi.org/10.1126/sciadv.aaq1012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This study was supported by the World Bank’s Africa Centre of Excellence in Agricultural Development and Sustainable Environment (CEADESE) with World Bank Grant No. ACE 023.

Author information

Author notes

  1. Kehinde A. Adeboye

    Present address: Department of Agricultural Technology, Ekiti State College of Agriculture and Technology, Isan-Ekiti, Nigeria

  2. Mande Semon

    Present address: Université Virtuelle de Côte D‘Ivoire (UVCI), Abidjan, Côte d’Ivoire

  3. Mamadou Fofana

    Present address: International Institute of Tropical Agriculture (IITA), Bukavu, Democratic Republic of Congo

Authors and Affiliations

  1. Center of Excellence in Agricultural Development and Sustainable Environment (CEADESE), Federal University of Agriculture, Abeokuta, Nigeria

    Kehinde A. Adeboye

  2. Africa Rice Center, Ibadan Station, Nigeria

    Kehinde A. Adeboye, Mande Semon & Mamadou Fofana

  3. Department of Crop Production and Horticulture, School of Agriculture, Lagos State Polytechnic, Ikorodu, Nigeria

    Oyeboade A. Oyetunde

  4. Department of Plant Breeding and Seed Technology, Federal University of Agriculture, Abeokuta, Nigeria

    Olusegun A. Oduwaye & Isaac O. Daniel

  5. Department of Animal Breeding and Genetic, Federal University of Agriculture, Abeokuta, Nigeria

    Ayotunde O. Adebambo

Authors
  1. Kehinde A. Adeboye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mande Semon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oyeboade A. Oyetunde
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olusegun A. Oduwaye
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ayotunde O. Adebambo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mamadou Fofana
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Isaac O. Daniel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Adeboye KA conceptualized the study, conducted the experiment, performed all analyses and drafted the manuscript; Semon M provided the plant materials, fine-tuned the concept and supervised the phenotyping; Oyetunde OA significantly contributed to data interpretation and review of literatures. Oduwaye OA, Adebambo AO and Daniel IO helped fine-tune the concept and supervise the study; Fofana M fine-tuned the concept and supervised the phenotyping. All authors revised and approved the manuscript.

Corresponding author

Correspondence to Kehinde A. Adeboye.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Adeboye, K.A., Semon, M., Oyetunde, O.A. et al. Diversity array technology (DArT)-based mapping of phenotypic variations among recombinant inbred lines of WAB638-1/PRIMAVERA under drought stress. Euphytica 217, 130 (2021). https://doi.org/10.1007/s10681-021-02860-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10681-021-02860-6

Keywords

Search

Navigation