Identification of putative QTLs for seedling stage phosphorus starvation response in finger millet (Eleusine coracana L. Gaertn.) by association mapping and cross species synteny analysis
[en] A germplasm assembly of 128 finger millet genotypes from 18 countries was evaluated for
seedling-stage phosphorus (P) responses by growing them in P sufficient (Psuf) and P deficient
(Pdef) treatments. Majority of the genotypes showed adaptive responses to low P condition.
Based on phenotype behaviour using the best linear unbiased predictors for each
trait, genotypes were classified into, P responsive, low P tolerant and P non-responsive
types. Based on the overall phenotype performance under Pdef, 10 genotypes were identified
as low P tolerants. The low P tolerant genotypes were characterised by increased shoot
and root length and increased root hair induction with longer root hairs under Pdef, than
under Psuf. Association mapping of P response traits using mixed linear models revealed
four quantitative trait loci (QTLs). Two QTLs (qLRDW.1 and qLRDW.2) for low P response
affecting root dry weight explained over 10% phenotypic variation. In silico synteny analysis
across grass genomes for these QTLs identified putative candidate genes such as Ser-Thr
kinase and transcription factors such as WRKY and basic helix-loop-helix (bHLH). The
QTLs for response under Psuf were mapped for traits such as shoot dry weight (qHSDW.1)
and root length (qHRL.1). Putative associations of these QTLs over the syntenous regions
on the grass genomes revealed proximity to cytochrome P450, phosphate transporter and
pectin methylesterase inhibitor (PMEI) genes. This is the first report of the extent of phenotypic
variability for P response in finger millet genotypes during seedling-stage, along with
the QTLs and putative candidate genes associated with P starvation tolerance
Disciplines :
Biotechnology
Author, co-author :
Ramakrishnan, M.
Stanislaus, Antony Ceasar ; Université de Liège - ULiège > Département des sciences de la vie > Génomique fonctionnelle et imagerie moléculaire végétale
Vinod
Duraipandiyan, V.
Ajeesh Krishna, TP
Upadhyaya
Ignacimuthu, S.
Language :
English
Title :
Identification of putative QTLs for seedling stage phosphorus starvation response in finger millet (Eleusine coracana L. Gaertn.) by association mapping and cross species synteny analysis
Publication date :
August 2017
Journal title :
PLoS ONE
eISSN :
1932-6203
Publisher :
Public Library of Science, United States - California
Volume :
12
Issue :
8
Pages :
1-27
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
Grant number: BT/PR15011/AGR/02/772/2010. http://www.dbtindia.nic.in/ (Department of Biotechnology)
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Igual J, Valverde A, Cervantes E, Velázquez E. Phosphate-solubilizing bacteria as inoculants for agriculture: use of updated molecular techniques in their study. Agronomie. 2001; 21:561–568. https://doi.org/10.1051/agro:2001145
Gyaneshwar P, Kumar GN, Parekh L, Poole P. Role of soil microorganisms in improving P nutrition of plants. Plant Soil. 2002; 245:83–93. https://doi.org/10.1023/A:1020663916259
Adhya TK, Kumar N, Reddy G, Podile AR, Bee H, Samantaray B. Microbial mobilization of soil phosphorus and sustainable P management in agricultural soils. Curr Sci. 2015; 108:1280–1287.
Schachtman DP, Reid RJ, Ayling SM. Phosphorus uptake by plants: from soil to cell. Plant Physiol. 1998; 116:447–453. https://doi.org/10.1104/pp.116.2.447 PMID: 9490752
Baker A, Ceasar SA, Palmer AJ, Paterson JB, Qi W, Muench SP, et al. Replace, reuse, recycle: improving the sustainable use of phosphorus by plants. J Exp Bot. 2015; 66:3523–3540. https://doi.org/10.1093/jxb/erv210 PMID: 25944926
Vinod KK, Heuer S. Approaches towards nitrogen- and phosphorus-efficient rice. AoB Plants. 2012; 2012:pls028. https://doi.org/10.1093/aobpla/pls028 PMID: 23115710
Herrera-Estrella L, Lopez-Arredondo D. Phosphorus: The underrated element for feeding the world. Trends Plant Sci. 2016; 21:461–463. https://doi.org/10.1016/j.tplants.2016.04.010 PMID: 27160806
Koide Y, Pariasca Tanaka J, Rose T, Fukuo A, Konisho K, Yanagihara S, et al. QTLs for phosphorus deficiency tolerance detected in upland NERICA varieties. Plant Breed. 2013; 132:259–265. https://doi.org/10.1111/pbr.12052
Correll DL. The role of phosphorus in the eutrophication of receiving waters: A review. J Environ Qual. 1998; 27:261–266. https://doi.org/10.2134/jeq1998.00472425002700020004x
