ANALYSIS OF GENETIC STRUCTURE AND DIVERSITY IN INDIAN MUSTARD [BRASSICA JUNCEA (L.) CZERN & COSS.] ASSESSED BY SSR MARKERS

V. K. Singh, R. Avtar, Mahavir*, N. Kumari, Manjeet, R. Kumar and R. S. Khedwal

Oilseeds Section, Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, Haryana, 125 004, India

*Corresponding Author’s Email: mahaveer.bishnoi9@gmail.com

ABSTRACT

In present endeavors, the genetic diversity and Bayesian structure of 88 genotypes of Indian mustard procured from four different centers viz., ICAR-DRMR Bharatpur, ICAR-IARI New Delhi, CCS HAU Hisar and PAU Ludhiana were studied using 59 genomic SSR markers, and their genetic liaison was explored. A total of 209 repeatable alleles were detected by 59 SSR markers in a size range of 50-1000 bp with maximum (7) fragments intensified by BG6, BG32, BG46 and BG71 markers. The average value of PIC and the mean expected heterozygosity (He) value from all the polymorphic primers were 0.49 and 0.56, respectively, which is an indicative of the presence of ample amount of genetic diversity among Indian mustard genotypes. All the 88 genotypes were grouped into four distinct clusters based on Jaccard’s dissimilarity coefficients and UNJ (Unweighted Neighbour Joining) methods, however, three subpopulations were predicted by bayesian structure analysis at delK = 3. The PCoA (Principal Coordinate Analysis) revealed 20.95% and 10.85% of variation, respectively, with 31.80% of cumulative variation. The present work indicates the presence of considerable genetic diversity among the Indian mustard genotypes, which could be used in future breeding programmes for developing mustard cultivars and germplasm management purposes.

Keywords: Indian mustard, genetic diversity, bayesian structure, SSRs.

https://doi.org/10.36899/JAPS.2022.1.0413
Published online June 14, 2021

INTRODUCTION

Global climate change and population explosion are the serious concerns for future food security. The world agriculture is predicted to be significantly affected by climate change, though the impact will vary by region and crop (Calicioglu et al., 2019). Rapeseed-mustard (Brassica) is one of the most important oilseed crops all over the world. All these crops generally belong to four species which are diverse and originated from the family Cruciferae/Brassicaceae (Wani et al., 2020). Brassica juncea (L.) Czern & Coss., usually known as ‘Indian mustard’ is an important member of the family Cruciferae and grown in major part of Indian subcontinent (Lakhanpal et al., 2018). It takes over approximately 25% share among all the oilseed crops in India (Yadav et al., 2019). Worldwide, it is the third largest oilseed crop after soybean and palm contributing 12.1 million tons of total consumable edible oil (Friedt and Snowdon, 2009). India is a major oilseed producer, consumer as well as importer globally but even then, about 57% of the total consumable vegetable oil is imported from other countries (Jat et al., 2019). The major reasons behind this are the low productivity of Indian mustard at the farmer’s fields. (Yadav et al., 2019). The population explosion and changing dietary pattern led to the increased demand of edible oils (Tripathi et al., 2019; Davis et al., 2016); therefore, it is urgent need to break yield plateau so that enhanced oil demand can be copped with. Genetic diversity is a key factor in generating variability in segregating populations and this variability is the attribute of heterosis produced by hybrids which are generated from genetically diverse lines (Goulet et al., 2017; Kaeppler, 2012). There are better chances to develop superior hybrids/cultivars through breeding programs if ample genetic variability is present in breeding material (Bhargava and Srivastava, 2019). Thus, breeders and geneticists can be assisted with better understanding of genetic diversity in B. juncea and this would be very helpful in accessing genetic variability present in germplasm and ultimately will lead the path to widen the genetic base of breeding material (Panjabi et al., 2019). Diversity among living organisms is a consequence of variations in DNA sequences and of environmental effects (Carvalho et al., 2019). Assessment of phenotype-based diversity is not so reliable due to the effect of environmental factors and G X E interactions (El-Soda et al., 2014). Therefore, the DNA based diversity assessment is really vital and good enough for differentiating genotypes based on their genetic architecture. Molecular markers have proven to be invaluable tools for assessing the extent of genetic variation and its distribution within species. Microsatellites or Simple Sequence Repeats (SSR) are highly polymorphic, species-specific and co-dominant markers, considered as a most promising tool for detecting genetic diversity in plants (Adhikari et al., 2017). Keeping in view of the above fact, the present study was carried out for assessing genetic diversity among various genotypes of B. juncea in order to find out the most diverse genotypes which can further assist breeding program. These utmost diverse genotypes will be employed for crossing program which can ultimately lead to favorable allele combinations in segregating populations.

