Theoretical and Applied Genetics

, Volume 112, Issue 1, pp 106–113 | Cite as

Grain yield responses to moisture regimes in a rice population: association among traits and genetic markers

  • G. H. Zou
  • H. W. Mei
  • H. Y. Liu
  • G. L. Liu
  • S. P. Hu
  • X. Q. Yu
  • M. S. Li
  • J. H. Wu
  • L. J. LuoEmail author
Original Paper


Drought is a major constraint to rice (Oryza sativa L.) production in rainfed and poorly irrigated environments. Identifying genomic regions influencing the response of yield and its components to water deficits will aid our understanding of the genetic mechanism of drought tolerance (DT) of rice and the development of DT varieties. Grain yield (GY) and its components of a recombinant inbred population developed from a lowland rice and an upland rice were investigated under different water levels in 2003 and 2004 in a rainout DT screening facility. Correlation and path analysis indicated that spikelet fertility (SF) was particularly important for grain yield with direct effect (P=0.60) under drought stress, while spikelet number per panicle (SN) contributed the most to grain yield (P=0.41) under well-watered condition. A total of 32 quantitative trait loci (QTLs) for grain yield and its components were identified. The phenotypic variation explained by individual QTLs varied from 1.29% to 14.76%. Several main effect QTLs affecting SF, 1,000-grain weight (TGW), panicle number (PN), and SN were mapped to the same regions on chromosome 4 and 8. These QTLs were detected consistently across 2 years and under both water levels in this study. Several digenic interactions among yield components were also detected. The identification of genomic regions associated with GY and its components under stress will be useful to improve drought tolerance of rice by marker-aided approaches.


Drought Stress Drought Tolerance Drip Irrigation Grain Yield Water Stress Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Additive effect


Additive × additive epistasis


Additive × environment interaction


Epistasis × environment interaction


Drought tolerance


Epistatic QTL


Grain yield


Main effect QTL


Panicle number


Quantitative trait locus


QTL × environment interaction


Recombinant inbred line


Spikelet fertility


Spikelet number per panicle


Simple sequence repeats


1,000-grain weight



We are grateful to Dr A. Blum and Dr J. C. O’Toole for their advice on the construction of the screening facility and on drought tolerant screening trials. This study was jointly supported by grants from Chinese Ministry of Science and Technology (973 plan, 2004B17200; 863 plan, 2003AA207010), Chinese Ministry of Agriculture (948 plan, 2001-101), Shanghai Municipal Science and Technology Commission (02ZC14082, 03DJ14014, 05DJ14008) and the Rockefeller Foundation (2004FS071), New York, USA.


