Genetic diversity analysis of Brassica juncea mutants through morphological and molecular characterization

Introduction

Indian mustard (Brassica juncea) belonging to the family cruciferae (Syn. Brassicaceae). It is the world’s third most important source of edible oil [9] and second most important in India accounting 30% of the total oilseeds production [27]. In India, the area under mustard cultivation is 6.07 million hectares producing about 8.70 million tonnes of seeds with average productivity of 1433 Kgha^-1[4]. Still, there is a big gap in productivity in India compared to the world (1811 kg.ha^-1: www ikissan.com). Developing high-yielding varieties is the only option to narrow down the gap. Improvement of any crop is determined by the availability of genetic variability in the germplasm and the heritability magnitude of yield-contributing traits [8]. However, most of the rapeseed-mustard varieties cultivated in India have a narrow genetic base which limits their improvement.  Existing germplasm needs strengthening which could be easily used in mustard crop improvement. Mutagenesis could pave the way for generating novel variability and the development of high-yielding varieties in mustard [11, 12].

A morphological characterization is a potential tool for the classification and identification of desirable variability. Genetic diversity analysis has been carried out in Indian mustard using Principal Component Analysis [19, 18, 14] and Hierarchical Cluster analysis [23] using morphological characters. Molecular marker has been extensively used to identify the distinctness of genotype or segregating populations [6]. The precise information regarding genetic divergence and relatedness between useful mutants can be achieved through molecular markers [20]. Identification of genomic regions associated with improved traits will help to develop improved cultivars using marker-assisted selection (MAS) [22]. Simple sequence repeats (SSRs) or microsatellites have been recognized as useful molecular markers for the analysis of genetic diversity, MAS, and population analysis [17, 22]. Among Brassica species, SSR markers have been developed in B. rapa (AA), B. nigra (BB), B. napus (AACC), and B. oleracea (CC) [32]. These markers have been employed to detect microsatellite loci in other related species due to the conserved nature of flanking sequences [27] and their application in the assessment of genetic variability in mustard [28]. Induced mutation generates variability which could be due to new alleles. Identification of such alleles with molecular markers could help in the selection of desirable traits as well as plants. Studies on morphological and molecular diversity analysis of induced mutations have not yet been reported in mustard and therefore present study was undertaken to evaluate the extent of variability for yield and related attributes of mutants over the parent through morphological and molecular characterization.

Materials and Methods

Development of mutant lines

Seed Treatment: Genetically pure, dry, and healthy seeds of Brassica juncea cv BIO 902 were selected to prepare five lots consisting of 300 seeds each. They were irradiated with 900, 1000, 1100, 1200, and 1300 Gy doses of gamma rays using a ⁶⁰Co source at BARC, Mumbai. For the combination treatment of gamma rays and EMS, gamma ray-exposed seeds from each treatment were treated with 0.5% aqueous solution of EMS after pre-soaking with sterilized distilled water for 12 hours. Seeds were washed 3-4 times under running tap water to remove residual EMS and were soaked on blotting paper before sowing.

Isolation of mutants: M₁ generation along with control was raised during 2014-15. Seeds from each M₁ plants were harvested separately at maturity.  A total of 1011M₁ plants were harvested. Their plant-to-row progeny consisting of total of 51000 plants was raised in M₂ generation during rabi 2015-16. All the recommended practices were followed to raise a good crop. Based on visual performance, twenty-six M₂ plants superior for seed yield were selected and observations on yield and yield components were recorded. These putative mutants were advanced to M₃ generation and sown along with 4 checks namely Pusa bold, BIO 902, Kranti, and Shatabdi during rabi 2016-2017 to study their breeding behaviour and yield potential. Yield and yield contributing characters were recorded at maturity. For confirmation of breeding behaviour and yield potential, identified mutants along with 4 checks namely Pusa bold, BIO 902, Kranti, and Shatabdi were raised in M₄ generation during 2017-18 in randomized block design with three replications at AICRP on Linseed and Mustard farm, College of Agriculture, Nagpur.

Observations:  During 2017-18, data were recorded on 5 randomly selected plants per replication for all quantitative characters namely number of lobes leaf^-1, leaf length, leaf width, petal length, petal width, days to maturity, plant height, main shoot length, primary branches plant^-1, siliqua length, siliqua beak length, no. of siliqua plant^-1 and  seed yield plant^-1 in each mutant line. Observations were recorded as per DUS UPOV 2011.

Statistical Analysis: Principal component analysis (PCA) and Hierarchical cluster analysis (HCA) analysis were performed using R-software. Principle component analysis using the principal component method was carried out and a principle component with an eigen value more than one was taken. Varimax rotation was used for a clear interpretation of results.

