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Euphytica

, 214:170 | Cite as

Molecular dissection of sugar related traits and it’s attributes in Saccharum spp. hybrids

  • Md. Sariful IslamEmail author
  • Xiping Yang
  • Sushma Sood
  • Jack C. Comstock
  • Fenggang Zan
  • Jianping Wang
Review
  • 279 Downloads

Abstract

Sugarcane is one of the most important crops in the tropical and sub-tropical regions worldwide because it supplies over half of the world’s sugar. The main goal of sugarcane breeding programs is releasing new cultivars with improved sugar content, disease resistance and agronomic traits. Molecular markers linked to the sugar yield would greatly facilitate the development of sugarcane cultivars with higher sugar content. In this study, quantitative trait loci (QTL) associated with sugar and yield related traits were identified using a segregating F1 population derived from two Saccharum spp. hybrids. Specifically, BRIX, POL, recoverable sugar content (SC), fiber content (FC), moisture content (MC), juice purity, stalk diameter (SD), and stalk weight (SW) data were collected from a replicated field trial of a bi-parental population. A total of 36 and nine QTL for sugar and yield related traits, respectively were identified using a high density genetic map with markers developed by genotyping-by-sequencing. Of the 45 detected QTL, seven QTL were associated with each of the three sugar related traits BRIX, POL, and SC; six QTL with FC and MC; three QTL with juice purity; four QTL with SD; and five QTL with SW. The QTL explained a total of phenotypic variations of 70.90, 61.80, 61.68, 68.67, 91.62, 33.00, 49.91, and 64.49% for BRIX, POL, SC, FC, MC, purity, SD, and SW, respectively. Upon validation, markers from the identified QTL would be useful in marker-assisted selection for selecting superior cultivars with these traits.

Keywords

Molecular marker Quantitative trait loci (QTL) Sugar Saccharum spp. 

Notes

Acknowledgements

We gratefully thank Dr. Neil Glynn for developing and providing the population. Authors also express their gratitude to Moaiad Kanaan, Ken Peterkin and Brittany Read at Sugarcane Field Station, USDA, ARS for their help during phenotyping and field trial. This research is financially supported by United States Department of Agriculture—Agricultural Research Service CRIS projects 6030-21000-006-00D and the Florida Sugar Cane League.

Compliance with ethical standards

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

10681_2018_2252_MOESM1_ESM.docx (18 kb)
Supplementary material 1 (DOCX 18 kb)

