Subscribe Now Subscribe Today
Research Article
 

Protein-based Fingerprinting in Cowpea Accessions [Vigna uniguiculata (L.) Walp] for Deciphering Genetic Relatedness



J.O. Phillip, O.U. Udensi, I.O. Phillip and S.E. Abimiku
 
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail
ABSTRACT

Background and Objective: A clear understanding of the extent of genetic diversity in a germplasm is very critical for maximal exploitation and utilization of inherent resource for conservation and subsequent improvement. This paper was aimed at evaluating the diversity in cowpea accessions using seed storage protein profiling. Materials and Methods: The analysis of the seed storage proteins of cowpea was undertaken at the analytical bioscience laboratory of the International Institute of Tropical Agriculture (IITA) using 15 cowpea accessions obtained from the germplasm of International Institute of Tropical Agriculture (IITA), Ibadan in March, 2017. Seed storage protein was extracted and subjected to sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). Distinct protein bands were scored for presence (“1”) and absence (“0”) and subjected to analyses using GenAlEx 6.5 and MSVP 3.21 software for genetic parameters. Results: SDS-PAGE analysis confirmed diversity in the seed storage proteins of cowpeas with two major clusters and sub-clusters based on population genetic distance differed according to location and this can be exploited to undertake hybridization of accessions TVu-4467 and/or TVu-8063 from Nigeria as well as TVu-12513 adopted from Zambia. Other genetic diversity parameters indicated the existence of genetic diversity in the 15 accessions. Conclusion: Taking the results together, the investigation confirmed it might be justifiable to carry out parental selection of cowpea accessions adopted from Nigeria, preferably TVu4467 and/or TVu8063 as well as TVu12513 adopted from Zambia for possible crossing to widen the genetic base of cowpea.

Services
Related Articles in ASCI
Similar Articles in this Journal
Search in Google Scholar
View Citation
Report Citation

 
  How to cite this article:

J.O. Phillip, O.U. Udensi, I.O. Phillip and S.E. Abimiku, 2019. Protein-based Fingerprinting in Cowpea Accessions [Vigna uniguiculata (L.) Walp] for Deciphering Genetic Relatedness. International Journal of Plant Breeding and Genetics, 13: 1-11.

DOI: 10.3923/ijpbg.2019.1.11

URL: https://scialert.net/abstract/?doi=ijpbg.2019.1.11
 
Received: July 28, 2018; Accepted: January 29, 2019; Published: March 05, 2019



INTRODUCTION

The economic hardship orchestrated by economic recession as well as global climatic conditions have imposed serious stress on Nigerian families, especially in terms of food security and nutrition. However, the unfortunate angle to this is the fact that Nigerians are still wanting the imported food instead of exploring and exploiting the local ones, which interestingly might be better nutrition-wise. African families have varying preference to foreign cowpea varieties against local ones in the following order: Niger (52%), Burkina Faso (50%), Togo (46%), Lagos (36%) and Ghana (18-21%)1. This situation is unfortunate especially taking into cognizance the economic recession in Nigeria. For emphasis, most children in developing countries and the internally displaced people (IDPs) in camps are malnourished as a result of protein deficiency and as such the urgent need to utilize available but nutrient-rich food crop as cowpea.

Cowpea (Vigna unguiculata (L.) Walp) is a global legume and presently is the second most important legume in Africa2-3. Nigeria being the largest producer as well as consumer accounts for 61% of production in Africa and 58% worldwide4. Regrettably, this increase in cowpea production in sub-saharan Africa not withstanding, its yield has remained one of the lowest among all food legumes. The Food and Agriculture Organization (FAO) estimated average cowpea yield in West Africa to be 483 kg ha1, which was still 50% below the estimated potential production yield5.

Plant breeding depends on the conservation and utilization of plant breeding resources for the purpose of improvement, which depends on clear understanding of the genetic variation and distinctiveness of the populations6. This underscores the importance attached to the analysis of genetic relationships in crop species7. Diversity in plant genetic resources (PGR) provides ample opportunity for plant breeders to develop new and improved cultivars with desirable characteristics8. Undoubtedly, various researchers have adopted different approaches in the bid to estimating genetic relatedness in a given germplasm with varying levels of informativeness and resolution power.

