• Research Article
  • |
  • Open Access

Genomic, Agro-morphological and Chemical Divergence of Wild Barley Populations Differentially Adapted to Microenvironments

  • Mutthanthirige Don Lalith Chandana Nishantha;
    • State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
    • Directorate of Agriculture and Livestock, Army Cantonment, Panagoda, Homagama, 10200, Sri Lanka
  • Diddugodage Chamila Jeewani;
    • State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
    • Grain Legumes and Oil Crops Research and Development Centre, Department of Agriculture, Angunakolapelessa, 82220, Sri Lanka
  • Bian Jianxin;
    • State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
  • Nie Xiaojun;
    • State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
  • Song Weining
    • State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China

  • Corresponding Author(s): Mutthanthirige Don Lalith Chandana Nishantha

  • Directorate of Agriculture and Livestock, Army Cantonment, Panagoda, Homagama, 10200, Sri Lanka

  • mdlcnishantha@gmail.com

  • Nishantha MDLC (2020).

  • This Article is distributed under the terms of Creative Commons Attribution 4.0 International License

Received : May 01, 2020
Accepted : Jun 18, 2020
Published Online : Jun 22, 2020
Journal : Journal of Plant Biology and Crop Research
Publisher : MedDocs Publishers LLC
Online edition : http://meddocsonline.org

Cite this article: Nishantha MDLC, Jeewani DC, Jianxin B, Xiaojun N, Weining S. Genomic, Agro-morphological and Chemical Divergence of Wild Barley Populations Differentially Adapted to Microenvironments. J Plant Biol Crop Res. 2020; 3(1): 1020.

Abstract

Genetic diversity is one of the most important indicator for germplasm assessment, molecular evaluation as well as speciation studies. In this study, ISJ molecular markers together with agro-morphological traits and near-infrared spectroscopy were used to investigate the diversity and divergence of two wild barley populations from Mt Gilboa, Israel, adapted to two microenvironments. High level of polymorphism was observed with ISJ markers and the significant differences were found in agro-morphological traits and near-infrared spectroscopy analysis.

The genetic variation (50.5% polymorphism) was recognized between the populations. Cluster analysis grouped genotypes into two clear groups suggesting that they have adapted to two different microenvironments. Some agromorphological traits such as plant height, number of tillers, days to flowering, flag leaf length and number of seeds per spike exhibited significant different at p ≤ 0.05 and p ≤ 0.001 probability levels and several chemical compounds such as fiber and crude protein content also exhibited significant different at p ≤ 0.05 and p ≤ 0.001 probability levels between microenvironments. Hence, our results showed the genomic divergence and adaptation of wild barley to microenvironments. Furthermore, the results demonstrated the effectiveness of ISJ markers, agro-morphological and chemical traits in detecting variation exerted by adaptation to microenvironments.

Keywords: Wild barley; Agro-morphological traits; Microenvironment; Environmental adaptation; Molecular markers; Polymorphism.

Introduction

      Barley ( Hordeum vulgare L. ) is one of the oldest cultivated cereal crop and the fourth largest cereal crop produced worldwide, quantity wise and it is utilized in the food industry, feed industry and malt production [1]. Also, being the most extensively adapted cereal grain species can be cultivated in fertile lands as well as desert lands [2]. Wild barley ( Hordeum spontaneum ) is the progenitor of cultivated barley varieties which offers considerable potential as a genetic resource for barley improvement. Naturally occurring wild barley populations are distributed primarily across the Fertile Crescent, Central Asia and Tibet [3-6]. The study of the origin, genetic variation and evolutionary relationship in barley is important for the conservation and restoration of biodiversity of wild germplasm [7]. By studying microenvironmental adaptation, we can have fair idea about the genomic divergence of wild barley adapted to different microenvironments.

      Molecular markers are vital and valuable tools in the representation and evaluation of genetic diversity within and between species and populations [8]. The concept of polymorphism is used to define genetic variation in a population, which has been extensively studied in recent years by several established scientific disciplines [9]. Molecular markers are robust tools for the detection of genetic variation within and among populations [10]. The use of intron-exon splice junction (ISJ) markers is an effective method for analysis of genetic diversities, cultivar identification, construction of genetic maps and molecular marker assisted breeding [11,12].

