Abstract
Key message
In this first genetic study on assessing leaf thickness directly in cereals, major and environmentally stable QTL were detected in barley and candidate genes underlying a major locus were identified.
Abstract
Leaf thickness (LT) is an important characteristic affecting leaf functions which have been intensively studied. However, as LT has a small dimension in many plant species and technically difficult to measure, previous studies on this characteristic are often based on indirect estimations. In the first study of detecting QTL controlling LT by directly measuring the characteristic in barley, large and stable loci were detected from both field and glasshouse trials conducted in different cropping seasons by assessing a population of 201 recombinant inbred lines. Four loci (locating on chromosome arms 2H, 3H, 5H and 6H, respectively) were consistently detected for flag leaf thickness (FLT) in each of these trials. The one on 6H had the largest effect, with a maximum LOD 9.8 explaining up to 20.9% of phenotypic variance. FLT does not only show strong interactions with flag leaf width and flag leaf area but has also strong correlations with fertile tiller number, spike row types, kernel number per spike and heading date. Though with reduced efficiency, these loci were also detectable from assessing second last leaf of fully grown plants or even from assessing the third leaves of seedlings. Taking advantage of the high-quality genome assemblies for both parents of the mapping population used in this study, three candidate genes underlying the 6H QTL were predicted based on orthologous analysis. These results do not only broaden our understanding on genetic basis of LT and its relationship with other traits in cereal crops but also form the bases for cloning and functional analysis of genes regulating LT in barley.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Afzal A, Duiker SW, Watson JE, Luthe D (2017) Leaf thickness and electrical capacitance as measures of plant water status. Trans ASABE 60(4):1063–1074. https://doi.org/10.13031/trans.12083
Alqudah AM, Schnurbusch T (2015) Barley leaf area and leaf growth rates are maximized during the pre-anthesis phase. Agronomy 5(2):107–129. https://doi.org/10.3390/agronomy5020107
Buchfink B, Reuter K, Drost HG (2021) Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods 18:366–368. https://doi.org/10.1038/s41592-021-01101-x
Campoli C, von Korff M (2014) Genetic control of reproductive development in temperate cereals. Adv Bot Res 72:131–158. https://doi.org/10.1016/B978-0-12-417162-6.00005-5
Capo-chichi LJA, Eldridge S, Elakhdar A, Kubo T, Brueggeman R, Anyia AO (2021) QTL mapping and phenotypic variation for seedling vigour traits in barley (Hordeum vulgare L.). Plants 10:1149. https://doi.org/10.3390/plants10061149
Chen ZT, Zheng Z, Luo W et al (2021) Detection of a major QTL conditioning trichome length and density on chromosome arm 4BL and development of near isogenic lines targeting this locus in bread wheat. Mol Breed 41:10. https://doi.org/10.1007/s11032-021-01201-8
Coneva V, Chitwood DH (2018) Genetic and developmental basis for increased leaf thickness in the Arabidopsis cvi ecotype. Front Plant Sci 9:322. https://doi.org/10.3389/fpls.2018.00322
Coneva V, Frank MH, de Luis Balaguer MA, Li M, Sozzani R, Chitwood DH (2017) Genetic architecture and molecular networks underlying leaf thickness in desert-adapted tomato Solanum pennellii. Plant Physiol 175:376–391. https://doi.org/10.1104/pp.17.00790
Diaz S, Hodgson J, Thompson K, Cabido M, Cornelissen JH, Jalili A, Montserrat-Marti G, Grime J, Zarrinkamar F, Asri Y (2004) The plant traits that drive ecosystems: evidence from three continents. J Veg Sci 15:295–304. https://doi.org/10.1111/j.1654-1103.2004.tb02266.x
Digel B, Tavakol E, Verderio G, Tondelli A, Xu X, Cattivelli L, Rossini L, von Korff M (2016) Photoperiod-H1 (Ppd-H1) controls leaf size. Plant Physiol 172(1):405–415. https://doi.org/10.1104/pp.16.00977
