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Full Research Article

Evaluation of Genetic Diversity in Linseed (Linum usitatissimum L.) Germplasm Through D2 Cluster Analysis and PCA

K
Korra Shankar1,2,*
0009-0002-3208-1936
N
Nalini Tiwari1
A
Achila Singh1
C
C.V. Sameer Kumar2
G
Guglothu Suresh2
1Oilseeds Research Farm, Kalyanpur, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur-208 002, Uttar Pradesh, India.
2Professor Jayashankar Telangana Agricultural University, Hyderabad-500 030, Telangana, India.

ABSTRACT

Background: Linseed is used as an oilseed and fiber crop and is extremely rich in high Omga-3 fatty acid content. The main aim of breeders in crop improvement programs is to develop high-yielding crop varieties. For this, we need to select diverse high-yielding genotypes to produce the heterotic cross combinations. This experiment aims to evaluate the genetic diversity among linseed germplasm for yield-related traits.

Methods: In this experiment, a total of 75 diverse linseed genotypes (Indigenous and three exotic) along with ten checks were investigated during Rabi-2019-20 in randomized block design using three replications at Oilseeds Research Farm, Kalyanpur, Kanpur (Uttar Pradesh). Observations were recorded on ten yield-related traits. Clustering of genotypes by using the D2 cluster of Tocher‘s method and Principal component analysis.

Result: Cluster analysis deciphered 75 genotypes into 16 distinct groups, with the highest number of individuals observed in cluster II. The greater average was recorded within clusters IV (14.34), II (13.20), XI (12.78) and I (6.31) order, whereas clusters between I and XI recorded the high between cluster distance. The results indicated that a total of 57.58% of variation was contributed by the first three principal components (PCA).

INTRODUCTION

A self-fertilizing winter season oilseed crop, linseed (Linum usitatissimum L.) is a member of the Linaceae family (Cloutier et al., 2012). It is believed that linseed originated in the Mediterranean region Darlington (1963) and Southwest Asia, specifically in India (Vavilov, 1935; Richharia, 1962). It has a somatic chromosome number of 2n=30, with other species exhibiting chromosomal variability (2n=16 to 60).  Although primarily self-pollinating, insect activity can result in up to 2% outcrossing (Dilman, 1928). The term “linseed” is used when the crop is cultivated for oilseed, whereas “fibre flax” or simply “flax” is commonly used in Europe when it is grown for fibre (Vaisey-Genser and Diane, 2003). Flaxseed is rich in fats, proteins and dietary fibre and is available in two primary varieties: brown and yellow or golden (also referred to as golden linseeds). Brown flaxseeds contain approximately 41% fat, 20% protein and a substantial 28% total dietary fibre. Also contains 7.7% moisture and 3.4% ash (left after combustion) (Gill, 1987). The oil content in linseed ranges from 33% to 45% (Arora et al., 2003) and it exhibits an inverse relationship with protein content. Flax fibres are widely used in the textile industry to make linen cloths, thread, rope and packaging materials, as well as in the production of currency notes (Mackiewicz-Talarczyk  et al., 2008). Flax fiber is highly valued for its exceptional durability and strength. It is soft, flexible and non-lignified with 80-90% cellulose. Linseed is well known for its high content of omega-3 fatty acids Smy´kal et al.  (2012) and it is responsible for its unique drying effect (Przybylski, 2005). These fatty acids contribute to cholesterol reduction and improve heart health (Westcott and Muir, 2003). Additionally, linseed oil is also used in the making of products like varnish, inks, paints and linoleum flooring due to its exceptional drying effect (Czemplik et al., 2011). Linseed is highly demanded for industrial applications due to its utilization in both edible and non-edible products. The lower productivity of linseed is due to poor fertility response and contains unsaturated fatty acid, linoleic acid causes the oxidation of oil thereby reducing the storage lifetime. To overcome these challenges, breeding efforts should focus on developing linseed lines with enhanced yield potential and reduced linoleic acid content, thereby improving productivity and oil quality. To accelerate crop improvement programs, selecting genetically diverse parents is essential for the creation of high-yielding varieties with improved climate resilience for diversified agroecological recovery. For this purpose, it is essential to quantify the diversity among parents (Govindaraj et al., 2015). The more diverse the parents, the greater the chances of achieving high heterotic lines for seed yield and its component characters.