These includes principal component analysis, divergence analysis and utilization of morphological and agronomical data7. However, due to the laborious and time-consuming nature of these methods in drawing meaningful conclusions from generated data, molecular marker-based techniques have been explored and exploited by plant breeders. Molecular markers are highly heritable and polymorphic enough to enable the discrimination of closely related genotypes. Studies have affirmed that molecular genetic techniques using DNA polymorphism have been increasingly used to characterize and identify a novel germplasm for use in the crop breeding process9. Other markers systems such as biochemical markers that incorporates the analysis of seed storage proteins and isozymes is an inexpensive technique that measures population subdivision, genetic diversity, gene flow, genetic structure of species and comparisons among species out-crossing rates, population structure and population divergence10.

Mature seeds of crops provide a very stable and convenient system for biochemical analysis for establishing relationship between parents and hybrids of plant species11, resolving the taxonomy and evolutionary problems of several crop plants12and for selecting desirable genotypes for breeding as well as crop improvement13. The polymorphism of seed storage proteins has been widely applied in plant classification14, screening of mutants for seed storage proteins15,16, germplasm resource analysis17,18, variety identification19,20 as well as genetic diversity analysis21,22.

Proteins are products of gene expression and their different sizes or biochemical characteristics are a function of the different alleles which encode different amino acid sequences23. Isozymes are useful in germplasm diversity analysis owing to its co-dominance nature24, ability to reveal absence of epistatic and pleitropic effects, ease of use as well as cost effectiveness10. Important to note is the fact that storage proteins are direct and stable products of genes, which obviously reflect DNA diversity. This DNA diversity can thus be easily detected by various electrophoresis, especially SDS-PAGE. The evaluation of genetic variations using protein markers requires the electrophoresis of seed proteins using SDS-PAGE, which is a potent tool for application in plant breeding11. The electrophoretic patterns of the proteins provides insight into the polymorphisms present in the accessions and the level of relatedness.

There are over 15,000 accessions and 2000 wild relatives of cowpea held at the International Institute of Tropical Agriculture, (IITA) Genebank25. Regrettably, non-utilization of majority of the germplasm collections due to genetic redundancy, duplicated lines, little knowledge of their genetic characterization is hampering the efforts of conservation and improvement26, especially with the incapacitation of most developing countries to sustainably feed its citizens. The implication is that these accessions need to be properly characterized, conserved and utilized for integration in breeding programmes. In this study, therefore, an attempt was made using protein-based profiling to generate information on 15 accessions of cowpeas from IITA germplasm with the following objectives:

Screen the cowpea accessions collected from IITA for genetic diversity
Identify/distinguish duplicated accessions from IITA
Identify desirable accessions for crop improvement

MATERIALS AND METHODS

Experimental materials: This work was carried out in March-April, 2017. Fifteen cowpea accessions were used for this study which were obtained from the germplasm collection of the International Institute of Tropical Agriculture, IITA, Ibadan, Nigeria. Details of the experimental materials were presented in Table 1.

Protein extraction: Cowpea seeds coats were removed and seed storage proteins were extracted as described by Jha and Ohri27. After powdering, it was homogenized using 0.5 M Tris-HCl buffer in the ratio of 1:10 (w/v). The paste was put into Eppendorf tubes and centrifuged at 13,000×g for 10 min. The supernatant was collected as protein fractions and stored at -20°C for analysis.

Protein profiling using SDS-PAGE: During analysis, the extracted protein was mixed with sample loading buffer (0.5 M Tries [pH 6.8] 50% glycerol, 10% SDS, 2-β mercaptoethanol and bromophenol blue [In vitro genTM pre-stained marker) in the ratio of 1:5, denatured at 95°C, cooled on ice and loaded on SDS-PAGE for separation28.

Constant current was applied while varying the voltage initially 100 V and later increased to 150 V. The resolving gel (30:0.8% acrylamide/Bis, 1.5 M Tries buffer (pH 8.8), 10% SDS, APS and TEMED concentrations was 12 and 15% while the stacking gel concentration was 5%. The staining gel was performed in 0.0025% Coomassie brilliant blue R250 containing 40% methanol and 20% acetic glacial acid (v/v) in distilled water at room temperature for about 4 h and gently agitated. Destaining was done in the same solution without dyes repeatedly until the protein bands were clearly seen. Electrophoresis was performed at room temperature until the blue marker reached the bottom of the gels.

Gel documentation and analysis: The banding patterns were visualized and photographed with VMR Trans-illuminator ECN 730-1386. Distinct bands were scored for presence or absence of bands and subjected to analysis. UPGMA-based dendrogram was constructed using Multivariate Statistical Package29 (MVSP) version 3.1 as well as Jaccard’s coefficient of similarity. Genetic diversity, genetic distance, Shannon’s information index, molecular variation and principal coordinate analysis (PCoA) were performed30 using GenAlEx 6.5.

