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Evaluacion de la diversidad genetica en genotipos de Brassica juncea (Brassicaceae) utilizando diferencias fenotipicas y marcadores SSR.

Assessment of genetic diversity in Brassica juncea (Brassicaceae) genotypes using phenotypic differences and SSR markers.

Brassica species, commonly called as rapeseed-mustard, are the third most important oilseed crops of the world after soybean and palm. China, India, Canada, Japan and Germany are the major rapeseed-mustard growing countries. These are the second most important oilseed crops of India, next to soybean. India is one of the largest rapeseed-mustard growing country occupying first position with 20.23% area and second position with 11.7% share to the global production (USDA, 2012). Four oleiferous Brassica species viz. Brassica juncea, B. napus, B. rapa and B. carinata are cultivated in about 6.39 million hectares area and produce 7.41 million tons in India (Kumar, Kumar & Kandpal, 2012). B. juncea (2n = 36, AABB genome), an allopolyploid commonly called as Indian mustard, contributes more than 80% to the total rapeseed-mustard production in the country and is an important component in the oilseed sector. It is known to be more drought tolerant and shattering resistant than B. napus and B. rapa, therefore, has an enormous cultivation potential in semi-arid areas. With the increasing population and improving life standards, per capita oil consumption has increased tremendously. To meet out the present oil requirements, there is an urgent need to increase the yield potential of B. juncea through genetic interventions.

The maximum utilization of any species for breeding and its adaptation to different environments depend on the level of genetic diversity it holds. Genetic distance among parents may be attributed to their differences for number of genes and their functional relations in a given environment (Nei, 1976). Evaluation of genetic divergence and relatedness among breeding materials has significant implications for the improvement of crop plants. Knowledge on genetic diversity in B. juncea could help breeders and geneticists to understand the structure of germplasm, predict which combinations would produce the best offsprings (Hu et al., 2007), and facilitate to widen the genetic basis of breeding material for selection (Qi, Yang & Zhang, 2008).

Genetic diversity among individuals or populations can be determined using morphological, biochemical and molecular approaches (Mohammadi & Prasanna, 2003). Assessment of genetic diversity in B. juncea using phenotypic characters has previously been done by many researchers (Gupta, Sekhon & Satija, 1991; Vaishnava, Sachan & Tewari, 2006; Alie, Singh, Tariq & Sharma, 2009; Singh, Arya, Chandra, Niwas & Salisbury, 2010). Isozyme loci have been used as markers in a number of genetic studies including genetic diversity in B. juncea (Kumar & Gupta, 1985; Arunachalam, Prabhu & Sujata, 1996). However, these parameters are influenced by environmental factors and the developmental stage of the plant. In B. juncea various marker systems have been used for assessing the genetic diversity. There is increasing number of reports where molecular markers like Restriction Fragment Length Polymorphism, (RFLP; Song, Osborn & Williams, 1988; Diers & Osborn, 1994; Hallden, Nilsson, Rading & Sall, 1994; Das Santos, Nienhuis, Skroch, Tivang & Slocum, 1994), Random Amplified Polymorphic DNAs, (RAPDs; Ghosh, Haque, Parvin, Akhter & Rahim, 2009; Yildirim, Yildirim, Ercisli, Agar & Karlidag, 2010; Khan et al., 2011), Amplified Fragment Length Polymorphism, (AFLP; Sun, Zhao, Song & Chen, 2001; Guo, Zhou, Ma & Cao, 2002; Zhao et al., 2005) and microsatellites or Simple Sequence Repeats (SSRs; Abbas, Farhatullah, Marwat, Khan & Munir, 2009; Wang et al., 2009; Redden, Vardy, Edwards, Raman & Batley, 2009) have been used to study genome organization, varietal differences and diversity analysis in Brassicas. SSRs are co-dominant, highly polymorphic PCR-based markers and are expected to be very powerful in cultivar discrimination. The fairly saturated linkage map for this class of marker is available in public domains (Panjabi et al., 2008).

Among various markers available for genetic analysis in plants, molecular markers are more efficient, precise and reliable in discriminating closely related species and cultivars (Mishra et al., 2011), even then, many breeding groups emphasize in morphological traits than molecular markers (Hu et al., 2007). Therefore, the present study was undertaken to estimate the genetic diversity of 44 B. juncea genotypes of diverse geographic origin and explore potential to evaluate the relationship of these genotypes based on quantitative trait data and microsatellite markers. It would be interesting to see relative efficiency of these two approaches in discriminating genotypes of B. juncea. Genetic distances will further help in identifying genetically diverse genotypes, which then can be utilized in creating valuable selectable variation.

MATERIALS AND METHODS

Plant material: Forty four B. juncea genotypes, including varieties/purelines from different agro-climatic zones of India and four genotypes of exotic origin (Australia, Poland and China) were taken for this study (Appendix 1).

Phenotypic evaluation: The present study was carried out at the Division of Genetics, Indian Agricultural Research Institute, New Delhi under normal field condition during winter 2010-11. Geographically, the experimental farm of IARI, New Delhi is situated at the altitude of 228.61m above mean sea level (28[degrees]38'23"N-77[degrees]09'27" E). The area has a semi-arid, sub-tropical climate having mean precipitation of about 700mm most of which is received in rainy season spreading from July to September. Alluvial soils (Typic Ustochrept) which are slightly alkaline (8.25pH) with clay loam texture and low organic matter was supplemented with 60kg of nitrogen (in two splits), 40kg of phosphorus, 20kg of potash and 40kg of sulphur per hectare to raise a healthy crop. In the pre-irrigated well cultivated field with pulverised soil, sowing was done with the help of hand plough and the depth of sowing was kept at 1.5-2cm. All the 44 genotypes were grown in the field in randomized block design with three replications. Each genotype was planted in a four-row plot of three meter length with a spacing of 30x10cm (Row x Plant). The crop was given three irrigations, first at vegetative stage, second at flower initiation, and third at seed development. The observations were recorded on 12 morphological traits viz., plant height (cm), days to maturity, point to first branch (cm), number of siliquae on main shoot, number of primary branches, number of secondary branches, main shoot length (cm), point to first siliqua (cm), siliqua length (cm), number of seeds per siliqua, seed yield per plant (g) and 1 000 seed weight (g) using standard methods. The data were recorded on five random but competitive plants except for days to maturity, where it was taken on plot basis.