      The ISJ markers had been used to identify genetic variation and mapping polymorphisms in several crop species such as, wheat, barley, faba, triticale, tritipyrum [12], cotton, mosses and Orthotrichum speciosum [13]. ISJ markers are profitable in term of cost, while showing higher polymorphism and good stability with reproducible bands [13]. Therefore, in this study we used ISJ markers to detect polymorphism in wild barley populations.

      Primarily, response to physical characteristics of the environment can be reflected as plant stress. The changes in abiotic factors such as temperature, climatic factors and chemicals create extrinsic stress which is considered the most vital stress agent [19-20]. In addition to that, competition, predation and parasitism, are considered as biotic stress factors, which may also cause for the development of stress [21]. Even though abiotic and biotic stresses may act as independent units, these two regularly act synergistically. For example, the organism that has suboptimal fitness due to abiotic stress, often suffers more from parasites and predators.

      Environmental stress could only be measured or valued in connection to the organism which is going through stress. These genetic changes in organism or population give rise by inbreeding or other changes in the genetic architecture of organisms or population can change the perception of a different unchanged environment [22]. In this study, we selected wild barley samples to investigate microenvironmental adaptation from Mt. Gilboa, Israel, which belongs to Fertile Crescent, where wild barley originated.

      The present study was conducted to evaluate the genetic diversity and genomic divergence of wild barley populations, differentially adapted to microenvironments in Mount Gilboa, Israel by using molecular markers, agro-morphological traits and NIR spectroscopy analysis.

Materials and methods

Plant materials

      In this study, we used 27 wild barley samples which were collected from Mount Gilboa mountain, Israel. Samples were collected from the top and bottom of the rock situated in Mount Gilboa, which had two different microenvironments where top of the rock faced harsh stress conditions due to high sun exposure, heat, wind and low amount of water compared to bottom of the rock which received low sun exposure, low heat, low wind and water stress. Among them, sixteen samples were collected from the bottom of the rock and eleven were from the top of the rock. Samples were arbitrarily named using the codes of B1 and B2 (Bottom population) for the samples obtained from the bottom of the rock (Low Elevation) and T1 and T2 (Top population) for the samples obtained from top of the rock (High Elevation).

DNA extraction

      DNA was extracted from fresh young leaves of above 27 wild barley accessions using the modified DS buffer method [23]. In brief, leaf tissues were grounded to a powder in 2 ml Eppendorf tubes in liquid nitrogen. The powder was then mixed with 0.6 ml of DS buffer (4% Sarkosyl, 0.1 M Tris-HC1, 10 mM EDTA, pH 8.0) and subsequently added 0.6 ml phenol/ chloroform/ isoamylalcohol (25:24:1). The whole mixture was shaken well for 30 seconds, left it on ice for 20 minutes and centrifuged at 10,000 rpm for 10 minutes. The aqueous phase was recovered and transferred to fresh tubes. Then, 0.6 ml of chloroform was added to the obtained solution and subsequently shaken well before centrifugation for 10 minutes.

      The upper phase was collected and 0.5 ml of isoproponol and 50 µl of 3 M NaAc were added to the tube. Then the tube was inverted gently for few times to precipitate the DNA and centrifuged at 10,000 rpm for 5 minutes. After discarding the supernatant, the pellet was washed two times with 70 % ethanol. Pellet was air dried at room temperature for twelve hours and then 50 µl of double distilled H2 O was added. DNA concentration was checked by Thermo Scientific NanoDrop 2000 spectrophotometer and quality was checked using 1% agarose gel electrophoresis.

Molecular markers analyses

      Seven ISJ primers were used in this study (Table 1). PCR amplification was carried out in a total volume of 20 µL reaction mixture containing 1.0 µL of template DNA, 2.0 µL of 10 x buffer, 1.6 µL dNTPs (2.5 mmol/L), 1.6 µL MgCl2 (25 mmol/L), 1 µmol/L primer, 0.2 µL of Taq polymerase (Takara) and 12.6 µL of double-distilled H2O. The amplification reaction was performed using Bio-Rad C1000 thermal cycler according to the following cycling program: Initial denaturation for 5 minutes at 94 0 C, followed by 9 thermal cycles of 1 minutes at 94 0 C, 108 seconds at 48 0 C, 2 minutes at 72 0 C, 20 thermal cycles of 1 minutes at 94 0 C, 90 seconds at 55 0 C, 2 minutes at 72 0 C and a final extension at 72 0 C for 10 minutes.

table 1 Table 1

Table 1: List of ISJ primers and its descriptive used in the study.

