Skip to main content
Log in

Association of SSR markers and morpho-physiological traits associated with salinity tolerance in sugar beet (Beta vulgaris L.)

  • Published:
Euphytica Aims and scope Submit manuscript

Abstract

The conventional screening methods for salinity tolerance are time-consuming, labor-intensive and have low throughput screening rate. Molecular marker-quantitative trait association can be used to increase the efficiency of a breeding program, especially for salinity tolerance. This study was carried out to find marker-trait association using regression analysis between 13 morpho-physiological traits and 104 simple sequence repeat (SSR) markers (from 18 SSR primer pairs) on a set of 168 genotype from 12 extreme salt tolerant and sensitive crossing parents (14 samples in each parent) during 2011 and 2012. The morpho-physiological traits included Ca2+, Na+ and K+ in leaf, quality related traits in root, root yield, sugar yield and white sugar yield which were field evaluated under saline and non-saline conditions in 2 years. Results of analysis of variance revealed a significant difference between genotypes for most of the studied traits in both environments. High estimates of broad-sense heritability with relatively low genetic advance were observed for ECS and MS (in stress conditions) and for ECS and α-N in root (in non-stress conditions). The result of regression analysis showed that in 2011, five markers [(FDSB1007 (c-284 bp), KWS (a-234 bp), SB06 (c-180 bp), FDSB502 (f-293 bp) and FDSB1027 (a-211 bp)] and in 2012, nine markers [KWS (f-250), KWS (h-266), USD29 (b-153), BQ588629 (f-196), SB07 (c-278), Bmb3 (b-268), SB04 (d-200), SB15 (d-164) and Bvm3 (e-131)] had significant effect on at least one trait in both environments. Two SSR markers (FDSB502 and Bmb3) were significantly associated with the key traits contributed to salinity tolerance such as leaf Na+ and leaf K+ and the highest root quality-related traits suggesting these as the appropriate markers to improve salinity tolerance of sugar beet. The efficiency of such markers in breeding programs for developing sugar beet cultivars with high salinity tolerance requires further investigation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Abbreviations

Ca2+ :

Calcium content

Na+ :

Sodium content

K+ :

Potassium content

α-N:

α-Amino nitrogen content

SC:

Sugar content

WSC:

White sugar content

ECS:

Extraction coefficient of sugar

MS:

Molasses sugar

RY:

Root yield

SY:

Sugar yield

WSY:

White sugar yield

References

  • Ahmadi M, Majidi Heravan E, Sadeghian SY, Mesbah M, Darvish F (2011) Drought tolerance variability in S1 pollinator lines developed from a sugar beet open population. Euphytica 178:339–349

  • Al-Karaki GN (2000) Growth, water use efficiency and sodium and potassium acquisition by tomato cultivars grown under salt stress. J Plant Nutr 23:1–8

    Article  CAS  Google Scholar 

  • Bänziger M, Edmeades GO, Beck D, Bellon M (2000) Breeding for drought and nitrogen stress tolerance in maize. CIMMYT, Mexico City

    Google Scholar 

  • Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664

    Article  Google Scholar 

  • Bhargava A, Shukla S, Katiyar RS, Ohri D (2003) Selection parameters for genetic improvement in Chenopodium grain on sodic soil. J Appl Hortic 5:45–48

    Google Scholar 

  • Bhargava A, Shukla S, Dixit BS, Bannerji R, Ohri D (2006) Variability and genotype × cutting interactions for different nutritional components in C. album L. Hortic Sci 33:29–38

    Google Scholar 

  • Bocianowski J (2012a) A comparison of two methods to estimate additive-by-additive interaction of QTL effects by a simulation study. J Theor Biol 308:20–24

    Article  PubMed  Google Scholar 

  • Bocianowski J (2012b) Analytical and numerical comparisons of two methods of estimation of additive × additive interaction of QTL effects. Sci Agric 69:240–246

    Article  Google Scholar 

  • Bocianowski J (2012c) The use of weighted multiple linear regression to estimate QTL-by-QTL epistatic effects. Genet Mol Biol 35(4):802–809