Molecular marker evaluation: DNA from 44 genotypes was isolated from young leaves using CTAB (Cetyl Trimethyl Ammonium Bromide) method as described by Murray & Thompson (1980), later modified by Doyle & Doyle (1990). After purification, DNA was quantified by analysing on 0.8% agarose gel with Hind III-cut [lambda] DNA as standard. The concentration of DNA in individual sample was determined based on the intensity of the bands in the [lambda] DNA ladder. Finally it was diluted to 20ng/[micro]L for PCR analysis. Microsatellite markers (SSR), 143 in number, spanning A and B genome were used to study DNA polymorphism (Appendix 2). These primers are known to express polymorphism among B. juncea genotypes. The amplification reaction was carried out in 10[micro]L reaction volume containing 10X Taq buffer, 1mM Mg[Cl.sub.2], 10mM dNTPs, 200pmole primers, one unit Taq DNA polymerase and 20ng template DNA. PCR amplification was programmed for 35 cycles after an initial denaturation cycle for five minutes at 94[degrees]C. Each cycle consisted of a denaturation step at 94[degrees]C for one minute, an annealing step at 58[degrees]C for one minute, and an extension step at 72[degrees]C for two minutes, following by extension cycle for seven minutes at 72[degrees]C in the final cycle. The amplified fragments were resolved on 2% agarose gel. Bands were scored as zero for absence and one for presence in each genotype.

Genetic distances based on phenotypic data: The phenotypic data recorded on 12 yield and yield related traits were subjected to analysis of variance (ANOVA). Using same phenotypic data, Manhattan dissimilarity coefficients (MD; Sokal & Michener, 1958) were calculated by pair-wise comparisons of varieties by using NTSYS-pc 2.02 programme (Rohlf, 1998). Based on an average linkage algorithm (UPGMA, unweighted pair group method with an arithmetic average), clustering of genotypes was done. To depict the similarity or dissimilarity among groups or individual genotypes Principal Coordinate Analysis (PCoA; Gower, 1966) was done using DARwin 6.0 programme.

Genetic distances based on SSR analysis: Utilizing binary data generated by SSR primers Jaccard's similarity coefficients (Jaccard, 1908) were calculated between genotypic pairs using NTSYS-pc 2.02 programme (Rohlf, 1998). From the similarity coefficients matrix, thus generated, the dissimilarity coefficients (JD; Genetic distances = 1-similarity coefficient) were calculated. The dissimilarity coefficient matrices were again subjected to PCoA to explore and establish similarity or dissimilarity among groups or individual genotypes.

Correlation between phenotypic and molecular genetic distance matrices: Simple correlation was calculated between the Jaccard's and Manhattan genetic distances matrices.

RESULTS

Phenotypic analysis: In the field evaluation trial during winter 2010-11, a total of 44 genotypes were evaluated in RBD. The analysis of variance for 12 yield and yield contributing traits revealed that, the genotypes taken for this investigation had significant genetic variation (Table 1). Plant height, point to first branch, number of secondary branches, point to first siliqua and number of siliquae on main shoot showed wider range for trait values. The most important agronomic trait, seed yield per plant showed a range from 5.90g to 15.59g.

Using the mean values of the 12 quantitative traits, Manhattan dissimilarity coefficients (MD) were calculated by pair-wise comparisons of varieties by using NTSYS-pc 2.02 programme. Manhattan dissimilarity coefficients ranged from 0.07-0.47 with an average of 0.23. The UPGMA based dendrogram scattered the genotypes in four different clusters (Fig. 1). The first cluster comprised of 25 genotypes from seven states of India. Pedigree analysis of these 25 genotypes revealed that Varuna and Pusa Bold, two most popular and widely adapted cultivars, or their derivatives are involved in development of these genotypes. The genotypes of this cluster are characterised by good seed yield, tall plant height and medium maturity ranging from 120 to 145 days.

The second cluster had 11 genotypes developed from HAU Hisar (Haryana), IARI New Delhi, CSAUA&T Kanpur (Uttar Pradesh), ZARS Morena (Madhya Pradesh), RAU Srig anganar (Rajasthan) and PAU Ludhiana (Punjab). These genotypes are good seed yielders, late maturing (>140 days), possessing small seed size and tall to very tall plant stature. Seven genotypes fell in cluster III of which four are having exotic origin, one is a resynthesized B. juncea and another was developed using one of the B. juncea mutant. These genotypes are characterized by small to very small seed size and low to medium seed yields. The fourth cluster included only one genotype viz., IC 355399A which has a peculiar siliqua orientation in bunches that puts it into a separate category.

SSR marker analysis: Among the 143 SSR primers used for polymorphism study, a total of 134 SSR were detected polymorphic with 355 amplified alleles. The average number of alleles per primer varied from one to six, while the size of the fragments ranged from 200bp to 400bp. The average percentage of polymorphism for each primer ranged from 4.34 to 37.5 per cent. Jaccard's similarity coefficients based on SSR data ranged from 0.38 to 0.83 with an average of 0.58.

The UPGMA based dendrogram representing genetic similarity among different accessions grouped the 44 genotypes into four clusters at 40% genetic distance (Fig. 2). First cluster comprised of nine varieties of which eight were developed at IARI, New Delhi. In six of these varieties, except two early maturing varieties Pusa Agrani and Pusa Tarak, Varuna is involved as one of the parents directly or through the ancestry. The ninth one, Varuna, is a very old selection from Varanasi (Uttar Pradesh) during seventies.

The cluster II had 11 genotypes which included two varieties viz., RH30 and Laxmi are from Haryana state and related by ancestry. Three double zero genotypes viz., EC 597325, EC 597318 and Heera falls adjacent to each other in this cluster. Genotypes viz., Rohini, GM 1, RGN 73 and JM 1 have Varuna as their immediate or distant ancestor, whereas, RLM 619 and JM 2 are mutants.

Sixteen genotypes fall in cluster III in which four are from Haryana, three each from Punjab and Rajasthan, two each from U.P. and Maharashtra, one from Gujarat state. The remaining genotype EC 399299 is of exotic origin, having good adaptation to the Indian conditions.

Cluster IV comprised of eight genotypes, which include bunchy type, appressed type, exotic material, somaclone and heat tolerant genotypes. These eight genotypes belong to four breeding programmes. The quality varieties viz., Pusa Karishma and Pusa Mustard 21, developed from IARI, New Delhi fall in cluster IV as these have been bred by using the exotic quality zero erucic acid lines ZEM-1 and ZEM-2. Another IARI bred variety Pusa Jaikisan, a somaclone variant from Varuna developed through tissue culture, also falls in the cluster IV, far away from majority of other IARI bred varieties as expected.

