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Euphytica

, 213:37 | Cite as

Identification of quantitative trait loci for panicle length and yield related traits under different water and P application conditions in tropical region in rice (Oryza sativa L.)

  • Ian Paul Navea
  • Maria Stefanie Dwiyanti
  • Jonghwa Park
  • Backki Kim
  • Sangbum Lee
  • Xing Huang
  • Hee-Jong Koh
  • Joong Hyoun Chin
Article
  • 394 Downloads

Abstract

Climate change is projected to have a serious impact on the yield potential of rice in tropical as well as in temperate countries. It is therefore essential to develop rice varieties which are climate change ready and with stable yield when grown under low inputs of irrigation water and fertilizer. In this study, the effects of the shift from temperate to tropical environment as well as the different levels of water regime-phosphorus application were evaluated using a set of temperate recombinant inbred lines (RILs) derived from a cross between Dasanbyeo (Tongil-type indica) and TR22183 (temperate japonica). Here, we have identified genetic mechanisms for yield stability mainly by observing the panicle length in the RILs and the parental lines. TR22183 grown in the Philippines showed no reduction in panicle length whereas the Dasanbyeo exhibited a considerable reduction in panicle length when grown in the Philippines compared to those grown in Korea. In the RILs, a total of 18 QTLs for panicle length were identified across 12 chromosomes except in chromosomes 6 and 7. There were six interesting panicle length QTLs, qPL1.4, qPL2.1, qPL2.2, qPL4.1, qPL9.2, and qPL11.2 on chromosomes 1, 2, 4, 9, and 11 respectively. They were clustered together with other yield-related QTLs such as spikelet number and grain number in two different years. Except for qPL2.1, all the beneficial alleles originated from TR22183. The panicle length QTLs were identified across different water-P treatments. Interestingly, qPL1.4, qPL2.1, qPL4.1, and qPL11.2 were constantly detected in the low-input tropical condition. No QTL for panicle length was identified in the parallel experiment conducted under temperate conditions in Korea suggesting that the QTLs identified in tropical conditions could be useful in breeding programs to develop rice varieties that have stable yield potential under a warming temperate climate.

Keywords

Climate change Phosphorus Panicle length Yield QTL EpQTL SNP 

Notes

Acknowledgements

This study was supported by a Grant from the Next-Generation BioGreen 21 Program (No. PJ01102401) of the Rural Development Administration, Korea. We would like to thank IRRI GSL and the MBAST staff of the International Rice Research Institute and Dr. Hong-Ryul Kim of Seoul National University in Korea. We also would like to thank Mr. Karl Jensen Victorio and Quedahm Chin for editing the manuscript thoroughly.

Supplementary material

10681_2016_1822_MOESM1_ESM.tif (1.8 mb)
Supplementary Fig. 1. Phenotypic distribution of panicle length of DT-RILs under four water-P conditions in Philippines (Exp12 and Exp14). DS: Dasanbyeo parental phenotype, TR: TR22183 parental phenotype, RF_0P: rainfed, no P application, IR_0P: irrigated, no P application, RF_60P: rainfed, normal P application, IR_60P: irrigated, normal P application (TIFF 1855 kb)
10681_2016_1822_MOESM2_ESM.tif (2 mb)
Supplementary Fig. 2. Agro-meteorological data of the experiments conducted under tropical conditions (TIFF 2018 kb)
10681_2016_1822_MOESM3_ESM.tif (11.5 mb)
Supplementary Fig. 3. M-QTLs and EpQTLs identified in this study (TIFF 11800 kb)
10681_2016_1822_MOESM4_ESM.tif (1.5 mb)
Supplementary Fig. 4. Comparison in panicle length of DT-RILs with different combinations of allele types in qPL2.1 and qPL9.2. DS: Dasanbyeo allele, TR: TR22183 allele, RF: rainfed condition, IR: irrigation condition, 0P: no P application, 60P: normal P application (TIFF 1513 kb)
10681_2016_1822_MOESM5_ESM.pdf (56 kb)
Supplementary Table 1. Distribution of agronomic traits in DT-RILs and parental lines in different P-water treatments in tropical conditions (PDF 57 kb)
10681_2016_1822_MOESM6_ESM.pdf (34 kb)
Supplementary Table 2. Correlation of panicle length to other yield-related traits (PDF 35 kb)
10681_2016_1822_MOESM7_ESM.pdf (57 kb)
Supplementary Table 3. Soil nature and property from the experiment conducted in tropical conditions (PDF 58 kb)
10681_2016_1822_MOESM8_ESM.pdf (59 kb)
Supplementary Table 4. Analysis of variance table using GLM analysis on P and water effect on panicle length (PDF 60 kb)
10681_2016_1822_MOESM9_ESM.pdf (124 kb)
Supplementary Table 5. Panicle length EpQTLs identified in this study (PDF 125 kb)
10681_2016_1822_MOESM10_ESM.pdf (44 kb)
Supplementary Table 6. Differential QTLs identified in different P-water treatments in the tropics (PDF 44 kb)
10681_2016_1822_MOESM11_ESM.pdf (73 kb)
Supplementary Table 7. Total PVE (%) of each trait under different P-water treatments (PDF 73 kb)
10681_2016_1822_MOESM12_ESM.pdf (168 kb)
Supplementary Table 8. QTLs identified in this study co-located with previously published QTLs (PDF 169 kb)

References

  1. Chen S, Zeng F, Pao Z, Zhang G (2008) Characterization of high-yield performance as affected by genotype and environment in rice. J Zhejiang Univ Sci B 9(5):363–370CrossRefPubMedPubMedCentralGoogle Scholar
  2. Chin JH, Kim JH, Jiang WZ, Chu SH, Woo MO, Han LZ, Brar DS, Koh HJ (2007) Identificaiton of subspecies-specific STS markers and their association with segregation distortion in rice (Oryza sativa L.). J Crop Sci Biotechnol 10(3):175–184Google Scholar
  3. Chivanno S, Souvannalath S, Lersupavithnapa B, Kerdsuk V, Thuan N (2008) Strategies for managing climate risks in the Lower Mekong River Basin: a place-based approach. In: Leary JAN (ed) Climate change and adaptation. Earthscan, London, pp 333–350Google Scholar
  4. Cho YI, Jiang WZ, Chin JH, Piao ZZ, Cho YI, McCouch SRM, Koh HJ (2007) Identification of QTLs associated with physiological nitrogen use efficiency in rice. Mol Cells 23:72–79PubMedGoogle Scholar
  5. Chung YS, Yoon MB, Kim HS (2004) On climate variations and changes observed in South Korea. Clim Change 66(1):151–161CrossRefGoogle Scholar
  6. Cui K, Peng S, Xing Y, Su S, Xu C, Zhang Q (2003) Molecular dissection of the genetic relationships of source-sink and transport tissue with yield traits in rice. Theor Appl Genet 106:649–658CrossRefPubMedGoogle Scholar
  7. Dong Y, Tsuzuki E, Lin D, Kamiunten H, Terao H, Matsuo M, Cheng S (2004) Molecular genetic mapping of quantitative trait loci for milling quality in rice. J Cereal Sci 40:109–114CrossRefGoogle Scholar
  8. Fan J, Oliphant A, Shen R, Kermani B, Garcia F, Gunderson K, Hansen M, Steemers F, Butler SL, Deloukas P, Galver L, Hunt S, McBride C, Bibikova M, Rubano T, Chen J, Wickham E, Doucet D, Chang W, Campbell D, Zhang B, Kruglyak S, Bentley D, Haas J, Rigault P, Zhou L, Stuelpnagel J, Chee MS (2003) Highly parallel SNP genotyping. Cold Spring Harb Symp Quant Biol 68:69–78CrossRefPubMedGoogle Scholar
  9. Haefele SM, Hijmans RJ (2007) Soil quality in rice-based rainfed lowlands of Asia: characterization and distribution. In: Aggarwal PK, Ladha JK, Singh RK, Devakumar C, Hardy B (eds), Science, technology, and trade for peace and prosperity. Proceedings of the 26th international rice research conference, 9–12 October 2006, New Delhi, India. Los Baños (Philippines) and New Delhi (India): International Rice Research Institute, Indian Council of Agricultural Research, and National Academy of Agricultural Sciences, pp 297–308Google Scholar
  10. Hittalmani S, Shashidhar H, Bagali P, Huang N, Sidhu J, Singh V (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
  11. Hua J, Xing Y, Xu C, Sun X, Yu S, Zhang Q (2002) Genetic dissection of an elite rice hybrid reveal that heterozygotes are not always advantageous for performance. Genetics 162:1885–1895PubMedPubMedCentralGoogle Scholar
  12. Huang X, Qian Q, Liu Z, Sun H, He S, Luo D, Xia G, Chu C, Li J, Fu X (2009) Natural variation at the DEP1 locus enhances grain yield in rice. Nat Genet 41:494–497CrossRefPubMedGoogle Scholar
  13. Jiang GH, Xu CG, Li XH, He YQ (2004) Characterization of the genetic basis for yield and its component traits of rice revealed by doubled haploid population. Yi Chuan Xue Bao 31:63–72PubMedGoogle Scholar
  14. Jiang W, Lee J, Chu SH, Ham TH, Woo MO, Cho YI, Chin JH, Han LZ, Xuan Y, Yuan D, Xu F, Dai Y, Yea JD, Koh HJ (2010) Genotype x environment interactions for chilling tolerance of rice recombinant inbred lines under different low temperature environments. Field Crop Res 117:226–236CrossRefGoogle Scholar
  15. Jiang W, Lee J, Jin YM, Qiao Y, Piao R, Jang SM, Woo MO, Kwon SW, Liu X, Pan HY, Du X, Koh HJ (2011) Identification of QTLs for seed germination capability after various storage periods using two RIL populations in rice. Mol Cells 31:385–392CrossRefPubMedPubMedCentralGoogle Scholar
  16. Kane S, Reilly J, Tobey J (1992) An emperical study of the economic effects of climate change on world agriculture. Clim Change 21:17–35CrossRefGoogle Scholar
  17. Kim B, Kim DG, Lee G, Seo J, Choi IY, Choi BS, Yang TJ, Kim KS, Lee J, Chin JH, Koh HJ (2014) Defining the genome structure of “Tongil” rice, an important cultivar in the Korean “Green Revolution”. Rice 7:22. doi: 10.1186/s12284-014-0022-5 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Li X, Qian Q, Fu Z, Wang Y, Xiong G, Zeng D, Liu X, Teng S, Hiroshi F, Yuan M, Luo D, Han B, Li J (2003) Control of tillering in rice. Nature 422:618–620CrossRefPubMedGoogle Scholar
  19. Li F, Liu W, Tang J, Chen J, Tong H, Hu B, Li C, Fang J, Chen M, Chu C (2010) Rice DENSE AND ERECT PANICLE 2 is essential for determining panicle outgrowth and elongation. Cell Res 20:838–849CrossRefPubMedGoogle Scholar
  20. Li M, Tang D, Wang K, Wu X, Lu L, Yu H, Gu M, Yan C, Cheng Z (2011a) Mutations in the F-box gene LARGER PANICLE improve the panicle architecture and enhance the grain yield in rice. Plant Biotechnol J 9:1002–1013CrossRefPubMedGoogle Scholar
  21. Li Y, Fan C, Xing Y, Jiang Y, Luo L, Sun L, Shao D, Xu C, Li X, Xiao J, He Y, Zhang Q (2011b) Natural variation in GS5 plays an important role in regulating grain size and yield in rice. Nat Genet 43:1266–1269CrossRefPubMedGoogle Scholar
  22. Lin HX, Qian HR, Zhuang JY, Lu J, Min SK, Xiong ZM, Hunag N, Zheng KL (1995) Interval mapping of QTLs for yield and other related characters in rice. Rice Genet Newsl 12:251–253Google Scholar
  23. Liu E, Liu Y, Wu G, Zeng S, Thi TG, Liang L, Liang Y, Dong Z, She D, Wang H, Zaid IU, Hong D (2016) Identification of a candidate gene for panicle length in rice (Oryza sativa L.) via association and linkage analysis. Front Plant Sci 7:596. doi: 10.3389/fpls.2016.00596 PubMedPubMedCentralGoogle Scholar
  24. Lu Z, Yu H, Xiong G, Wang J, Jiao Y, Liu G, Jing Y, Meng X, Hu X, Qian Q, Fu X, Wang Y, Li J (2013) Genome-wide binding analysis of the Transcription Activator IDEAL PLANT ARCHITECTURE1 reveals a complex network regulating rice plant architecture. Plant Cell 25:3743–3759CrossRefPubMedPubMedCentralGoogle Scholar
  25. Mao BB, Cai WJ, Zhang ZH, Hu ZL, Li P, Zhu LH, Zhu YG (2003) Characterization of QTLs for harvest index and source-sink characters in a DH population of rice (Oryza sativa L.). Yi Chuan Xue Bao 30:1118–1126PubMedGoogle Scholar