Donovan LA, Maherali H, Caruso CM, Huber H, de Kroon H (2011) The evolution of the worldwide leaf economics spectrum. Trends Ecol Evol 26(2):88–95. https://doi.org/10.1016/j.tree.2010.11.011
Elberse I, Vanhala T, Turin J, Stam P, Van Damme J, Van Tienderen P (2004) Quantitative trait loci affecting growth-related traits in wild barley (Hordeumspontaneum) grown under different levels of nutrient supply. Heredity 93:22–33. https://doi.org/10.1038/sj.hdy.6800467
Gao S, Zheng Z, Powell J, Habib A, Stiller J, Zhou M, Liu C (2019) Validation and delineation of a locus conferring Fusarium crown rot resistance on 1HL in barley by analysing transcriptomes from multiple pairs of near isogenic lines. BMC Genomics 20:650. https://doi.org/10.1186/s12864-019-6011-8
Garnier E, Shipley B, Roumet C, Laurent G (2001) A standardized protocol for the determination of specific leaf area and leaf dry matter content. Funct Ecol 15:688–695. https://doi.org/10.1046/j.0269-8463.2001.00563.x
Goddard ME (1992) A mixed model for analyses of data on multiple genetic markers. Theor Appl Genet 83:878–886. https://doi.org/10.1007/BF00226711
Habib A, Shabala S, Shabala L, Zhou M, Liu C (2016) Near-isogenic lines developed for a major QTL on chromosome arm 4HL conferring Fusarium crown rot resistance in barley. Euphytica 209:555–563. https://doi.org/10.1007/s10681-015-1623-9
He T, Hill CB, Angessa TT, Zhang XQ, Chen K, Moody D, Telfer P, Westcott S, Li C (2019) Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection. J Exp Bot 70(20):5603–5616. https://doi.org/10.1093/jxb/erz332
Hu J, Wang Y, Fang Y, Zeng L, Xu J, Yu H, Shi Z, Pan J, Zhang D, Kang S, Dong ZhuLI, G, Guo L, Zeng D, Zhang G, Xie L, Xiong G, Li J, Qian Q, (2015) A rare allele of GS2 enhances grain size and grain yield in rice. Mol Plant 8:1455–1465. https://doi.org/10.1016/j.molp.2015.07.002
Li JW, Yang JP, Fan PP, Song JL, Li DS, Ge CS, Chen WY (2009) Responses of rice leaf thickness, SPAD readings and chlorophyll a/b ratios to different nitrogen supply rates in paddy field. Field Crops Res 114:426–432. https://doi.org/10.1016/j.fcr.2009.09.009
Liebsch D, Palatnik JF (2020) MicroRNA miR396, GRF transcription factors and GIF co-regulators: a conserved plant growth regulatory module with potential for breeding and biotechnology. Curr Opin Plant Biol 53:31–42. https://doi.org/10.1016/j.pbi.2019.09.008
Liu YX, Yang XM, Ma J, Wei YM, Zheng YL, Ma HX, Yao JB, Manners JM, Liu CJ (2010) Plant height affects crown rot severity in wheat (Triticumaestivum L.). Phytopathology 100:1276–1281. https://doi.org/10.1094/PHYTO-05-10-0142
Liu CG, Zhou XQ, Chen DG, Li LJ, Li JC, Chen YD (2014) Natural variation of leaf thickness and its association to yield traits in indica rice. J Integr Agric 13(2):316–325. https://doi.org/10.1016/S2095-3119(13)60498-0
Liu H, Zwer P, Wang HB, Liu CJ, Lu ZY, Wang YX, Yan GJ (2016) A fast generation cycling system for oat and triticale breeding. Plant Breed 135(5):574–579. https://doi.org/10.1111/pbr.12408
Liu M, Li Y, Ma Y, Zhao Q, Stiller J, Feng Q, Tian Q, Liu D, Han B, Liu C (2020) The draft genome of a wild barley genotype reveals its enrichment in genes related to biotic and abiotic stresses compared to cultivated barley. Plant Biotechnol J 18:443–456. https://doi.org/10.1111/pbi.13210
Lu W, Deng M, Guo F et al (2016) Suppression of OsVPE3 enhances salt tolerance by attenuating vacuole rupture during programmed cell death and affects stomata development in rice. Rice 9:65. https://doi.org/10.1186/s12284-016-0138-x
Ma J, Yan GJ, Liu CJ (2012) Development of near-isogenic lines for a major QTL on 3BL conferring Fusarium crown rot resistance in hexaploid wheat. Euphytica 183:147–152. https://doi.org/10.1007/s10681-011-0414-1