Principal Coordinate Analysis: To visualize the similarity or dissimilarity among groups or individual genotypes Principal Coordinate Analysis (PCoA) was done using DARwin 6.0 programme (Fig. 3). The PCoA analysis further confirmed the positions and grouping of genotypes. PCoA based on genetic distance matrix of phenotypic data (Fig. 3A) showed scattering of 'Pusa' varieties in two right hand side quadrants. The single zero cultivars viz., Pusa Mustard 21 and Pusa Karishma were placed in one quarter. In comparison to the grouping done by phenotypic data, the grouping of genotypes based on SSR marker data is observed to be more informative and convincing (Fig. 3B). All the IARI developed 'Pusa' varieties were clustered in one quadrant. The varieties viz., Pusa Karishma and Pusa Mustard 21, specifically developed for better oil quality (low erucic acid) were grouped together in other quadrant. Double zero genotypes viz., EC 597318, EC 597325 and Heera though are placed in different quadrants, in this case, but their position is much closer to each other. The PCoA based on molecular data is better in differentiating related genotypes of common origin and parentage.

Correlation between phenotypic and molecular genetic distance matrices: Simple correlation between phenotypic variation, estimated by Manhattan distances using all morphological characters and SSR marker based distance matrices was low (r = 0.11) and non significant. Thus, indicating that the two methods were independent in assessing genetic diversity.

DISCUSSION

The assessment of genetic diversity is not only important for crop improvement efforts but also for efficient management and protection of germplasm resources. But these estimates of genetic diversity can be biased by the choice of data (Phenotypic and molecular marker). Therefore, in present study both types of data have been used to measure unbiased diversity estimation.

The material taken for this study exhibit significant genetic variation for all the 12 yield and yield contributing traits. Inclusion of four exotic collections from Poland, Australia and China along with Indian genotypes, developed from different national breeding programmes located in different regions of the country, contributed significantly to this variation. Such significant genetic variation has also been reported by Vaishnava et al. (2006), Alie et al. (2009), Singh et al. (2010) and Yadava, Sapra, Sujata, Dass & Prabhu (2009) on metric traits in B. juncea.

Manhattan dissimilarity coefficients delineated 44 genotypes into four clusters in this study and differentiated these genotypes predominantly based on their maturity, seed yield, seed size and plant height. This method was also used by Sheikh, Banga, Banga and Najeeb (2011) to estimate genetic diversity among 24 stable introgressed progenies of B. carinata developed through interspecific hybridization between B. napus x B. carinata and B. juncea x B. carinata.

The genetic diversity study in B. juncea has been previously carried out using isozyme markers (Kumar & Gupta, 1985), morphological traits (Gupta et al., 1991; Pradhan, Sodhi, Mukhopadhyay & Pental, 1993) and molecular markers (Huangfu, Song & Qiang, 2009). SSRs, being a potential marker system not much used in research and breeding of B. juncea. Limited work considering SSR markers has been reported in B. juncea (Hopkins et al., 2006).

The molecular marker analysis by using 143 SSR markers successfully differentiated 44 B. juncea genotypes into four different groups. Out of the nine genotypes falling in cluster I, six were having Varuna or its derivatives as one of the parent. Similar results were reported by Jain, Bhatia, Banga, Prakash & Lakshmikumaran (1994) and Srivastava, Gupta, Pental & Pradhan (2001). All the three double zero genotypes viz., EC 597325, EC 597318 and Heera falls in cluster II. Mutants and somaclonal variants were delineated into cluster IV. This shows the effectiveness of SSR markers in identifying the close pedigree relationship in breeding material. A similar result regarding effectiveness of SSR markers in monitoring genetic diversity for yield component traits as well as quality traits have also been reported by Plieske & Struss (2001) and Charters, Robertson, Wilkinson & Ramsay (1996), respectively. Similar types of studies using SSR markers have also been done in B. napus (Uzunova & Ecke, 1999; Batley et al., 2003; Hopkins et al., 2006). In addition to microsatellite markers, other marker systems were also used by various researchers for genetic diversity studies in Brassica spp. Malode, Shingnapure, Waghmare & Sutar (2010) analyzed 20 genotypes of Brassica spp. including exotic, Indian and mutants using RAPD primers and grouped the genotypes into four clusters. Similar findings have also been observed in our study with SSR markers. In the present study, a good proportion of polymorphic markers was detected that would be useful in identification of interspecific hybrids as well as monitoring of genes introgression to desirable genetic backgrounds.

In comparison to PCoA based grouping done by phenotypic data, the grouping of genotypes based on SSR marker data is more informative and convincing. All the IARI developed 'Pusa' varieties were clustered in one quadrant. The varieties specifically developed for better oil quality (low erucic acid) were grouped together in other quadrant because one of the parents of these single zero varieties has exotic origin. The genetic information based on molecular data enables the accurate grouping of genotypes sharing common lineage or genotypes developed for specific objectives. Wang et al. (2009) also used PCoA to delineate and visualise 405 individuals and 48 varieties of B. napus into four cluster.

The low correlation between genetic distances calculated from the two approaches could be due to the fact that DNA markers reports genetic variation also in non coding regions which hardly have an effect on phenotype. On the other hand, quantitative traits are influenced by environmental factors and their phenotype is a product of genotype x environment interaction. Plants may be morphologically similar, but this does not necessarily imply genetic similarity, since different genetic bases can result in similar phenotypic expression (Khan, von Witzke-Ehbrecht, Maass & Becker, 2009). A large portion of variation detected by molecular markers is non-adaptive and is, therefore, not subject to either natural or artificial selection as compared with phenotypic characters, which in addition to selection pressure are influenced by the environment (Vieira et al., 2007). It can be concluded that SSR markers, which are free from environmental influences, are the stronger tools than quantitative trait data in discriminating B. juncea genotypes based on pedigree and origin. Information on genetic distances based on microsatellite markers shall be preferred in creating selectable genetic variation using genotypes which are genetically apart.

APPENDIX 1
List of B. juncea genotypes and their pedigree/description

Sr.   Designation        Pedigree/description
No.