  1. Ali ML, Pathan MS, Zhang J, Bai G, Sarkarung S, Nguyen HT (2000) Mapping QTLs for root traits in a recombinant inbred population from two indica ecotypes of rice. Theor Appl Genet 101:756–766CrossRefGoogle Scholar
  2. Blum A, Munns R, Passioura JB, Turner NC, Sharp RE, Boyer JS, Nguyen HT, Hsiao TC (1996) Letters to the editors: genetically engineered plants resistance to soil drying and salt stress: how to interpret osmotic relations. Plant Physiol 110:1051–1053PubMedPubMedCentralGoogle Scholar
  3. Boonjung H, Fukai S (1996) Effects of soil water deficit at different growth stages on rice growth and yield under upland conditions. 2. Phenology, biomass production and yield. Field Crops Res 48:47–55CrossRefGoogle Scholar
  4. Clark LJ, Aphale SL, Barraclough PB (2000) Screening the ability of rice roots to overcome the mechanical impedance of wax layers: importance of test conditions and measurement criteria. Plant Soil 219:187–196CrossRefGoogle Scholar
  5. Cooper M, Rajatasereekul S, Immark S, Fukai S, Basnayake J (1999) Rainfed lowland rice breeding strategies for Northeast Thailand. I. Genotypic variation and genotype×environment interactions for grain yield. Field Crops Res 64:131–151CrossRefGoogle Scholar
  6. Courtois B, McLaren G, Sinha PK, Prasad K, Yadav R, Shen L (2000) Mapping QTLs associated with drought avoidance in upland rice. Mol Breed 6:55–66CrossRefGoogle Scholar
  7. Crosson P (1995) Natural resource and environmental consequences of rice production. In: Fragile lives in fragile ecosystems, Proceedings of the international rice research conference, IRRI, Los Banos, Manila, Philippines, pp 83–100Google Scholar
  8. Dey MM, Upadhyaya HK (1996) Yield loss due to drought, cold and submergence in Asia. In: Evenson RE, Herdt RW, Hossain M (eds) Rice research in Asia: progress and priorities. CAB International, Wallingford, pp 291–303Google Scholar
  9. Fukai S, Pantuwan G, Jongdee B, Coope M (1999) Screening for drought resistance in rainfed lowland rice. Field Crops Res 64:61–74CrossRefGoogle Scholar
  10. Hemamalini GS, Shashidhar HE, Hittalmani S (2000) Molecular marker assisted tagging of morphological and physiological traits under two contrasting moisture regimes at peak vegetative stage in rice. Euphytica 112:69–78CrossRefGoogle Scholar
  11. Hittalmani S, Shashidhar HE, Bagali PG, Ning Huang, Sidhu JS, Singh VP, Khush GS (2002) Molecular mapping of quantitative trait loci for plant growth, yield and yield related traits across three diverse locations in a doubled haploid rice population. Euphytica 125:207–214CrossRefGoogle Scholar
  12. Insightful Corporation (2001) S-plus 6 for windows, User’s guide. Seattle, WA, USAGoogle Scholar
  13. Kamoshita A, Wade J, Ali L, Pathan S, Zhang J, Sarkarung S, Nguyen T (2002a) Mapping QTLs for root morphology of a rice population adapted to rainfed lowland conditions. Theor Appl Genet 104:880–893CrossRefPubMedGoogle Scholar
  14. Kamoshita A, Zhang J, Siopongco J, Sarkarung S, Nguyen HT Wade LJ (2002b) Effects of phenotyping environment on identification of quantitative trait loci for rice root morphology under anaerobic conditions. Crop Sci 42:255–265CrossRefPubMedGoogle Scholar
  15. Lanceras JC, Pantuwan G, Jongdee B, Toojinda T (2004) Quantitative trait loci associated with drought tolerance at reproductive stage in rice. Plant Physiol 135:384–399CrossRefPubMedPubMedCentralGoogle Scholar
  16. Lebreton C, Lazic-Jancic V, Steed A, Pekic S, Quarrie SA (1995) Identification of QTL for drought responses in maize and their use in testing causal relationships between traits. J Exp Bot 46:853–865CrossRefGoogle Scholar
  17. Lilley JM, Ludlow MM, McCouch SR, O’Toole JC (1996) Locating QTL for osmotic adjustment and dehydration tolerance in rice. J Exp Bot 47:1427–1436CrossRefGoogle Scholar
  18. Lincoln SE, Daly MJ, Lander E (1992) Constructing genetic maps with MAPMAKER/EXP 3.0. Whitehead Institute Technical report, 3rd edn. Whitehead Institute, CambridgeGoogle Scholar
  19. Liu HY, Zou GH, Liu GL, Hu SP, Li MS, Mei HW, Yu XQ, Luo LJ (2005) Correlation analysis and QTL identification for canopy temperature, leaf water potential and spikelet fertility in rice under contrasting moisture regimes. Chinese Sci Bull 50:130–139CrossRefGoogle Scholar
  20. Mather K, Jinks JL (1982) Biometrical genetics, 3 edn. Chapman & Hall, LondonCrossRefGoogle Scholar
  21. McCouch SR, Cho YG, Yano M, Paul E, Blinstrub M, Morishima H, Kinoshita T (1997) Suggestion for QTL nomenclature. Rice Genet Newslett 14:11–13Google Scholar
  22. Pantuwan G, Fukai S, Cooper M, Rajatasereekul S, O’Toole JC (2002) Yield response of rice (Oryza sativa L.) genotypes to different types of drought under rainfed lowlands. Part 3. Plant factors contributing to drought resistance. Field Crops Res 73:181–200CrossRefGoogle Scholar
  23. Price AH, Steele KA, Gorham J, Bridges JM, Moore BJ, Evans JL, Richardson P, Jones RGW (2002a) Upland rice grown in soil-filled chambers and exposed to contrasting water-deficit regimes: I. Root distribution, water use and plant water status. Field Crops Res 76:11–24CrossRefGoogle Scholar
  24. Price AH, Steele KA, Moore BJ, Jones RGW (2002b) Upland rice grown in soil-filled chambers and exposed to contrasting water-deficit regimes: II. Mapping QTL for root morphology and distribution. Field Crops Res 76:25–43CrossRefGoogle Scholar
  25. Rajatasereekul S, Sriwisut S, Porn-uraisanit P, Ruangsook S, Mitchell JH, Fukai S (1997) Phenology requirement for rainfed lowland rice in Thailand and Lao PDR. In: Breeding strategies for rainfed lowland rice in drought-prone environments, Proceedings of an international workshop, Ubon Ratchatani, Thailand, 1996. ACIAR, Canberra, Australia, pp 97–103Google Scholar
  26. Shashidhar HE, Hemamalini GS, Hittalmani S (1999) Molecular marker-assisted tagging of morphological and physiological traits at the peak vegetative stage: two contrasting moisture regimes. In: Ito O, O’Toole J, Hardy B (eds) Genetic improvement of rice for water-limited environments. IRRI, Los Banos, pp 239–256Google Scholar
  27. Tripathy JN, Zhang JX, Robin S, Nguyen TT, Nguyen HT (2000) QTLs for cell-membrane stability mapped in rice (Oryza sativa L.) under drought stress. Theor Appl Genet 100:1197–1202CrossRefGoogle Scholar
  28. Wade LJ, McLaren CG, Quintana L, Harnpichitvitaya D, Rajatasereekul S, Sarawgi AK, Kumar A, Ahmed HU, Sarwoto, Singhf AK, Rodriguez R, Siopongco J, Sarkarung S (1999) Genotype by environment interactions across diverse rainfed lowland rice environments. Field Crop Res 64:35–50CrossRefGoogle Scholar
  29. Wang DL, Zhu J, Li ZK, Paterson AH (1999) Mapping QTLs with epistatic effects and QTL × environment interactions by mixed linear model approaches. Theor Appl Genet 99:1255–1264CrossRefGoogle Scholar
  30. Yadav R, Courtois B, Huang N, McLaren G (1997) Mapping genes controlling root morphology and root distribution in a double-haploid population of rice. Theor Appl Genet 94:619–632CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • G. H. Zou
    • 1
    • 2
  • H. W. Mei
    • 2
  • H. Y. Liu
    • 2
    • 3
  • G. L. Liu
    • 1
    • 2
  • S. P. Hu
    • 2
  • X. Q. Yu
    • 2
  • M. S. Li
    • 2
  • J. H. Wu
    • 2
  • L. J. Luo
    • 1
    • 2
  1. 1.Huazhong Agricultural UniversityWuhanChina
  2. 2.Shanghai Agrobiological Gene CenterShanghaiChina
  3. 3.Shanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations