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.
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References
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):363–370
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):175–184
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 333–350
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 23:72–79
Chung YS, Yoon MB, Kim HS (2004) On climate variations and changes observed in South Korea. Clim Change 66(1):151–161
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 106:649–658
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 40:109–114
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 68:69–78
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 297–308
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 125:207–214
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 162:1885–1895
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 41:494–497
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 31:63–72
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 117:226–236
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 31:385–392
Kane S, Reilly J, Tobey J (1992) An emperical study of the economic effects of climate change on world agriculture. Clim Change 21:17–35
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
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 422:618–620
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 20:838–849
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 9:1002–1013
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 43:1266–1269
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 12:251–253
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
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 25:3743–3759
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 30:1118–1126
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):19579–19584
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
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 3:265–279
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):1208–1219
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 101:9971–9975
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 119:1497–1506
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):216–228
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 122:1439–1449
Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8(6):e66428
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 39:623–630
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 576:441–450
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):3276–3287
Thomson MJ (2014) High-throughput SNP genotyping to access crop improvement. Plant Breed Biotechnol 2:195–212
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 107:479–493
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 29:875–886
Tobey J, Reilly J, Kane S (1992) Economic implications of global climate change for world agriculture. J Agric Resour Econ 17:195–204
Van Kauwenbergh S, Steward M, Mikkelsen R (2013) World reserves of phosphate rock: a dynamic and unfolding story. Better Crops 97:18–20
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):1255–1264
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 119:85–91
Wassmann R, Dobermann A (2007) Climate change adaptation through rice production in regions with high poverty levels. SAT eJournal 4(1):1–24
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):2952–2960
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 92:230–244
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 105:248–257
Yonemaru JI, Yamamoto T, Fukuoka S, Uga Y, Hori K, Yano M (2010) Q-TARO: QTL annotation rice online database. Rice 3(2):194–203
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 112:1052–1062
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 1:253–276
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 28:649–658
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.
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Ian Paul Navea and Maria Stefanie Dwiyanti have equally contributed to this work.
An erratum to this article is available at http://dx.doi.org/10.1007/s10681-017-1838-z.
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10681_2016_1822_MOESM1_ESM.tif
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)
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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
Supplementary Table 1. Distribution of agronomic traits in DT-RILs and parental lines in different P-water treatments in tropical conditions (PDF 57 kb)
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Supplementary Table 4. Analysis of variance table using GLM analysis on P and water effect on panicle length (PDF 60 kb)
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Navea, I.P., Dwiyanti, M.S., Park, J. et al. 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.). Euphytica 213, 37 (2017). https://doi.org/10.1007/s10681-016-1822-z
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DOI: https://doi.org/10.1007/s10681-016-1822-z