References

  1. Aitken KS, Jackson PA, McIntyre CL (2006) Quantitative trait loci identified for sugar related traits in a sugarcane (Saccharum spp.) cultivar × Saccharum officinarum population. Theor Appl Genet 112:1306–1317CrossRefGoogle Scholar
  2. Aitken KS, McNeil MD, Hermann S, Bundock PC, Kilian A, Heller-Uszynska K, Henry RJ, Li J (2014) A comprehensive genetic map of sugarcane that provides enhanced map coverage and integrates high-throughput Diversity Array Technology (DArT) markers. BMC Genom 15:152CrossRefGoogle Scholar
  3. Alwala S, Kimbeng CA, Veremis JC, Gravois KA (2009) Identification of molecular markers associated with sugar-related traits in a Saccharum interspecific cross. Euphytica 167:127–142CrossRefGoogle Scholar
  4. Andru S, Pan YB, Thongthawee S, Burner DM, Kimbeng CA (2011) Genetic analysis of the sugarcane (Saccharum spp.) cultivar ‘LCP 85-384’. I. Linkage mapping using AFLP, SSR, and TRAP markers. Theor Appl Genet 123:77–93CrossRefGoogle Scholar
  5. Balsalobre TWA, Mancini MC, Pereira GdS, Anoni CO, Barreto FZ, Hoffmann HP, de Souza AP, Garcia AAF, Carneiro MS (2016) Mixed modeling of yield components and brown rust resistance in sugarcane families. Agron J 108:1824–1837CrossRefGoogle Scholar
  6. Balsalobre TW, da Silva Pereira G, Margarido GR, Gazaffi R, Barreto FZ, Anoni CO, Cardoso-Silva CB, Costa EA, Mancini MC, Hoffmann HP, de Souza AP, Garcia AA, Carneiro MS (2017) GBS-based single dosage markers for linkage and QTL mapping allow gene mining for yield-related traits in sugarcane. BMC Genom 18:72CrossRefGoogle Scholar
  7. Carlier JD, Reis A, Duval MF, d’Eeckenbrugge GC, Leitao JM (2004) Genetic maps of RAPD, AFLP and ISSR markers in Ananas bracteatus and A-comosus using the pseudo-testcross strategy. Plant Breeding 123:186–192CrossRefGoogle Scholar
  8. Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140CrossRefGoogle Scholar
  9. Chen CX, Bowman KD, Choi YA, Dang PM, Rao MN, Huang S, Soneji JR, McCollum TG, Gmitter FG (2008) EST-SSR genetic maps for Citrus sinensis and Poncirus trifoliata. Tree Genet Genomes 4:1–10CrossRefGoogle Scholar
  10. Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971PubMedPubMedCentralGoogle Scholar
  11. Daniels J, Roach BT (1987) Taxonomy and evolution. In: Heinz DJ (ed) Sugarcane improvement through breeding. Elsevier Press, Amsterdam, pp 7–84CrossRefGoogle Scholar
  12. D’Hont A (2005) Unraveling the genome structure of polyploids using FISH and GISH; examples of sugarcane and banana. Cytogenet Genome Res 109:27–33CrossRefGoogle Scholar
  13. D’Hont A, Ison D, Alix K, Roux C, Glaszmann JC (1998) Determination of basic chromosome numbers in the genus Saccharum by physical mapping of ribosomal RNA genes. Genome 41:221–225CrossRefGoogle Scholar
  14. Eksteen A, Singels A, Ngxaliwe S (2014) Water relations of two contrasting sugarcane genotypes. Field Crop Res 168:86–100CrossRefGoogle Scholar
  15. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379CrossRefGoogle Scholar
  16. Garcia AA, Kido EA, Meza AN, Souza HM, Pinto LR, Pastina MM, Leite CS, Silva JA, Ulian EC, Figueira A, Souza AP (2006) Development of an integrated genetic map of a sugarcane (Saccharum spp.) commercial cross, based on a maximum-likelihood approach for estimation of linkage and linkage phases. Theor Appl Genet 112:298–314CrossRefGoogle Scholar
  17. Garcia AA, Mollinari M, Marconi TG, Serang OR, Silva RR, Vieira ML, Vicentini R, Costa EA, Mancini MC, Garcia MO, Pastina MM, Gazaffi R, Martins ER, Dahmer N, Sforca DA, Silva CB, Bundock P, Henry RJ, Souza GM, van Sluys MA, Landell MG, Carneiro MS, Vincentz MA, Pinto LR, Vencovsky R, Souza AP (2013) SNP genotyping allows an in-depth characterisation of the genome of sugarcane and other complex autopolyploids. Sci Rep 3:3399CrossRefGoogle Scholar
  18. Garrison E, Marth G (2012) Haplotype-based variant detection from short-read sequencing. arXiv 1207.3907Google Scholar
  19. Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES (2014) TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS ONE 9:e90346CrossRefGoogle Scholar
  20. Grivet L, Arruda P (2002) Sugarcane genomics: depicting the complex genome of an important tropical crop. Curr Opin Plant Biol 5:122–127CrossRefGoogle Scholar