Mbithi-Mwikya S, Camp JV, Yiru Y, Huyghebaert A. Nutrient and antinutrient changes in finger millet (Eleusine coracana) during sprouting. Lebensm -Wiss u-Technol. 2000; 33:9–14. https://doi.org/10.1006/fstl.1999.0605
Heuer S, Chin JH, Gamuyao R, Haefele SM, Wissuwa M. Molecular breeding for phosphorus-efficient rice. In: Varshney RK, Tuberosa R. (Eds.) Translational genomics for crop breeding: Abiotic stress, yield and quality, Volume 2. John Wiley and Sons Ltd., Chichester, UK. 2013. p. 65–82. https://doi.org/10.1002/9781118728482.ch5
Vinod KK. Enhancing nutrient starvation tolerance in rice. In: Jaiwal PK, Singh RP, Dhankher OP. (Eds.) Genetic Manipulation in Plants for Mitigation of Climate Change. Springer. 2015. p. 117–142. https://doi.org/10.1007/978-81-322-2662-8_6
Vance CP, Uhde-Stone C, Allan DL. Phosphorus acquisition and use: critical adaptations by plants for securing a nonrenewable resource. New Phytol. 2003; 157:423–447. https://doi.org/10.1046/j.14698137.2003.00695.x
Espindula LF, Minella E, Delatorre CA. Low-P tolerance mechanisms and differential gene expression in contrasting wheat genotypes. Pesq Agrop Bras. 2009; 44:1100–1105. https://doi.org/10.1590/S0100-204X2009000900005
Kumar V, Singh TR, Hada A, Jolly M, Ganapathi A, Sachdev A. Probing phosphorus efficient low phytic acid content soybean genotypes with phosphorus starvation in hydroponics growth system. Appl Biochem Biotechnol. 2015; 177:689–699. https://doi.org/10.1007/s12010-015-1773-1 PMID: 26239443
Goron TL, Raizada MN. Genetic diversity and genomic resources available for the small millet crops to accelerate a new green revolution. Front Plant Sci. 2015; 6:157. https://doi.org/10.3389/fpls.2015.00157 PMID: 25852710
Padulosi S, Bhag Mal, Ravi SB, Gowda J, Gowda KTK, Shanthakumar G, et al. Food security and climate change: Role of plant genetic resources of minor millets. Indian J Plant Genet Resour. 2009; 22:1–16.
Kumar A, Metwal M, Kaur S, Gupta AK, Puranik S, et al. Nutraceutical value of finger millet (Eleusine coracana (L.) Gaertn.), and their improvement using omics approaches. Front Plant Sci. 2016; 7:934. https://doi.org/10.3389/fpls.2016.00934 PMID: 27446162
Mirza N, Sharma N, Srivastava S, Kumar A. Variation in popping quality related to physical, biochemical and nutritional properties of finger millet genotypes. Proc Natl Acad Sci India Sect B Biol Sci. 2015; 85:507–515. https://doi.org/10.1007/s40011-014-0384-x
Kumar A, Mirza N, Charan T, Sharma N, Gaur VS. Isolation, characterization and immunolocalization of a seed dominant CaM from finger millet (Eleusine coracana L. Gartn.) for studying its functional role in differential accumulation of calcium in developing grains. Appl Biochem Biotechnol. 2014; 172:2955–2973. https://doi.org/10.1007/s12010-013-0714-0 PMID: 24469585
Ceasar SA, Ignacimuthu S. Genetic engineering of millets: current status and future prospects. Biotechnol Lett. 2009; 31:779–788. https://doi.org/10.1007/s10529-009-9933-4 PMID: 19205896
Singh UM, Chandra M, Shankhdhar SC, Kumar A. Transcriptome wide identification and validation of calcium sensor gene family in the developing spikes of finger millet genotypes for elucidating its role in grain calcium accumulation. PLoS One. 2014; 9:e103963. https://doi.org/10.1371/journal.pone.0103963 PMID: 25157851
Kumar A, Yadav S, Panwar P, Gaur V, Sood S. Identification of anchored simple sequence repeat markers associated with calcium content in finger millet (Eleusine coracana). Proc Natl Acad Sci India Sect B Biol Sci. 2015; 85:311–317. https://doi.org/10.1007/s40011-013-0296-1
Gupta AK, Gaur VS, Gupta S, Kumar A. Nitrate signals determine the sensing of nitrogen through differential expression of genes involved in nitrogen uptake and assimilation in finger millet. Funct Integr Genomics. 2013; 13:179–190. https://doi.org/10.1007/s10142-013-0311-x PMID: 23435937
Gupta S, Gupta SM, Gupta AK, Gaur VS, Kumar A. Fluctuation of Dof1/Dof2 expression ratio under the influence of varying nitrogen and light conditions: involvement in differential regulation of nitrogen metabolism in two genotypes of finger millet (Eleusine coracana L.). Gene. 2014; 546:327–335. https://doi.org/10.1016/j.gene.2014.05.057 PMID: 24875415
Kumar A, Gupta N, Gupta AK, Gaur VS. Identification of biomarker for determining genotypic potential of nitrogen-use-efficiency and optimization of the nitrogen inputs in crop plants. J Crop Sci Biotechnol. 2009; 12:183–194. https://doi.org/10.1007/s12892-009-0105-9