RESULTS

Number of bands in the accessions: The electropherogram from SDS-PAGE revealed a total of 17 polypeptide bands from which 9 bands were monomorphic and 6 were polymorphic. The band sizes ranged from 15.44-251.39 kDa. The accessions with the highest number of bands were accessions TVu595, TVu5590, TVu12277, TVu13076, TVu14046, TVu14085, TVu14114 and TVu15323 each with 14 bands; accessions TVu10710, TVu4467, TVu8063 and TVu15695 followed with 13 bands each. Accessions TVu11707 and TVu11883 had 12 bands while accession TVu12513 had 11 bands.

UPGMA-based dendrogram of cowpea accessions investigated: Four major clusters, I-IV were detected based on Jaccard’s similarity coefficient of protein bands.

Table 1:Germplasm description of the 15 accessions of cowpea [V. unguiculata (L.) Walp] used

Fig. 1:Relationship among 15 accessions of V. unguiculata L. Walp

Fig. 2:Percentages molecular variance in two cowpea populations (West and East Africa)

To a large extent, clustering pattern was based on population except two accessions, TVu15323 (Chad) and TVu15695 (Nigeria) that were clustered together with accessions in cluster IV. Dendrogram clustered cowpea accessions based on countries of collection/adoption into IITA germplasm. TVu596, TVu10710, TVu4467 and TVu8063 from Nigeria were clustered together while TVu5590 and TVu12277 adopted from Niger were also clustered together with infiltration of TVu11707 from Zambia (Fig. 1).

UPGMA-based Jaccard’s coefficient of similarity: Jaccard’s coefficient of similarity showed that the highest coefficient of similarity 1.00, which was between TVu14114 (Central African Republic [CAR]) and TVu14085 (CAR); TVu15323 (Chad), TVu14085 (CAR) and TVu14114 (CAR).TVu4467 (Nigeria) share 0.929 coefficient of similarity with TVu596 as well as between TVu8063 and TVu596. However, the lowest coefficient of similarity was 0.412, which was between TVu12513 (Zambia), TVu4467 and TVu8063 (Nigeria). Additionally, TVu12513 (Zambia) share coefficient similarity of 0.471 with TVu596 (Nigeria) as well as between TVu15695 (Nigeria), TVu11707 (Zambia) and TVu11883 (Malawi) (Table 2).

Genetic distance of cowpea accessions: The result from genetic distance revealed that the widest distance (3.61) were observed between TVu13076 (Nigeria) and TVu10710; TVu14046 (CAR) and TVu11707 (Zambia) while narrowest (1.73) was observed between TVu15695 (Nigeria) and TVu11707 (Zambia) and between TVu14114 (CAR) and TVu12513 (Zambia) (Table 3).

Analysis of molecular variation of cowpea accessions (AMOVA): Molecular variation of cowpea accessions showed that among population variance was zero percent while within population variation was 100%. However, attempt was made to resolve molecular variation of the accessions based on loci. Result revealed that for locus 1 among population was 7% while within population was 93%. Percentage molecular variance in locus 6 was observed to be 30% for among population variation and 70% for within population variance. For locus 14, among population variance was 23% while within population variance 77% (Fig. 2-5).

Pooled genetic parameters over loci and population: For pooled population, genetic diversity was 0.369±0.018, unbiased genetic diversity was 0.396±0.019 while Shannon’s information index was 0.550±0.020. However, based on population, genetic diversity was 0.377±0.019, unbiased genetic diversity was 0.399±0.020 while Shannon’s information index was 0.561±0.021 for West African population.

Table 2: UPGMA Jaccard’s coefficient similarity matrix

Table 3: Genetic distance across the accessions

Table 4:Pooled genetic parameters over loci per population
Population 1 (West Africa): TVu-595, TVu-10710, TVu-4467, TVu-5590, TVu-8063, TVu-12277, TVu-13076, TVu-15323, Tvu-15695. Population 2 (East Africa): TVu-11701, TVu-11883, TVu-12513, TVu-14046, TVu-14085, Tvu-14114. N: Number in population, Na: Number of alleles, Ne: Number of effective alleles, I: Shannon’s information index, He: Genetic diversity, uHe: Unbiased diversity

Fig. 3:Percentages molecular variance in locus 1 across the cowpea accessions investigated

Fig. 4:Percentages molecular variance in locus 6 across the cowpea accessions investigated

For East African population, the result revealed 0.361±0.030, 0.394±0.033 and 0.539±0.035 for genetic diversity, unbiased genetic diversity and Shannon’s information index, respectively (Table 4).