MATERIALS AND METHODS

A total of 75 genotypes were used in this experiment to assess the genetic diversity (Table 1) including three exotic (Canada) and other diverse regions of Indian including checks (T-397, Shekhar, Parvathi, Rajan, Gaurav, Surya, Ruchi, Meera, Rashmi and Shikha) of linseed (Linum usitatissimum L.) during Rabi season 2019-20. The location of this experiment is at Oilseeds Research Farm, Kalyanpur with the collaboration of C.S. Azad University of Agriculture and Technology, Kanpur, Uttar Pradesh, (India). The experiment was carried out in a randomized block design with a spacing of 30x7 cm using three replications. The equally competitive plants within a row were chosen for taking observation on ten yield component traits like days for 50 percent blooming, primary branches, secondary branch number, height of plant (cm), capsule number, seeds per capsule, days needed for maturity, 1000 seed weight, oil content of each line (%) and yield of single plant. A total of 25 grams of seeds from each genotype was used for oil content estimation using the NMR technique. The multivariate methods based on Tocher’s cluster analysis, which Rao (1952) and Mahalanobis (1936) described and PCA were used to evaluate the phenotypic divergence among the accessions and calculate the genetic distance using D2 statistics.

Table 1: List of Linseed accession utilized in this experiment.

RESULTS AND DISCUSSION

Clustering of genotypes
 
Members within the same cluster are expected to be in a high close relationship in terms of the characteristics being studied than members from different clusters. D2 clustering pattern deciphered that a total of 75 genotypes were grouped into 16 distinct clusters, displayed in Table 2 and Fig 1. Cluster II exhibited the greatest number of genotypes, consisting of 38, while cluster IV recorded the second maximum number of genotypes with 17. Cluster XI composed of five genotypes follows Cluster I, which contains three genotypes. The lowest number of genotypes was found in clusters III, V, VI, VII, VIII, IX, X, XII, XIII, XIV, XV and XVI, which had a single genotype each. The exotic germplasms, Hermes, Redwood 65 and AR-2 were classified into clusters, II, XVI and X, respectively. Similar patterns of clustering were corroborated by Kant et al., (2011); Meena et al., (2021) and Kumar and Kumar (2021). The intra-cluster and inter-cluster distance estimations of D2 values were presented in Table 3. Cluster IV (14.34) has recorded the highest intra-cluster distance, followed by cluster II (13.20), cluster XI (12.78) and cluster I (6.31) in descending order. Only one genotype was present in clusters X, XI, VIII, VII, IV and III (0.00), which exhibited the lowest intra-cluster D value. The highest intra-cluster distance was recorded in descending order between cluster XI and cluster I (40.62), cluster V and XI (38.13), cluster V and cluster XIV (35.57), cluster I and cluster XIV (34.73) and cluster VI and cluster XV (33.98). Due to the greater inter-cluster distances among these clusters, crossing between these clusters can result in hybrids with better heterosis (Acquaah, 2012). Similar types of results in diversity studies in linseed have been previously confirmed by (Tewari et al., 2020; Nizar and Mulani, 2015; Pali and Mehta, 2015). The lowest inter-cluster distance was recorded among clusters VI and X (8.79), then between VII and XII (9.99), III and VII (10.88), III and VIII (11.72) and cluster III and cluster XIV (11.73).

​

Table 2: Composition of seventy-three linseed genotypes into different clusters.



Fig 1: Dendrograms showing grouping of 75 linseed genotypes generated using D2 cluster analysis.



​

Table 3: Average intra and inter cluster distance in linseed.