      Finally, the PCR amplified products were separated by gelelectrophoresis in 2 % agarose gels with 1xTAE buffer. Gels were stained with ethidium bromide and imaged in Biometra (UV-solo model) gel documentation system. Each PCR reaction was carried out twice and only reproducible bands were considered for analysis.

Molecular data analysis

      Polymorphic Information Content (PIC) values for each ISJ primer were calculated according to the formula;

      PIC=1-Σ(Pij)2

      where Pij is the frequency of the ith pattern revealed by the j th primer summed across all patterns revealed by the primers [24].

      Marker index (MI) was obtained by the formula;

      MI = PIC × number of polymorphic bands as proposed by Powell et al [25] and used by Milbourne et al [26]. Effective multiplex ratio (EMR) is defined as the product of the fraction of polymorphic loci and the number of polymorphic loci. The ability of the primers to distinguish genotypes was assessed by calculating their resolving power (Rp) as where Ib is band informativeness, Ιb=1–[2× (0.5–pi)] and pi is the proportion of genotypes containing band I [27].

      Rp=Σ Ib

      Pair-wise genetic similarity (GS) between individuals for each marker system was estimated using the Jaccard coefficient [28]. All the GS matrices were subjected to appropriate clustering methods using NTSYSpc 2.02 software.

Agro-morphological characterization

      The experiment was carried out during two crop seasons of 2015-2016 and 2016-2017 at the experimental field of Northwest Agriculture and Forest University, Shaanxi, China (N 34°10́, E 108°10́) under rainfed condition. The altitude of the area is 525 m and the climate is semi-humid prone to semi-arid with an average annual temperature of 13 0 C and average annual rain fall of 600 mm.

      The rainfall and average temperature in the two crop seasons (October 2015 to June 2016 and October 2016 to June 2017) were 214.8 mm and 389.6 mm and 13.6 0 C and 12.4 0 C respectively. Weeds were controlled manually. Pests and diseases were controlled by conventional methods.

      Twenty-seven wild barley accessions collected from the bottom and top of the rock at Mt Gilboa, Israel were used for the experiment. Those all accessions were planted in two locations in the experimental field where the first location was at a lower elevation and the second location was located at comparatively higher elevation representing two microenvironments in Mt Gilboa. Line evaluation was carried out in 5 m rows with 30 cm x 45 cm spacing for within rows and between rows. Five plants per each row were randomly selected representing one plant per one meter and agro-morphological data were recorded for each plant.

      Ten agro-morphological traits including plant height (PH), total number of tillers (NT), days to flowering (DF), flag leaf length (FLL), flag leaf width (FLW), peduncle length (PL), spike length (SL), awn length (AL), number of seeds per spike (NSS) and 1000 seeds weight (TSW) were recorded according to the descriptors of barley published by International Plant Genetic Resources Institute (IPGRI), Rome, Italy. To identify the significant differences, variation and correlation within/between the populations and elevations, ANOVA and correlation analysis were performed using IBM SPSS 23.0 software.

NIR spectroscopy analysis

      The NIR analysis of wild barley seeds was carried out using Perten Diode array DA7250 NIR analysis system, Perten Instruments, Sweden, according to the protocol described by the manufacturer. The contents of Protein (Wet base), Oil (Dry base), Fiber (Fixed value=10), Crude protein (Dry basis), Starch (Wet base) and Amylose (Wet base) were measured by using NIR spectroscopy. Each sample was analyzed in duplicate as separately prepared replicates in a rotating sample cup. For each sample, four scans were performed and the results were averaged.

Results and Discussion

      The potential use of wild barley germplasm has to be exploited largely especially where the wild barley originated. Fertile Crescent is recognized as the originated center of wild and cultivated barley where high genetic diversity was reported4 . Therefore, to study microenvironmental adaptation and genetic divergence of wild barley, we have selected wild barley germplasms from Israel, as it represents the Fertile Crescent. Wild barley grown in the majority of Israeli climatic, topographic and edaphic habitats experiences many extremely unfavorable conditions [29]. Wild barley populations sampled in this area offered the unique advantage of allowing a comparison between genetic and ecogeographic diversity and finding correlations between genetic parameters and environmental parameters.