    Article  PubMed Central  PubMed  Google Scholar 

  • Bocianowski J (2013) Epistasis interaction of QTL effects as a genetic parameter influencing estimation of the genetic additive effect. Genet Mol Biol 36:93–100

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Bocianowski J (2014) Estimation of epistasis in doubled haploid barley populations considering interactions between all possible marker pairs. Euphytica 196:105–115

    Article  Google Scholar 

  • Bocianowski J, Krajewski P (2009) Comparison of the genetic additive effect estimators based on phenotypic observations and on molecular marker data. Euphytica 165:113–122

    Article  Google Scholar 

  • Bocianowski J, Mikołajczyk K, Bartkowiak-Broda I (2012) Determination of fatty acid composition in seed oil of rapeseed (Brassica napus L.) by mutated alleles of the FAD3 desaturase genes. J Appl Genet 53:27–30

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Bocianowski J, Nowosad K (2015) Mixed linear model approaches in mapping QTLs with epistatic effects by a simulation study. Euphytica. doi:10.1007/s10681-014-1329-4

    Google Scholar 

  • Bocianowski J, Kozak M, Liersch A, Bartkowiak-Broda I (2011) A heuristic method of searching for interesting markers in terms of quantitative traits. Euphytica 181:89–100. doi:10.1007/s10681-011-0424-z

    Article  Google Scholar 

  • Bocianowski J, Seidler-Łożykowska K (2012) The relationship between RAPD markers and quantitative traits of caraway (Carum carvi L.). Ind Crops Prod 36:135–139. doi:10.1016/j.indcrop.2011.08.019

    Article  CAS  Google Scholar 

  • Collins NC, Tardieu F, Tuberosa R (2008) Quantitative trait loci and crop performance under abiotic stress: where do we stand? Plant Physiol 147:469–486. doi:10.1104/pp.108.118117

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Cureton AN, Burns MJ, Ford-Lloyd BV, Newbury HJ (2002) Development of simple sequence repeat (SSR) markers for the assessment of gene flow between sea beet (Beta vulgaris ssp. maritima) populations. Mol Ecol Notes 2:402–403

    Article  CAS  Google Scholar 

  • Dadkhah AR, Grrifiths H (2006) The effect of salinity on growth, inorganic ions and dry matter partitioning in sugar beet cultivars. J Agric Sci Technol 8:199–210

    Google Scholar 

  • de los Reyes BG, McGrath JM (2003) Cultivar-specific seedling vigor and expression of a putative oxalate oxidase germin-like protein in sugar beet (Beta vulgaris L.). Theor Appl Genet 107:54–61

    PubMed  Google Scholar 

  • do Nascimento IR, Maluf WR, Figueira AR, Menezes CB, de Resende JTV, Faria MV, Nogueira DW (2009) Marker assisted identification of topspovirus resistant tomato genotypes in segregating progenies. Sci Agric 66:298–303

    Article  Google Scholar 

  • Dudley JW (1993) Molecular markers in plant improvement: manipulation of genes affecting quantitative traits. Crop Sci 33:660–668

    Article  CAS  Google Scholar 

  • Edmeades GO, Bolaños J, Chapman SC, Laftte HR, Bänziger M (1999) Selection improves drought tolerance in tropical maize populations: I. Gains in biomass, grain yield, harvest index. Crop Sci 39:1306–1315. doi:10.2135/cropsci1999.3951306x

    Article  Google Scholar 

  • Edwards MD, Stuber CW, Wendel JF (1987) Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action. Genetics 116:113–125

    PubMed Central  CAS  PubMed  Google Scholar 

  • Hasthanasombut S, Ntui V, Supaibulwatana K, Mii M, Nakamura I (2010) Expression of Indica rice OsBADH1 gene under salinity stress in transgenic tobacco. Plant Biotechnol Rep 4:75–83

    Article  Google Scholar 

  • Heuer B, Plaut Z (1989) Photosynthesis and osmotic adjustment of two sugar beet cultivars grown under saline conditions. J Exp Bot 40:437–440

    Article  Google Scholar 

  • Irzykowska I, Bocianowski J (2008) Genetic variation, pathogenicity and mycelial growth rate differentiation between Gaeumannomyces graminis var. tritici isolates derived from winter and spring wheat. Ann Appl Biol 152:369–375