Mascher M, Gundlach H, Himmelbach A, Beier S, Twardziok SO, Wicker T, Radchuk V, Dockter C, Hedley PE, Russell J (2017) A chromosome conformation capture ordered sequence of the barley genome. Nature 544:427–433. https://doi.org/10.1038/nature22043
Matsushika A, Makino S, Kojima M, Mizuno T (2000) Circadian waves of expression of the APRR1/TOC1 family of pseudo-response regulators in Arabidopsis thaliana: insight into the plant circadian clock. Plant Cell Physiol 41(9):1002–1012. https://doi.org/10.1093/pcp/pcd043
Muir CD, Pease JB, Moyle LC (2014) Quantitative genetic analysis indicates natural selection on leaf phenotypes across wild tomato species (Solanum sect. Lycopersicon; Solanaceae). Genetics 198:1629–1643. https://doi.org/10.1534/genetics.114.169276
Mulki MA, von Korff M (2016) CONSTANS controls floral repression by up-regulating VERNALIZATION2 (VRN-H2) in barley. Plant Physiol 170(1):325–337. https://doi.org/10.1104/pp.15.01350
Murchie EH, Hubbart S, Chen Y, Peng S, Horton P (2002) Acclimation of rice photosynthesis to irradiance under field conditions. Plant Physiol 130:1999–2010. https://doi.org/10.1104/pp.011098
Van Ooijen JW (2009) MapQTL® 6: Software for the mapping of quantitative trait loci in experimental populations of diploid species. Kyazma B.V., Wageningen, Netherlands. pp 1–60. https://www.kyazma.nl/index.php/MapQTL/
Paterson AH, Lander ES, Hewitt JD, Peterson S, Linoln SE, Tanksley SD (1988) Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature 335:721–736. https://doi.org/10.1038/335721a0
Pauli D, White JW, Andrade-Sanchez P, Conley MM, Heun J, Thorp KR, French AN, Hunsaker DJ, Carmo-Silva E, Wang G (2017) Investigation of the influence of leaf thickness on canopy reflectance and physiological traits in upland and Pima cotton populations. Front Plant Sci 8:1405. https://doi.org/10.3389/fpls.2017.01405
Peng S, Khush GS, Virk P, Tang Q, Zou Y (2008) Progress in ideotype breeding to increase rice yield potential. Field Crops Res 108(1):32–38. https://doi.org/10.1016/j.fcr.2008.04.001
Poorter H, Van Rijn CP, Vanhala TK, Verhoeven KJ, De Jong YE, Stam P, Lambers H (2005) A genetic analysis of relative growth rate and underlying components in Hordeum spontaneum. Oecologia 142:360–377. https://doi.org/10.1007/s00442-004-1705-1
Poorter H, Niinemets Ü, Poorter L, Wright IJ, Villar R (2009) Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytol 182:565–588. https://doi.org/10.1111/j.1469-8137.2009.02830.x
Pruneda-Paz JL, Breton G, Para A, Kay SA (2009) A functional genomics approach reveals CHE as a component of the Arabidopsis circadian clock. Science 323(5920):1481–1485. https://doi.org/10.1126/science.1167206
Roderick M, Berry SL, Noble I, Farquhar G (1999) A theoretical approach to linking the composition and morphology with the function of leaves. Funct Ecol 13:683–695. https://doi.org/10.1046/j.1365-2435.1999.00368.x
Sexton PJ, Peterson CM, Boote KJ, White JW (1997) Early-season growth in relation to region of domestication, seed size and leaf traits in common bean. Field Crops Res 52(1–2):69–78. https://doi.org/10.1016/S0378-4290(96)03452-1
Smith WK, Bell DT, Shepherd KA (1998) Associations between leaf structure, orientation and sunlight exposure in five Western Australian communities. Am J Bot 85:56–63. https://doi.org/10.2307/2446554
SpagnolettiZeuli P, Qualset C (1990) Flag leaf variation and the analysis of diversity in durum wheat. Plant Breed 105:189–202. https://doi.org/10.1111/j.1439-0523.1990.tb01196.x
Strable J (2020) Sugars inform the circadian clock how to shape rice shoots via the strigolactone pathway. Plant Cell 32(10):3043–3044. https://doi.org/10.1105/tpc.20.00615
Sun P, Zhang W, Wang Y, He Q, Shu F, Liu H, Deng H (2016) OsGRF4 controls grain shape, panicle length and seed shattering in rice. J Integ Plant Biol 58:836–847. https://doi.org/10.1111/jipb.12473
Taiz L, Zeiger E (2006) Chapter 8: Photosynthesis: Carbon Reactions. Plant Physiol, 4th ed; Sinauer Associates, Inc: Sunderland, MA, USA:160–162.