 
Cluster means (Average)
 
There was a considerable variation between 16 clusters concerning cluster average for distinct traits, as revealed by intra-cluster means ten characters shown in Table 4. Clusters XIV (89.00 days), cluster XIII (83.00 days), cluster X (82.00 days) and cluster VII (81.00 days) were recorded the highest average and clusters IX (64.00 days) and XV (65.00) had the lowest average for days for 50 percent blooming. The greater cluster mean for the primary branch was obtained for cluster X (7.67) followed by cluster IX (7.33), cluster VI (5.33) and cluster XIV (5.00). The lowest cluster mean was observed for cluster V (2.67). Clusters X (31), IX (30.33), XVI (29.00) and VI (20.67) showed the highest cluster average value in descending order for the secondary branch and cluster I (8.67) recorded the lowest average. For plant height, clusters I (104.00), XVI (102.67), V (79.00) and VI (75.33) were observed the highest cluster average and cluster XV (49.00) had the lowest average. The highest cluster average was observed in clusters VI (172.00), XI (165.80), X (156.33) and XVI (132.33) and the lowest cluster mean was in cluster XV (36.00) for capsules per plant. Cluster V (7.67) exhibited the lowest average and clusters VIII (9.33), III (9.00), XIV (8.59) and XI (8.93) for trait, seeds per capsule. When it comes to days taken for maturity, clusters XIV (146.33), XII (145.00), X (141.00) and I (140.67) demonstrated the highest cluster average and cluster VIII (126.00) indicated the lowest mean. Clusters IX (45.50), V (42.00), IV (39.24) and I (38.67) recorded the highest average oil content. In contrast, Cluster XIV (24.86) recorded the lowest mean. The maximum average of 1000-seed weight was observed in clusters VI (8.84), X (8.71), XII (8.52) and XIV (8.26). On the other hand, cluster XIV (4.18) had the lowest average weight. The character seed yield recorded the maximum average in cluster XIII (12.47) then in cluster X (10.12), cluster VI (10.09), cluster IX (9.37) and cluster XI (9.35). The clusters V and XIV (2.82) were noticed the lowest average. Ranjana et al., (2019) and Meena et al., (2021) have obtained similar results.

Table 4: Cluster means of different characters to genetic diversity in linseed.


 
The contribution of different traits towards the divergence
 
According to average D2, the highest contribution (presented in Table 5) was from the capsule number (35.75%) followed by plant height (19.96%), oil content (18.70%), seed yield (9.48%), test weight (8.72%). The other characteristics viz., days needed for 50 per cent blooming, maturity duration, primary branch, secondary branch and seeds per capsule recorded negligible contribution. These characteristics, like the capsules per plant, plant height, grain yield and oil percentage should be emphasized to choose the appropriate parents for crossing. Tewari et al., (2013) also followed the high contribution of capsule number and seed yield toward total genetic divergence.

​

Table 5: Per cent contribution of ten traits towards total genetic divergence.


 
Principal component analysis (PCA)
 
A multivariate approach (Crossa, 1990) like Principal component analysis (PCA) is used to determine how the different traits contribute to overall variability and to offer a basis for choosing characteristics. The primary goal of PCA is to compress the total variation from studied variables into a smaller set of factors (Sharma, 1998; Brejda et al., 2000). In this study, a total of ten principal components (PCs) were extracted, equivalent to the number of traits studied and it revealed the four most informative PCs with eigenvalues of more than one which accounted for 68.38 per cent cumulative variance (Table 6). However, more than 50 per cent of the variance in the population was explained by three major PCs (PC1 25.88%; PC2-18.58% and PC3-13.12%). Dabalo et al., (2020); Guei et al., (2005) confirmed these findings. As a result, parameters showed positive weight in the first three PCs considered to be more crucial (Patial et al., 2019). The secondary branch number of each plant (0.54), primary branch number (0.49), capsule number (0.47), seed yield (0.45), days needed for 50 percent blooming (0.13) and maturity duration (0.07) recorded positive weightage in PC1 while other traits were negative weight (Fig 2B). In PC2, the parameters viz., duration of 50 per cent blooming (0.32), seeds per fruit (0.27) and capsules (0.16) showed positive loading while other traits showed negative loading. In PC3, plant height (0.71), days needed for 50 per cent blooming (0.61) and maturity duration (0.31) showed highest positive loading and the remaining parameters showed negative loading. Generally, only a single component was chosen from these determined classes. The secondary branch of each plant was the better choice, as it exhibited the highest loading from PC1. Similarly, the time taken for 50 per cent blooming, plant length and seed number were best choices for the second, third and fourth principal components, respectively. PCA clearly demonstrated that the secondary branch of a plant, days to 50 per cent flowering, plant height and seeds per capsule were the most important traits showing a strong effect on total variation.