Marker analysis: DNA extraction of ten best-performing mutants along with parent Bio 902 and check Kranti was carried out using the CTAB method and purification was performed using phenol: chloroform. DNA quantification was carried out using a spectrophotometer. Polymerase Chain Reaction (PCR) was undertaken in the thermo cycler. Initial denaturation of DNA was done at 95^°C for 5 minutes, followed by 35 cycles of denaturation at 95^°C for 30 seconds, annealing at 55^°C for 30 seconds, and extension at 72^°C was performed.  Twenty SSR primer sets (Table 1) were used to investigate the genetic relationships and diversity among 10 high-yielding mutants with parent Bio 902 and Kranti. All SSR fragments were scored manually. Polymorphism Information Content (PIC) value was calculated. Distance-based cluster analysis was performed and a dendrogram based on the unweighted pair group method of the arithmetic mean (UPGMA) was constructed using Jaccard’s similarity coefficient with the help of DARwin [16].

Results and discussion

Genetic diversity is a pre–requisite in the crop improvement program. Seed yield potential in Indian mustard has stagnated and therefore generation of variability through induced mutagenesis looks to be a good option to isolate desirable mutants. We have isolated desirable mutations for seed yield (table 2).  The seed yield of mutants was found in the range of 2536- 3276 Kg ha^-1 which is 1.4 to 27% higher than the best check Kranti (2501Kg/ha). Siliqua plant^-1 has a positive association with higher seed yield (table 3).

Principal component analysis (PCA):

PCA reveals the major contributor to the total variation at each distinct point and the eigen value is often used to determine the number of major principal components to be explained. PCA for the data revealed that the first six principle components (PCs) had eigen values greater than one and altogether explained 74.24% of total accumulated variability (table 4). A maximum eigen value of 2.65 was observed for the first PC and a minimum of 1.10 for sixth PC. The first principal component accounts for the maximum proportion i.e.17.17% of total variability among all variables followed by second (16.17%), third (13.91%), fourth (10.92%), fifth (8.28%), and sixth (7.36%) PCA.

The analysis without rotation of axes failed to load all the variables signifying that it could not offer much information regarding the idea of the correlation between the variables and the principal components. Varimax Rotation was applied and this resulted in the loading of all the variables on different principal components. Factors loadings of different variables thus obtained are presented in table 4. The first principal factor (PF1) is ascribed to four variables i.e. days to maturity, leaf length, leaf width, no. of lobes plant^-1and could be designated as maturity factor. PF2 represents for high loadings of three variables i.e. petal length, siliqua beak length, and siliqua density. PF3 has high loadings of two variables i.e. plant height and siliqua plant^-1 which could be called as siliqua factor. PF4 has four variables viz. branches plant^-1, siliqua angle, siliqua texture, and seed yield plant^-1 and could be designated as yield and angle factor as it enabled high loadings of seed yield along with its most important component trait viz. primary branches plant^-1 as well as siliqua angle. PF5 has only one variable viz. petal width. Singh et al. (2013) also reported the loading of similar types of variables on a common principal factor (PF) in Indian mustard. It revealed that the number of primary branches/plant, number of secondary branches/plant, seed yield/plant, days to flowering, plant height, days to maturity, seeds/siliqua, and main shoot length were the major distinct variability contributing traits which accounted for more than half (58.70%) of the total variation (66.98%) in the set of 26 mutants and 4 checks. Thus, the successful transformation of 15 morphological variables into five independent principal factors by means of a grouping of similar types of variables on different principal factors elaborated and explained 66.98% of the variability of the original set. [14] also reported  similar results in Indian mustard. [30] observed that the four principal components in 18 genotypes of Indian mustard contributed 86% towards total variability.

Hierarchical cluster analysis (HCA)  The based cluster analysis was performed to see the relationship among mutants (Figure 1), parents, and checks.  Total genotypes are classified in eight clusters (Fig. 1). A Maximum number of mutants fall in cluster VIII having 20 mutants (Fig. 1) followed by cluster IV with three mutants, cluster VI with two genotypes, and one each in cluster I,II,III,V,VII.  Cluster II, III, VI, VII, and VIII mutants were found completely diverse from the checks and ACNMM 9 was found closely related to checks Pusa Bold and Shatabdi. Among the eight selected mutants, ACNMM 1, ACNMM 3, ACNMM 12, ACNMM 17, ACNMM 20, ACNMM 22, and ACNMM 23 were all found closely related to each other and observed in cluster VIII and ACNMM 9 found closely related with check parent variety Pusa Bold and check Shatabdi. Check Kranti was the only genotype placed in cluster I. The checks Pusa Bold and Shatabdi were placed in cluster IV along with the genotype ACNMM 9 which was a high-yielding genotype. Check BIO 902 was placed in cluster V. Those genotypes that were diverse from checks were placed in cluster VIII.

Marker analysis

Two main aspects of genetic diversity, marker informativeness (polymorphic and overall efficiency of informative fragment detection) and marker performance (overall efficacy of a primer set used in determining polymorphism level, genetic diversity, and discriminatory power) were evaluated. Out of 20 SSR primers screened for 10 mutants, parents, and checks, 12 primers viz., Na14E08, Na10G10, OI11B05, OI12E03, OI10F06, Ni4H05, Ni2E12, Ni2H06, Ni2Co1, Ni4G09b, Ra2DO4, OI10F09 were found monomorphic and eight primers viz., Na12E01, Na10E02, OI10E05, Na12A08, Na12D04, Na12F11, Ra2E12, Ra2A11 were found polymorphic.  PCR Product size ranged from 100 to 350bp. 200 SSR markers for genetic diversity analysis in 87 Indian mustard varieties in that they observed unique fingerprints for varietal identification [26].