Principal coordinate analysis (PCoA) of cowpea accessions: Principal coordinate analysis results revealed two major clusters with four sub-clusters. Sub-cluster 1 had TVu8063, TVu596 and TVu11883 while sub-cluster 2 had TVu4467, TVu15695, TVu14046 and TVu14085. Importantly is that the clustering was not based on region where the accessions were adopted from with some exceptions. From the three axes, cumulative percentage contribution to variation in the cowpea accessions were 16.39, 29.05 and 39.27% for principal coordinate 1, 2 and 3. Total contribution to variability was 84.71% (Fig. 6).

Fig. 5:Percentage molecular variance at locus 14 across the cowpea accessions investigated

DISCUSSION

Protein profile analysis of cowpea accessions using SDS-PAGE revealed 17 bands with a range of molecular weights from 8.30-251.39 kDa and six polymorphic bands detected. These results are corroborated by the findings of two findings31,32. The polymorphic bands were observed in TVu596, TVu10710, TVu4467, TVu5590, TVu8063 and TVu11883 with low molecular weights ranging from 15.44-14.85 kDa. A study reported that seven cowpea landraces were accessed for genetic variability in their seed proteins33. Using SDS-PAGE, the pattern observed from the electrophoresis of seed proteins revealed molecular heterogeneity in the total proteins.

Fig. 6:Principal coordinate analysis (PCoA) based on SDS-PAGE data illustrating the genetic relationship among 15 accessions of cowpea and populations (West and East Africa) from GenALex

The common bands discovered in the 7 landraces studied indicated proximal relationship in the varieties while distinct bands were equally seen which indicated varietal differences. The nature of protein profiles as a tool for biochemical fingerprinting in cowpea has been established31. However, a low degree of polymorphism was observed in the cowpea cultivars studied. On the strength of the outcome of the results by Singh et al.34, there is the presence of enough genetic divergence on the basis of seed storage in the pea cultivars studied. This outcome prompted the recommendation of KPMR-906 with wide genetic diversity for utilization in crop improvement programmes.

It had been reported that cowpea is among the 5th top food legumes that are grown worldwide and has a presence on every continent except Oceania and Australia31. The west African sub-region contributes about 95% of global cowpea production, with Nigeria being the largest producer in the world32. This notwithstanding, cowpea landraces are dramatically disappearing and unfortunately being replaced by improved, higher yielding modern cultivars from modern breeding programmes35,36 and this has resulted to erosion of crop genetic diversity31.

Successful conservation of germplasm largely depends on the understanding of the extent of genetic diversity within and among crop species37. There is therefore an urgency to ensure that the diversity in landraces is characterized and subsequently conserved. Germplasm with wider genetic base provides enough buffer and resilience against climatic and environmental challenges31. Seed storage proteins play important role in the defence against insect and pathogens as well as act as nitrogen and energy sources38. The reliability of protein data is obviously hinged on the fact that seed storage proteins are highly independent of environmental factors, which are comparable to those of other molecular markers. Electrophoretic techniques in plant classifications utilize gel medium supports, which contributes to the reliability of data generated therein39-41.

The UPGMA-based dendrogram generated two major clusters with sub-clusters, which was based generally on the region the accessions were adopted. Cluster I included accessions which were highly polymorphic while other clusters were monomorphic with the exception of TVs11883 in cluster III which was polymorphic. The correlation of low molecular weight and protein polymorphism can be further explored. A positive association between high molecular weight and monomorphism was expressed by Atoyebi et al.42. This was sharply contradicted by the report of Eid32.

Interestingly also is the fact that the result on principal coordinate analysis (PCoA) agreed with that of the dendrogram. The clustering together of TVu15695 (Nigeria) in cluster II and TVu11883 (Malawi) and TVu11707 (Zambia) in cluster I could be explained from the standpoint that there might have been introgressions before adoption. For instance, TVu15695 is a breeding/research material, which obviously should be as a result of hybridization. Clustering together of cowpea accessions is an indication that they might share common genetic characteristics. It should be noted that the existence of common bands among accessions is an indication that species bearing them share close genetic affinity as well as common ancestry. Protein bands are coded for by genes. The implication of the above is that genes have been fixed in species bearing them over evolutionary time.