MATERIALS AND METHODS

A total of 88 diverse genotypes were procured from four different centers of India viz., Directorate of Rapeseed-Mustard Research (DRMR), Bharatpur; Indian Agricultural Research Institute (IARI), New Delhi; CCS Haryana Agricultural University (CCS HAU), Hisar and Punjab Agricultural University (PAU), Ludhiana; including 14 genotypes of exotic origin (maintained at IARI). All the genotypes taken were grown in paired rows with standard package and practices at Research Area of Oilseeds Section, Department of Genetics and Plant Breeding, CCS HAU, Hisar during rabi, 2019-20. List of 88 genotypes used in the present study is given in Table 1.

Table 1: List of 88 genotypes of Indian mustard used in present study.

Molecular marker evaluation: Fresh and young leaves were taken for genomic DNA isolation of 88 diverse genotypes and 2% CTAB (Cetyl Trimethyl Ammonium Bromide) was employed for the purpose (Saghai-Maroof et al., 1984) with slight alterations. For removal of RNA contamination, isolated genomic DNA was subjected to RNase treatment. Quantity and integrity of DNA was assessed by 0.8% agarose gel while taking λ DNA (50ng/µl) as reference standard. DNA was finally diluted to 50ng/μL and further stored at -20°C for PCR amplification. Genomic DNA of all 88 genotypes was amplified using a set of 59 random primers (Table 2). The PCR amplification reaction for all SSR markers comprised a 20μL master mix containing 2.0μL of 10X buffer, 0.4μL of dNTP mix (10mM), 0.5μL of each forward and reverse primer (10μmol/L) (Integrated DNA Technologies), 0.5μL of Taq DNA polymerase (5U/μL) (New England Biolabs, Inc.), 2μL of template DNA (50ng/μL), and 13.1μL of ddH₂O employing a thermal cycler (Bio Rad T100, Thermal Cycler).

Table 2: List of 59 polymorphic SSR markers.

After an initial strand separation step of five minutes at 94°C, PCR was programmed for 35 cycles. Each cycle comprised of a denaturation step (one minute at 94°C), annealing step (variable annealing temperature for 30s), extension step (one minute at 72°C), followed by final extension (7 minutes at 72°C). The amplified products were resolved on 2% agarose gel. Band sizes of amplified products were analyzed by comparing with DNA ladder (50 bp) loaded in the gels for estimating the appropriate size of bands. The SSR bands obtained in gels were scored manually for absence and presence as zero and one, respectively in each genotype and binary data-set was created. The informativeness of the microsatellite markers used for differentiating Indian mustard genotypes was ascertained by expected heterozygosity (He) = 1- ; where pi is the frequency of the i^th allele and i is the total number of alleles at all loci as per Liu (1998). PIC (polymorphism information content) was calculated using the formula PIC_j = 1- ; where i is i^th allele of the j^th marker, n is the number of the j^th marker’s alleles, pi is allele frequency (Botstein et al., 1980). Genetic dissimilarities/number of clusters and factorial analysis among the Indian mustard genotypes were analyzed with the help of DARwin 6.0 software (Perrier and Jacquemoud-Collet, 2006). Un-weighted Neighbor-joining (UNJ) tree was constructed by dissimilarity matrix generated by software. Evaluation of population structure and number of gene pool was performed with Bayesian model-based cluster analysis using the STRUCTURE version 2.3.4 software. (Pritchard et al., 2000). For the analysis, number of presumed population (K) was set from 1 to 12; for each fixed K, 10 independent runs were assessed while taking that each run comprised of 50,000 burn-in period and 100,000 iterations. Structure Harvester v6.0 (Earl and vonHoldt, 2012) was employed for calculating optimum value of K by analyzing the delK statistics and L (K) (Evanno et al., 2005).