1     Varuna             Selection from Varansi Local
2     Pusa Bold          Varuna/BIC 1780
3     Pusa Jagannath     Varuna/Synthetic juncea
4     Pusa Agrani        Early maturing Brassica juncea/Synthetic
                           amphidiploid
                           (Brassica campestris var. toria/
                           Brassica nigra)
5     Pusa Jaikisan      Somaclone of Varuna
6     Pusa Mahak         Pusa Bold/Glossy mutant
7     Pusa Vijay         Synthetic Brassica juncea/VSL 5
8     Pusa Tarak         Agra Local/Poorbi Raya
9     Pusa Bahar         Pusa Rai 28/Varuna//Pusa Rai 30/T 6342
10    Pusa mustard 27    Divya/Pusa Bold//PR666EPS///PR704EPS2
11    RH 30              Selection from P 26/3-1
12    Laxmi              PR 15/RH 30A
13    Vardan             Biparental mating involving Varuna,
                           Keshari, CSU 10 and IB 1775, IB 1786
                           and IB 1866
14    Swarn Jyoti        Selection from germplasm Line RC 1670
15    RH 819             Prakash/Bulk pollen
16    RL 1359            RLM 514/Varuna
17    PBR 91             RLM 511/PR 18//CM 1
18    PBR 97             DIR 202/PR 3/V 3///RLM 619/Varuna
19    NRCDR 02           MDOC 43/NBPGR 36
20    Kranti             Selection from germplasm collected from
                           Kanpur Dehat
21    RGN 48             RSM-204/B-75
22    Pusa Mustard 21    Pusa Bold/ZEM 2
23    Pusa Karishma      Pusa Barani/ZEM 1
24    BEC 144            Exotic collection from Poland
25    EC597318           Exotic collection from Australia
26    EC 597325          Exotic collection from Australia
27    Heera              A low glucosinolate genetic stock
                           IC-296501
28    GM 1               MR 71-3-2/TM 4
29    GM 2               Selection from material collected from
                           Vendancha, Gujarat
30    JM 1               Pusa Bold/L 6
31    JM 2               Mutant of RL 9
32    TM 4               Varuna/TM 1
33    TM 22              TM 1/TM 4
34    EC 399299          Exotic collection from China
35    RGN 73             RGN 8/Pusa Bold
36    CS 54              B 380/NDR 8603
37    RLM 619            Gamma rays induced mutant of RL 18
38    Rohini             Selection from natural population of
                           Varuna
39    Vasundhra          RH 839/RH 30
40    Aravali (RN 393)   Krishna/RS 50
41    BPR 541-4          MDOC 8/PCR 7
42    BPR 543-2          TM 2/PCR 9202
43    IC355399A          Bunchy mutant
44    NPJ 128            Appressed mutant

