Skip to main content
Beni-Suef University Journal of Basic and Applied Sciences
Download PDF

Identification and mapping of QTL associated with some traits related for drought tolerance in wheat using SSR markers

Beni-Suef University Journal of Basic and Applied Sciences volume 11, Article number: 38 (2022) Cite this article

Abstract

Background

Wheat is the most important crop around the world. Drought stresses affect wheat production and their characterization. Most of the traits that are affected by drought are quantitative traits, so detection of the quantitative trait’s loci (QTLs) related to these traits is very important for breeder and wheat producers. In this trend, 285 F2 individuals from crosses between four bread wheat genotypes (Triticum aestivum L.), i.e., Sakha93, Sids1, Sakha94, and Gemmiza9, were used for identified QTLs associated with plant height (PH) and leaf wilting (LW). Single marker analysis and composite interval mapping (CIM) were used.

Results

A total of 116 QTLs loci were detected which covered 19 chromosomes out of the 21 chromosomes of wheat. PH and LW had 74 and 42 QTLs loci, respectively. On the other hand, chromosome 7A showed to bear the highest number of QTLs loci (15 loci). While chromosome 1A beard the highest number of QTLs loci related to PH (10 loci), chromosome 2B and 7A beard the highest number of QTLs related LW. We highly recommend our finding to help breeders in wheat breeding programs to improve plant height and leaf wilting.

Conclusion

Our investigation concluded that SSR markers have high efficiency in the identification of QTLs related to abiotic stress; also the CIM method had more advanced priority for QTLs mapping.

1 Background

Wheat is the most important crop that contributes to nutritional and food security around the world. Wheat is one of the strategic crops in Egypt, and the wheat breeding program to produce superior varieties is one of the important breeding programs that many researchers are concerned with. Despite this importance, there is relatively little research in the field of identifying QTLs responsible for some yield-related traits, especially by using the Molecular markers technique. Therefore, this research is an important step to identify the QTLs associated with some drought-affected traits in order to contribute to the development of drought-tolerant wheat cultivars. Although many QTLs related to plant height (PH) were detected in wheat, no QTL related to wilting was detected. On the other hand, abiotic stresses (drought, cold, heat, and salt) affected wheat productivity, while drought stress affects about 1 billion hectares of global agricultural soil including sodic and saline soils [1].

Among the environmental stresses, drought is the important one that affects the development and growth of crops. Drought still to be a major challenge to researchers and breeders. Factors that affect responses of plants to drought stress include genotype, stage of growth, duration and stress severity, physiological process of growth, different genes expression patterns, the different activity of respiration patterns and photosynthesis activity, and environmental factors [2,3,4,5]. A drought had effects on genes expression, so various responsive genes related to drought were featured [6]. Gene’s role could be featured by gene expression to high levels of resistance between varieties (Ouvard et al., 1995). Drought influence plants in levels of protein, production of antioxidant, osmotic adjustment, the composition of the hormone, depth and extension of the root, stomata closing and opening, photosynthesis inhibition, chlorophyll decreasing content, transpiration reduction, and growth inhibition [7,8,9,10]. Drought can also cause pollen sterility, loss in grain yield, and abscisic acid accumulation [11].

Recent techniques like molecular methods must be appropriate useful identification tools for some clonal variation, stress tolerance, and genetic stability establishment [12,13,14,15,16].

The main goal of quantitative trait loci (QTL) analysis is to answer the question of whether phenotypic differences are depending on a few loci with quite large effects, or to many loci, each with midget effects. Remington and Purugganan [17] said, “It appears that a substantial proportion of the phenotypic variation in many quantitative traits can be explained with few loci of large effect, with the remainder due to numerous loci of small effect” [17,18,19].

QTLs can be categorized to constitutive QTL, that detected with most environments (their effects are stable across environmental conditions); and adaptive QTL, that detected with specific conditions of the environment (expression increasing with a level of environmental factor) like QTL that increases drought tolerance [20]. The sensitivity to environmental stress could be explained due to the regulations response (e.g., transcription) of the QTL gene to hint of environment. Meanwhile, response differences may cause by an indirect effect (e.g., larger root systems genotypes will be less affected by water or nutrient deficit, so genes controlling root development may underpin QTLs defined by stomatal conductance, or biomass accumulation). On the other hand, QTLs that caused an alteration in flowering time often affect yield against water or nutrient deficit because the duration of the crop life cycle affects the timing and intensity of the stress experienced by the plants [21]

Many QTLs and molecular markers are related to genes responsible for drought tolerance or sensitivity [22]. Advances in genomic and molecular technologies develop molecular markers which could be useful for QTLs identification. DNA markers based on the polymerase chain reaction (PCR) were the most notable ones among markers that used in studying the genetic characterization of wheat, sequence tagged microsatellite sites (STMSs) and/or simple sequence repeats (SSRs) [23], amplified fragment length polymorphisms (AFLP) [15], and chloroplast simple sequence repeats (cpSSR) [24]. SSR markers had an advantage in wheat molecular studies because it has a co-dominant type of inheritance, a large number of genomes, reproducibility, locus specificity, and high informational content. Moreover, their high polymorphism ratio, chromosome specificity, multiallelic nature, and wide distribution throughout the wheat genome are observed [25, 26].

SSR markers used to identify QTLs related to yield traits such as harvest index and thousand-grain weight [27], to study D genome-based genetic diversity in terms of drought tolerance [28], to study the physiological and genetic characterization wheat genotypes against the drought and temperature tolerance [29], and to detect the quantitative trait loci (QTL) for various traits [30, 31]. The aim of our investigation is to construct QTLs mapping for some traits related to drought tolerance in wheat.

2 Methods

2.1 Wheat materials

A total of 285 F2 individuals from crosses between four bread wheat genotypes (Triticum aestivum L.), i.e., Sakha93, Sids1, Sakha94, and Gemmiza9, were used for QTL analysis of four traits related to drought tolerance. The parents were chosen from a previous study [32], as representing a wide range of diversity for several agronomic characters. The parents were supplied by Field Crops Research Institute, Agricultural Research Center, Giza, Egypt. Table 1 presents the Parent’s name, pedigree, and drought stress.

Table 1 Names, pedigree, and drought stress response of the parental genotypes used in the study according to previous findings from the maize breeding program in Egypt
Full size table

2.2 Field experiment and drought tolerance assessment

The four parental wheat varieties were sown at the Experimental Farm of Genetics Department, Faculty of Agriculture, Mansura University (31.0449° N, 31.3537° E). Then, these varieties were crossed to produce possible crosses, i.e., Cross 1 (H1) = (Sakha93 × Gemmiza9), Cross 2 (H2) = (Sakha94 × Gemmiza9) , and Cross 3 (H3) = (Sakha93 × Sids1) according to Habiba et al. [32]. F2 and their parents were evaluated for drought tolerance at two drought treatments. They were sown in pots (25 cm.) containing sand and clay (2:1 v/v). Irrigation was given as normal irrigation for control and one irrigation 45 days after planting irrigation, i.e., two irrigations were given through the whole season for drought treatment. Pots were fertilized with P2O5, in one dose during soil preparing and Nitrogen was added by ammonia injection in one dose after soil preparing and before 4 days from planting. The trial was arranged in randomized complete blocks design with three replications. The experiment was conducted with 13/11 day/night photoperiod, 20/15 °C day/night temperature, and relative humidity of about 85%. Data were recorded on plant height (PH in cm) and leaf wilting (LW = per day to wilting).

2.3 DNA extraction and SSR markers amplification

DNA extracted from green leaves from each genotype was collected from ten-day seedlings germinated from seeds of each genotype according to Khaled and Esh [33] and Khaled et al. [16]. A set of 143 SSRs from the Wheat database (BARC, CFA, CFD, GWM, WMC, WMSX, BARC, XGWM, XPSP, and XWMC) and new 52 SSRs from the Cotton database (JESPR) involve the 21 chromosomes of wheat (References). Out of the 530 SSR primers, 195 (143 of wheat primers and 52 of cotton primers) have polymorphism to distinguish the genotypes and are used for mapping. Amplification was performed as follows,94 °C for 1 min (one cycle); 94 °C for 20 s, 50–55 °C for 35 s, 72 °C for 45 s (35 cycles), and final extension at 72 °C for 45 s (one cycle). Then hold at 4 °C (infinite). The PCR products were conducted to electrophoresis at 90 V, in 2% agarose gel containing 0.5 μg/ml ethidium bromide for approximately 2 h, using 0.5 × TBE buffer, along with a DNA ladder. The gel was visualized under UV.