    Article  CAS  Google Scholar 

  • Irzykowska L, Bocianowski J, Baturo-Cieśniewska A (2013a) Association of mating-type with mycelium growth rate and genetic variability of Fusarium culmorum. Cent Eur J Biol 8:701–711

    CAS  Google Scholar 

  • Irzykowska L, Werner M, Bocianowski J, Karolewski Z, Frużyńska-Jóźwiak D (2013b) Genetic variation of horse chestnut and red horse chestnut and trees susceptibility to Erysiphe flexuosa and Cameraria ohridella. Biologia 68:851–860

    Article  Google Scholar 

  • Javidfar F, Ripley VL, Roslinsky V, Zeinali H, Abdmishani C (2006) Identification of molecular markers associated with oleic and linolenic acid in spring oilseed rape (Brassica napus). Plant Breed 125:65–71

    Article  CAS  Google Scholar 

  • Johnson HW, Robinson HF, Comstock RE (1955) Estimates of genetic and environmental variability in soybean. Agron J 47:314–318

    Article  Google Scholar 

  • Kamruzzahan MM, Hossain RI, Alam MF (2000) Variability and correlation studies in tomato (Lycopersicon esculantum Mill.). Bangladesh J Genet Biotechnol 1:21–26

    Google Scholar 

  • Kozak M, Bocianowski J, Rybiński W (2013) Note on the use of coefficient of variation for data from agricultural factorial experiments. Bulg J Agric Sci 19:644–646

    Google Scholar 

  • Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756

    PubMed Central  CAS  PubMed  Google Scholar 

  • Laurent V, Devaus P, Thiel T, Viard F, Mielordt S, Touzet P, Quillet MC (2007) Comparative effectiveness of sugar beet microsatellite markers isolated from genomic libraries and GenBank ESTs to map the sugar beet genome. Theor Appl Genet 115:793–805

    Article  CAS  PubMed  Google Scholar 

  • Liu H, Wang Q, Yu M, Zhang Y, Wu Y, Zhang H (2008) Transgenic salt-tolerant sugar beet (Beta vulgaris L.) constitutively expressing an Arabidopsis thaliana vacuolar Na+/H + antiporter gene, AtNHX3, accumulates more soluble sugar but less salt in storage roots. Plant, Cell Environ 31:1325–1334

    Article  CAS  Google Scholar 

  • Marschner H, Kykin A, Kuiper PJC (1981) Differences in salt tolerance of three sugar beet genotypes. Physiol Plant 51:234–238

    Article  CAS  Google Scholar 

  • Martin E, Cravero V, Espósito A, López Anido F, Milanesi L, Cointry E (2008) Identification of markers linked to agronomic traits in globe artichoke. Aust J Crop Sci 1:43–46

    CAS  Google Scholar 

  • McGrath JM, Trebbi D, Fenwick A, Panella L, Schulz B, Laurent V, Barnes S, Murray SC (2007) An open-source first-generation molecular genetic map from a sugar beet × table beet cross and its extension to physical mapping. Crop Sci 47:27–44

    Article  Google Scholar 

  • Mcharo M, LaBonte DR, Clark C, Hoy M, Oard JH (2005) Molecular marker variability for southern root-knot nematode resistance in sweetpotato. Euphytica 144:125–132

    Article  CAS  Google Scholar 

  • Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    PubMed Central  CAS  PubMed  Google Scholar 

  • Miano DW, LaBonte DR, Clark AC (2008) Identification of molecular markers associated with sweet potato resistance to sweet potato virus disease in Kenya. Euphytica 160:15–24

    Article  CAS  Google Scholar 

  • Murray MG, Thompson WF (1998) Rapid isolation of high molecular weight plant DNA. Nucl Acid Res 8:4321–4325

    Article  Google Scholar 

  • Ober ES, Rajabi A (2010) Abiotic stress in sugar beet. Sugar Tech 12:294–298. doi:10.1007/s12355-010-0035-3

    Article  CAS  Google Scholar 

  • Pawłowicz I, Rapacz M, Bocianowski J (2008) Identification of AFLP markers linked with low-temperature resistance in introgressions transferred from Festuca arundinacea to Lolium multiflorum. Plant Breed Seed Sci 58:3–10