      There are different criteria for the estimation of genetic diversity such as pedigree analysis32, agro-morphological traits [30-31], biochemical markers [32-33] and molecular markers [34-35]. In this study, we used molecular markers, agro-morphological traits and biochemical markers to evaluate genetic diversity.

Polymorphism and genetic variation analyzed by ISJ markers

      Seven ISJ primers were used in this study and primer sequences and their properties are summarized in table 1. Those ISJ primers produced 85 bands with an average of 6.14 bands per marker and 43 out of 85 bands (50.5%) were polymorphic. All primers except primer R1 detected polymorphism where primer R2 showed the highest polymorphic percentage (75%) while primer R1 showed the lowest polymorphic percentage (0 %). The number of fragments amplified by each reaction ranged from zero (Primer R1) to twelve (Primer R2) with a mean of 6.14. The band fragment size varied from 100 bp to 2000 bp and PIC values were ranged from 0.0 to 0.94 with an average of 0.76, thus indicating sufficient variability in wild barley populations. The Ib, Rp, EMR and MI values were ranged from 0 to 0.13, 0 to 1.19, 0 to 9.00 and 0 to 8.50 respectively. Primer R3 showed the highest Rp value (1.19), while primer R2 showed the highest EMR (9.00), PIC (0.94) and MI (8.50) values. Primers E4 and R3 showed the highest Ib value (0.13) (Table 1). Moreover, primers E1, E2, R2, R3, R4 and R5 which recorded higher values of PIC, MI, EMR and Rp were identified as more informative in distinguishing wild barley genotypes. Primer R1 has not shown any polymorphic bands and due to that, it cannot use to distinguish wild barley genotypes.

      PIC values greater than 0.50 indicate that, those markers enable sufficient level of polymorphism [36]. PCR amplification profile obtained with primer R4 is shown in figure 1. Number of studies have been conducted to evaluate the genetic relationships among different barley genotypes using molecular markers such as RADP [37], SSR[38] and ISJ[11,39,40]. Our results are comparable with the results reported by previous authors. Genetic similarities (GS) among barley genotypes were calculated using Jaccard’s similarity coefficient and used to construct a dendrogram using NTSYSpc 2.02 software. Two groups were recognized in the dendrogram (Figure 2) with UPGMA algorithm for constructing cluster.

...

Figure 1:

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Figure 2:

      Cluster analysis showed a clear separation between two groups as Top population (T1-1 to T2-7) and Bottom population (B1-1 to B2-7). Accession T1-6 as an outlier, was grouped together in the bottom cluster, since it showed more similarity to the bottom cluster. Similar kind of cluster analysis have been conducted to identify genotype groups in previous studies [39, 41,42].

Diversity of agro-morphological traits

      In both growing seasons (2015-2016 and 2016-2017), mean, standard deviation, maximum, minimum and coefficient of variation (CV %) for the traits under both conditions (Low Elevation and High Elevation) in both populations (Top and Bottom populations) are indicated in table 2. Analysis of variance (ANOVA) within the microenvironments (between populations) is shown in table 3 and ANOVA between microenvironments (between elevations) is shown in table 4. Figure 3 demonstrates a graphical illustration of variation of agro-morphological traits.

...

Figure 3:

table 2 Table 2

Table 2: Summary of statistics of agro-morphological traits.

table 3 Table 3

Table 3: ANOVA of agro-morphological traits within microenvironments (Between populations).

table 4 Table 4

Table 4: ANOVA of agro-morphological traits between microenvironments (Between elevations).

      Plant height, number of tillers, flag leaf length, flag leaf width, peduncle length, spike length, number of seeds per spike and thousand seed weight were comparatively higher in the samples tested from bottom of the rock (LE) than top of the rock (HE) in both seasons. This is due to the adaptation to the shade effect, comparatively higher fertility and water availability in the bottom of the rock. Furthermore, all above parameters were low in top of the rock due to the harsh climatic and edaphic conditions they faced. Statistical analysis clearly indicates these microenvironmental adaptations (Table 2).