Kumar A, Kanwal P, Gupta AK, Singh B, Gaur VS. A full-length Dof1 transcription factor of finger millet and its response to a circadian cycle. Plant Mol Biol Rep. 2014; 32:419–427. https://doi.org/10.1007/s11105-013-0653-5
Gupta SM, Arora S, Mirza N, Pande A, Lata C, et al. Finger Millet: a “Certain” Crop for an “Uncertain” future and a solution to food insecurity and hidden hunger under stressful environments. Front Plant Sci. 2017; 8.643. https://doi.org/10.3389/fpls.2017.00643 PMID: 28487720
Dida M, Srinivasachary, Ramakrishnan S, Bennetzen J, Gale M, Devos K. The genetic map of finger millet, Eleusine coracana. Theor Appl Genet. 2007; 114:321–332. https://doi.org/10.1007/s00122-006-0435-7 PMID: 17103137
Srinivasachary, Dida MM, Gale MD, Devos KM. Comparative analyses reveal high levels of conserved colinearity between the finger millet and rice genomes. Theor Appl Genet. 2007; 115:489–499. https://doi.org/10.1007/s00122-007-0582-5 PMID: 17619853
Babu BK, Agrawal PK, Pandey D, Jaiswal JP, Kumar A. Association mapping of agro-morphological characters among the global collection of finger millet genotypes using genomic SSR markers. Mol Biol Rep. 2014a; 41:5287–5297. https://doi.org/10.1007/s11033-014-3400-6 PMID: 24861452
Babu BK, Agrawal PK, Pandey D, Kumar A. Comparative genomics and association mapping approaches for opaque2 modifier genes in finger millet accessions using genic, genomic and candidate gene-based simple sequence repeat markers. Mol Breed. 2014b; 34:1261–1279. https://doi.org/10.1007/s11032-014-0115-2
Babu BK, Dinesh P, Agrawal PK, Sood S, Chandrashekara C, Bhatt JC, et al. Comparative genomics and association mapping approaches for blast resistant genes in finger millet using SSRs. PLoS One. 2014c; 9:e99182. https://doi.org/10.1371/journal.pone.0099182 PMID: 24915067
Ramakrishnan M, Ceasar SA, Duraipandiyan V, Vinod KK, Kalpana K, Al-Dhabi NA, et al. Tracing QTLs for leaf blast resistance and agronomic performance of finger millet (Eleusine coracana (L.) Gaertn.) genotypes through association mapping and in silico comparative genomics analyses. PLoS One. 2016a; 11:e0159264. https://doi.org/10.1371/journal.pone.0159264 PMID: 27415007
Shimizu A, Kato K, Komatsu A, Motomura K, Ikehashi H. Genetic analysis of root elongation induced by phosphorus deficiency in rice (Oryza sativa L.): fine QTL mapping and multivariate analysis of related traits. Theor Appl Genet. 2008; 117:987–996. https://doi.org/10.1007/s00122-008-0838-8 PMID: 18641966
Richardson AE, Lynch JP, Ryan PR, Delhaize E, Smith FA, Smith SE, et al. Plant and microbial strategies to improve the phosphorus efficiency of agriculture. Plant Soil. 2011; 349:121–156. https://doi.org/10.1007/s11104-011-0950-4
Wissuwa M, Wegner J, Ae N, Yano M. Substitution mapping of Pup1: a major QTL increasing phosphorus uptake of rice from a phosphorus-deficient soil. Theor Appl Genet. 2002; 105:890–897. https://doi.org/10.1007/s00122-002-1051-9 PMID: 12582914
Gamuyao R, Chin JH, Pariasca-Tanaka J, Pesaresi P, Catausan S, Dalid C, et al. The protein kinase Pstol1 from traditional rice confers tolerance of phosphorus deficiency. Nature. 2012; 488:535–539. https://doi.org/10.1038/nature11346 PMID: 22914168
Josephs EB, Lee YW, Stinchcombe JR, Wright SI. Association mapping reveals the role of purifying selection in the maintenance of genomic variation in gene expression. Proc Natl Acad Sci USA. 2015; 112:5390–15395. https://doi.org/10.1073/pnas.1503027112 PMID: 26604315
Tabien R, Li Z, Paterson A, Marchetti M, Stansel J, Pinson S. Mapping QTLs for field resistance to the rice blast pathogen and evaluating their individual and combined utility in improved varieties. Theor Appl Genet. 2002; 105:313–324. https://doi.org/10.1007/s00122-002-0940-2 PMID: 12582534
Gupta S, Kumari K, Muthamilarasan M, Parida S, Prasad M. Population structure and association mapping of yield contributing agronomic traits in foxtail millet. Plant Cell Rep. 2014; 33:881–893. https://doi.org/10.1007/s00299-014-1564-0 PMID: 24413764
Sehgal D, Skot L, Singh R, Srivastava RK, Das SP, Taunk J, et al. Exploring potential of pearl millet germplasm association panel for association mapping of drought tolerance traits. PLoS One. 2015; 10:e0122165. https://doi.org/10.1371/journal.pone.0122165 PMID: 25970600
Wissuwa M, Gamat G, Ismail AM. Is root growth under phosphorus deficiency affected by source or sink limitations? J Exp Bot. 2005; 56:1943–1950. https://doi.org/10.1093/jxb/eri189 PMID: 15911558
Mori A, Fukuda T, Vejchasarn P, Nestler J, Pariasca-Tanaka J, Wissuwa M. The role of root size versus root efficiency in phosphorus acquisition of rice. J Exp Bot. 2016; 67:1179–1189. https://doi.org/10.1093/jxb/erv557 PMID: 26842979