    Google Scholar 

  • Reinefeld E, Emmerich A, Baumgarten G, Winner C, Beiß U (1974) Zur voraussage des melassezuckers aus rubenanalysen. Zucker 27:2–15

    CAS  Google Scholar 

  • Richards CM, Brownson M, Mitchell SE, Kresovich S, LE Panella (2004) Polymorphic microsatellite markers for inferring diversity in wild and domesticated sugar beet (Beta vulgaris). Mol Ecol Notes 4:243–245

    Article  CAS  Google Scholar 

  • Rozema J, Flowers TJ (2008) Crops for a salinized world. Science 322:1478–1480

    Article  CAS  PubMed  Google Scholar 

  • Schneider K, Borchardt DC, Schafer-Pregl R, Nagl N, Glass C, Jeppsson A, Gebhardt C, Salamini F (1999) PCR-based cloning and segregation analysis of functional gene homologues in Beta vulgaris. Mol Gen Genet 262:515–524

    Article  CAS  PubMed  Google Scholar 

  • Schneider K, Schäfer-Pregl R, Borchardt DC, Salamini F (2002) Mapping QTLs for sucrose content, yield and quality in a sugar beet population fingerprinted by EST-related markers. Theor Appl Genet 104:1107–1113

    Article  CAS  PubMed  Google Scholar 

  • Schuelke M (2000) An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol 18:233–234

    Article  CAS  PubMed  Google Scholar 

  • Shukla S, Bhargava A, Chatterjee A, Srivastava A, Singh SP (2006) Genotypic variability in vegetable amaranth (Amaranthus tricolor L.) for foliage yield and its contributing traits over successive cuttings and years. Euphytica 151:103–110. doi:10.1007/s10681-006-9134-3

    Article  CAS  Google Scholar 

  • Tuberosa R, Salvi S, Sanguineti MC, Landi P, Maccaferri M, Conti S (2002) Mapping QTLs regulating morpho-physiological traits and yield: case studies, shortcomings and perspectives in drought-stressed maize. Ann Bot (London) 89:941–963. doi:10.1093/aob/mcf134

    Article  CAS  Google Scholar 

  • Uno Y, Kanechi M, Inagaki N, Sugimoto M, Maekawa S (1996) The evaluation of salt tolerance during germination and vegetative growth of asparagus, table beet and sea aster. J Jpn Soc Hortic Sci 65:579–585

    Article  CAS  Google Scholar 

  • Weber Z, Irzykowska L, Bocianowski J (2005) Analysis of mycelial growth rates and RAPD-PCR profiles in a population of Gaeumannomyces graminis var. tritici originating from wheat plants grown from fungicide-treated seed. J Phytopathol 153:318–324

    Article  CAS  Google Scholar 

  • Wolko Ł, Bocianowski J, Antkowiak W, Słomski R (2015) Genetic diversity and population structure of wild pear (Pyrus pyraster (L.) Burgsd.) in Poland. Open Life Sci 10(1):19–29. doi:10.1515/biol-2015-0003

    CAS  Google Scholar 

  • Wu GQ, Liang N, Feng RJ, Zhang JJ (2013) Evaluation of salinity tolerance in seedlings of sugar beet (Beta vulgaris L.) cultivars using proline, soluble sugars and cation accumulation criteria. Acta Physiol Plant 35:2665–2674. doi:10.1007/s11738-013-1298-6

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The molecular marker analysis of this research was performed at Julius Kühn-Institut, Institute for Breeding Research on Agricultural Crops in Germany. The authors are grateful to Dr. Lothar Frese and Dr. Marion Nachtigall for providing facilities and their excellent assistance to perform the molecular part of this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zahra Abbasi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abbasi, Z., Majidi, M.M., Arzani, A. et al. Association of SSR markers and morpho-physiological traits associated with salinity tolerance in sugar beet (Beta vulgaris L.). Euphytica 205, 785–797 (2015). https://doi.org/10.1007/s10681-015-1408-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10681-015-1408-1

Keywords

Navigation