2.4 Linkage map and QTLs analysis

Single marker analysis (SMA) and composite interval mapping (CIM) were used to localize the QTL associated with drought tolerance in wheat using 285 plants of an F2 population derived from crosses between four bread wheat genotypes using QTL IciMapping v4.2.5.3 software [34] depending on Kosambi mapping function. The logarithm of odds (LOD) threshold of higher than 3 was used. Segregation ratios of the genotypes classes at each locus were tested using the chi-square test (p < 0.01). The linkage mapping was compared with previous maps. The QTL analysis was also performed using IciMapping v4.2.5.3 software by combined analysis of adjusted means of the phenotypic trait value and genotyping data via inclusive composite interval mapping (ICIM) algorithm for additive gene effect with function inbuilt in the software. The walking speed chosen for all QTLs was 1 cM and the LOD threshold was calculated by 1000 permutation and p = 0.05.

2.5 Statistical analysis

The collected data were subjected to analysis of variance of the split-plot design and significant differences were estimated according to Bernardo [35]. The analyses of variance (ANOVA) were calculated using SPSS v25 and MS-Excel v365. Values of means, standard deviation, correlation coefficients, and plots showing the distribution of phenotypic data for different traits were determined using SPSS v25 and MS-Excel365. QTLs map was constructed using QTL IciMapping v4.2.5.3 software [34]

3 Results

3.1 Phenotypic evaluation

The phenotypic variations between parents and their hybrids (i.e., Sakha93 (S93), Sids1(Sids), Sakha94 (S94), Gemmiza9 (G9), H1, H2, and H3) were evaluated for plant height (PH) and leaf wilting (LW). Mean, standard error, standard deviation, and coefficient of variance (CV %) are presented in Table 2. Analysis of variance and correlation are presented in Table 3.

Table 2 Mean, standard error, standard deviation, and coefficient of variance of parents and their crosses for plant height (PH) and leaf wilting (LW) traits
Full size table
Table 3 Analysis of variance (ANOVA) of plant height (PH) and leaf wilting (LW)
Full size table

Data presented in Table 2 and Fig. 1 illustrated that genotypes (parents and their crosses) exhibited significant variations among studied traits. Due to the results of agronomic traits, Sakha93 and Sids1 were considered drought-tolerant genotypes; and Gemmaza9 and Sakha94 were the sensitive ones. While Sakha93 and Sids1 surpass the others in LW and PH, their cross (H3) was on average. In general, significant variation between tolerant and sensitive genotypes was observed.

Fig. 1
figure 1

Response of different parental genotypes to drought stress, i.e., the effect of drought on plant height (cm) and leaf wilting (days) of Wheat genotypes

Full size image

Figure 1 and Table 2 reveal that Sakha93 recorded the highest value of PH within parents, while Sids1 surpassed all genotypes and hybrids for their survival against drought treatment (LW value). On other hand, the cross H1 had the highest PH among parents and their crosses, while H1 was in average LW. Variations for all the traits were significantly observed for treatments, genotypes, and genotype × treatments under drought conditions (p < 0.05).

The coefficient of variation (CV) was lower for all traits, while LW was the highest among them. Because mean is used in calculating CVs, increasing mean were expected to produce smaller coefficients of variation. Phenotypic correlations ranged widely among traits under drought conditions and control. Correlations were significantly positive (p < 0.05) between genotypes and both PH and LW.

3.2 Construction of linkage map

A genetic map was constructed for plant height and leaf wilting using 195 SSR markers of that 79 SSRs on A chromosomes were mapped, 69 SSRs on B chromosomes, and 47 SSRs on D chromosomes. Chromosomes 1A and 2B beard highest markers that coverage of 19 SSRs, and the chromosome 7D had the lowest one that 3 SSRs coverage it. The genetic length that the linkage map covered was 5057.4858 cM and the average inter marker distance was 25.9358 cM (Fig. 2).