Wissuwa M, Kretzschmar T, Rose TJ. From promise to application: root traits for enhanced nutrient capture in rice breeding. J Exp Bot. 2016; 67:3605–3615. https://doi.org/10.1093/jxb/erw061 PMID: 27036129
Lambers H, Shane MW, Cramer MD, Pearse SJ, Veneklaas EJ. Root structure and functioning for efficient acquisition of phosphorus: Matching morphological and physiological traits. Ann Bot. 2006; 98:693–713. https://doi.org/10.1093/aob/mcl114 PMID: 16769731
He Z, Ma Z, Brown KM, Lynch JP. Assessment of inequality of root hair density in Arabidopsis thaliana using the Gini coefficient: a close look at the effect of phosphorus and its interaction with ethylene. Ann Bot. 2005; 95:287–293. https://doi.org/10.1093/aob/mci024 PMID: 15546931
Li J, Xie Y, Dai A, Liu L, Li Z. Root and shoot traits responses to phosphorus deficiency and QTL analysis at seedling stage using introgression lines of rice. J Genet Genomics. 2009; 36:173–183. https://doi.org/10.1016/S1673-8527(08)60104-6 PMID: 19302973
Chen J, Xu L, Cai Y, Xu J. QTL mapping of phosphorus efficiency and relative biologic characteristics in maize (Zea mays L.) at two sites. Plant Soil. 2008; 313:251–266. https://doi.org/10.1007/s11104-008-9698-x
Zhu J, Kaeppler SM, Lynch JP. Mapping of QTL controlling root hair length in maize (Zea mays L.) under phosphorus deficiency. Plant Soil. 2005; 270:299–310. https://doi.org/10.1007/s11104-004-1697-y
Liang Q, Cheng X, Mei M, Yan X, Liao H. QTL analysis of root traits as related to phosphorus efficiency in soybean. Ann Bot. 2010; 106:223–234. https://doi.org/10.1093/aob/mcq097 PMID: 20472699
Su JY, Zheng Q, Li HW, Li B, Jing RL, Tong YP, et al. Detection of QTLs for phosphorus use efficiency in relation to agronomic performance of wheat grown under phosphorus sufficient and limited conditions. Plant Sci. 2009; 176:824–836. https://doi.org/10.1016/j.plantsci.2009.03.006
Yuan Y, Gao M, Zhang M, Zheng H, Zhou X, et al. QTL Mapping for Phosphorus Efficiency and Morphological Traits at Seedling and Maturity Stages in Wheat. Front Plant Sci. 2017; 8:614. https://doi.org/10.3389/fpls.2017.00614 PMID: 28484481
Yan X, Liao H, Beebe SE, Blair MW, Lynch JP. QTL mapping of root hair and acid exudation traits and their relationship to phosphorus uptake in common bean. Plant soil. 2004; 265:17–29. https://doi.org/10.1007/s11104-005-0693-1
Van De Wiel CM, Van Der Linden CG, Scholten O. Improving phosphorus use efficiency in agriculture: opportunities for breeding. Euphytica. 2016; 207:1–22. https://doi.org/10.1007/s10681-015-1572-3
Liu G, James D, Thai P. Induction of root hair growth in a phosphorus-buffered culture solution. Agric Sci China. 2006; 5:370–376. https://doi.org/10.1016/S1671-2927(06)60063-1
Ramakrishnan M, Ceasar S, Duraipandiyan V, Al-Dhabi N, Ignacimuthu S. Assessment of genetic diversity, population structure and relationships in Indian and non-Indian genotypes of finger millet (Eleusine coracana (L.) Gaertn) using genomic SSR markers. Springerplus. 2016b; 5:120. https://doi.org/10.1186/s40064-015-1626-y PMID: 26900542
Ramakrishnan M, Ceasar SA, Duraipandiyan V, Al-Dhabi NA, Ignacimuthu S. Using molecular markers to assess the genetic diversity and population structure of finger millet (Eleusine coracana (L.) Gaertn.) from various geographical regions. Genet Resour Crop Evol. 2016c; 63:361–376. https://doi.org/10.1007/s10722-015-0255-1
Ceasar SA, Hodge A, Baker A, Baldwin SA. Phosphate concentration and arbuscular mycorrhizal col-onisation influence the growth, yield and expression of twelve PHT1 family phosphate transporters in foxtail millet (Setaria italica). PLoS One. 2014; 9:e108459. https://doi.org/10.1371/journal.pone.0108459 PMID: 25251671
Slabaugh E, Held M, Brandizzi F. Control of root hair development in Arabidopsis thaliana by an endoplasmic reticulum anchored member of the R2R3-MYB transcription factor family. Plant J. 2011; 67:395–405. https://doi.org/10.1111/j.1365-313X.2011.04602.x PMID: 21477080
Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012; 9:671–675. https://doi.org/10.1038/nmeth.2089 PMID: 22930834
Lin G, Chai J, Yuan S, Mai C, Cai L, Murphy RW, et al. VennPainter: A tool for the comparison and identification of candidate genes based on venn diagrams. PLoS One. 2016; 11:e0154315. https://doi.org/10.1371/journal.pone.0154315 PMID: 27120465
Doyle JJ, Doyle JL. Isolation of plant DNA from fresh tissue. Focus. 1990; 12:13–15.
Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000; 155:945–959. PMCID: PMC1461096 PMID: 10835412
Ramasamy R, Ramasamy S, Bindroo B, Naik V. STRUCTURE PLOT: a program for drawing elegant STRUCTURE bar plots in user friendly interface. SpringerPlus. 2014; 3:1–3. https://doi.org/10.1186/2193-1801-3-431
Karandikar R (2006) On the Markov Chain Monte Carlo (MCMC) method. Sadhana. 2006; 31:81–84. https://doi.org/10.1007/BF02719775
Meyn SP, Tweedie RL. Markov Chains and Stochastic Stability. Communications and Control Engineering Series. Springer, London. 1993.
Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology. 2005; 14:2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x PMID: 15969739
Earl DA, vonHoldt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genet Resour. 2012; 4:359–361. https://doi.org/10.1007/s12686-011-9548-7
Endelman JB, Jannink JL. Shrinkage estimation of the realized relationship matrix. G3 (Bethesda). 2012; 2:1405–1413. https://doi.org/10.1534/g3.112.004259 PMID: 23173092
Bradbury P, Zhang Z, Kroon D, Casstevens T, Ramdoss Y, Buckler E. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007; 23:2633–2635. https://doi.org/10.1093/bioinformatics/btm308 PMID: 17586829
Yu J, Buckler ES. Genetic association mapping and genome organization of maize. Curr Opin Biotechnol. 2006; 17:155–160. https://doi.org/10.1016/j.copbio.2006.02.003 PMID: 16504497
Li MX, Yeung JMY, Cherny SS, Sham PC. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference data-sets. Hum Genet. 2012; 131: 747–756. https://doi.org/10.1007/s00439-011-1118-2 PMID: 22143225
Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet. 2012; 44:825–830. https://doi.org/10.1038/ng.2314 PMID: 22706313
Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012; 40:D1178–D1186. https://doi.org/10.1093/nar/gkr944 PMID: 22110026
Meyer F, Paarmann D, D’souza M, Olson R, Glass EM, Kubal, et al. The metagenomics RAST server–a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics. 2008; 9:386. https://doi.org/10.1186/1471-2105-9-386 PMID: 18803844
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2007; 36:D480–D484. https://doi.org/10.1093/nar/gkm882 PMID: 18077471
MacDonald GK, Bennett EM, Potter PA, Ramankutty N. Agronomic phosphorus imbalances across the world’s croplands. Proc Natl Acad Sci USA. 2011; 108:3086–3091. https://doi.org/10.1073/pnas.1010808108 PMID: 21282605
Sattari S, Bouwman A, Rodríguez RM, Beusen A, Van Ittersum M. Negative global phosphorus budgets challenge sustainable intensification of grasslands. Nat Commun. 2016; 7:10696. https://doi.org/10.1038/ncomms10696 PMID: 26882144
Abel S, Ticconi CA, Delatorre CA. Phosphate sensing in higher plants. Physiol Plant. 2002; 115:1–8. https://doi.org/10.1034/j.1399-3054.2002.1150101.x PMID: 12010462
Del Amor F, Martinez V, Cerda A. Salinity duration and concentration affect fruit yield and quality, and growth and mineral composition of melon plants grown in perlite. HortScience. 1999; 34:1234–1237.
Bird C. Soils and plant nutrients. In: Bird C (ed) The fundamentals of horticulture: Theory and practice. Royal horticultural society, Cambridge University Press, Cambridge. 2014.