  26. Mao H, Sun S, Yao J, Wang C, Yu S, Xu C, Li X, Zhang Q (2010) Linking differential domain functions of the GS3 protein to natural variation of grain size in rice. Proc Natl Acad Sci USA 107(45):19579–19584CrossRefPubMedPubMedCentralGoogle Scholar
  27. 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 6:33CrossRefPubMedPubMedCentralGoogle Scholar
  28. 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 3:265–279CrossRefGoogle Scholar
  29. Nakagawa H, Tanaka A, Tanabata T, Ohtake M, Fujioka S, Nakamura H, Ichikawa H, Mori M (2012) SHORT GRAIN1 decreases organ elongation and brassinosteroid response in rice. Plant Physiol 158(3):1208–1219CrossRefPubMedGoogle Scholar
  30. Peng S, Huang J, Sheehy J, Laza R, Visperas R, Zhong X, Centeno GS, Khush G, Cassman K (2004) Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci USA 101:9971–9975CrossRefPubMedPubMedCentralGoogle Scholar
  31. Piao R, Jiang W, Ham TH, Choi MS, Qiao Y, Chu SH, Park JH, Woo MO, Jin Z, An G, Lee J, Koh HJ (2009) Map-based cloning of the ERECT PANICLE 3 gene in rice. Theor Appl Genet 119:1497–1506CrossRefPubMedGoogle Scholar
  32. Qi W, Sun F, Wang Q, Chen M, Huang Y, Feng YQ, Luo X, Yang J (2011) Rice ethylene-response AP2/ERF factor OsEATB restricts internode elongation by down-regulating a gibberellin biosynthetic gene. Plant Physiol 157(1):216–228CrossRefPubMedPubMedCentralGoogle Scholar
  33. Qiao Y, Piao R, Shi J, Lee SI, Jiang W, Kim BK, Lee J, Han L, Ma W, Koh HJ (2011) Finemapping and candidate gene analysis of dense and erect panicle 3, DEP3, which confers high grain yield in rice (Oryza sativa L.). Theor Appl Genet 122:1439–1449CrossRefPubMedGoogle Scholar
  34. Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8(6):e66428CrossRefPubMedPubMedCentralGoogle Scholar
  35. Song X, Huang W, Shi M, Zhu M, Lin H (2007) A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet 39:623–630CrossRefPubMedGoogle Scholar
  36. Srikanth B, Subhakara R, Surekha K, Subrahmanyam D, Voleti S, Neeraja C (2016) Enhanced expression of OsSPL14 gene and its association with yield components in rice (Oryza sativa) under low nitrogen conditions. Gene 576:441–450CrossRefPubMedGoogle Scholar
  37. Tabuchi H, Zhang Y, Hattori S, Omae M, Shimizu-Sato S, Oikawa T, Qin Q, Nishimura M, Kitano H, Xie H, Fang X, Yoshida H, Kyozuka J, Chen F, Sato Y (2011) LAX PANICLE2 of rice encodes a novel nuclear protein and regulates the formation of axillary meristems. Plant Cell 23(9):3276–3287CrossRefPubMedPubMedCentralGoogle Scholar
  38. Thomson MJ (2014) High-throughput SNP genotyping to access crop improvement. Plant Breed Biotechnol 2:195–212CrossRefGoogle Scholar
  39. Thomson MJ, Tai TH, McClung AM, Lai XH, Hinga MH, Lobos KB, Xu Y, Martinez CP, McCouch SR (2003) Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theor Appl Genet 107:479–493CrossRefPubMedGoogle Scholar
  40. Thomson MJ, Zhao K, Wright M, McNally K, Rey J, Tung CW, Reynolds A, Scheffler B, Eizenga G, McClung A, Kim H, Ismail AM, de Ocampo M, Mojica C, Reveche MY, Dilla-Ermita CJ, Mauleon R, Leung H, Bustamante C, McCouch SR (2012) High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Mol Breed 29:875–886CrossRefGoogle Scholar