Fig. 2
figure 2figure 2figure 2

Linkage maps revealed the position and distribution the QTLs related to plant height and leaf wilting among Wheat chromosomes. QTL name on the right side and centimorgan (cM) distance on the left. A colored bar represents the CI (confidence interval) of QTL identified through single-locus analysis

Full size image

The number of QTLs covered by each chromosome is presented in Tables 4, 5, and 6. Data in Table 4 revealed that QTLs related to plant height (PH) and leaf wilting (LW) were distributed among all chromosome sets except chromosomes 20 and 21.

Table 4 Chromosomes ID and their QTLs loci related to plant height and leaf wilting for nineteen chromosomes out of twenty-one wheat chromosomes
Full size table
Table 5 Position, characters, and distribution the QTLs related to plant height among nineteen chromosomes out of twenty-one wheat chromosomes
Full size table
Table 6 Position, characters, and distribution the QTLs related to leaf wilting among nineteen chromosomes out of twenty-one wheat chromosomes
Full size table

The nineteen chromosomes were shown to bear 116 QTLs where plant height had 74 QTLs loci and 42 loci for leaf wilting. Out of the observed 116 QTLs, chromosome 19 (7A = 15) beard the highest QTLs number for both the studied traits, followed by chromosome 5 (2B = 14) and chromosome 1 (1A = 13). Among the two traits (plant height and leaf wilting), chromosome 1(1A) exhibited the highest number of QTLs that related to plant height trait (= 10) followed by chromosome 3 (1D) and 19 (7A) which recorded 7 QTLs loci, while chromosomes 5 (2B) and 19 (7A) showed the highest number of QTLs related to leaf wilting.

4 Discussion

Traits such as plant height and leaf wilting, historically, have been subjected to strong selective natural and artificial pressure, to improve the adaptation of bread wheat to different climatic conditions and to increase the grain yield [36,37,38,39]. However, these same traits are not only important for increasing crop yield potential, but they are also useful in determining the adaptation to climate change [40]. In the present work, the genetic control of two traits was investigated to identify associated QTLs. Variations for all the traits were significantly observed for treatments, genotypes, and genotype × treatments under drought conditions (p < 0.05). The coefficient of variation (CV) was lower for all traits, while LW among them was the highest. Because mean used in calculating CVs, increasing mean were expected to produce smaller coefficients of variation. Phenotypic correlations ranged widely among traits under drought conditions and control. Correlations were significantly positive (p < 0.05) between genotypes and plant height, similarly between plant height and LW. However, significant negative correlations (p < 0.05) were exhibited between LW and genotypes. This was consistent with previous reports in wheat and also in other cereal species such as rice and barley, indicating a high response to selection of these traits [41,42,43]. Continuous distribution or absence of discrete segregating classes for PH and LW suggested that its inheritance is either determined by a large number of genes with small effects or by a few major genes with substantial environmental effects. The presence of transgressive segregants in all traits investigated suggested that each of the parental cultivars had desirable and undesirable alleles in various proportions for loci governing these traits.

A total of 530 high-quality SSR markers were used to build the genetic map, and as expected, most of them were placed on genomes A and B, in line with previous results [44, 45]. Wen et al. [46] showed that the D genome had fewer markers than the A and B genomes in the high-density consensus map in common wheat. A total of 28 and 10 QTLs were found in the F2 populations. The comparative QTL analysis of genomes A and B between F2 populations showed that 55 QTLs for PH could be considered to be adjacent and nearly overlapping. Pearson rank between the assessed traits revealed that PH was correlated with the LW, in agreement with Rabbi and Hisam [45], Bilgrami et al. [47], and Mecha et al. [48]. In our study, several QTLs for PH and LW co-localized on the same chromosome, suggesting that they were not distributed evenly in the wheat genome, but they tended to cluster in particular chromosome regions (Table 4).

5 Conclusions

Most of the traits that are affected by drought are quantitative traits, so detection of the QTLs related to these traits is very important for breeder and wheat producers. In this trend, QTLs for plant height (PH) and days to wilting (W) were studied. A total of 116 QTLs loci were detected which covered 19 chromosomes out of the 21 chromosomes of wheat. Chromosome 7A showed to bear the highest number of QTLs loci (15 loci). While chromosome 1A beard the highest number of QTLs loci related to PH (10 loci), chromosome 2B and 7A beard the highest number of QTLs related to surviving (days to wilting). We highly recommend our finding to help breeders in wheat breeding programs to improve plant height and survival (days to wilting). SSR markers are useful for the detection of QTLs related to abiotic stress like drought. Liu et al. [49] detected seven QTLs related to PH on chromosomes 1B, 4B (two regions), 6A (two regions), 6D and 7A; on the other hand, Wang et al. [50] identified two major QTLs related to PH on chromosomes 4B and 6D.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