Undoubtedly, the higher the similarity coefficient, the more phylogenetically related the species are and vice versa. Jaccard’s coefficient of similarity revealed that TVu12513 (Zambia) was not phylogenetically related to TVu4467, TVu596 and TVu8063 (Nigeria) on the basis of coefficient of similarity. This result corroborated with the result on the dendrogram. Similarly, TVu15695 (Nigeria) was genetically distinct from TVu11707 (Zambia) and TVu11883 (Malawi) thus the grouping into different clusters. Surprisingly, TVu14046 (Central African Republic) share 0.933 (93.3%) similarity with TVu13067 (Nigeria). This might be explained from the premise that TVu13076 is a breeding/research material, which might be a product from a cross related to TVu14046. This similarity could also be explained as duplication43.

Result obtained from the genetic distance also proved that TVu13076 (Nigeria) is genetically distinct from accessions TVu10710, TVu14046 (CAR) and TVu11707 (Zambia) as their genetic distances were 3.61, which was the widest. What it does suggest is that the wider the genetic distance, the more distinct the species are. Given the extent of genetically un-relatedness of some cowpea species, especially between Nigeria accessions and Zambia accessions, one wonders the underlying factor surrounding the analysis of molecular variation result obtained where within population variation was 100%. The understanding was that by clustering accessions into different clusters should mean that they should show molecular variations. However, the probable explanation is that variation was not significant enough to cause visible variability.

This work also attempted to resolve the molecular variation of the different accessions based on loci the population notwithstanding. Comparing locus 1, 6 and 14, molecular variation among population were 7, 30 and 23% while within population molecular variation were 93, 70 and 77%, respectively. Worthy to note is that genes are located on different loci encoding proteins. The difference observed in molecular variations among the cowpea accessions based on the three loci may not have made significant impact on the molecular variations comparing the populations. Going by other genetic parameters such as genetic diversity, unbiased diversity and Shannon’s information index derived from protein banding pattern, revealed that west African cowpea accessions are more diverse than east African population. This might be attributed to the two accessions, TVu13076 and TVu15695 that are used as breeding/research materials. What this might suggest is that the two accessions could encode genes whose proteins produce outstanding qualities that adds to the total variability.

From principal coordinate analysis (PCoA) percentage cumulative variability contribution from the 3 axes are 16.39, 29.05 and 39.27% giving total contribution of 84.71% to the variability observed, especially within population. The efficiency and informativeness of any technique adopted to essay genetic diversity might be enhanced exponentially if many of such techniques are compared and more importantly are assessed in combination44,45. Though RAPD markers were used to evaluate diversity in cowpea accessions obtained from IITA, Ibadan, Nigeria46, the outcomeshowed that Asian cowpea accessions were genetically distinct from African accessions. This notwithstanding, it was wondered how cowpea accessions adopted from Nigeria and Benin both from West Africa were genetically distinct. However, this was explained by attributing it to plant breeders introducing species from diverse geographical backgrounds in the bid to widen the genetic base among existing stock47. This present study using protein profiling showed higher genetic parameters such as genetic diversity, unbiased diversity and Shannon’s information index in West African cowpea accessions when compared with the east African accessions. There is in existence a coherent relationship between the extent of genetic diversity within landraces and geographical distribution of the landraces6. This work reported a geographical or region-based clustering which corroborates with the result on genetic distance. The implication of this within population diversity reported in this present study is that parental selection should be made among accessions that are genetically distinct. From this result it might be justifiable enough to suggest selection, especially between cowpea accessions from Nigeria, probably TVu4467 and/or TVu8063 and TVu12513 adopted from Zambia. Due to the higher genetic parameters, the possibility is that there was lower intra-population similarity and higher proportion of polymorphic loci resulting in increased heterozygosity48. This might be the underlying reason for the differentials that exist between the two populations.

Higher genetic diversity might provide genetic barrier against different biotic and abiotic stresses49,50. The knowledge of genetic diversity that exists within and among genotypes of any crop is fundamental to estimating the potential of genetic gain in breeding programmes and for effective conservation and sustainable utilization of available genetic resources.

Accessions with similar banding patterns are recommended for further studies to understand their agronomic and biochemical traits to provide information for their better management and utilization.

CONCLUSION

Seed storage protein profiles bear genetic imprints and as such play useful roles in plant identification and characterization. The outcome of this study revealed genetic variation in 15 accessions of cowpea as revealed by its protein profiles. The utilization of SDS-PAGE as a cost effective technique was able to detect inherent variation and thus offering useful information for breeders.