RESULTS

SSR polymorphism: In present endeavors, all the 88 Indian mustard genotypes were inspected with 59 polymorphic SSR markers and merely steadfast and explicit fragments intensified by the primers were scored (Table 2). A total of 209 repeatable alleles were detected, which varied from 2 to 7 with an average of 3.54 alleles per locus. The overall fragment length of PCR amplified products ranged between 50 bp (BG5, BG19, BG42 and BG57) to 1000 bp (BG37). Among 59 polymorphic markers, 19 produced minimums of 2 alleles whereas, four primers namely, BG6, BG32, BG46 and BG71 produced a maximum of 7 alleles (Table 3). The percent polymorphism ranged between 33.33 to 100% with an average of 87.99%. Of 59 primers, 40 primers had highest percent polymorphism (100%). The PIC value indicates allelic disparity and frequency between the studied genotypes. In this study, the PIC value varied from 0.08 (BG20) to 0.84 (BG6 and BG32) with mean value of 0.49 for all the studied genotypes, demonstrating moderate discriminating capability of the SSR markers used for this study (Table 3). Twenty-eight SSR markers have reported PIC values of more than 0.50. Markers with high PIC value are considered as suitable markers for explaining the genetic diversity among genotypes. The average expected heterozygosity/gene diversity (He) was recorded to be 0.56 with maximum value for BG6 (0.86) and minimum values for BG1 and BG20 (0.09), respectively.

Table 3: Genetic diversity indices of 59 SSR markers for 88 Indian mustard genotypes.

PIC: Polymorphic Information Content; He: Expected Heterozygosity

Genetic distance and cluster analysis: The dissimilarity coefficients of tested genotypes varied from 0.135 to 0.512 (Figure 1). Based on the genetic dissimilarity, MST-11-14-32 was found most dissimilar from DRMRB-15, DRMRB-20, M-108 and RH-401 B with dissimilarity coefficient of 0.512, 0.499, 0.498 and 0.492, respectively. On the other hand, a minimum dissimilarity value of 0.135 was found between genotypes, CS-52 and DRMRB-12. Dendrogram was constructed based on Jaccard’s dissimilarity coefficient and UNJ method, which grouped all 88 genotypes of Indian mustard into four major clusters (Figure 2). Cluster-I, comprised of 49 genotypes which was further subdivided into two sub clusters i.e., C-Ia and C-Ib. Sub cluster C-Ia included 27 genotypes originating from all the four centers, without clear majority from any particular center whereas, sub cluster C-Ib comprised of 22 genotypes representing 7 each from Bharatpur and Ludhiana centers, 5 from Delhi center and 3 form Hisar center. Cluster-II includes 5 assorted genotypes from three centers, with no clear majority from any center. Cluster-III consisted of 32 genotypes, maintaining/originating from the entire four centers, but most of the genotypes were from Delhi (12) and Hisar (11) center. The smallest cluster-IV contained only 2 genotypes namely RH-0305-1 and NRCDR-02 from Hisar and Delhi center, respectively.

A PCoA had also been performed in order to better understand the distribution of variation and to categorize any genetic information associated with studied genotypes from the previous dendrogram (Figure 3). It clearly showed the presence of three major groups among the 88 genotypes of our study. The results of PCoA further supported results obtained by dendogram and STRUCTURE. The first two axes of PCoA demonstrated 20.95% and 10.85% of variation, respectively with a 31.80% of cumulative variation (Table 4).