APPENDIX 2
List of SSR primers used in polymorphism study

Locus code   Forward primer

Na10-A08     CATGGTTAAAACAATGGCCC
Na10-A09     TCTTGAGCAAAGAAACTTGG
Na10-B01     CAAGTGTCTGCTAGGTGGGG
Na10-B04     GCGTCGAGAGAGATCGAGAG
Na10-B07     GCCTTAGATTAGATGGTCGCC
Na10-B08 *   AGAGAAAAACACTTCCCGCC
Na10-B10     GTCGGGTTTGAGTGAGTTGG
Na10-B11     TTTAACAACAACCGTCACGC
Na10-C01     TTTTGTCCCACTGGGTTTTC
Na10-C03     TTGGGTGTCTTTGTTACCCC
Na10-C06 *   TGGATGAAAGCATCAACGAG
Na10-C08     GTTTGGTTCAGAGGCAGAGG
Na10-D03     ATGATTTGCCTTGAAATGCC
Na10-D07     CTACTTTGATGGACACTTGCC
Na10-D08     TCCATTCATTAAAATCGGCG
Na10-D09     AAGAACGTCAAGATCCTCTGC
Na10-D11     GAGACATAGATGAGTGAATCTGGC
Na10-E02     TCGCGCATGTAATCAAAATC
Na10-E08     TCGGGGTTTGTTGTGAGG
Na10-F01     ACCCCTGTGCAGACTCTTAT
Na10-F06     CTCTTCGGTTCGATCCTCG
Na12-A01     GCATGCTCTTGATGAACGAA
Na12-B09     ACGGAAGATCAAACAGCTCC
Na12-E06A    TTGGGTTGACTACTCGGTCC
Na14-D07     GCATAACGTCAGCGTCAAAC
Ni1-A04      TCCTCCTACTTTGATACTTGC
Ni2-A07      GGAACCCAACAAGTGAGTCC
Ni2-A12 *    ACGATGGGTTCTTCTTGTCG
Ni2-B01      AAGGAGATTGTTTTTGGGGC
Ni2-C01      GAGTATGAGAGATGGGAATCCG
Ni2-C06      CACTGGGATACAAGCCCTTC
Ni2-C12      ACATTCTTGGATCTTGATTCG
Ni2-D07      ACCAAAGCTGATCTCCAACC
Ni2-E10      ACTGCTTCAGCACGACCC
Ni2-E12      TTATCTGCTTGTCTTGGGGC
Ni2-F02      TGCAACGAAAAAGGATCAGC
Ni2-F11 *    AAAGGGTTTCAATTTCACGC
Ni2-G08      TCGACCAACAGAGAATGAAGAG
Ni2-H06      CATCAGATCCGACGAAATCC
Ni3-G04B     ATACTCGGGATAGGTGTGCG
Ni3-H07      GCTGTGATTTTAGTGCACCG
Ni4-A02      AGGACCACTGGGATACAAGC
Ni4-A03      ACACAGAAACATCAAACATACC
Ni4-A04      ATGTGGTCTTTCCCAGTTGC
Ni4-C06      CAGAGGCGAAAACGAGAGAG
Ni4-E08      GATTTTGAGGAAGCGGAGG
Ra2-A01      TTCAAAGGATAAGGGCATCG
Ra2-A02      AACCTCCGACGTGTGTGTG
Ra2-A04      AAAAACTCCTCTTCAACG
Ra2-A05      GCTAGTTTACGCGGCGG
Ra2-A06      CATTATGTATGTATGTGTGTGTGTGTG
Ra2-A07      CCGACACCGCTAGTTAGTCC
Ra2-A09      AATATATCGTCGGAGTCGGG
Ra2-A10      CCAGTGTGTGTGTGTGTGTG
Ra2-A11      GACCTATTTTAATATGCTGTTTTACG
Ra2-B01      TGTTGTAGCCTAACCCGGAG
Ra2-B02      GATGGTTTTTCGTTTTCACG
Ra2-B05      AGGCGCGTTTATACATCGG
Ra2-B07      TTTAACTGCTGCAGGTCGC
Ra2-B08      CAATTCATTTGTGACCACACC
Ra2-B12      TCTGAGTGAGTGTGTGTGTGTG
Ra2-C01      ATAGTAAGCGTCGCTCGTGG
Ra2-C05      GTAACCCGACGGTTGTATGG
Ra2-C07      ATTTCCGAATCGGGAGTTTC
Ra2-C08      AGAGTGTGCGAGCTTAACGG
Ra2-C09      ATCCCCTTCATCATCCTCG
Ra2-C11      CGCCTATTTCACACACACAC
Ra2-C12      CTTGAGTGTGTGTGTATGTGTGC
Ra2-D01      ACGATGCGATCGATAAATCC
Ra2-D03      CATGACTGAATCTTTGTGTGTGTG
Ra2-D04      TGGATTCTCTTTACACACGCC
Ra2-D06      CGCGTGTGGGTGTGTG
Ra2-D07      GTTTTCGCGTCTTTGGACTC
Ra2-E02 *    TGGTTATCGTTGTATGGGTGG
Ra2-E03 *    AGGTAGGCCCATCTCTCTCC
Ra2-E04      ACACACAACAAACAGCTCGC
Ra2-E07      ATTGCTGAGATTGGCTCAGG
Ra2-E09      TTCATACCATCGAGTTTGCG
Ra2-E12      TGTCAGTGTGTCCACTTCGC
Ra2-F01      AAATTGTTGTGTTTAAAAATGTC
Ra2-F04      CCTACAAACACATAAATAAAGAGAGAG
Ra2-F09 *    AGCCGTTATTATCGTCGTGG
Ra2-F11      TGAAACTAGGGTTTCCAGCC
Ra2-G02      GGGTTATTTCACGCAACTCG
Ra2-G04      AAAACGACGTCATATTGGGC
Ra2-G05      GCCAACTTAATTGATGGGGTC
Ra2-G08      ATGTCCGGATAACCGAATCC
Ra2-G09      ACAGCAAGGATGTGTTGACG
Ra2-G10      GAGACTCTCTCTCTCTCTCTCT
Ra2-G11      TATGTGTGTGTGTGTGTGCG
Ra2-H01      ACGTCGTCACACCAAACG
Ra2-H02 *    AGTGCGCACGATGTGTAAAA
Ra2-H03      AATTAGTTGCGTGTCCTGGC
Ra2-H04      GAAGACAAGAGATCATGGGAGG
Ra2-H07      ATCATCAATCCTGACGAGGC
Ra2-H08      ACAATGCGCGTGTTTCTCC
Ra2-H09      TATGTGCGCCTGTTAGTGTG
Ra2-H10      GCGCGTGTAGGCTACGTC
Ra3-B10      CCTACCACGGTTCTGATTCTTC
Ra3-C01      ACTCGGTGGAACGTCTTTG
Ra3-C04 *    CTAACCTCAGACGGAGACGG
Ra3-C09      CCTACCTCCGATAGTCCAATG
BRMS-001     GATCTTCTCTCCAAAACTCTCT
BRMS-049     ACGAATTGAATTGGACAGAG
BRMS-003     GAACGCGCAACAACAAATAGTG
BRMS-011     CCGTAAGGAATATTGAGGCA
BRMS-014     TCGCCAATAGAACCCAAAACTT
BRMS-015     TCCCGTATCAATGGCGTAACAG
BRMS-016     GGAAAGGGAAGCTTCATATC
BRMS-017     TGAATTGAAAGGCATAAGCA
BRMS-024     GATCAAATAACGAACGGAGAGA
BRMS-034     TCGGATTTGCATGTTCCTGACT
BRMS-040     TGCCTCCTCTCATTTTTTCTCC
BRMS-048     AACTTTGCTTCCACTGATTTTT
BRMS-050     GATCAAGGCTACGGAGAGAGAG
BRMS-056     TATCGGTACTGATTCGCTCTTCAAC
BRMS-088     TATCGGTACTGATTCGCTCTTCAAC
BN12A        GCCGTTCTAGGGTTTGTGGGA
BN38A        TGGTAACTGGTAACCGACGAAAATC
BRMS-006     TGGTGGCTTGAGATTAGTTC
Na10-F07     ACAAACAAAGCCTCCCAACC
Na10-F08     AAACTTGCTTTCGAGGATGG
Na12-F09     TTGCACACATACCAGATGCC
Na10-F10     TATGTGTGTGTGTGTGTGCG
Na12-F11     CCTCACATCGTCTTCTTCATCC
Na12-F12     CGTTCTCACCTCCGATAAGC
Ni2-B06      CTAAGTCCCCATTTATGC
Ni2-B09      ACGGAAGATCATACAGCACT
Ni4-B06      GGTAAGAAAATGTCTGCGCC
Ra1-A04      GTTTTCCAAATTATCCCCCG
Ra1-F03      AACTCGCTTTTACCGTCGTC
Ra1-F09      AAAACGGATAAACGTCACCG
Ra1-G07      ATCGACATCGAACGAAAAGC
Ra1-H08      GTCGATGATCACGGAAGAGG
Ra1-H11      CGCTAATGTGTGGTGGATTG
Ra2-B09      ACATTATACGAATCCTTGTCCC
Ra2-C03      AGACCGGTGTCATCATTATTATC
Ra2-E01      TCTATATTAACGCGCGACGG
Ra2-E11      GGAGCCAGGAGAGAAGAAGG
Ra2-G03      ACTTGTAATGCACTCGCACG
Ra2-H06      GAATTCAGAGGTATCTACACGGC
Ra2-H11      ACATGGAGCTTCTCCTTTCG
Ra2-H12      ACAGACGCACACAGACGC