  41. Tobey J, Reilly J, Kane S (1992) Economic implications of global climate change for world agriculture. J Agric Resour Econ 17:195–204Google Scholar
  42. Van Kauwenbergh S, Steward M, Mikkelsen R (2013) World reserves of phosphate rock: a dynamic and unfolding story. Better Crops 97:18–20Google Scholar
  43. Wang D, Zhu J, Li Z, Paterson A (1999) Mapping QTLs with epistatic effects and QTL× environment interactions by mixed linear model approaches. Theor Appl Genet 99(7):1255–1264CrossRefGoogle Scholar
  44. Wang J, Nakazaki T, Chen S, Chen W, Saito H, Tsukiyama T, Okumoto Y, Xu Z, Tanisaka T (2009) Identification an characterization of the erect-pose panicle gene EP conferring high grain yield in rice (Oryza sativa L.). Theor Appl Genet 119:85–91CrossRefPubMedGoogle Scholar
  45. Wassmann R, Dobermann A (2007) Climate change adaptation through rice production in regions with high poverty levels. SAT eJournal 4(1):1–24Google Scholar
  46. Wright MH, Tung CW, Zhao K, Reynolds A, McCouch SR, Bustamante CD (2010) ALCHEMY: a reliable method for automated SNP genotype calling for small batch sizes and highly homozygous populations. Bioinformatics 26(23):2952–2960CrossRefPubMedPubMedCentralGoogle Scholar
  47. Xiao J, Li J, Yuan L, Tanksley SD (1996) Identification of QTLs affecting traits of agronomic importance in a recombinant inbred population derived from a subspecific rice cross. Theor Appl Genet 92:230–244CrossRefPubMedGoogle Scholar
  48. Xing Z, Tan F, Hua P, Sun L, Xu G, Zhang Q (2002) Characterization of the main effects, epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice. Theor Appl Genet 105:248–257CrossRefPubMedGoogle Scholar
  49. Yonemaru JI, Yamamoto T, Fukuoka S, Uga Y, Hori K, Yano M (2010) Q-TARO: QTL annotation rice online database. Rice 3(2):194–203CrossRefGoogle Scholar
  50. Yoon DB, Kang KH, Kim HJ, Ju HG, Kwon SJ, Suh JP, Jeong OY, Ahn SN (2006) Mapping quantitative trait loci for yield components and morphological traits in an advanced backcross population between Oryza grandiglumis and the O. sativa japonica cultivar Hwaseongbyeo. Theor Appl Genet 112:1052–1062CrossRefPubMedGoogle Scholar
  51. Zuo J, Li J (2013) Molecular dissection of complex agronomic traits in rice: a team effort by Chinese scientists in recent years. Nat Sci Rev 1:253–276CrossRefGoogle Scholar
  52. Zuo S, Kang H, Li Q, Chen Z, Zhang Y, Liu W, Wang G, Chen H, Pan X (2014) Genome-wide association analysis on genes controlling panicle traits of varieties from international rice core collection bank and its breeding utilization. Chin J Rice Sci 28:649–658Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Plant Breeding, Genetics, and Biotechnology DivisionInternational Rice Research InstituteLos BanosPhilippines
  2. 2.Division of Plant Science, Plant Genomics and Breeding Institute, and Research Institute of Agriculture and Life SciencesSeoul National UniversitySeoulKorea
  3. 3.Graduate School of Integrated BioindustrySejong UniversitySeoulKorea

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