At the moment, a plethora of advanced tools are available for plant diversity studies. The current thinking favours the usage of DNA marker systems. However, the limitation imposed by the cost of these DNA marker systems makes biochemical markers the method of choice at least as a baseline. Considering the size of the global cowpea germplasm, more work is advocated to completely characterize them, identify distinct accessions and recommend genome-wide crosses to generate more variation that can be adopted to engender food security. It is suggested though implicitly from the results taken together that parental selection of cowpea accessions be done in accession(s) adopted from Nigeria, preferably TVu4467 and/or TVu8063 as well as TVu12513 adopted from Zambia for possible crossing. Looking to the future, DNA marker systems can be incorporated for the characterization of crop plants.

SIGNIFICANCE STATEMENTS

This study has revealed variation in the accessions under consideration as it relates to cowpea production in Nigeria which is directly beneficial in quelling malnutrition as it is considered as the poor man’s meat. This research will provide other scientists with requisite information to undertake breeding programmes andhence, broadening the genetic base which is both important for crop improvement and conservation of germplasm.

ACKNOWLEDGMENT

The authors acknowledge the management of the germplasm resource unit of International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria for providing the seeds as well as the Plant Genetic Resource Management and Genomics Unit of the Department of Genetics and Biotechnology for her financial assistance.

REFERENCES
1:  Quaye, W., K. Adofo, E.S. Buckman, G. Frempong, J. Jongerden and G. Ruivenkamp, 2011. A socio-economic assessment of cowpea diversity on the Ghanaian market: Implications for breeding. Int. J. Consum. Stud., 35: 679-687.
CrossRef  |  Direct Link  |  

2:  NRC., 2006. Lost Crops of Africa. Vol. 2, National Academies Press, Washington DC., ISBN-13: 978-0309103336.

3:  FAO., 2009. FAO quarterly bulletin of statistics. Food and Agricultural Organization of United Nations, Rome, Italy.

4:  Ronner, E. and K.E. Giller, 2013. Background information on agronomy, farming systems and ongoing projects on grain legumes in Uganda. N2Africa, Characterization Uganda, January 2013, pp: 1-34.

5:  FAO., 2001. FAOSTAT agricultural data. Food and Agricultural Organization of United Nations, Rome, Italy.

6:  Ghalmi, N., M. Malice, J.M. Jacquemin, S.M. Ounane, L. Mekliche and J.P. Baudoin, 2010. Morphological and molecular diversity within Algerian cowpea (Vigna unguiculata (L.) Walp.) landraces. Genet. Resour. Crop Evol., 57: 371-386.
CrossRef  |  Direct Link  |  

7:  Udensi, O.U. and N.E. Edu, 2015. Evaluation and identification of genetic variation pattern in cowpea [Vigna unguiculata (L.) Walp] accessions using multivariate analyses. J. Basic Applied Sci., 11: 149-158.
Direct Link  |  

8:  Govindaraj, M., M. Vetriventhan and M. Srinivasan, 2015. Importance of genetic diversity assessment in crop plants and its recent advances: An overview of its analytical perspectives. Genet. Res. Int., Vol. 2015. 10.1155/2015/431487

9:  O'Neill, R., R.J. Snowdon and W. Kohler, 2003. Population genetics: Aspects of biodiversity. Prog. Bot., 64: 115-137.
CrossRef  |  Direct Link  |  

10:  Mondini, L., A. Noorani and M.A. Pagnotta, 2009. Assessing plant genetic diversity by molecular tools. Diversity, 1: 19-35.
CrossRef  |  Direct Link  |  

11:  Singh, P.K., A. Kumar, N. Srivastava, R.M. Agarwal and S.S. Bhagyawant, 2012. Association of protein profiling and agronomic traits in chickpea as revealed using SDS-PAGE. J. Cell Tissue Res., 12: 3279-3284.
Direct Link  |  

12:  Ghafoor, A., Z. Ahmad, A.S. Qureshi and M. Bashir, 2002. Genetic relationship in Vigna mungo (L.) Hepper and V. radiata (L.) R. Wilczek based on morphological traits and SDS-PAGE. Euphytica, 123: 367-378.
CrossRef  |  Direct Link  |  

13:  Nisar, M., A. Ghafoor, M.R. Khan, H. Ahmad, A.S. Qureshi and H. Ali, 2007. Genetic diversity and geographic relationship among local and exotic chickpea germplasm. Pak. J. Bot., 39: 1575-1581.