  21. Irvine JE (1999) Saccharum species as horticultural classes. Theor Appl Genet 98:186–194CrossRefGoogle Scholar
  22. Islam MS, Thyssen GN, Jenkins JN, Fang DD (2015) Detection, validation, and application of genotyping-by-sequencing based single nucleotide polymorphisms in Upland cotton. Plant Genome 8:1–10CrossRefGoogle Scholar
  23. Islam MS, Thyssen GN, Jenkins JN, Zeng L, Delhom CD, McCarty JC, Deng DD, Hinchliffe DJ, Jones DC, Fang DD (2016a) A MAGIC population-based genome-wide association study reveals functional association of GhRBB1_A07 gene with superior fiber quality in cotton. BMC Genom 17:903CrossRefGoogle Scholar
  24. Islam MS, Zeng L, Thyssen GN, Delhom CD, Kim HJ, Li P, Fang DD (2016b) Mapping by sequencing in cotton (Gossypium hirsutum) line MD52ne identified candidate genes for fiber strength and its related quality attributes. Theor Appl Genet.  https://doi.org/10.1007/s00122-00016-02684-00124
  25. Jansen RC, Stam P (1994) High resolution of quantitative traits into multiple loci via interval mapping. Genetics 136:1447–1455PubMedPubMedCentralGoogle Scholar
  26. Knapp SJ, Stroup WW, Ross WW (1985) Exact confidence intervals for heritability on a progeny mean basis. Crop Sci 25:192–194CrossRefGoogle Scholar
  27. Legendre BL (1992) The core/press method for predicting the sugar yield from cane for use in cane payment. Sugar J 50:1–7Google Scholar
  28. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Proc GPD (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079CrossRefGoogle Scholar
  29. Liu GF, Zhou HK, Hu H, Zhu ZH, Hayat Y, Xu HM, Yang J (2007) Genetic analysis for brix weight per stool and its component traits in sugarcane (Saccharum officinarum). J Zhejiang Univ Sci B 8:860–866CrossRefGoogle Scholar
  30. Liu PW, Chandra A, Que YX, Chen PH, Grisham MP, White WH, Dalley CD, Tew TL, Pan YB (2016) Identification of quantitative trait loci controlling sucrose content based on an enriched genetic linkage map of sugarcane (Saccharum spp. hybrids) cultivar ‘LCP 85-384’. Euphytica 207:527–549CrossRefGoogle Scholar
  31. Lu F, Lipka AE, Glaubitz J, Elshire R, Cherney JH, Casler MD, Buckler ES, Costich DE (2013) Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genet 9:e1003215CrossRefGoogle Scholar
  32. Margarido GR, Pastina MM, Souza AP, Garcia AA (2015) Multi-trait multi-environment quantitative trait loci mapping for a sugarcane commercial cross provides insights on the inheritance of important traits. Mol Breed 35:175CrossRefGoogle Scholar
  33. McCouch S, Cho Y, Yano P, Blinstrub M, Morishima H, Kinoshita T (1997) Report on QTL nomenclature. Rice Genet Newsl 14:11–13Google Scholar
  34. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303CrossRefGoogle Scholar
  35. Ming R, Liu SC, Moore PH, Irvine JE, Paterson AH (2001) QTL analysis in a complex autopolyploid: genetic control of sugar content in sugarcane. Genome Res 11:2075–2084CrossRefGoogle Scholar
  36. Ming R, Wang W, Draye X, Moore H, Irvine E, Paterson H (2002) Molecular dissection of complex traits in autopolyploids: mapping QTLs affecting sugar yield and related traits in sugarcane. Theor Appl Genet 105:332–345CrossRefGoogle Scholar
  37. Nibouche S, Raboin LM, Hoarau JY, D’Hont A, Costet L (2012) Quantitative trait loci for sugarcane resistance to the spotted stem borer Chilo sacchariphagus. Mol Breed 29:129–135CrossRefGoogle Scholar
  38. Pace J, Gardner C, Romay C, Ganapathysubramanian B, Lubberstedt T (2015) Genome-wide association analysis of seedling root development in maize (Zea mays L.). BMC Genom 16:47CrossRefGoogle Scholar
  39. Palhares AC, Rodrigues-Morais TB, Van Sluys MA, Domingues DS, Maccheroni W Jr, Jordao H Jr, Souza AP, Marconi TG, Mollinari M, Gazaffi R, Garcia AA, Vieira ML (2012) A novel linkage map of sugarcane with evidence for clustering of retrotransposon-based markers. BMC Genet 13:51CrossRefGoogle Scholar
  40. Pastina MM, Malosetti M, Gazaffi R, Mollinari M, Margarido GR, Oliveira KM, Pinto LR, Souza AP, van Eeuwijk FA, Garcia AA (2012) A mixed model QTL analysis for sugarcane multiple-harvest-location trial data. Theor Appl Genet 124:835–849CrossRefGoogle Scholar