      Average values of agro-morphological traits including plant height, number of tillers, days to flowering, flag leaf length, flag leaf width and 1000 seeds weight were higher in 2017 than 2016. Plant growth is comparatively higher in 2017 due to high rainfall during the 2016-2017 growing season. However, average peduncle length is low in 2017 than 2016. When the plant height is high, peduncle length is low and they have negative correlation with each other. This is different with other agromorphological characters (Figure 3).

ANOVA between populations

      Plant height and number of tillers were significantly different in both 2016 and 2017 seasons between populations. Flag leaf length, spike length and number of seeds per spike were significantly different in both 2016 and 2017 seasons between populations, except between 2016 HE Btm and Top. Days to flowering was significant in both 2016 and 2017 seasons, between populations, except between 2016 LE Btm and Top. Peduncle length was significant only in 2017 in both LE and HE (Table 3).

ANOVA between elevations

      All the agro-morphological traits, except days to flowering and 1000 seeds weight, were significant in both 2016 and 2017 seasons between elevations. Days to flowering was significant only in 2017 between elevations whereas 1000 seeds weight was significant only in 2017 between LE and HE Btm (Table 4).

      Agro-morphological characters are useful tools frequently use to evaluate the diversification and to establish the description of a genotype in crops including barley8 . Agro-morphological characterization is a first step towards conservation and utilization of plant genetic resources. When assessing genetic diversity, the use of agro-morphological variation provides greater complementary information to molecular markers characterization [43].

      Along with the results described above, CV % is higher in all agro-morphological traits in high elevation, except, days to flowering and 1000 seeds weight. Except leaf width, number of effective tillers and awn length, all higher CV % values were recorded in Top populations (HE Top). Lower CV % values were observed for agro-morphological traits in low elevation, except for the peduncle length (Table 2). As a whole it implies higher CV % obtained from populations of high elevation and lower CV % values obtained from populations of low elevation. It emphasizes that except for the days to flowering, growth rate of high elevation population is higher than low elevation population. These CV % values differentiate the variation of both wild barley populations.

      Significant variations were observed for all agro-morphological traits indicating sufficient genetic variation and diversity in two microenvironments (Table 3 and 4). Similar type of results were also observed in several previous studies [44-46]. These agro-morphological traits analysis confirmed the microenvironmental adaptation of both Top and Bottom wild barley populations obtained from two different microenvironments in Mt. Gilboa, Israel.

NIR spectroscopy analysis

      The mean, standard deviation, minimum, maximum and coefficient of variation values of NIR spectroscopy analysis of protein (wet base), oil (dry base), fiber (fixed value=10), crude protein (dry basis), starch (wet base) and amylose (wet base) in 2015-2016 and 2016-2017 growing seasons in both Bottom and Top populations are shown in table 5.

table 5 Table 5

Table 5: Descriptive statistics of NIR spectroscopy analysis.

      Analysis of variance of the chemical composition of NIR spectroscopy analysis in 2015-2016 seasons between the top and bottom populations showed a significant difference in protein, oil, fiber, crude protein and starch composition at P ≤ 0.05 probability level. Fiber and crude protein contents were shown significant difference at P ≤ 0.05 probability level between top and bottom populations in 2016-2017 while oil and amylose were shown significant different at P ≤ 0.001 probability level.

      ANOVA of chemical composition of NIR spectroscopy analysis in both seasons, between Top and Btm populations, oil, fiber and crude protein showed a significant difference at P ≤ 0.05 probability level (Table 6). Many studies have been conducted to investigate effect of environmental effect on the chemical composition of barley [47-50] and it is a good indicator for the analysis of genetic divergence. This NIR spectroscopy analysis confirmed the microenvironmental adaptation in relation to the chemical composition of both Top and Bottom wild barley populations studied from two different microenvironments in Mt. Gilboa, Israel.

table 6 Table 6

Table 6: ANOVA of chemical properties between microenvironments.

      Characterization of differently adapted wild barley populations is an important aspect for the evaluation and preservation of wild germplasm. Such germplasms have undergone local environmental adaptations through natural selection, mutations and genetic drift for a particular geographic region over many generations [51]. Also climatic conditions and epigenetic factors play a major role in the evolution by representing significant levels of variation in response to the selection stress in the environment [41]. Present study depicted the clear variations of genetical, agro-morphological and chemical characteristics in wild barley population differently adapted to microenvironments.