AFLP:

Amplified fragment length polymorphisms

CIM:

Composite interval mapping

cM:

Centimorgan

CPSSR:

Chloroplast simple-sequence repeats

CV:

Coefficient of variance

DNA:

Deoxyribonucleic acid

ICIM:

Inclusive composite interval mapping

LOD:

Logarithm of odds

LW:

Leaf wilting

PCR:

Polymerase chain reaction

PH:

Plant height

QTLs:

Quantitative trait loci

SMA:

Single marker analysis

SSR:

Simple-sequence repeats

STMS:

Sequence-tagged microsatellite

TBE:

Tris-borate-EDTA

References

  1. FAO. State of Food and Agriculture (2020) Overcoming water challenges in agriculture. Rome. https://doi.org/10.4060/cb1447en

    Article  Google Scholar 

  2. Anjum SA, Ashraf U, Tanveer M, Khan I, Hussain S, Shahzad B, Zohaib A, Abbas F, Saleem MF, Ali I, Wang LC (2017) Drought induced changes in growth, osmolyte accumulation and antioxidant metabolism of three maize hybrids. Front Plant Sci. https://doi.org/10.3389/fpls.2017.00069

    Article  PubMed  PubMed Central  Google Scholar 

  3. Eftekhari A, Baghizadeh A, Yaghoobi MM, Abdolshahi R (2017) Differences in the drought stress response of DREB2 and CAT1 genes and evaluation of related physiological parameters in some bread wheat cultivars. Biotechnol Biotechnol Equip 31:709–716. https://doi.org/10.1080/13102818.2017.1316214

    Article  CAS  Google Scholar 

  4. El-Moneim DA, Alqahtani MM, Abdein MA, Germoush MO (2020) Drought and salinity stress response in wheat: physiological and TaNAC gene expression analysis in contrasting Egyptian wheat genotypes. J Plant Biotechnol 47:1–14. https://doi.org/10.5010/JPB.2020.47.1.001

    Article  Google Scholar 

  5. Itam M, Mega R, Tadano S, Abdelrahman M, Matsunaga S, Yamasaki Y, Akashi K, Tsujimoto H (2020) Metabolic and physiological responses to progressive drought stress in bread wheat. Sci Rep 10:1–14. https://doi.org/10.1038/s41598-020-74303-6

    Article  CAS  Google Scholar 

  6. He C, Du Y, Fu J, Zeng E, Park S, White F, Zheng J, Liu S (2020) Early drought-responsive genes are variable and relevant to drought tolerance. G3 10:1657–1670. https://doi.org/10.1534/g3.120.401199

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lawlor DW, Cornic G (2002) Photosynthetic carbon assimilation and associated metabolism in relation to water deficits in higher plants. Plant Cell Environ 25:275–294. https://doi.org/10.1046/j.0016-8025.2001.00814.x

    Article  CAS  PubMed  Google Scholar 

  8. Szegletes ZS, Erdei L, Tari I, Cseuz L (2000) Accumulation of osmoprotectants in wheat cultivars of different drought tolerance. Cereal Res Commun 28(4):403–410. https://doi.org/10.1007/bf03543622

    Article  CAS  Google Scholar 

  9. Yordanov I, Velikova V, Tsonev T (2000) Plant responses to drought, acclimation, and stress tolerance. Photosynthetica 38:171–186. https://doi.org/10.1023/A:1007201411474

    Article  CAS  Google Scholar 

  10. Zhu JK (2002) Salt and drought stress signal transduction in plants. Annu Rev Plant Biol 53:247–273. https://doi.org/10.1146/annurev.arplant.53.091401.143329

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Xuemei Ji, Shiran B, Wan J, Lewis DC, Jenkins CLD, Condon AG, Richards RA, Dolferus R (2010) Importance of pre-anthesis anther sink strength for maintenance of grain number during reproductive stage water stress in wheat. Plant Cell Environ 33:926–942. https://doi.org/10.1111/j.1365-3040.2010.02130.x