​

Table 6: Principal components extracted with Eigen values, percentage of variance explained and factor loading of different traits.


       
The scree plot illustrated the percentage difference between principal components and eigenvalues (Fig 2A). PC1 showed 25.88 percent variability with an Eigenvalue of 2.588 in this study. The first component (PC1) recorded the highest variance compared to other PCs (Fig 2A). Due to greater variance explained by the first component, genotypes selected from this group might be helpful in breeding programs for trait improvement. The selection of clusters from PC1 is highly beneficial in trait enhancement approaches, as it exhibits the greater variability (Fig 3). The biplot diagrams represent how the trait interacts and which genotypes perform better towards traits (Fig 2B).

Fig 2A: Scree plot showing the proportion of total variance explained by each principal component.



Fig 2B: PCA biplot illustrating the spatial distribution of genotypes across the first two principal components and the corresponding trait loadings.



Fig 3: 2-dimensional PCA biplot, represents the relationships among clusters of data, variables and the contribution of variables to the principal components.

CONCLUSION

The outcomes of this experiment demonstrated the substantial genetic diversity among all the accessions evaluated. The clustering analysis revealed 16 distinct groups, highlighting the potential for heterosis through strategic hybridization among more distantly related clusters. The capsule number appeared as the most critical trait influencing divergence, later on the height of plant and oil content. Principal component analysis indicated that the first three PC contributed a total of 57.58% of the variance. Overall, this experiment revealed that the selection of diverse genotypes may lead to improvement in linseed yield and quality by heterotic cross combinations.

ACKNOWLEDGEMENT

The authors immensely thank the Oilseeds Research Farm of CSAUAT, Kanpur, Uttar Pradesh from the Plant Breeding and Genetics department, for rendering necessary facilities and for their moral support during the time of investigation.
 
Informed consent
 
No animals are involved during research.

CONFLICT OF INTEREST

The authors confirm that no competing exist.

REFERENCES


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  2. Cloutier, S., Ragupathy, R., Miranda, E., Radovanovic, N., Reimer, E., Walichnowski, A., Ward, K., Rowland, G., Duguid, S. and Banik, M. (2012). Integrated consensus genetic and physical maps of flax (Linum usitatissimum L.). Theoretical Applied Genetics. 125: 1783-1795. 

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  4. Czemplik, M., Boba, A., Kostyn, K., Kulma, A., Mitula, A., Sztajnert, M., Wróbel Kwiatkowska, Zuk, M., Jan J., Szopa J., Telichowska, K.S. (2011). Flax Engineering for Biomedical Application. In: Biomedical Engineering, Trends, Research and Technologies, [(Eds.) Komorowska, M.A. and Olsztynska-Janus, S.]. InTech, Rijeka, Croatia. pp. 407-434.

  5. Dabalo, D.Y., Singh, B.C.S., Weyessa, B. (2020). Genetic variability and association of characters in linseed (Linum usitatissimum L.) plant grown in central Ethiopia region. Saudi Journal of Biological Sciences. 27(8): 2192-2206. 

  6. Darlington, C.D. (1963). Chromosome Botany and the Origins of Cultivated Plants. London, George Allen and Unwin Ltd.

  7. Dillman, A.C. (1928). Natural crossing in flax. Journal of the American Society of Agronomy. pp 279-286. 

  8. Acquaah, G. (2012). Principles of Plant Genetics and Breeding, John Wiley and Sons, Hoboken, NJ, USA. 

  9. Gill, K.S. (1987). Introduction of Linseed. ICAR Publication, Krishi Anusandhan Bhawan, Pusa, New Delhi. pp 1-11.

  10. Govindaraj, M., Vetriventhan, M., Srinivasan, M. (2015). Importance of genetic diversity assessment in crop plants and its recent advances: an overview of its analytical perspectives. Genetics Research International. pp 1-14.