Table 4: Percentage of variation explained by the first 3 axes in PCoA of 88 genotypes Indian mustard

Bayesian structure analysis: A model-based cluster analysis was carried out using STRUCTURE v2.3.3, to determine the genetic association among individual mustard genotypes. The magnitude of delK was obtained by comparing the logarithmic likelihood [L(K)] and estimates of the LnP(D) which ranged from 1 to 10 for the different delKs. The best value of delK was recorded at 3 (Figure 4). At this point, the grouping of genotypes largely supported the results of dendrogram and PCoA. The maximum likelihood and delK (delK = 3) values suggested that all the 88 genotypes of Indian mustard were predominantly allocated into three subpopulations (SP) viz., SP1 (Red), SP2 (Green) and SP3 (Blue) (Figure 5). Of the 88 genotypes, approximately 23 genotypes had pure and 65 genotypes maintained their identity with admixture of alleles of other accessions.

DISCUSSION

Assessment of the genetic diversity and population structure of a given crop species offers valuable information which is needed to broaden the narrow genetic base and selection of parental lines for initiation of crop improvement programme. Crossing of genotypes within the cluster will not lead to desirable segregates as compared to genotypes of different cluster sowing to high genetic similarity among the genotypes within a cluster (Kachare et al., 2019). Due to co-dominant nature and high level of reproducibility, SSR markers have been frequently used in genetic diversity studies in Indian mustard (Vinu et al., 2013; Panigrahi et al., 2018). A total of 209 repeatable alleles were intensified by 59 polymorphic SSR primers in 88 genotypes with an average of 3.54 alleles per locus. The number of alleles detected in present study is significantly higher than previous reports (Sudan et al., 2016; Avtar et al., 2016). This difference arisen might be due to different sampling size, different genotypes being used in previous studies and different SSR primers taken for the study. In general, the PIC value point towards efficacy of markers in linkage analysis during inheritance study among parental lines and hybrids, whereas expected heterozygosity suggests average heterozygosity within the population (Osuman et al., 2020). Mean PIC value of 0.49 with a range of 0.08 to 0.84 across 209 polymorphic loci showed nearly complete correspondence with the previous study (Avtar et al., 2016; Singh et al., 2017). It is reported that high diversity of locus is demonstrated in PIC value greater than 0.5 (Botstein et al., 1980). In our study, 28 primers had a PIC value higher than 0.5, suggesting their efficacy in identification of genotypes along with extreme support in detecting the incidence of polymorphism at a particular SSR locus. Markers with high PIC values such as BG 6, BG 32, BG 33 and BG 99 (PIC >0.80) could be effectively used in genetic diversity studies of Indian mustard. The SSR polymorphism perceived in present study is in agreement with prior findings (Gupta et al., 2014; Prajapat et al., 2014). In our study, the average estimates of He (0.56) was higher than mean PIC value (0.49) which is closer to our expectations since He will always be greater than PIC value. In Indian mustard, limited reports are available on genetic diversity along with high polymorphism using SSR markers. Of 59 polymorphic primers, 40 primers gave 100% polymorphism. The average polymorphic percentage (87.99%) revealed the considerable polymorphism in the studied molecular markers though it was slightly lower than previous study (Avtar et al., 2016 and Singh et al., 2013) where 100 % polymorphism in Indian mustard genotypes has been reported. This difference might be arisen due to different genotypes and markers being used.