    Article  CAS  Google Scholar 

  12. Al-Taweel SK, Abdel-Aziz RM, Rabea KM, Khaled KAM (2019) Studying cDNA SCoT in response to salinity stress in Stevia rebaudiana Bertoni. SABRAO J Breed Genet 51:281–294

    Google Scholar 

  13. Al-Taweel SK, Azzam CR, Khaled KA, Abdel-Aziz RM (2021) Improvement of Stevia (Stevia rebaudiana Bertoni) and steviol glycoside through traditional breeding and biotechnological approaches. SABRAO J Breed Genet 53:88–111

    Google Scholar 

  14. Bennici A, Anzidei M, Vendramin GG (2004) Genetic stability and uniformity of Foeniculum vulgare Mill. regenerated plants through organogenesis and somatic embryogenesis. Plant Sci 166:221–227. https://doi.org/10.1016/j.plantsci.2003.09.010

    Article  CAS  Google Scholar 

  15. Khaled KA, El-Arabi NI, Sabry NM, El-Sherbiny S (2018) Sugarcane genotypes assessment under drought condition using amplified fragment length polymorphism. Biotechnology 17:120–127. https://doi.org/10.3923/biotech.2018.120.127

    Article  CAS  Google Scholar 

  16. Khaled KA, El-Demardash IS, Amer EAM (2015) Genetic polymorphism among some sugarcane germplasm collections as revealed by RAPD and ISSR analyses. Life Sci J 12:159–167. https://doi.org/10.7537/marslsj120315.21

    Article  CAS  Google Scholar 

  17. Remington DL, Purugganan MD (2003) Candidate genes, quantitative trait loci, and functional trait evolution in plants. Int J Plant Sci 164:S7–S20

    Article  CAS  Google Scholar 

  18. Mackay TFC (2004) The genetic architecture of quantitative traits: lessons from Drosophila. Curr Opin Genet Dev 14:253–257

    Article  CAS  Google Scholar 

  19. Roff DA (2007) A centennial celebration for quantitative genetics. Evolution 61:1017–1032

    Article  Google Scholar 

  20. Vargas M, van Eeuwijk FA, Crossa J, Ribaut JM (2006) Mapping QTLs and QTL × environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods. Theor Appl Genet 112:1009–1023

    Article  CAS  Google Scholar 

  21. Reynolds M, Tuberosa R (2008) Translational research impacting on crop productivity in drought-prone environments. Curr Opin Plant Biol 11:171–179

    Article  Google Scholar 

  22. Budak H, Hussain B, Khan Z, Ozturk NZ, Ullah N (2015) From genetics to functional genomics: improvement in drought signaling and tolerance in wheat. Front Plant Sci 6:1012. https://doi.org/10.3389/fpls.2015.01012

    Article  PubMed  PubMed Central  Google Scholar 

  23. Prasad M, Varshney RK, Roy JK, Balyan HS, Gupta PK (2000) The use of microsatellites for detecting DNA polymorphism, genotype identification and genetic diversity in wheat. Theor Appl Genet 100:584–592. https://doi.org/10.1007/s001220050077

    Article  CAS  Google Scholar 

  24. Tomar RSS, Deshmukh RK, Naik KB, Tomar SMS (2014) Development of chloroplast-specific microsatellite markers for molecular characterization of alloplasmic lines and phylogenetic analysis in wheat. Plant Breeding 133:12–18. https://doi.org/10.1111/pbr.12116

    Article  CAS  Google Scholar 

  25. Ahmed HGMD, Kashif M, Rashid MAR, Sajjad M, Zeng Y (2020) Genome wide diversity in bread wheat evaluated by SSR markers. Int J Agric Biol 24:263–272. https://doi.org/10.17957/IJAB/15.1433

    Article  CAS  Google Scholar 

  26. Mwadzingeni L, Shimelis H, Rees DJG, Tsilo TJ (2017) Genome-wide association analysis of agronomic traits in wheat under drought-stressed and non-stressed conditions. PLoS ONE 12:e0171692. https://doi.org/10.1371/journal.pone.0171692

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Golabadi M, Arzani A, Maibody SM, Tabatabaei BS, Mohammadi SA (2011) Identification of microsatellite markers linked with yield components under drought stress at terminal growth stages in durum wheat. Euphytica 177:207–221. https://doi.org/10.1007/s10681-010-0242-8