Rose TJ, Impa SM, Rose MT, Pariasca-Tanaka J, Mori A, Heuer S, et al. Enhancing phosphorus and zinc acquisition efficiency in rice: a critical review of root traits and their potential utility in rice breeding. Ann Bot. 2013; 112:331–345. https://doi.org/10.1093/aob/mcs217 PMID: 23071218
Niu YF, Chai RS, Jin GL, Wang H, Tang CX, Zhang YS. Responses of root architecture development to low phosphorus availability: a review. Ann Bot. 2013; 112:391–408. https://doi.org/10.1093/aob/mcs285 PMID: 23267006
Giehl RF, Von Wirén N. Root nutrient foraging. Plant Physiol. 2014; 166:509–517. https://doi.org/10.1104/pp.114.245225 PMID: 25082891
Péret B, Desnos T, Jost R, Kanno S, Berkowitz O, Nussaume L. Root architecture responses: in search of phosphate. Plant Physiol. 2014; 166:1713–1723. https://doi.org/10.1104/pp.114.244541 PMID: 25341534
Vejchasarn P, Lynch JP, Brown KM. Genetic variability in phosphorus responses of rice root phenotypes. Rice. 2016; 9:29. https://doi.org/10.1186/s12284-016-0102-9 PMID: 27294384
Samal D, Kovar J, Steingrobe B, Sadana US, Bhadoria PS, Claassen N. Potassium uptake efficiency and dynamics in the rhizosphere of maize (Zea mays L.), wheat (Triticum aestivum L.), and sugar beet (Beta vulgaris L.) evaluated with a mechanistic model. Plant Soil. 2010; 332:105–121. https://doi.org/10.1007/s11104-009-0277-6
Lynch JP. Root phenes for enhanced soil exploration and phosphorus acquisition: tools for future crops. Plant Physiol. 2011; 156:1041–1049. https://doi.org/10.1104/pp.111.175414 PMID: 21610180
Lynch JP, Chimungu JG, Brown KM. Root anatomical phenes associated with water acquisition from drying soil: targets for crop improvement. J Exp Bot. 2014; 65:6155–6166. https://doi.org/10.1093/jxb/eru162 PMID: 24759880
Fitter AH. Architecture and biomass allocation as components of the plastic response of root systems to soil heterogeneity. In: Caldwell MM, Pearcy RW. (Eds) Exploitation of environmental heterogeneity by plants: Ecophysiological processes above- and below- ground. San Diego: Academic Press. 1994; 305–323. https://doi.org/10.1016/B978-0-12-155070-7.50016–0
Reiter RS, Coors J, Sussman M, Gabelman W. Genetic analysis of tolerance to low-phosphorus stress in maize using restriction fragment length polymorphisms. Theor Appl Genet. 1991; 82:561–568. https://doi.org/10.1007/BF00226791 PMID: 24213334
Li YD, Wang YJ, Tong YP, Gao JG, Zhang JS, Chen SY. QTL mapping of phosphorus deficiency tolerance in soybean (Glycine max L. Merr.). Euphytica. 2005; 142:137–142. https://doi.org/10.1007/s10681-005-1192-4
Wissuwa M, Yano M, Ae N. Mapping of QTLs for phosphorus-deficiency tolerance in rice (Oryza sativa L.). Theor Appl Genet. 1998; 97:777–783. https://doi.org/10.1007/s001220050955
Hutchings MJ. Some statistical problems associated with determinations of population parameters for herbaceous plants in the field. New Phytol. 1975; 74:349–363. https://doi.org/10.1111/j.1469-8137.1975.tb02622.x
Chaubey C, Senadhira D, Gregorio G. Genetic analysis of tolerance for phosphorous deficiency in rice (Oryza sativa L.). Theor Appl Genet. 1994; 89:313–317. https://doi.org/10.1007/BF00225160 PMID: 24177847
Kavanová M, Lattanzi FA, Grimoldi A, Schnyder H. Phosphorus deficiency decreases cell division and elongation in grass leaves. Plant Physiol. 2006; 141:766–775. https://doi.org/10.1104/pp.106.079699 PMID: 16648218
Luquet D, Zhang BG, Dingkuhn M, Dexet A, Clément-Vidal A. Phenotypic plasticity of rice seedlings: case of phosphorus deficiency. Plant Prod Sci. 2005; 8:145–151. https://doi.org/10.1626/pps.8.145
Thuynsma R, Valentine A, Kleinert A. Phosphorus deficiency affects the allocation of below-ground resources to combined cluster roots and nodules in Lupinus albus. J Plant Physiol. 2014; 171:285–291. https://doi.org/10.1016/j.jplph.2013.09.001 PMID: 24129121
Raghothama K. Phosphate acquisition. Annu Rev Plant Physiol Plant Mol Biol. 1999; 50:665–693. https://doi.org/10.1146/annurev.arplant.50.1.665 PMID: 15012223
Li K, Xu Z, Zhang K, Yang A, Zhang J. Efficient production and characterization for maize inbred lines with low-phosphorus tolerance. Plant Sci. 2007; 172:255–264. https://doi.org/10.1016/j.plantsci.2006.09.004
Miguel MA, Postma JA, Lynch JP. Phene synergism between root hair length and basal root growth angle for phosphorus acquisition. Plant Physiol. 2015; 167:1430–1439. https://doi.org/10.1104/pp.15.00145 PMID: 25699587