Genetic distance offers a measure of the degree of relatedness between genotypes of a species (Garcia et al., 2004) which helps us in genetic improvement of a population (Liu et al., 2019). A distance base tree was generated using UNJ method and Jaccard’s dissimilarity coefficient which grouped all genotypes into four clusters. Each cluster contained genotypes from all four locations (except cluster IV). Cluster I was further subdivided into two subgroups C-Ia and C-Ib. Our findings are in consistent with preceding studies where Thakur et al. (2020) also obtained five subgroups while evaluating 78 genotypes of B. carinata and Sun et al. (2018) where they also grouped 25 wild B. juncea populations into two major groups. Clustering of genotypes based on SSR markers revealed that genotypes procured/originated from a particular center did not group together in same cluster, however genotypes from same center strewn into different clusters. This suggested that grouping of genotypes was independent of geographic province. It could be due to the interchange of genotypes from one place to another. Prior reports of various researchers indicate that geographic diversity is not an inevitable criterion of its genetic diversity hence choice of elite lines based on their diversity estimates could be effective in mustard cultivars development programme (Singh et al., 2013; Teklewold and Becker, 2006). Jaccard’s dissimilarity coefficients ranged from 0.135 to 0.512 among 88 genotypes of Indian mustard. Based on dissimilarity coefficient, MST-11-14-32 was found most diverse from DRMRB-15, DRMRB-20, M-108 and RH-401 B genotypes. These diverse genotypes can be used effectively in the mustard breeding programme for selection of some desirable recombinants. Results obtained by molecular analyses revealed ample genetic diversity between studied genotypes of Indian mustard. Earlier reports further strengthened the present finding that the SSR markers can be used effectively to estimate genetic distances among Indian mustard genotypes (Abbas et al., 2009). Similar results concerning efficiency of SSR markers regarding genetic diversity for yield and its component traits have been also reported by Sudan et al., 2016; Vinu et al., 2013; Tian et al., 2017.

Next to delK (K = 3), bayesian structure analysis allocated the 88 genotypes of Indian mustard into subpopulations (SP1, SP2 and SP3) consisting of few genotypes of pure genetic make-up however others got admixtures of alleles from other subpopulations. This admixture may be the result of random mating involved in development of these breeding lines (Thakur et al., 2020). This is in agreement with earlier finding of Thakur et al. (2020) who reported an admixture of alleles in addition to few pure forms in B. carinata A. Braun using 212 SSR markers. Likewise, Sun et al. (2018) also reported the two lineages among 25 wild B. juncea populations using 11 SSR markers with admixture of Northern and Southern lineages in few accessions. Of the few pure form genotypes, we found that CS-52, a salt tolerant variety and RH-119, a thermo-tolerant and high yielding variety recommended for cultivation in Zone-II were found to be 100% pure. The SSR markers clearly assigned genotypes into heterotic groups based on the source populations and individuals of similar genetic background. The outcomes of population structure analysis were further confirmed by the results of UNJ based clustering and PCoA. Parallel results were reported in B. juncea genotypes using SSR markers, where structure analysis and UNJ based dendrogram grouped all the 23 genotypes into three sub-populations and three clusters, respectively (Sudan et al., 2016).