Locus code   Reverse primer

Na10-A08     CAAGAAACACCATCATTTCTCA
Na10-A09     CAAACTGAGCCATACACAAAGG
Na10-B01     TCGATCGAAGAAACCAGACC
Na10-B04     CTCACCGTCACTGCTTCATC
Na10-B07     ACTTCAGCTCCGATTTGCC
Na10-B08 *   GTGAGCTTTGCGAAACACG
Na10-B10     CATCGCAGATCCTTCTCTCC
Na10-B11     CTCCTCCTCCATCAATCTGC
Na10-C01     GGAAACTAGGGTTTTCCCTTC
Na10-C03     ACCGAGAAGACTGATACGGG
Na10-C06 *   ATCAATCAACACAAGCTGCG
Na10-C08     CTATCGCTGCAGAAGAAGGG
Na10-D03     GATGAAACAATAACCTGAGACACAC
Na10-D07     TCTGAAGTTGATTAGTCGGTCC
Na10-D08     TTCTGATCCCTTTCTCTCCC
Na10-D09     ACCACCACGGTAGTAGAGCG
Na10-D11     CATTAGTTGTGGACGGTCGG
Na10-E02     TGTGACGCATCCGATCATAC
Na10-E08     GAGGAGGATGCTAAGAGTGAGC
Na10-F01     GGAATCGCATAGGAGAGCAA
Na10-F06     TTTTTAACAGGAACGGTGGC
Na12-A01     GCTTCAACCTCTCAATCGCT
Na12-B09     TGAGCGACCCATTCTTTAGG
Na12-E06A    CCGTTGATTTGGCTAAGACC
Na14-D07     CTGCGGGACACATAACTTTG
Ni1-A04      ACGTCAAATACTTCACTGCC
Ni2-A07      AGAGCTTGAGACACATAACACC
Ni2-A12 *    CAAGAAACTTTCGAGGAACCC
Ni2-B01      AAGACTAATAAACACACGGCG
Ni2-C01      GACTGAGCAGCTTGGAGACC
Ni2-C06      ACAATTTGAAGTACAAAACTCTC
Ni2-C12      AAAGGTCAAGTCCTTCCTTCG
Ni2-D07      ACTCTTCGAATTCTTTTCC
Ni2-E10      CACATGTAAACTCTCCCACAGG
Ni2-E12      AAGGAAATCGTCTCACTTGG
Ni2-F02      TGCTAATTGAGCAATAGTGATTCC
Ni2-F11 *    GGGAAACATACTCACCACGC
Ni2-G08      TTTCCCCATGAACACATTTC
Ni2-H06      TCCTTTGGACTGTGAAAAACG
Ni3-G04B     CATGTGGCAATCCTACATTTAC
Ni3-H07      AGCCGTTGATGGAATTTTTG
Ni4-A02      ATTTGGAGCTGCGTACTTCG
Ni4-A03      GGACCGGTTTTATTTGTTCG
Ni4-A04      CATCCTCTGCTTTAGTGGGC
Ni4-C06      TTTATAGACTTCCCGTGGGC
Ni4-E08      CAAAGCACTGAGAGAGAGAGAGAG
Ra2-A01      TCTTCTTCTTTTGTTGTCTTCCG
Ra2-A02      TCATCACCACCATCACCATC
Ra2-A04      CCCAAAGTTAGGTTTTAATGTAATCTC
Ra2-A05      AAACGACATCGGCAAAGAAG
Ra2-A06      TCTTGGTTGACTTCATAAACGG
Ra2-A07      AGACAAATTTATTACTTACCTGC
Ra2-A09      CAAGATGACATCGGACCACC
Ra2-A10      TTTAACAGATAGCGCAGTGGTC
Ra2-A11      ACCTCACCGGAGAGAAATCC
Ra2-B01      TTATGACGTAATATTATATGTAACTTG
Ra2-B02      TCAGCTGTCACGTCTTGTCG
Ra2-B05      GAAGGCATTTCTTTTCCACG
Ra2-B07      GGGCAAATGTGATAAATCCG
Ra2-B08      GTCCACGTATTGTGCGTAGG
Ra2-B12      ATTACGTTCGGTCCAAGCAC
Ra2-C01      AACCCTTTATGGGAAAACGG
Ra2-C05      CGCAATATGAGTTCCCTTCC
Ra2-C07      ACTTGCAAACGCACACACAC
Ra2-C08      TCCACGCTATATTTCCGACC
Ra2-C09      TCTGGACTGATCAGAACTCGG
Ra2-C11      GTGTTACACGCTCACAACGC
Ra2-C12      GCACGCTACCCGTTACCAC
Ra2-D01      CACAACTATACACGTGCGCC
Ra2-D03      CACTGACACCAGCAACGG
Ra2-D04      CAAACCAAAATGTGTGAAGCC
Ra2-D06      TCGACACAGTTTCAGCCAAG
Ra2-D07      CTGCAGCGCTGTCTAT
Ra2-E02 *    ACGCACACTCTACACTCTACAC
Ra2-E03 *    CCAAAACTTGCTCAAAACCC
Ra2-E04      AACATCAAACCTCTCGACGG
Ra2-E07      CCTACACTTGCGATCTTCACC
Ra2-E09      TTCAGTAAGTAACCCTTAATTTACACG
Ra2-E12      AAGAGAAACCCAATAAAGTAGAACC
Ra2-F01      TGAATCGAAGGAAAAGGACG
Ra2-F04      AACAACATAAAAGATTCATTTCG
Ra2-F09 *    TCATTGCATCAGATTGTCGG
Ra2-F11      CTTCACCATGGTTTTGTCCC
Ra2-G02      ACACAGGCGGGTTACATAGC
Ra2-G04      CGCTTCTTCTTCTCAGTCTCG
Ra2-G05      CCTCAATGTTCTCTCTCTCTCTCTC
Ra2-G08      GAAGCTTTTCAATTTTTAAGTTCTCTC
Ra2-G09      GATGAGCCTCTGGTTCAAGC
Ra2-G10      AATACGTGTGTGCCACCAAA
Ra2-G11      TATCCGTGTCCGTGTATCCC
Ra2-H01      GTTTGCCGACGAAAGAGG
Ra2-H02 *    CACTAACGCATTATTTTATATGGGTG
Ra2-H03      TGTTATTAAATCCTTTGGACGC
Ra2-H04      TGAAATCGGTTTGATTCTTCG
Ra2-H07      CGCGCACACACACACAC
Ra2-H08      GTGATCACAAGACACCGCC
Ra2-H09      ACATCGTTTAGCATGCTCCC
Ra2-H10      CGGCCGCGGCAACTG
Ra3-B10      TGTCCAATTTTGGGCAAATC
Ra3-C01      GTTGTGTGGAATTGTGAGCG
Ra3-C04 *    CTTTAAACTCCGACCAACCG
Ra3-C09      TTTTATTGGAGGGAAAGGGG
BRMS-001     AAAGTCCAAGCTAAATTACAAA
BRMS-049     CAGATGGGAGTCAAGTCAAC
BRMS-003     CGCGTCACAATCGTAGAGAATC
BRMS-011     TTCCCAATTCTCAAACGGTA
BRMS-014     CATCTCCATTGCTGCATCTGCT
BRMS-015     CGATGCTGACATTATTGTGGCG
BRMS-016     CTGGAAAGCATACACTTTGG
BRMS-017     CAGCCTCCACCACTTATTCT
BRMS-024     GAGCCAAGAAAGGACCTAAGAT
BRMS-034     CCGATACACAACCAGCCAACTC
BRMS-040     TGACCGAGAGGTTCACAAGTAA
BRMS-048     TTGCTTAACGCTAAATCCATAT
BRMS-050     CGTGACGCTAGAGTAATCGAGT
BRMS-056     ATCGGTTGTTATTTGAGAGCAGATT
BRMS-088     ATCGGTTGTTATTTGAGAGCAGATT
BN12A        GAGGAAGTGAGAGCGGGAAATCA
BN38A        ACGCTGTCTTCAGGTCCCACTC
BRMS-006     ACTCGAAGCCTAATGAAAAG
Na10-F07     TCACACAACTTGTTCAATCTTGC
Na10-F08     AAACCAGTTGACTCCATCGG
Na12-F09     ACTCGAAGAGAAGATAAGGTC
Na10-F10     TCCGTTTGATTGGGTCTCTC
Na12-F11     TCACATCAGTCCATGGTTCC
Na12-F12     TCCGATGTAGAATCAGCAGC
Ni2-B06      ACAGATCTAATGCGCTTGGG
Ni2-B09      TGAGCGACCCATACTATAGA
Ni4-B06      TTGCTGCAACTTCTCATTCG
Ra1-A04      TTCCCATGGTTTCTAGAGGG
Ra1-F03      CAAGACGTGGAGCTGAAGTG
Ra1-F09      GAACAGTCTTACACCCGATTTAG
Ra1-G07      TCACCCTCTACCTCCACCAC
Ra1-H08      CTTGACAGCTACGGTTTGTCC
Ra1-H11      ACCGGAGCGGTTTACATAAC
Ra2-B09      GTCATGACATTTCGCGAGG
Ra2-C03      CCTCTCTGCAGAACTGCTCC
Ra2-E01      GCACACACACACTCAAACCC
Ra2-E11      CCCAAAACTTCCAAGAAAAGC
Ra2-G03      TGGAGATTATTCCGCTGTCC
Ra2-H06      TAACAAAGACCCTGCGTTCC
Ra2-H11      GCGTGCACACACACACAC
Ra2-H12      ACAAATACCGGAGCATCGTC