Liu Y, Mi G, Chen F, Zhang J, Zhang F. Rhizosphere effect and root growth of two maize (Zea mays L.) genotypes with contrasting P efficiency at low P availability. Plant Sci. 2004; 167:217–223. https://doi.org/10.1016/j.plantsci.2004.02.026
Bates T, Lynch J. Stimulation of root hair elongation in Arabidopsis thaliana by low phosphorus availability. Plant Cell Environ. 1996; 19:529–538. https://doi.org/10.1111/j.1365-3040.1996.tb00386.x
Schmidt W, Schikora A. Different pathways are involved in phosphate and iron stress-induced alterations of root epidermal cell development. Plant Physiol. 2001; 125: 2078–2084. https://doi.org/10.1104/pp.125.4.2078 PMID: 11299387
Gahoonia TS, Nielsen NE. Root traits as tools for creating phosphorus efficient crop varieties. Plant Soil. 2004; 260:47–57. https://doi.org/10.1023/B:PLSO.0000030168.53340.bc
Keyes SD, Daly KR, Gostling NJ, Jones DL, Talboys P, Pinzer BR, et al. High resolution synchrotron imaging of wheat root hairs growing in soil and image based modelling of phosphate uptake. New Phytol. 2013; 198:1023–1029. https://doi.org/10.1111/nph.12294 PMID: 23600607
Wang L, Liao H, Yan X, Zhuang B, Dong Y. Genetic variability for root hair traits as related to phosphorus status in soybean. Plant Soil. 2004; 261:77–84. https://doi.org/10.1023/B:PLSO.0000035552.94249.6a
Nestler J, Keyes SD, Wissuwa M. Root hair formation in rice (Oryza sativa L.) differs between root types and is altered in artificial growth conditions. J Exp Bot. 2016; 67:3699–3708. https://doi.org/10.1093/jxb/erw115 PMID: 26976815
Abdurakhmonov I, Abdukarimov A. Application of association mapping to understanding the genetic diversity of plant germplasm resources. Int J Plant Genomics. 2008; 2008:1–18. https://doi.org/10.1155/2008/574927 PMID: 18551188
Singh BD, Singh AK. Marker-assisted plant breeding: Principles and practices. Springer, New Delhi. 2015; 217–256. https://doi.org/10.1007/978-81-322-2316-0
Pettersson FH, Anderson CA, Clarke GM, Barrett JC, Cardon LR, Morris, et al. Marker selection for genetic case–control association studies. Nat. Protocols. 2009; 4:743–752. https://doi.org/10.1038/nprot.2009.38 PMID: 19390530
Zhang J, Song Q, Cregan PB, Jiang GL. Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theor Appl Genet. 2016; 129:117–130. https://doi.org/10.1007/s00122-015-2614-x PMID: 26518570
Tian C, Gregersen PK, Seldin MF. Accounting for ancestry: population substructure and genome-wide association studies. Hum Mol Genet. 2008; 17:R143–R150. https://doi.org/10.1093/hmg/ddn268 PMID: 18852203
Hittalmani S, Mahesh HB, Shirke MD, Biradar H, Uday G, et al. Genome and Transcriptome sequence of Finger millet (Eleusine coracana (L.) Gaertn.) provides insights into drought tolerance and nutraceu-tical properties. BMC Genomics. 2017; 18:465. https://doi.org/10.1186/s12864-017-3850-z PMID: 28619070
De Villiers S, Michael V, Manyasa E, Saiyiorri A, Deshpande S. Compilation of an informative microsatellite set for genetic characterization of East African finger millet (Eleusine coracana). Electron J Biotechnol. 2015; 18:77–82. https://doi.org/10.1016/j.ejbt.2014.12.001
McCouch SR, CGSNL. Gene nomenclature system for rice. Rice. 2008; 1:72–84. https://doi.org/10.1007/s12284-008-9004-9
Wei L, Liu Y, Dubchak I, Shon J, Park J. Comparative genomics approaches to study organism similarities and differences. J Biomed Inform. 2002; 35:142–150. https://doi.org/10.1016/S1532-0464(02)00506-3 PMID: 12474427
Sarkar A, Soueidan H, Nikolski M. Identification of conserved gene clusters in multiple genomes based on synteny and homology. BMC Bioinformatics. 2011; 12:S18. https://doi.org/10.1186/1471-2105-12-S9-S18 PMID: 22151970
Bennetzen JL, Schmutz J, Wang H, Percifield R, Hawkins J, Pontaroli AC, et al. Reference genome sequence of the model plant Setaria. Nat Biotech. 2012; 30:555–561. https://doi.org/10.1038/nbt.2196 PMID: 22580951
Zhang G, Liu X, Quan Z, Cheng S, Xu X, Pan S, et al. Genome sequence of foxtail millet (Setaria italica) provides insights into grass evolution and biofuel potential. Nat Biotech. 2012; 30:549–554. https://doi.org/10.1038/nbt.2195 PMID: 22580950
Nirgude M, Babu BK, Shambhavi Y, Singh UM, Upadhyaya HD, Kumar A. Development and molecular characterization of genic molecular markers for grain protein and calcium content in finger millet (Eleusine coracana (L.) Gaertn.). Mol Biol Rep. 2014; 41:1189–1200. https://doi.org/10.1007/s11033-013-2825-7 PMID: 24477581