Thirulogachandar V, Alqudah AM, Koppolu R, Rutten T, Graner A, Hensel G, Kumlehn J, Bräutigam A, Sreenivasulu N, Schnurbusch T, Kuhlmann M (2017) Leaf primordium size specifies leaf width and vein number among row-type classes in barley. Plant J 91(4):601–612. https://doi.org/10.1111/tpj.13590
Tsukaya H (2013) Does ploidy level directly control cell size? Counterevidence Arab Genet Plos One 8:e83729. https://doi.org/10.1371/journal.pone.0083729
Van Camp W (2005) Yield enhancement genes: seeds for growth. Curr Opin Biotechnol 16:147–153. https://doi.org/10.1016/j.copbio.2005.03.002
Vile D, Garnier E, Shipley B, Laurent G, Navas M-L, Roumet C, Lavorel S, Díaz S, Hodgson JG, Lloret F (2005) Specific leaf area and dry matter content estimate thickness in laminar leaves. Ann Bot 96:1129–1136. https://doi.org/10.1093/aob/mci264
Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93:77–78. https://doi.org/10.1093/jhered/93.1.77
Wang G, Schmalenbach I, von Korff M et al (2010) Association of barley photoperiod and vernalization genes with QTLs for flowering time and agronomic traits in a BC2DH population and a set of wild barley introgression lines. Theor Appl Genet 120:1559–1574. https://doi.org/10.1007/s00122-010-1276-y
Wanga MA, Shimelis H, Mashilo J, Laing MD (2021) Opportunities and challenges of speed breeding: a review. Plant Breed 140:185–194. https://doi.org/10.1111/pbr.12909
White AC, Rogers A, Rees M, Osborne CP (2016) How can we make plants grow faster? a source–sink perspective on growth rate. J Exp Bot 67:31–45. https://doi.org/10.1093/jxb/erv447
White JW, Montes-R C (2005) Variation in parameters related to leaf thickness in common bean (Phaseolus vulgaris L ). Field Crops Res 91(1):7–21. https://doi.org/10.1016/j.fcr.2004.05.001
Wickham H (2019) Package ‘tidyverse’, pp1–5. https:/tidyverse.tidyverse.org
Witkowski E, Lamont BB (1991) Leaf specific mass confounds leaf density and thickness. Oecologia 88:486–493. https://doi.org/10.1007/BF00317710
Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z et al (2004) The worldwide leaf economics spectrum. Nature 428(6985):821–827. https://doi.org/10.1038/nature02403
Xue S, Xu F, Li G, Zhou Y, Lin M, Gao Z, Su X, Xu X, Jiang GZ, Zhang S (2013) Fine mapping TaFLW1, a major QTL controlling flag leaf width in bread wheat (Triticumaestivum L.). Theor Appl Genet 126:1941–1949. https://doi.org/10.1007/s00122-013-2108-7
Yan W, Li HB, Cai SB, Ma HX, Rebetzke GJ, Liu CJ (2011) Effects of plant height vary with type I and type II resistance of Fusarium head blight in wheat (Triticumaestivum L.). Plant Pathol 60:506–512. https://doi.org/10.1111/j.1365-3059.2011.02426.x
Yan G, Liu H, Wang H, Lu Z, Wang Y, Mullan D, Hamblin J, Liu C (2017) Accelerated generation of selfed pure line plants for gene identification and crop breeding. Front Plant Sci 8:1786. https://doi.org/10.3389/fpls.2017.01786
Yao Y, Zhang P, Liu H et al (2017) A fully in vitro protocol towards large scale production of recombinant inbred lines in wheat (Triticumaestivum L.). Plant Cell Tiss Organ Cult 128:655–661. https://doi.org/10.1007/s11240-016-1145-8
Yin X, Kropff MJ, Stam P (1999a) The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley. Heredity 82:415–421. https://doi.org/10.1038/sj.hdy.6885030
Yin X, Stam P, Dourleijn CJ, Kropff M (1999b) AFLP mapping of quantitative trait loci for yield-determining physiological characters in spring barley. Theor Appl Genet 99:244–253. https://doi.org/10.1007/s001220051230
Zhang B, Ye W, Ren D, Tian P, Peng Y, Gao Y, Ruan B, Wang L, Zhang G, Guo L (2015) Genetic analysis of flag leaf size and candidate genes determination of a major QTL for flag leaf width in rice. Rice 8:2. https://doi.org/10.1186/s12284-014-0039-9
Zheng Z, Wang HB, Chen GD, Yan GJ, Liu CJ (2013) A procedure allowing up to eight generations of wheat and nine generations of barley per annum. Euphytica 191:311–316. https://doi.org/10.1007/s10681-013-0909-z
Zhou H, Luo W, Gao S, Ma J, Zhou M, Wei Y, Zheng Y, Liu Y, Liu C (2021) Identification of loci and candidate genes controlling kernel weight in barley based on a population for which whole genome assemblies are available for both parents. Crop J 9(4):854–861. https://doi.org/10.1016/j.cj.2020.07.010
Acknowledgements
The authors wish to thank Caritta Eliasson for her technical supports.
Funding
The funding was provided by CSIRO (Grant No. R-10191-01), and the Key Scientific and Technological Research Project of Henan Province (Grant No. 192102110030).
Author information
Authors and Affiliations
Contributions
JJ conceived the study. CL, ZZ and HH designed the experiments. ZZ, HH, SG, HZ, WL, and UK conducted the experiments, collected, and analysed data. ZZ, CL and HH prepared the first draft of the manuscript. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest.
Additional information
Communicated by Maria von Korff.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Zheng, Z., Hu, H., Gao, S. et al. Leaf thickness of barley: genetic dissection, candidate genes prediction and its relationship with yield-related traits. Theor Appl Genet 135, 1843–1854 (2022). https://doi.org/10.1007/s00122-022-04076-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00122-022-04076-1