    Article  Google Scholar 

  28. Faheem M, Mahmood T, Shabbir G, Akhtar N, Kazi AG, Mujeeb-Kazi A (2015) Assessment of D-genome based genetic diversity in drought tolerant wheat germplasm. Int J Agric Biol 17:4. https://doi.org/10.17957/IJAB/14.0018

    Article  CAS  Google Scholar 

  29. Ramya P, Jain N, Singh PK, Singh GP, Prabhu KV (2015) Population structure, molecular and physiological characterisation of elite wheat varieties used as parents in drought and heat stress breeding in India. Ind J Genet Pl Breed 75:250–252. https://doi.org/10.5958/0975-6906.2015.00038.3

    Article  Google Scholar 

  30. Kumari S, Jaiswal V, Mishra VK, Paliwal R, Balyan HS, Gupta PK (2018) QTL mapping for some grain traits in bread wheat (Triticum aestivum L.). Physiol Mol Biol Plants 24:909–920. https://doi.org/10.1007/s12298-018-0552-1

    Article  PubMed  PubMed Central  Google Scholar 

  31. Liu RX, Wu FK, Xin YI, Yu LIN, Wang ZQ, Liu SH, Mei DENG, Jian MA, Wei YM, Zheng YL, Liu YX (2020) Quantitative trait loci analysis for root traits in synthetic hexaploid wheat under drought stress conditions. J Integr Agric 19:1947–1960. https://doi.org/10.1016/S2095-3119(19)62825-X

    Article  CAS  Google Scholar 

  32. Habiba R, El-Adl A, El-Mageed A, Gomaa K (2018) Effect of genotypes and polyethylene glycol on immature embryo culture in wheat. J Agric Chem Biotechnol 9:175–180. https://doi.org/10.21608/JACB.2018.35237

    Article  Google Scholar 

  33. Khaled AMK, Esh AMH (2008) High quality genomic DNA impurities-free from sugar crops and other plant tissue. In: Proceedings of international conference (IS-2008), Al Arish, Egypt

  34. Meng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3:269–283. https://doi.org/10.1016/j.cj.2015.01.001

    Article  Google Scholar 

  35. Bernardo R (2002) Breeding for quantitative traits in plants, vol 1. Stemma Press, p 369

    Google Scholar 

  36. Law CN, Worland AJ (1997) Genetic analysis of some flowering time and adaptive traits in wheat. New Phytol 13:19–28. https://doi.org/10.1046/j.1469-8137.1997.00814.x

    Article  Google Scholar 

  37. Lewis S, Faricelli ME, Appendino ML, Valárik M, Dubcovsky J (2008) The chromosome region including the earliness per se locus Eps-A m1 affects the duration of early developmental phases and spikelet number in diploid wheat. J Exp Bot 59:3595–3607. https://doi.org/10.1093/jxb/ern209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Richards RA (1988) A tiller inhibitor gene in wheat and its effect on plant growth. Aust J Agric Res 39:749–757. https://doi.org/10.1071/AR9880749

    Article  Google Scholar 

  39. Sanchez-Garcia M, Bentley AR (2019) Global journeys of adaptive wheat genes. In: Miedaner T, Korzun V (eds) Applications of genetic and genomic research in cereals, 1st edn. Elsevier, Amsterdam, pp 183–200. https://doi.org/10.1016/B978-0-08-102163-7.00009-0

    Chapter  Google Scholar 

  40. De Vita P, Taranto T (2019) Durum wheat (Triticum turgidum ssp. durum) breeding to meet the challenge of climate change. In: Al-Khayri JM, Jain SM, Johnson DV (eds) Advances in plant breeding strategies: cereals. Springer, pp 471–524

    Chapter  Google Scholar 

  41. Abinasa M, Ayana A, Bultosa G (2011) Genetic variability, heritability and trait associations in durum wheat (Triticum turgidum L. var. durum) genotypes. African J Agric Res 6:3972–3979

    Google Scholar 

  42. Iqbal M, Navabi A, Salmon DF, Yang RC, Murdoch BM, Moore SS, Spaner D (2007) Genetic analysis of flowering and maturity time in high latitude spring wheat: genetic analysis of earliness in spring wheat. Euphytica 154:207–218. https://doi.org/10.1007/s10681-006-9289-y