  11. Guei, R.G., Sanni, K.A. and Fawole, A.F.J. (2005). Genetic diversity of rice (Oryza sativa L.). Agronomie Africaine. 5: 17-28. 

  12. Brejda, J.J., Moorman, T.B., Moorman, D.L. and Dao, T.H. (2000). Identification of regional soil quality factors and indicators I. Central and southern high plains. Soil Science Society of America Journal. 64(6): 2115-2124.

  13. Kant, R., Chauhan, M.P., Srivastava, R.K. and Yadav, R. (2011). Genetic divergence analysis in linseed (Linum usitatissimum L.). Indian Journal of Agricultural Research. 45: 59-64.

  14. Kumar, N. and Kumar, V. (2021). Assessment of genetic diversity in linseed germplasm using morphological traits. Electronic Journal of Plant Breeding. 12(1): 66-73.

  15. Mackiewicz-Talarczyk, M., Barriga-Bedoya, J., Mankowski, J. and Pniewska, I. (2008). Global flax market situation. ID 97 International Conferences on Flax and Other Bast Plants.  pp 408-412.

  16. Mahalanobis, P.C. (1936). On the Generalized Distance in Statistics. In: Proceedings of the National Academy of Science (India). 2: 49-55.

  17. Meena, A.K., Kumar, M. (2021). Assessment of genetic diversity in linseed (Linum usitatissimum L.) genotypes. Electronic Journal of Plant Breeding. 12(2): 597-601.

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  19. Pali, V. and Mehta, N. (2015). Character association analysis for seeds yield and its components in linseed (Linum usitatissimum L.). Trends in Biosciences. 8(17):  4573- 4576.

  20. Patial, R., Paul, S., Sharma, D., Sood, V.K., Kumar, N. (2019). Morphological characterization and genetic diversity of linseed (Linum usitatissimum L.). Journal of Oilseeds and Research. 36: 8-16. 

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    Full Research Article

    Evaluation of Genetic Diversity in Linseed (Linum usitatissimum L.) Germplasm Through D2 Cluster Analysis and PCA

    K
    Korra Shankar1,2,*
    0009-0002-3208-1936
    N
    Nalini Tiwari1
    A
    Achila Singh1
    C
    C.V. Sameer Kumar2
    G
    Guglothu Suresh2
    1Oilseeds Research Farm, Kalyanpur, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur-208 002, Uttar Pradesh, India.
    2Professor Jayashankar Telangana Agricultural University, Hyderabad-500 030, Telangana, India.

    ABSTRACT

    Background: Linseed is used as an oilseed and fiber crop and is extremely rich in high Omga-3 fatty acid content. The main aim of breeders in crop improvement programs is to develop high-yielding crop varieties. For this, we need to select diverse high-yielding genotypes to produce the heterotic cross combinations. This experiment aims to evaluate the genetic diversity among linseed germplasm for yield-related traits.

    Methods: In this experiment, a total of 75 diverse linseed genotypes (Indigenous and three exotic) along with ten checks were investigated during Rabi-2019-20 in randomized block design using three replications at Oilseeds Research Farm, Kalyanpur, Kanpur (Uttar Pradesh). Observations were recorded on ten yield-related traits. Clustering of genotypes by using the D2 cluster of Tocher‘s method and Principal component analysis.

    Result: Cluster analysis deciphered 75 genotypes into 16 distinct groups, with the highest number of individuals observed in cluster II. The greater average was recorded within clusters IV (14.34), II (13.20), XI (12.78) and I (6.31) order, whereas clusters between I and XI recorded the high between cluster distance. The results indicated that a total of 57.58% of variation was contributed by the first three principal components (PCA).