Locus code   Repeat               Source

Na10-A08     (CT)21               B. napus
Na10-A09     (GA)26               B. napus
Na10-B01     (GA)46               B. napus
Na10-B04     (GA)40               B. napus
Na10-B07     (CT)29               B. napus
Na10-B08 *   (CT)38               B. napus
Na10-B10     (GGCVAVT)18          B. napus
Na10-B11     (CT)28               B. napus
Na10-C01     (GA)19               B. napus
Na10-C03     (CT)52               B. napus
Na10-C06 *   (GA)58               B. napus
Na10-C08     (CT)18               B. napus
Na10-D03     (GT)11               B. napus
Na10-D07     (CA)11-(TA)5         B. napus
Na10-D08     GA                   B. napus
Na10-D09     (GT)11               B. napus
Na10-D11     (GA)27               B. napus
Na10-E02     (GA)24               B. napus
Na10-E08     (GA)42               B. napus
Na10-F01     (GT)15-(GA)70        B. napus
Na10-F06     (CCG)6               B. napus
Na12-A01     (CT)34               B. napus
Na12-B09     (GGC)6               B. napus
Na12-E06A    (CT)23               B. napus
Na14-D07     (CCG)3               B. napus
Ni1-A04      (CT)25               B. nigra
Ni2-A07      (T)10-(GA)32         B. nigra
Ni2-A12 *    (CT)20               B. nigra
Ni2-B01      GT                   B. nigra
Ni2-C01      (GA)9                B. nigra
Ni2-C06      (GA)60               B. nigra
Ni2-C12      (GA)43               B. nigra
Ni2-D07      (A)11                B. nigra
Ni2-E10      (CT)24               B. nigra
Ni2-E12      (CT)53               B. nigra
Ni2-F02      (CT)2                B. nigra
Ni2-F11 *    (GA)22               B. nigra
Ni2-G08      (GT)10-(GA)26        B. nigra
Ni2-H06      (CT)17               B. nigra
Ni3-G04B     (AG)18               B. nigra
Ni3-H07      (CA)11-(TA)5         B. nigra
Ni4-A02      (GA)31               B. nigra
Ni4-A03      (CT)50               B. nigra
Ni4-A04      (GA)30-(GAA)16       B. nigra
Ni4-C06      (GA)20               B. nigra
Ni4-E08      (CT)47               B. nigra
Ra2-A01      (GA)19               B. rapa
Ra2-A02      (GT)36               B. rapa
Ra2-A04      (GA)58               B. rapa
Ra2-A05      (GGC)6               B. rapa
Ra2-A06      (GT)47               B. rapa
Ra2-A07      (GT)9                B. rapa
Ra2-A09      (GGC)9               B. rapa
Ra2-A10      (GT)107              B. rapa