  41. Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J, Gundlach H, Haberer G, Hellsten U, Mitros T, Poliakov A, Schmutz J, Spannagl M, Tang H, Wang X, Wicker T, Bharti AK, Chapman J, Feltus FA, Gowik U, Grigoriev IV, Lyons E, Maher CA, Martis M, Narechania A, Otillar RP, Penning BW, Salamov AA, Wang Y, Zhang L, Carpita NC, Freeling M, Gingle AR, Hash CT, Keller B, Klein P, Kresovich S, McCann MC, Ming R, Peterson DG, Mehboobur R, Ware D, Westhoff P, Mayer KF, Messing J, Rokhsar DS (2009) The Sorghum bicolor genome and the diversification of grasses. Nature 457:551–556CrossRefGoogle Scholar
  42. Pinto LR, Garcia AAF, Pastina MM, Teixeira LHM, Bressiani JA, Ulian EC, Bidoia MAP, Souza AP (2010) Analysis of genomic and functional RFLP derived markers associated with sucrose content, fiber and yield QTLs in a sugarcane (Saccharum spp.) commercial cross. Euphytica 172:313–327CrossRefGoogle Scholar
  43. Poland JA, Brown PJ, Sorrells ME, Jannink JL (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7:e32253CrossRefGoogle Scholar
  44. Schon CC, Utz HF, Groh S, Truberg B, Openshaw S, Melchinger AE (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits. Genetics 167:485–498CrossRefGoogle Scholar
  45. Singh RK, Singh SP, Tiwari DK, Srivastava S, Singh SB, Sharma ML, Singh RB, Mohapatra T, Singh NK (2012) Genetic mapping and QTL analysis for sugar yield-related traits in sugarcane. Euphytica 191:333–353CrossRefGoogle Scholar
  46. Van Ooijen J (2006) JoinMap 4.0: software for the calculation of genetic linkage maps in experimental populations. Kyazma BV, WageningenGoogle Scholar
  47. Voorrips R (2002) MapChart: software for the graphical presentationof linkage maps and QTLs. J Heredity 93:77–78CrossRefGoogle Scholar
  48. Vuong TD, Sonah H, Meinhardt CG, Deshmukh R, Kadam S, Nelson RL, Shannon JG, Nguyen HT (2015) Genetic architecture of cyst nematode resistance revealed by genome-wide association study in soybean. BMC Genom 16:593CrossRefGoogle Scholar
  49. Waclawovsky AJ, Sato PM, Lembke CG, Moore PH, Souza GM (2010) Sugarcane for bioenergy production: an assessment of yield and regulation of sucrose content. Plant Biotechnol J 8:263–276CrossRefGoogle Scholar
  50. Wang J, Roe B, Macmil S, Yu Q, Murray JE, Tang H, Chen C, Najar F, Wiley G, Bowers J, Van Sluys MA, Rokhsar DS, Hudson ME, Moose SP, Paterson AH, Ming R (2010) Microcollinearity between autopolyploid sugarcane and diploid sorghum genomes. BMC Genom 11:261CrossRefGoogle Scholar
  51. Wang S, Basten CJ, Zeng ZB (2012) Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NCGoogle Scholar
  52. Wu KK, Burnquist W, Sorrells ME, Tew TL, Moore PH, Tanksley SD (1992) The detection and estimation of linkage in polyploids using single-dose restriction fragments. Theor Appl Genet 83:294–300CrossRefGoogle Scholar
  53. Yang J, Zhu J, Williams RW (2007) Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics 23:1527–1536CrossRefGoogle Scholar
  54. Yang X, Song J, You Q, Paudel DR, Zhang J, Wang J (2017a) Mining sequence variations in representative polyploid sugarcane germplasm accessions. BMC Genom 18:594CrossRefGoogle Scholar
  55. Yang X, Sood S, Glynn N, Islam MS, Comstock J, Wang J (2017b) Constructing high-density genetic maps for polyploid sugarcane (Saccharum spp.) and identifying quantitative trait loci controlling brown rust resistance. Mol Breed 37:116CrossRefGoogle Scholar
  56. Yang X, Islam MS, Sood S, Maya S, Hanson EA, Comstock J, Wang J (2018) Identifying quantitative trait loci (QTLs) and developing diagnostic markers linked to orange rust resistance in sugarcane (Saccharum spp.). Front Plant Sci 9:350CrossRefGoogle Scholar
  57. Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468PubMedPubMedCentralGoogle Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2018

Authors and Affiliations

  • Md. Sariful Islam
    • 1
    Email author
  • Xiping Yang
    • 2
  • Sushma Sood
    • 1
  • Jack C. Comstock
    • 1
  • Fenggang Zan
    • 3
  • Jianping Wang
    • 2
  1. 1.Sugarcane Production Research UnitUSDA ARSCanal PointUSA
  2. 2.Agronomy DepartmentUniversity of FloridaGainesvilleUSA
  3. 3.Sugarcane Research InstituteYunnan Academy of Agricultural SciencesKaiyuanChina

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