14:  Portyanko, V.A., N.R. Sharopova and A.A. Sozinov, 1998. Characterisation of European oat germ plasm: Allelic variation at complex avenin loci detected by acid polyacrylamide gel electrophoresis. Euphytica, 102: 15-27.
CrossRef  |  Direct Link  |  

15:  Kumamaru, T., H. Satoh, N. Iwata, T. Omura, M. Ogawa and K. Tanaka, 1988. Mutants for rice storage proteins. 1: Screening of mutants for rice storage proteins of protein bodies in the starchy endosperm. Theor. Applied Genet., 76: 11-16.
CrossRef  |  Direct Link  |  

16:  Qu, L., X. Wei, S. Hikaru, O. Masahiro and K. Toshihiro, 2001. Seed storage glutelin α-2 subunit lacking mutants in rice. Acta Botanica Sinica, 43: 1167-1171.
Direct Link  |  

17:  Sun, Y., H. Nakayama, K. Okuno and K. Ise, 2000. Variation in endosperm protein of rice germplasm in Yunnan province. Acta Agron Sin, 26: 438-443.
Direct Link  |  

18:  Yu, C.M. and P.D. Chen, 2003. Study on common wheat (T. aestlvum L.) germplasm for waxy protein. J. Nanjing Agric. Univ., 26: 1-6.

19:  Song, T., D. Zheng and Y. Liu, 1996. Identification of corn varieties using lactate polyacrylamide gel electrophoresis of seed albumins and globulins. Acta Bot. Sinica, 38: 599-604.
Direct Link  |  

20:  Zhang, C.Q., Y.P. Yin, R.Q. Gao and J.Z. Jia, 1998. Polymorphism of seed protein and identification of cotton cultivar. Sci. Agric. Sin, 31: 16-19.
Direct Link  |  

21:  Gan, L. and X. Wang, 1999. Genetic diversity analysis of rape seeds for the storage protein. J. Huazhong Agric. Univ., 18: 528-532.
Direct Link  |  

22:  Yang, T.Y., Y.H. Shen, X.G. Huang, J.H. He and G.Z. Wu, 2005. Identification of genetic diversity in the foxtail millet (Setaria italic L. Beauv.) by A-PAGE. Acta Agron Sin, 131: 131-133.
Direct Link  |  

23:  Yang, H.B., W.H. Kang and B.C. Kang, 2015. Methods for Developing Molecular Markers. In: Current Technologies in Plant Molecular Breeding, Koh, H.J., S.Y. Kwon and T. Michael (Eds.)., Springer Science+Business Media, Germany.

24:  Xu, Y., 2010. Molecular Plant Breeding. CAB International, UK., ISBN-13: 9781845933920, Pages: 734.

25:  IITA., 2002. IITA crops and farming systems. International Institute of Tropical Agriculture (IITA), Nigeria.

26:  Vijayan, S., 2005. Statistical analysis of influence of morphological characters on yield in selected vegetable crops. Ph.D. Thesis, Dharwad University of Agricultural Sciences, Dharwad.

27:  Jha, S.S. and D. Ohri, 2002. Comparative study of seed protein profiles in the genus Pisum. Biol. Plant., 45: 529-532.

28:  Laemmli, U.K., 1970. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature, 227: 680-685.
CrossRef  |  Direct Link  |  

29:  Kovach, W.L., 2007. MVSP-A Multivariate Statistical Package for Windows, Version 3.1. Kovach Computing Services, Pentraeth, Wales, UK.

30:  Peakall, R. and P.E. Smouse, 2006. genalex 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes, 6: 288-295.
CrossRef  |  Direct Link  |  

31:  Mahfouz, H.M., 2016. Assessment of genetic diversity in cowpea (Vigna unguicalata) using SDS-PAGE, random amplified polymorphic DNA (RAPD) and Inter-Simple Sequence Repeat (ISSR) markers. Egypt. J. Genet. Cytol., 44: 75-91.
Direct Link  |  

32:  Eid, M.H., 2018. Evaluation of genetic diversity in barley (Hordeum vulgare) genotypes using protein profiling. J. Plant Breed. Genet., 6: 23-31.