      Adaptation in basic terms can be stated as the process of change in an organism to conform successfully with new environmental conditions whereby the organism or group of organisms acquires characteristics involving changes in morphology, physiology or behavior that tend to develop their survival and reproductive success in the particular environment [22]. Those phenotypical changes can occur within a set genotype. As a result, phenotype adaptation which is called “phenotypic plasticity” takes place. This has the potential to change its phenotype according to existing conditions in the environment. Moreover, adaptation can also happen through changes in allele frequencies and it is an outcome of the selection pressure exerted by the environment. This mechanism is known as evolutionary adaptation or genotypic adaptation [52].

      The variation in agro-morphological traits is usually determined by both genetic makeup of plant and environmental influences and interactions between them. Breeding programs based on both genomic information and genetic information are quick and accurate than conventional breeding. The characterized barley genotypes were mainly classified according to genetic, morpho-agronomic and chemical characterization which was complex and of multigenic characters. Such characters can be hardly influenced by various environmental conditions and therefore are liable to subjective evolution.

Conclusion

      The populations analyzed in the present study have been characterized for genetic diversification in several ways such as genetically using ISJ molecular markers, using agro-morphological traits and biochemical analysis using NIR spectroscopy. In this study, we found that wild barley samples obtained from two different microenvironments, under the main common environment in Mt. Gilboa, Israel, have genomic divergence and differentially adapted to the particular microenvironments. Furthermore, our results demonstrated the effectiveness of ISJ molecular markers, agro-morphological traits and chemical characteristics for detecting variation and thus in monitoring the impact exerted by adaptation to the microenvironment on genetic divergence.

Acknowledgments

      This work was mainly funded by the 948 Program of Chinese Ministry of Agriculture (Grant No.2016-X16) and partially supported by the National Natural Science Foundation of China (Grant No. 31401373).