    INTRODUCTION

    A self-fertilizing winter season oilseed crop, linseed (Linum usitatissimum L.) is a member of the Linaceae family (Cloutier et al., 2012). It is believed that linseed originated in the Mediterranean region Darlington (1963) and Southwest Asia, specifically in India (Vavilov, 1935; Richharia, 1962). It has a somatic chromosome number of 2n=30, with other species exhibiting chromosomal variability (2n=16 to 60).  Although primarily self-pollinating, insect activity can result in up to 2% outcrossing (Dilman, 1928). The term “linseed” is used when the crop is cultivated for oilseed, whereas “fibre flax” or simply “flax” is commonly used in Europe when it is grown for fibre (Vaisey-Genser and Diane, 2003). Flaxseed is rich in fats, proteins and dietary fibre and is available in two primary varieties: brown and yellow or golden (also referred to as golden linseeds). Brown flaxseeds contain approximately 41% fat, 20% protein and a substantial 28% total dietary fibre. Also contains 7.7% moisture and 3.4% ash (left after combustion) (Gill, 1987). The oil content in linseed ranges from 33% to 45% (Arora et al., 2003) and it exhibits an inverse relationship with protein content. Flax fibres are widely used in the textile industry to make linen cloths, thread, rope and packaging materials, as well as in the production of currency notes (Mackiewicz-Talarczyk  et al., 2008). Flax fiber is highly valued for its exceptional durability and strength. It is soft, flexible and non-lignified with 80-90% cellulose. Linseed is well known for its high content of omega-3 fatty acids Smy´kal et al.  (2012) and it is responsible for its unique drying effect (Przybylski, 2005). These fatty acids contribute to cholesterol reduction and improve heart health (Westcott and Muir, 2003). Additionally, linseed oil is also used in the making of products like varnish, inks, paints and linoleum flooring due to its exceptional drying effect (Czemplik et al., 2011). Linseed is highly demanded for industrial applications due to its utilization in both edible and non-edible products. The lower productivity of linseed is due to poor fertility response and contains unsaturated fatty acid, linoleic acid causes the oxidation of oil thereby reducing the storage lifetime. To overcome these challenges, breeding efforts should focus on developing linseed lines with enhanced yield potential and reduced linoleic acid content, thereby improving productivity and oil quality. To accelerate crop improvement programs, selecting genetically diverse parents is essential for the creation of high-yielding varieties with improved climate resilience for diversified agroecological recovery. For this purpose, it is essential to quantify the diversity among parents (Govindaraj et al., 2015). The more diverse the parents, the greater the chances of achieving high heterotic lines for seed yield and its component characters.

    MATERIALS AND METHODS

    A total of 75 genotypes were used in this experiment to assess the genetic diversity (Table 1) including three exotic (Canada) and other diverse regions of Indian including checks (T-397, Shekhar, Parvathi, Rajan, Gaurav, Surya, Ruchi, Meera, Rashmi and Shikha) of linseed (Linum usitatissimum L.) during Rabi season 2019-20. The location of this experiment is at Oilseeds Research Farm, Kalyanpur with the collaboration of C.S. Azad University of Agriculture and Technology, Kanpur, Uttar Pradesh, (India). The experiment was carried out in a randomized block design with a spacing of 30x7 cm using three replications. The equally competitive plants within a row were chosen for taking observation on ten yield component traits like days for 50 percent blooming, primary branches, secondary branch number, height of plant (cm), capsule number, seeds per capsule, days needed for maturity, 1000 seed weight, oil content of each line (%) and yield of single plant. A total of 25 grams of seeds from each genotype was used for oil content estimation using the NMR technique. The multivariate methods based on Tocher’s cluster analysis, which Rao (1952) and Mahalanobis (1936) described and PCA were used to evaluate the phenotypic divergence among the accessions and calculate the genetic distance using D2 statistics.

    Table 1: List of Linseed accession utilized in this experiment.