33:  Alghamdi, S.S., M.A. Khan, H.M. Migdadi, E.H. El-Harty, M. Afzal and M. Farooq, 2019. Biochemical and molecular characterization of cowpea landraces using seed storage proteins and SRAP marker patterns. Saudi J. Biol. Sci., 26: 74-82.
CrossRef  |  Direct Link  |  

34:  Singh, B.K., A.K. Singh, A.H. Hotti, J. Kumar and S.K. Singh, 2017. Diversity analysis through SDS-PAGE of seed storage protein of pea genotypes. Res. Environ. Life Sci., 10: 449-552.
Direct Link  |  

35:  FAOSTAT., 2012. Final 2011 data and preliminary 2012 data for 5 major commodity aggregates. FAOSTAT., Rome.

36:  Bao, S., Y. He, X. Zong, L. Wang and L. Li et al., 2008. Collection of pea (Pisum sativum) and faba bean (Vicia faba) germplasm in Yunnan. Plant Genet. Resour. Newslett., 156: 11-22.
Direct Link  |  

37:  Chenbang, H., L. Yujiao, W. Kunlun, Y. Mingyi and F. Qinhua et al., 2008. Collecting and surveying landraces of pea (Pisum sativum) and faba bean (Vicia faba) in Qinghai province of China. Plant Genet. Resour. Newslett., 156: 1-10.
Direct Link  |  

38:  Karuri, H.W., E.M. Ateka, R. Amata, A.B. Nyende, A.W.T. Muigai, E. Mwasame and S.T. Gichuki, 2010. Evaluating diversity among Kenyan sweet potato genotypes using morphological and SSR markers. Int. J. Agric. Biol., 12: 33-38.

39:  Ehsanpour, A.A., B. Shojaie and F. Rostami, 2010. Characterization of seed storage protein patterns of four Iranian Pistachios using SDS-PAGE. Natural Sci., 2: 737-740.

40:  Mohammed, T.R., S.K. Khalifa and R.M. Salah El-Dine, 2006. Leave protein electrophoretic profile and chromosome numbers of some Araceae. Int. J. Agric. Biol., 8: 231-234.

41:  Sadia, M., S.A. Malik, M.A. Rabbani and S.R. Pearce, 2009. Electrophoretic characterization and the relationship between some Brassica species. Electron. J. Biol., 5: 1-4.
Direct Link  |  

42:  Atoyebi, O.J., J.O. Faluyi and O.O. Oyedapo, 2014. Investigation of the genetic diversity of selected wild and cultivated Sorghum germplasm using sodium dodecyl sulphate polyacrylamide gel electrophoresis. Greener J. Biol. Sci., 4: 18-20.

43:  Marris, A., 1992. Characterization in isoenzyme of Brazilian cultivars of Hordeum vulgare. Bachelor Thesis, University of Federal Rio Grand do Sul, Porto Alegre, R.S.

44:  Khan, A.I., Y.B. Fu and I.A. Khan, 2009. Genetic diversity cultivars as revealed by simple sequence repeat markers. Commun. Sci., 4: 21-30.

45:  Basha, S.D., G. Francis, H.P.S. Makkar, K. Becker and M. Sujatha, 2009. A comparative study of biochemical traits and molecular markers for assessment of genetic relationships between Jatropha curcas L. germplasm from different countries. Plant Sci., 176: 812-823.
CrossRef  |  Direct Link  |  

46:  Udensi, O.U., A. Okon, E.V. Ikpeme, O.O. Onyokpo and F.U. Ogban, 2015. Assessing the genetic diversity in cowpea (Vigna unguiculata L. Walp) accessions obtained from IITA, Nigeria using random amplified polymorphic DNA (RAPD). Int. J. Plant Breed. Genet., 10: 12-22.
CrossRef  |  Direct Link  |  

47:  Singh, B.D., 2004. Plant Breeding. 4th Edn., Kalyani Publisher, India.

48:  Hughes, A.R. and J.J. Stachowicz, 2004. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. Proc. Natl. Acad. Sci. USA., 101: 8998-9002.
CrossRef  |  Direct Link  |  

49:  Hajjar, R., D.I. Jarvis and B. Gemmill-Herren, 2008. The utility of crop genetic diversity in maintaining ecosystem services. Agric. Ecosyst. Environ., 123: 261-270.
CrossRef  |  Direct Link  |  

50:  Sakiyama, N.S., 2000. DNA markers for coffee tree breeding. Proceedings of the 3rd International Seminar on Biotechnology in the Coffee Agro-Industry, November 30, 2000, Londrina, Brazil, pp: 179-185.

©  2020 Science Alert. All Rights Reserved