References

  1. Arngren M, Hansen PW, Eriksen B, Larsen J, Larsen R. Analysis of pregerminated barley using hyperspectral image analysis. J. Agric. Food Chem. 2011; 59: 11385-11394.
  2. Newman C, Newman RA. Brief History of Barley Foods. Cereal Foods World. 2006; 51: 4-7.
  3. Harlan JR, Zohary D. Distribution of Wild Wheats and Barley. Science. 1966; 153: 1074-1080.
  4. Nevo E. Origin, evolution, population genetics and resources for breeding of wild barley, Hordeum spontaneum, in Shewry P.R, eds. the Fertile Crescent, Barley: genetics, biochemistry, molecular biology and biotechnology, C.A.B. International, The Alden Press, Oxford. 1992: 19-43.
  5. Zohary D, Hopf M. The origin and spread of cultivated plants in West Asia, Europe and the Nile Valley, in Domestication of plants in the Old World, Clarendon Press, Oxford, England. 1993: 226- 227.
  6. Wei K, Xue D, Jin X, Wu F, Zhang . Genotypic and environmental variation of β-amylase activity, β- glucan and protein fraction contents in Tibetan wild barley. J. Zhejiang Univ. 2009; 35: 639 - 644.
  7. Wang A, Yu Z, Ding Y. Genetic diversity analysis of wild close relatives of barley from Tibet and the Middle East by ISSR and SSR markers. C. R. Biol. 2009; 332: 393-403.
  8. Russell J, Fuller J, Young G, Thomas B, Macaulay M, et al. Discriminating between barley genotypes using microsatellite markers. Genome. 1997; 40: 442-450.
  9. Nagy S, Poczai P, Cernák I, Gorji AM, Hegedűs G, et al. PICcalc: an online program to calculate polymorphic information content for molecular genetic studies. Biochem. Genet. 2012; 50: 670- 672.
  10. Tsuda Y, Goto S, Ide Y. RAPD Analysis of Genetic Variation Within and Among Four Natural Populations of Betula maximowicziana. Silvae Genet. 2004; 53: 234-239.
  11. Yu CL, Liu JX, Qi GC, Luo XJ, Liu XC, et al. IT-ISJ Markers and Its Application to the Genetic Diversity in Barley, Barley and Cereal Sciences. 2012; 4: 001.
  12. Zheng J, Zhang ZS, Li C, Qun W, Hu M, et al. Intron-targeted intron-exon splice conjunction (IT-ISJ) marker and its application in construction of upland cotton linkage map. Agric. Sci. China. 2008; 7: 1172-1180.
  13. Pardo DC, Terracciano S, Giordano S, Spagnuolo V. Molecular Markers Based on PCR Methods: A Guideline for Mosses. Cryptogam Bryol. 2014; 35: 229-246.
  14. Bednař P, Papoušková B, Müller L, Barták P, Stávek J, et al. Utilization of capillary electrophoresis/mass spectrometry (CE/MSn) for the study of anthocyanin dyes. J. Sep. Sci. 2005; 28: 1291- 1299.
  15. Missang E, Crepin G, Sylvain R, Catherine M. Flavonols and anthocyanins of bush butter, Dacryodes edulis (G. Don) HJ Lam, fruit, Changes in their composition during ripening. J. Agric. Food Chem. 2003; 51: 475-7480.
  16. Williams CA, Greenham J, Harborne JB, Kong JM, Chia LS, et al. Tatsuzawa F. Acylated anthocyanins and flavonols from purple flowers of Dendrobium cv.‘Pompadour’. Biochem. Syst. Ecol. 2002; 30. 667-675.
  17. Lees D, Francis F. Standardization of pigment analyses in cranberries. Hort Science. 1972; 7: 83-84.
  18. Liu Y, Chen X, Ouyang A. Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry. Lebensm. Wiss. Technol. 2008; 41: 1720-1725.
  19. Sørensen J, Norry F, Scannapieco A, Loeschcke V. Altitudinal variation for stress resistance traits and thermal adaptation in adult Drosophila buzzatii from the New World. J. Evol. Biol. 2005; 18: 829-837.
  20. Lindgren B, Laurila A. Proximate causes of adaptive growth rates: growth efficiency variation among latitudinal populations of Rana temporaria. J. Evol. Biol. 2005; 18: 820-828.
  21. Relyea R. The heritability of inducible defenses in tadpoles. J. Evol. Biol. 2005; 18: 856-866.
  22. Bijlsma RG, Loeschcke V. Environmental stress, adaptation and evolution: an overview. J. Evol. Biol. 2005; 18: 744-749.
  23. Weining S, Langridge P. Identification and mapping of polymorphisms in cereals based on the polymerase chain reaction. Theor. Appl. Genet. 1991; 82: 209-216.
  24. Botstein D, White R, Skolnick MH,Davis RW. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 1980; 32: 314-331.
  25. Powell W, Morgante M, Andre C, Hanafey M, Vogel J, et al. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol. Breed. 1996; 2: 225-238.
  26. Milbourne D, Meyer R, Bradshaw JE, Baird E, Bonar N, et al. Comparison of PCR-based marker systems for the analysis of genetic relationships in cultivated potato. Mol. Breed. 1997; 3: 127-136.
  27. Prevost A,Wilkinson MJ. New system of comparing PCR primers applied to ISSR fingerprinting of potato cultivars. Theor. Appl. Genet. 1999; 98: 107-112.