    RESULTS AND DISCUSSION

    Clustering of genotypes
     
    Members within the same cluster are expected to be in a high close relationship in terms of the characteristics being studied than members from different clusters. D2 clustering pattern deciphered that a total of 75 genotypes were grouped into 16 distinct clusters, displayed in Table 2 and Fig 1. Cluster II exhibited the greatest number of genotypes, consisting of 38, while cluster IV recorded the second maximum number of genotypes with 17. Cluster XI composed of five genotypes follows Cluster I, which contains three genotypes. The lowest number of genotypes was found in clusters III, V, VI, VII, VIII, IX, X, XII, XIII, XIV, XV and XVI, which had a single genotype each. The exotic germplasms, Hermes, Redwood 65 and AR-2 were classified into clusters, II, XVI and X, respectively. Similar patterns of clustering were corroborated by Kant et al., (2011); Meena et al., (2021) and Kumar and Kumar (2021). The intra-cluster and inter-cluster distance estimations of D2 values were presented in Table 3. Cluster IV (14.34) has recorded the highest intra-cluster distance, followed by cluster II (13.20), cluster XI (12.78) and cluster I (6.31) in descending order. Only one genotype was present in clusters X, XI, VIII, VII, IV and III (0.00), which exhibited the lowest intra-cluster D value. The highest intra-cluster distance was recorded in descending order between cluster XI and cluster I (40.62), cluster V and XI (38.13), cluster V and cluster XIV (35.57), cluster I and cluster XIV (34.73) and cluster VI and cluster XV (33.98). Due to the greater inter-cluster distances among these clusters, crossing between these clusters can result in hybrids with better heterosis (Acquaah, 2012). Similar types of results in diversity studies in linseed have been previously confirmed by (Tewari et al., 2020; Nizar and Mulani, 2015; Pali and Mehta, 2015). The lowest inter-cluster distance was recorded among clusters VI and X (8.79), then between VII and XII (9.99), III and VII (10.88), III and VIII (11.72) and cluster III and cluster XIV (11.73).

    ​

    Table 2: Composition of seventy-three linseed genotypes into different clusters.



    Fig 1: Dendrograms showing grouping of 75 linseed genotypes generated using D2 cluster analysis.



    ​

    Table 3: Average intra and inter cluster distance in linseed.


     
    Cluster means (Average)
     
    There was a considerable variation between 16 clusters concerning cluster average for distinct traits, as revealed by intra-cluster means ten characters shown in Table 4. Clusters XIV (89.00 days), cluster XIII (83.00 days), cluster X (82.00 days) and cluster VII (81.00 days) were recorded the highest average and clusters IX (64.00 days) and XV (65.00) had the lowest average for days for 50 percent blooming. The greater cluster mean for the primary branch was obtained for cluster X (7.67) followed by cluster IX (7.33), cluster VI (5.33) and cluster XIV (5.00). The lowest cluster mean was observed for cluster V (2.67). Clusters X (31), IX (30.33), XVI (29.00) and VI (20.67) showed the highest cluster average value in descending order for the secondary branch and cluster I (8.67) recorded the lowest average. For plant height, clusters I (104.00), XVI (102.67), V (79.00) and VI (75.33) were observed the highest cluster average and cluster XV (49.00) had the lowest average. The highest cluster average was observed in clusters VI (172.00), XI (165.80), X (156.33) and XVI (132.33) and the lowest cluster mean was in cluster XV (36.00) for capsules per plant. Cluster V (7.67) exhibited the lowest average and clusters VIII (9.33), III (9.00), XIV (8.59) and XI (8.93) for trait, seeds per capsule. When it comes to days taken for maturity, clusters XIV (146.33), XII (145.00), X (141.00) and I (140.67) demonstrated the highest cluster average and cluster VIII (126.00) indicated the lowest mean. Clusters IX (45.50), V (42.00), IV (39.24) and I (38.67) recorded the highest average oil content. In contrast, Cluster XIV (24.86) recorded the lowest mean. The maximum average of 1000-seed weight was observed in clusters VI (8.84), X (8.71), XII (8.52) and XIV (8.26). On the other hand, cluster XIV (4.18) had the lowest average weight. The character seed yield recorded the maximum average in cluster XIII (12.47) then in cluster X (10.12), cluster VI (10.09), cluster IX (9.37) and cluster XI (9.35). The clusters V and XIV (2.82) were noticed the lowest average. Ranjana et al., (2019) and Meena et al., (2021) have obtained similar results.

    Table 4: Cluster means of different characters to genetic diversity in linseed.


     
    The contribution of different traits towards the divergence
     
    According to average D2, the highest contribution (presented in Table 5) was from the capsule number (35.75%) followed by plant height (19.96%), oil content (18.70%), seed yield (9.48%), test weight (8.72%). The other characteristics viz., days needed for 50 per cent blooming, maturity duration, primary branch, secondary branch and seeds per capsule recorded negligible contribution. These characteristics, like the capsules per plant, plant height, grain yield and oil percentage should be emphasized to choose the appropriate parents for crossing. Tewari et al., (2013) also followed the high contribution of capsule number and seed yield toward total genetic divergence.