Ra2-A11      (CT)51               B. rapa
Ra2-B01      (CA)21               B. rapa
Ra2-B02      (CA)40               B. rapa
Ra2-B05      (GT)36               B. rapa
Ra2-B07      (CA)45               B. rapa
Ra2-B08      (GT)33               B. rapa
Ra2-B12      (GT)43               B. rapa
Ra2-C01      (GT)37               B. rapa
Ra2-C05      (CA)34               B. rapa
Ra2-C07      (GT)26               B. rapa
Ra2-C08      (CA)33               B. rapa
Ra2-C09      (GA)42               B. rapa
Ra2-C11      (CA)27               B. rapa
Ra2-C12      (GT)58               B. rapa
Ra2-D01      (CA)35               B. rapa
Ra2-D03      (GT)42               B. rapa
Ra2-D04      (CA)14               B. rapa
Ra2-D06      (GT)12               B. rapa
Ra2-D07      (GT)23               B. rapa
Ra2-E02 *    (GT)51               B. rapa
Ra2-E03 *    (CT)18               B. rapa
Ra2-E04      (GA)19               B. rapa
Ra2-E07      (GA)19               B. rapa
Ra2-E09      (GT)17               B. rapa
Ra2-E12      (GA)32               B. rapa
Ra2-F01      (CT)70               B. rapa
Ra2-F04      (GA)52               B. rapa
Ra2-F09 *    (GT)16               B. rapa
Ra2-F11      (CT)34               B. rapa
Ra2-G02      (CA)20               B. rapa
Ra2-G04      (GA)52               B. rapa
Ra2-G05      (GT)14-(GA)36        B. rapa
Ra2-G08      (GA)38               B. rapa
Ra2-G09      (CT)19               B. rapa
Ra2-G10      (CT)27               B. rapa
Ra2-G11      (GT)45               B. rapa
Ra2-H01      (CA)32               B. rapa
Ra2-H02 *    (GT)40               B. rapa
Ra2-H03      (GT)62               B. rapa
Ra2-H04      (CT)58               B. rapa
Ra2-H07      (GT)31               B. rapa
Ra2-H08      (GT)32               B. rapa
Ra2-H09      (GT)40               B. rapa
Ra2-H10      (GT)45               B. rapa
Ra3-B10      (CA)34               B. rapa
Ra3-C01      (AC)73               B. rapa
Ra3-C04 *    (CGG)6               B. rapa
Ra3-C09      (AC)18               B. rapa
BRMS-001     (GA)25               B. rapa
BRMS-049     (CT)26               B. rapa
BRMS-003     (CT)19               B. rapa
BRMS-011     (GA)18               B. rapa
BRMS-014     (TC)15               B. rapa
BRMS-015     (TG)4,(GA)20         B. rapa
BRMS-016     (TC)20               B. rapa
BRMS-017     (CA)33               B. rapa
BRMS-024     (GA)24               B. rapa
BRMS-034     (GA)18               B. rapa
BRMS-040     (GA)49(GT)4          B. rapa
BRMS-048     (TC)21               B. rapa
BRMS-050     (AAT)4(TC)19(TTC)3   B. rapa
BRMS-056     (GA)13               B. rapa
BRMS-088     unknown              B. rapa
BN12A        unknown              B. napus
BN38A        (TG)11               B. napus
BRMS-006     (GA)34               B. rapa
Na10-F07     (GA)18               B. napus
Na10-F08     (GCC)5               B. napus
Na12-F09     (GA)48               B. napus
Na10-F10     (GT)45               B. napus
Na12-F11     (CT)20               B. napus
Na12-F12     (CCG)7               B. napus
Ni2-B06      (GA)28               B. nigra
Ni2-B09      (GGC)6               B. nigra
Ni4-B06      (GT)9                B. nigra
Ra1-A04      (CT)41               B. rapa
Ra1-F03      (TCC\/GCC)18         B. rapa
Ra1-F09      (GT)31               B. rapa
Ra1-G07      (TG)17               B. rapa
Ra1-H08      (AGG\/CGG)8          B. rapa
Ra1-H11      (GT)61               B. rapa
Ra2-B09      (CCG)7               B. rapa
Ra2-C03      (TTA)6               B. rapa
Ra2-E01      (GT)32               B. rapa
Ra2-E11      (CT)24               B. rapa
Ra2-G03      (GT)44               B. rapa
Ra2-H06      (CA)35               B. rapa
Ra2-H11      (GT)34               B. rapa
Ra2-H12      (CG)8-(CA)47         B. rapa

* monomorphic primers


ACKNOWLEDGMENTS

Senior author is thankful to Indian Agricultural Research Institute, New Delhi for providing her financial assistance in the form of fellowship and for giving best of knowledge and resources for conducting this research required for partial fulfilment of M.Sc. in Genetics.

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Vinu V (1), Naveen Singh (1), Sujata Vasudev (1), Devendra Kumar Yadava (1), Sushil Kumar (2), Sugandh Naresh (1), Sripad Ramachandra Bhat (2) & Kumble Vinod Prabhu (1)

(1.) Division of Genetics, Indian Agricultural Research Institute, New Delhi-110 012; vinu.kathu@gmail.com, n.singhk@rediffmail.com, sujatavasudev@gmail.com, dkygenet@gmail.com, khusboo.mehak@gmail.com, kvinodprabhu@rediffmail.com

(2.) NRC on Plant Biotechnology, IARI Campus, New Delhi-110 012; sushil254386@yahoo.com, srbhat22@rediffmail.com

Received 12-XI-2012. Corrected 20-VI-2013. Accepted 22-VII-2013.

TABLE 1
Mean sum of squares, mean performance and range of 12 phenotypic
traits recorded on 44 Indian mustard genotypes

Source                     Replications   Genotypes    ESS    SEM

d.f.                            2            42        86
Plant height                 35.17 *      1056.05 *   78.38   6.26
Days to maturity               2.93       224.00 *    0.43    0.46
Point to first branch        654.21 *     1306.01 *   82.61   6.43
No. of primary branches/       1.21        5.44 *     0.81    0.63
  plant
Secondary branches/plant       2.7         64.96 *    10.01   2.24
Main shoot length            159.64 *     640.45 *    87.55   6.62
Point to first siliqua       35.32 *       27.96 *    11.16   2.36
No. of siliquae on main      37.01 *      211.49 *    38.6    4.39
  shoot
Siliqua length                 0.22        0.72 *     0.06    0.17
No. of seeds /siliqua          0.45        4.53 *     1.35    0.82
Seed yield /plant              1.38        20.08 *    5.52    1.66
1000 seed weight               0.02        2.57 *     0.05    0.16

Source                     CD 5%    Mean        Range

d.f.
Plant height               29.5    211.58   171.30-253.83
Days to maturity           2.18    141.12   112.00-161.00
Point to first branch      30.28   62.46    21.67-108.50
No. of primary branches/   2.99     5.78      3.67-9.67
  plant
Secondary branches/plant   10.54    12.9     5.33-35.33
Main shoot length          31.18   74.58    14.00-106.50
Point to first siliqua     11.13    7.39     2.33-21.00
No. of siliquae on main    20.7    48.29     31.67-75.67
  shoot
Siliqua length             0.81     3.51      1.78-4.45
No. of seeds /siliqua      3.88    13.31     10.13-15.70
Seed yield /plant          7.83    11.96     5.90-15.59
1000 seed weight           0.77     4.73      3.02-6.18

* significant at the 0.05 probability level.
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Author:V., Vinu; Singh, Naveen; Vasudev, Sujata; Kumar Yadava, Devendra; Kumar, Sushil; Naresh, Sugandh; Ra
Publication:Revista de Biologia Tropical
Date:Dec 1, 2013
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