    Article  CAS  Google Scholar 

  43. Shrimali J, Shekhawat AS, Kumari S (2017) Genetic variation and heritability studies for yield and yield components in barley genotypes under normal and limited moisture conditions. J Pharmacogn Phytochem 6:233–235

    Google Scholar 

  44. Berkman PJ, Visendi P, Lee HC, Stiller J, Manoli S, Lorenc MT, Lai K, Batley J, Fleury D, Šimková H et al (2013) Dispersion and domestication shaped the genome of bread wheat. Plant Biotechnol J 11:564–571. https://doi.org/10.1111/pbi.12044

    Article  CAS  PubMed  Google Scholar 

  45. Rabbi A, Hisam SM (2017) Genetics of drought tolerance in hard red spring wheat in the Northern United States of America. Ph.D. Thesis, North Dakota State University, Fargo, ND, USA

  46. Wen W, He Z, Gao F, Liu J, Jin H, Zhai S, Qu Y, Xia X (2017) A high-density consensus map of common wheat integrating four mapping populations scanned by the 90k SNP array. Front Plant Sci 8:1–14. https://doi.org/10.3389/fpls.2017.01389

    Article  CAS  Google Scholar 

  47. Bilgrami SS, Ramandi HD, Shariati V, Razavi K, Tavakol E, Fakheri BA, Mahdi Nezhad N, Ghaderian M (2020) Detection of genomic regions associated with tiller number in Iranian bread wheat under different water regimes using genome-wide association study. Sci Rep 10:1–17. https://doi.org/10.1038/s41598-020-69442-9

    Article  CAS  Google Scholar 

  48. Mecha B, Alamerew S, Assefa A, Dutamo D, Assefa E (2017) Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.) genotypes. Adv Plants Agric Res 6:1–10. https://doi.org/10.15406/apar.2017.06.00226

    Article  Google Scholar 

  49. Liu DC, Gao MQ, Guan RX, Li RZ, Cao SH, Guo XL, Zhang AM (2002) Mapping quantitative trait loci for plant height in wheat (Triticum aestivum L.) using a F2:3 population. Yi Chuan Xue Bao 29(8):706–711

    CAS  PubMed  Google Scholar 

  50. Wang Z, Hu H, Jiang X, Tao Y, Lin Y, Wu F, Hou S, Liu S, Li C, Chen G, Liu Y (2020) Identification and validation of a novel major quantitative trait locus for plant height in common wheat (Triticum aestivum L.). Front Genet 11:602495. https://doi.org/10.3389/fgene.2020.602495

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The work was carried out and supported by the Genetic department, faculty of Agriculture, Beni-Suef University and the Genetic department, faculty of Agriculture, Mansoura University.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Faculty of Agriculture, Beni-Suef University, New Beni Suef City, Beni Suef Governorate, Egypt

    Khaled Adly Mohamed Khaled

  2. Faculty of Agriculture, Mansoura University, Elgomhouria St., Mansoura City, 35516, Egypt

    Rehab Mohamed Mohamed Habiba & Mohamed Hassan Abd El-Aziz

  3. Horticulture Department, Faculty of Agriculture, Benghazi University, Benghazi, Libya

    Jamila Abdelhafeeth Bashasha

Authors
  1. Khaled Adly Mohamed Khaled
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rehab Mohamed Mohamed Habiba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jamila Abdelhafeeth Bashasha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed Hassan Abd El-Aziz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors were involved in conceptualization and methodology; KAMK, MHA, and RMMH helped in application of drought treatments and DNA extraction from all samples; KAMK contributed to PCR and bioinformatics analysis; writing—original draft preparation; MHA, RMMH, and JAB were involved in review and editing. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Khaled Adly Mohamed Khaled.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khaled, K.A.M., Habiba, R.M.M., Bashasha, J.A. et al. Identification and mapping of QTL associated with some traits related for drought tolerance in wheat using SSR markers. Beni-Suef Univ J Basic Appl Sci 11, 38 (2022). https://doi.org/10.1186/s43088-022-00212-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43088-022-00212-4

Keywords

  • Wheat
  • QTLs
  • Mapping
  • Composite interval mapping (CIM)
  • Plant height
  • Leaf wilting
  • Simple sequence repeats
  • SSR