    ​

    Table 5: Per cent contribution of ten traits towards total genetic divergence.


     
    Principal component analysis (PCA)
     
    A multivariate approach (Crossa, 1990) like Principal component analysis (PCA) is used to determine how the different traits contribute to overall variability and to offer a basis for choosing characteristics. The primary goal of PCA is to compress the total variation from studied variables into a smaller set of factors (Sharma, 1998; Brejda et al., 2000). In this study, a total of ten principal components (PCs) were extracted, equivalent to the number of traits studied and it revealed the four most informative PCs with eigenvalues of more than one which accounted for 68.38 per cent cumulative variance (Table 6). However, more than 50 per cent of the variance in the population was explained by three major PCs (PC1 25.88%; PC2-18.58% and PC3-13.12%). Dabalo et al., (2020); Guei et al., (2005) confirmed these findings. As a result, parameters showed positive weight in the first three PCs considered to be more crucial (Patial et al., 2019). The secondary branch number of each plant (0.54), primary branch number (0.49), capsule number (0.47), seed yield (0.45), days needed for 50 percent blooming (0.13) and maturity duration (0.07) recorded positive weightage in PC1 while other traits were negative weight (Fig 2B). In PC2, the parameters viz., duration of 50 per cent blooming (0.32), seeds per fruit (0.27) and capsules (0.16) showed positive loading while other traits showed negative loading. In PC3, plant height (0.71), days needed for 50 per cent blooming (0.61) and maturity duration (0.31) showed highest positive loading and the remaining parameters showed negative loading. Generally, only a single component was chosen from these determined classes. The secondary branch of each plant was the better choice, as it exhibited the highest loading from PC1. Similarly, the time taken for 50 per cent blooming, plant length and seed number were best choices for the second, third and fourth principal components, respectively. PCA clearly demonstrated that the secondary branch of a plant, days to 50 per cent flowering, plant height and seeds per capsule were the most important traits showing a strong effect on total variation.

    ​

    Table 6: Principal components extracted with Eigen values, percentage of variance explained and factor loading of different traits.


           
    The scree plot illustrated the percentage difference between principal components and eigenvalues (Fig 2A). PC1 showed 25.88 percent variability with an Eigenvalue of 2.588 in this study. The first component (PC1) recorded the highest variance compared to other PCs (Fig 2A). Due to greater variance explained by the first component, genotypes selected from this group might be helpful in breeding programs for trait improvement. The selection of clusters from PC1 is highly beneficial in trait enhancement approaches, as it exhibits the greater variability (Fig 3). The biplot diagrams represent how the trait interacts and which genotypes perform better towards traits (Fig 2B).

    Fig 2A: Scree plot showing the proportion of total variance explained by each principal component.



    Fig 2B: PCA biplot illustrating the spatial distribution of genotypes across the first two principal components and the corresponding trait loadings.



    Fig 3: 2-dimensional PCA biplot, represents the relationships among clusters of data, variables and the contribution of variables to the principal components.

    CONCLUSION

    The outcomes of this experiment demonstrated the substantial genetic diversity among all the accessions evaluated. The clustering analysis revealed 16 distinct groups, highlighting the potential for heterosis through strategic hybridization among more distantly related clusters. The capsule number appeared as the most critical trait influencing divergence, later on the height of plant and oil content. Principal component analysis indicated that the first three PC contributed a total of 57.58% of the variance. Overall, this experiment revealed that the selection of diverse genotypes may lead to improvement in linseed yield and quality by heterotic cross combinations.

    ACKNOWLEDGEMENT

    The authors immensely thank the Oilseeds Research Farm of CSAUAT, Kanpur, Uttar Pradesh from the Plant Breeding and Genetics department, for rendering necessary facilities and for their moral support during the time of investigation.
     
    Informed consent
     
    No animals are involved during research.

    CONFLICT OF INTEREST

    The authors confirm that no competing exist.

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