The polymorphic information content (PIC) values of eight SSR loci were calculated across 10 mutant genotypes and are presented in Table 5. Out of 18 amplified bands, four were monomorphic and 14 were polymorphic. The percent polymorphism varied from 66.67 to 100% with an average of 77%. The maximum numbers of polymorphic bands were three in the Na10E02 primer with 100% polymorphism. Polymorphic information content values ranged from 0.08 to 0.48 with an average of 0.34 per primer combination. The primer Na12A08 observed minimum polymorphism with a PIC Value of 0.27. Among the primers used in the present study, Na10E02 was highly informative since it recorded high PIC, EMR, and MI values of 0.48, 3, and 1.21 respectively. SSR marker-based genetic diversity analysis has also been performed in Indian mustard which differs in Alternaria brassicicola tolerance [7]. In this study, nineteen SSR markers were found to be highly informative out of 41 with a polymorphic information content value > 0.50. 

 Assessment of genetic diversity on the basis of SSR markers

For enabling better exploitation of genetic resources, it is desirable to know the genetic diversity at molecular levels [13]. Thus, distance-based cluster analysis was performed and a dendrogram based on the unweighted pair group method of the arithmetic mean (UPGMA) was constructed using Jaccard’s similarity coefficient (Figure 2). Similar work was also conducted by [28] where genotypes were grouped into four clusters based on genetic distances. Also, [29] used both morphological and molecular markers for diversity analysis and [24] and [5] revealed the grouping of various genotypes into various clusters. The result suggested that there are 3 clades. The first clade consists of four mutant lines along with Bio902. The second clade consists of 5 mutant lines, whereas the third clade consists of one mutant which is the variety, Kranti. The mutants were grouped into three major clusters; cluster 1 separated into two sub-clusters having ACNMM9, ACNMM4, ACNMM17, ACNMM19 in one sub-cluster and  the check variety BIO-902 into separate sub clusters indicating these mutant lines were closely related to BIO-902.

Similarly, cluster 2 was also divided into two sub-clusters having ACNMM23, ACNMM22, ACNMM13, ACNMM14 in one sub-cluster and ACNMM in the other whereas ACNMM7 separated into 2 sub-clusters  indicating these mutant lines were distantly related to any of the given parents and check variety due to variation caused by mutagenesis and were found independent. Cluster 3 was again separated into 2 sub-clusters one with check variety Kranti and another with ACNMM15 which indicates ACNMM15 is closely related to check variety Kranti. Similar work was also conducted by Singh et. Al. (2016) where used the 150 genic-SSR markers, among them 65 markers (43.3%) were found polymorphic and amplified products of varying sizes in the range of 100-400 bp while 85 SSRs (56.6%) were monomorphic. Diversity  analysis conducted by [15] using SSR markers  showed a maximum PIC of 0.252 for primer Ra1F03.

Conclusion: The observations suggested that SSRs are proficient in evaluating genetic variation and relationships among different mutants. Further, the findings of this study will be useful for DNA fingerprinting, and varietal identification which could help during background selection for marker-assisted backcross breeding programs. Out of the 10 superior high-yielding mutants selected for molecular analysis, mutant ACNMM9 was similar to checks both in morphological as well as molecular studies. Mutants ACNMM 4, ACNMM 17, ACNMM 19, and ACNMM 15 were found in distinct clusters as of checks morphologically but in the same cluster as that of checks in molecular analysis.

Future scope of the study: Mutants ACNMM 23, ACNMM 22, ACNMM 14, ACNMM 7, and ACNMM 13 were found in different clusters than checks both morphologically as well as in molecular studies. Hence, those genotypes which were diverse morphologically and in molecular studies must be further be evaluated in the yield trials.

Conflict of interest: Authors has no any conflict of interest.

Acknowledgment: We would like to thank AICRP on Mustard for providing seed material and research field for conduction of research trial and to the Board of Research in Nuclear Sciences (BRNS) for providing the funding to undertake research on mutation breeding research in mustard.

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Figures:

Fig 1. Dendrogram showing different clusters of 30 mutants along with checks

Fig 2.Dendrogram derived from banding pattern of SSR marker analysis of 12 genotypes

Table 1: List of 20 SSR markers used in the present study

Table 2.  Seed yield and its attributes of stable mutant lines compared with parent and checks

* Mutants and varieties used for molecular marker analysis.

Table 3. Estimates correlation coefficients of yield and yield contributing traits

Table 4. Total variance of different principal components

Table 5. Factor loadings of characters with respect to different principal factors (verimax rotation)

Table 6. Details of informative markers based on PIC, EMR and MI