  28. Jaccard P. Nouvelles recherches sur la distribution florale. Bull. Soc. Vaud. Sc. Nat. 1908; 44: 223 -270.
  29. Nevo E, Beiles A, Zohary D. Genetic resources of wild barley in the Near East: structure, evolution and application in breeding. Biol. J. Linnean. Soc. 1986; 27: 355-380.
  30. Marić S, Bede M, Martinčić J, Guberac V. Variability of some winter wheat traits from breeding process. Sjemenarstvo. 1998; 15:421-433.
  31. Casadesús J, Kaya Y, Bort J, Nachit MM, Araus JL, et al. Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Ann. Appl. Biol. 2007; 150: 227-236.
  32. Cox TS, Lookhart GL, Walker DE, Harrell LG, Albers LD, et al. Genetic relationships among hard red winter wheat cultivars as evaluated by pedigree analysis and gliadin polyacrylamide gel electrophoretic patterns. Crop Sci. 1985; 25: 1058-1063.
  33. Metakovsky E, Branlard G. Genetic diversity of French common wheat germplasm based on gliadin alleles. Theor. Appl. Genet. 1998; 96: 209-218.
  34. Manifesto MM, Schlatter A. Hopp HE, Suárez EY, Dubcovsky J. Quantitative evaluation of genetic diversity in wheat germplasm using molecular markers. Crop Sci. 2001; 41: 682-690.
  35. Pagnotta MA, Mondini L, Atallah MF. Morphological and molecular characterization of Italian emmer wheat accessions. Euphytica. 2005; 146: 29-37.
  36. Španić V, Buerstmayr H, Drezner G. Assessment of genetic diversity of wheat genotypes using microsatellite markers. Period. Biol. 2012; 114: 37-42.
  37. Shi Y, Bian H, Han N, Pan J, Tong W, et al. Genetic variation analysis by RAPD of some barley cultivars in China, Zuo Wu Xue Bao.2004; 30: 258-265.
  38. Ferreira JR, Pereira JF, Turchetto C, Minella E, Consoli L, et al. Assessment of genetic diversity in Brazilian barley using SSR markers. Genet. Mol. Biol. 2016; 39: 86-96.
  39. Drikvand R, Salahvarzi E, Salahvarzi A, Hossinpour T. Study of genetic diversity among rainfed barley genotypes using ISJ markers and morphological traits.J. Agric. Sci. 2012; 4: 137.
  40. Hua W, Zhang X, Zhu J, Shang Y, Wang J, et al. A study of genetic diversity of colored barley (Hordeum vulgare L.) using SSR markers. Genet. Resour. Crop Evol. 2015; 62: 395-406.
  41. Demissie A, Bjørnstad Å. Phenotypic Diversity of Ethiopian Barleys in Relation to Geographical Regions, Altitudinal Range and Agro-Ecological Zones: As an Aid to Germplasm Collection and Conservation Strategy. Hereditas.1996; 124: 17-29.
  42. Verma S, Goyal V, Verma A, Kumar D, Singh J. Evaluating genetic variation in barley varieties at molecular level. Int. J. Trop Agric, 2015; 33: 2217-2221.
  43. Cortese LM, Honig J, Miller C, Bonos SA. Genetic diversity of twelve switchgrass populations using molecular and morphological markers. Bioenergy Res. 2010; 3: 262-271.
  44. Abebe TD, Bauer AM, Léon J. Morphological diversity of Ethiopian barleys (Hordeum vulgare L.) in relation to geographic regions and altitudes. Hereditas. 2010; 147: 154-164.
  45. Ahmad Z, Ajmal SU, Munir M, Zubair M, Masood MS. Genetic diversity for morpho-genetic traits in barley germplasm. Pak. J. Bot. 2008; 40: 1217-1224.
  46. Ibrahim O, Mohamed MH, Tawfik M, Badr EA. Genetic diversity assessment of barley (Hordeum vulgare L.) genotypes using cluster analysis. Inter. J. of Acad. Res. 2011; 3: 81-85.
  47. Tester RF, Karkalas J. The effects of environmental conditions on the structural features and physico-chemical properties of starches. Starke. 2001; 53: 513-519.
  48. Mpofu A, Sapirstein HD, Beta T. Genotype and environmental variation in phenolic content, phenolic acid composition and antioxidant activity of hard spring wheat. J. Agric. Food Chem. 2006; 54: 1265-1270.
  49. Kiseleva VI, Genkina NK, Tester R, Wasserman LA, Popov AA, et al. Annealing of normal, low and high amylose starches extracted from barley cultivars grown under different environmental conditions. Carbohydr. Polym.2004; 56: 157-168.
  50. Hang A, Obert D, Gironella AIN, Burton CS. Barley amylose and β-glucan: Their relationships to protein, agronomic traits and environmental factors. Crop Science. 2007; 47: 1754-1760.
  51. Hedrick PW. Sex: differences in mutation, recombination, selection, gene flow and genetic drift. Evolution. 2007; 61: 2750-2771.
  52. Sorensen JG, Norry FM, Scannapieco AC, Loeschcke V. Altitudinal variation for stress resistance traits and thermal adaptation in adult Drosophila buzzatii from the New World. J. Evol. Biol. 2005; 18: 829-837.

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