Bennetzen JL, Freeling M. The unified grass genome: synergy in synteny. Genome Res. 1997; 7:301–306. https://doi.org/10.1101/gr.7.4.301 PMID: 9110169
Flavell R, Bennett M, Smith J, Smith D. Genome size and the proportion of repeated nucleotide sequence DNA in plants. Biochem Genet. 1974; 12:257–269. https://doi.org/10.1007/BF00485947 PMID: 4441361
Chin JH, Gamuyao R, Dalid C, Bustamam M, Prasetiyono J, Moeljopawiro S, et al. Developing rice with high yield under phosphorus deficiency: Pup1 sequence to application. Plant Physiol. 2011; 156:1202–1216. https://doi.org/10.1104/pp.111.175471 PMID: 21602323
Taj G, Agarwal P, Grant M, Kumar A. MAPK machinery in plants: Recognition and response to different stresses through multiple signal transduction pathways. Plant Signal Behav. 2010; 5:1370–1378. https://doi.org/10.4161/psb.5.11.13020 PMID: 20980831
Shimada Y, Goda H, Nakamura A, Takatsuto S, Fujioka S, et al. Organ-specific expression of brassi-nosteroid-biosynthetic genes and distribution of endogenous brassinosteroids in Arabidopsis. Plant Physiol. 2003; 131:287–297. https://doi.org/10.1104/pp.013029 PMID: 12529536
Jia H, Ren H, Gu M, Zhao J, Sun S, Zhang X, et al. The phosphate transporter gene OsPht1; 8 is involved in phosphate homeostasis in rice. Plant Physiol. 2011; 156:1164–1175. https://doi.org/10.1104/pp.111.175240 PMID: 21502185
Chen ZH, Nimmo GA, Jenkins GI, Nimmo HG. BHLH32 modulates several biochemical and morphological processes that respond to Pi starvation in Arabidopsis. Biochem J. 2007; 405:191–198. https://doi.org/10.1042/BJ20070102 PMID: 17376028
Jiang J, Ma S, Ye N, Jiang M, Cao J, et al. WRKY transcription factors in plant responses to stresses. J Integr Plant Biol. 2017; 59:86–101. https://doi.org/10.1111/jipb.12513 PMID: 27995748
Wu Y, Yang L, Cao A, Wang J. Gene Expression Profiles in Rice Developing Ovules Provided Evidence for the Role of Sporophytic Tissue in Female Gametophyte Development. PLoS One. 2015; 10:e0141613. https://doi.org/10.1371/journal.pone.0141613 PMID: 26506227
Wang M, Yuan D, Gao W, Li Y, Tan J, Zhang X. A comparative genome analysis of PME and PMEI families reveals the evolution of pectin metabolism in plant cell walls. PLoS One. 2013; 8:e72082. https://doi.org/10.1371/journal.pone.0072082 PMID: 23951288
Devaiah BN, Karthikeyan AS, Raghothama KG. WRKY75 transcription factor is a modulator of phosphate acquisition and root development in Arabidopsis. Plant Physiol. 2007; 143:1789–1801. https://doi.org/10.1104/pp.106.093971 PMID: 17322336
Li X, Duan X, Jiang H, Sun Y, Tang Y, Yuan Z, et al. Genome-wide analysis of basic/helix-loop-helix transcription factor family in rice and Arabidopsis. Plant Physiol. 2006; 141:1167–1184. https://doi.org/10.1104/pp.106.080580 PMID: 16896230
Yi K, Wu Z, Zhou J, Du L, Guo L, Wu Y, et al. OsPTF1, a novel transcription factor involved in tolerance to phosphate starvation in rice. Plant Physiol. 2005; 138:2087–2096. https://doi.org/10.1104/pp.105.063115 PMID: 16006597
Dai X, Wang Y, Zhang WH. OsWRKY74, a WRKY transcription factor, modulates tolerance to phosphate starvation in rice. J Exp Bot. 2016; 67:947–960. https://doi.org/10.1093/jxb/erv515 PMID: 26663563
Kurotani KI, Hattori T, Takeda S. Overexpression of a CYP94 family gene CYP94C2b increases inter-node length and plant height in rice. Plant Signal Behav. 2015; 10:e1046667. https://doi.org/10.1080/15592324.2015.1046667 PMID: 26251886
Li Y, Zhang J, Zhang X, Fan H, Gu M, Qu H, et al. Phosphate transporter OsPht1; 8 in rice plays an important role in phosphorus redistribution from source to sink organs and allocation between embryo and endosperm of seeds. Plant Sci. 2015; 230:23–32. https://doi.org/10.1016/j.plantsci.2014.10.001 PMID: 25480005
Xu P, Cai XT, Wang Y, Xing L, Chen Q, Xiang CB. HDG11 upregulates cell-wall-loosening protein genes to promote root elongation in Arabidopsis. J Exp Bot. 2014; 65:4285–4295. https://doi.org/10.1093/jxb/eru202 PMID: 24821957
Miah G, Rafii MY, Ismail MR, Puteh AB, Rahim HA, Islam KN, et al. A review of microsatellite markers and their applications in rice breeding programs to improve blast disease resistance. Int J Mol Sci. 2013; 14:22499–22528. https://doi.org/10.3390/ijms141122499 PMID: 24240810
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