REFERENCES

1. Abbas, S.J., Farhatullah, K.B. Marwat, I. A. Khan and I. Munir (2009). Molecular analysis of genetic diversity in Brassica Pak. J. Bot. 41(1):167-176.
2. Adhikari, S., S. Saha, A. Biswas, T.S. Rana, T.K. Bandyopadhyay and P. Ghosh (2017). Application of molecular markers in plant genome analysis: a review. The Nucleus. 60(3):283-297.
3. Avtar, R., B. Rani, M. Jattan, Manmohan, N. Kumari and A. Rani (2016). Genetic diversity analysis among elite gene pool of Indian mustard using SSR markers and phenotypic variations. The Bioscan. 11(4):3035-3044.
4. Bhargava, A. and S. Srivastava (2019). Plant Breeding. In: Participatory plant breeding: concept and applications. Springer, Singapore. pp. 29-68.
5. Botstein, D., R.L. White, M. Skolnick and R.W. Davis (1980). Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32:314-331.
6. Calicioglu, O., A. Flammini, S. Bracco, L. Bellù and R. Sims (2019). The future challenges of food and agriculture: An integrated analysis of trends and solutions. 11(1):222.
7. Carvalho, Y.G., L.C. Vitorino, U.J. de Souza and L.A. Bessa (2019). Recent trends in research on the genetic diversity of plants: implications for conservation. 11(4):62.
8. Davis, K.F., J.A. Gephart, K.A. Emery, A.M. Leach, J.N. Galloway and P. D’Odorico (2016). Meeting future food demand with current agricultural resources. Global Environ. Chang. 39:125-132.
9. Earl, D.A. and B.M. VonHoldt (2012). STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the evanno method. Conserv. Genet. Resour. 4:359-361.
10. El-Soda, M., M. Malosetti, B.J. Zwaan, M. Koornneef and M.G. Aarts (2014). Genotype × environment interaction QTL mapping in plants: lessons from Arabidopsis. Trends Plant Sci. 19(6):390-398.
11. Evanno, G., S. Regnaut and J. Goudet (2005). Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14:2611-2620.
12. Friedt, W. and R. Snowdon (2009). Oilseed rape. In Oil crops. Springer, New York. Pp. 91-126.
13. Garcia, A.A.F., L.L. Benchimol, A.M.M. Barbosa, I.O. Geraldi, C.L. Souza and A.P. de Souza (2004). Comparison of RAPD, RFLP, AFLP and SSR markers for diversity studies in tropical maize inbred lines. Genet. Mol. Biol. 27(4):579-88.
14. Goulet, B.E., F. Roda and R. Hopkins (2017). Hybridization in plants: old ideas, new techniques. Plant Physiol. 173(1):65-78.
15. Gupta, N., S.M. Zargar, M. Gupta and S.K. Gupta (2014). Assessment of genetic variation in Indian mustard (Brassica juncea) using PCR based markers. Mol. Plant. Breed. 5(3):10-17.
16. Jat, R.S., V.V. Singh, P. Sharma and P.K. Rai (2019). Oilseed rassica in India: demand, supply, policy perspective and future potential. OCL. 26:8
17. Kachare, S., S. Tiwari, N. Tripathi and V.V. Thakur (2019). Assessment of genetic diversity of soybean (Glycine max) genotypes using qualitative traits and microsatellite markers. Agric. Res. 9:23-34.doi: 10.1007/s40003-019-00412-y.
18. Kaeppler, S. (2012). Heterosis: many genes, many mechanisms-end the search for an undiscovered unifying theory. Sch. Res. Notices. doi:10.5402/2012/682824.
19. Lakhanpal, N., D. Verma, R. Kaur and K. Singh (2018). Characterization of cold responsive uncoupling protein1 (UCP1) gene from Brassica juncea (Czern. and Coss.). J. Plant Biochem. Biot.27(1):108-117.
20. Liu, B.H. (1998). Statistical genomics: Linkage, mapping and QTL analysis. CRC Press, Boca Raton, Florida, USA
21. Liu, F.M., Z. Hong, D.P. Xu, H.Y. Jia, N.N. Zhang, X.J. Liu, Z.J. Yang and M.Z. Lu (2019). Genetic diversity of endangered endemic Dalbergia odorifera revealed by SSR markers. Forests. 10:225.
22. Osuman, A.S., B. Badu-Apraku, B.E. Ifie, P. Tongoona, E. Obeng-Bio and A.L. Garcia-Oliveira (2020). Genetic diversity, population structure and inter-trait relationships of combined heat and drought tolerant early-maturing maize inbred lines from West and Central Africa. Agronomy. 10:1324 DOI: 10.3390/agronomy10091324.
23. Panigrahi, P., K.K. Panigrahi, and S. Bhattacharya (2018). SSR marker based DNA fingerprinting and diversity studies in mustard (Brassica juncea). Electron. J. Plant Breed. 9(1):25-37.
24. Panjabi, P., S.K. Yadava, N. Kumar, R. Bangkim and N. Ramchiary (2019). Breeding Brassica juncea and rapa for sustainable oilseed production in the changing climate: progress and prospects. In genomic designing of climate-smart oilseed crops. Springer, Cham. Pp. 275-369.
25. Perrier and Jacquemoud-Collet (2006). DARwin software [Internet]: Available from: http://http//darwin. cirad.fr.
26. Prajapat, P., N. Sasidharan, M. Kumar and V. Prajapati (2014). Molecular characterization and genetic diversity analysis in four Brassica species using microsatellite markers. The Bioscan. 9(4):1521-1527.
27. Pritchard, J.K., M. Stephens and P. Donnelly (2000). Inference of population structure using multilocus genotype data. Genetics. 155:945-959.
28. Saghai-Maroof, M.A., K.M. Soliman, R.A. Jorgensen and R.W. Allard (1984). Ribosomal DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal location and population dymnamics. Proc. Natl. Acad. Sci. 81: 8014-8018.
29. Singh, K.H., R. Shakya, A.K. Thakur, D.K. Chauhan and J.S. Chauhan (2013). Genetic diversity in Indian mustard [Brassica juncea (L.) Czern & Coss.] as revealed by agronomic traits and RAPD markers. Natl. Acad. Sci. Lett. 36(4)419-427.
30. Singh, S., V.V. Singh, S. Ambawat, M. Dubey and D. Singh (2017). Screening and estimation of allelic differentiation in Indian mustard using SSR markers for background selection. Int. J. Curr. Microbiol. Appl. Sci. 6(9):2506-2516.
31. Sudan, J., P. Khajuria, S.K. Gupta and R. Singh (2016). Analysis of molecular diversity in Indian and Exotic genotypes of Brassica juncea using SSR markers. Indian J. Genet. 76(3):361-364.
32. Sun, X.Q., Y.Q. Qu, M.M. Li, X.L. Song and Y.Y. Hang (2018). Genetic diversity, genetic structure and migration routes of wild Brassica juncea in China assessed by SSR markers. Genet. Resour. Crop Evol. doi: 10.1007/s10722-018-0628-3.
33. Teklewold, A. and H.C. Becker (2006). Geographic pattern of genetic diversity among 43 Ethiopian mustard (Brassica carinata Braun) accessions as revealed by RAPD analysis. Genet. Resour. Crop Evol. 53:1173-1185.
34. Thakur, A.K., K.H. Singh, N. Parmar, D. Sharma, D.C. Mishra, L. Singh, J. Nanjundan and S. Yadav (2020). Population structure and genetic diversity as revealed by SSR markers in Ethiopian mustard (Brassica carinata Braun): a potential edible and industrially important oilseed crop. Genet. Resour. Crop Evol. doi: 10.1007/s10722-020-00988-3.
35. Tian, H., J. Yan, S.A. Channa, R, Zhang, Y, Guo and S. Hu (2017). Analysis of the genome genetic diversity among Brassica napus, B. rapa and juncea accessions using specific simple sequence repeat markers. Pak. J. Bot. 49(1):125-132.
36. Tripathi, A.D., R. Mishra, K.K. Maurya, R.B. Singh and D.W. Wilson (2019). Estimates for world population and global food availability for global health. In the role of functional food security in global health. Academic Press. Pp. 3-24.
37. Vinu, V., N. Singh, S. Vasudev, D.K. Yadava, S. Kumar, S. Naresh, S.R. Bhat and K.V. Prabhu (2013). Assessment of genetic diversity in Brassica juncea (Brassicaceae) genotypes using phenotypic differences and SSR markers. Rev. Biol. Trop. 61(4):1919-1934.
38. Wani, S.H., A.K. Thakur and Y.J. Khan (2020). Brassica improvement: molecular, genetics and genomic perspectives. Springer Nature.
39. Yadav, M.S., S. Godika, D.K. Yadava, N. Ahmad, N. Mehta, K. Bhatnagar, V.K. Agrawal, A. Kumar, L. Thomas and C. Chattopadhyay (2019). Prioritizing components of package of integrated pest management in Indian mustard (Brassica juncea) in India for better economic benefit. Crop Prot.120:21-29.