Inter-cluster Distance Research Articles

Diversity analysis for cooking quality traits in North-East Indian rice landraces

In North Eastern India, rice is the most common food consumed. Many different rice cultivars have been developed over the past 60 years. The dynamic variations in grain quality improvement among the main cultivars have seldom been the subject of investigation. The study assessed 20 rice landraces for eight variables related to cooking quality to identify various genotypes for potential breeding programme and to determine the minimum number of components that can best explain the total diversity. Out of the eight PCs, three PCs exhibited more than 1.0 eigen values and exhibited 78.71 % total variability among the characters. KBAC, CLBR, KLBR and KB were associated with PC I, which explained 37.11% of the overall variance. From the cluster analysis, the 20 rice landraces were divided into seven different groups. Between cluster VII and III and between cluster VII and V there was maximum inter- cluster divergence, indicating that the genotypes found in these clusters were genetically more distinct from one another than genotypes found in any other cluster. Landraces including Dhansiri, Phourin Nakuppi, Moniram, Ranjit, Mahsuri, Chakhao Sempak and Disang were found to be genetically divergent and would produce prospective segregating populations that may be employed as source materials for improving quality attributes through appropriate selection, according to the inter-cluster distances.

Genetic Diversity Analysis for Yield and Yield Attributing Traits in Finger Millet (Eleusine coracana L.)

The present investigation was carried out to assess the genetic variability, heritability, genetic advance, genetic diversity in 37 finger millet genotypes for 15 quantitative traits during Kharif, 2022 at Field Experimentation Centre, Department of Genetics and Plant Breeding, Naini Agricultural Institute, Sam Higginbottom University of Agricxulture Technology and Sciences, Uttar Pradesh in Randomized Block Design with 3 replications. The analysis of variance for mean sum of squares due to genotypes showed significant differences for all the 15 quantitative characters. The genotype, IE-184 followed by IE-195, IE-185, IE-191, IE-199, IE-180 exhibited highest grain Yield per Plant, while IE-111, IE-150, IE-169, IE-170, IE-175, IE-168 genotypes are early in maturity. Whereas, genotypic and phenotypic coefficient of variation were found high for flag leaf length, number of productive tillers, number of fingers per ear, finger length. High heritability and high Genetic advance were recorded in plant height, flag leaf length, ear width, number of fingers per ear, finger width. Clustering by Tocher’s Method has grouped all 37 genotypes into 6 clusters, in which Cluster-I contains more (16) genotypes and Cluster-IV, Cluster-V, Cluster-VI contains less (1) genotypes. According to Mahalanobis Euclidean Distance (not to scale), the maximum Inter-Cluster distance is recorded between Cluster-I and Cluster-VI (10.52) followed by Cluster-II and Cluster-V (10.44), Cluster-II & Cluster-III (9.35), Cluster-II & Cluster-IV (9.21). Plant height followed by grain yield per plant and days to 50% flowering traits contributes the highest percentage towards the overall divergence among the genotypes. Hence the selection of genotypes based on the above-mentioned characters and Inter Cluster Distance will be useful for crop improvement in Finger Millet.

Identification of B and R lines on maldandi cytoplasm and assessing their genetic diversity in rabi sorghum [Sorghum bicolor (L.) Moench

Fifty-four superior minicore collections were crossed with maldandi based male sterile line (M31-2A) to study restoration patterns andto classify maintainer (B) and restorer (R) lines. Of 54 mini core collections, only one came out as a strong restorer (IS 19450), 12 partialrestorers and 41 maintainers. Further, these 13 partial restorers (12 partial restorers and 1 strong restorer) and 41 maintainers alongwith M35-1 were subjected to diversity analysis for eleven characters. A total of 55 genotypes were clustered into seven clusters. Outof which, cluster–I and cluster-II had 34 and 16 genotypes, respectively, and five were solitary clusters having single genotypes. Thehighest inter-cluster distance was noted between cluster-VI and VII followed by cluster V and VI, and cluster-II and VII, indicating ample diversity available among them. Therefore, the genotypes of these clusters can be used as parents for crossing in the hybridization program to obtain desirable and excellent segregants. However, divergent maintainers against the strong restorer (IS 19450) can be used for the development of maldandi based hybrids.

Elucidation of genetic diversity through Multivariate analysis in brinjal (Solanum melongena L.) landraces for varietal improvement

Genetic diversity plays a crucial role in harnessing the potential of crop genotypes for genetic improvement. It helps to identify, preserve, and utilize diverse landraces effectively. This research aimed to assess the level of genetic diversity and traits enhancing genetic variation among 46 brinjal genotypes (Solanum melongena L.) in preparation for a special breeding programme to utilize the potential landraces. Genetic divergence among 46 genotypes of brinjal for eleven characters was assessed using the D2 method. The studied genotypes were categorized into eight clusters. Cluster I with 15 genotypes was the largest followed by Cluster IV (14 genotypes), Cluster V (7 genotypes), Cluster VII (4 genotypes), Cluster III (3 genotypes), Clusters II, VI, and VIII with one genotype each. The intra-cluster D2 values ranged from 0.00 (Cluster II, VI and VII) to 103.53 (Cluster III). The inter-cluster distances varied from 117.00 to 791.17. Cluster II and Cluster VII had the greatest inter-cluster distance (791.17), followed by Cluster II and VIII (673.41), and Cluster VII and Cluster V had the smallest inter-cluster distance (117.00). Crossing genotypes in clusters with wide inter-cluster distances improves recombinant isolation in segregating generations. The three traits- number of flowers per inflorescence (23.8%), average fruit weight (17.68%) and days to first flowering (16.81%) altogether contribute to 57.8% of the total divergence. The combination of these traits could be useful to produce high-yielding climate resilient varieties from valuable landraces.

Multivariate analysis for assessing genetic diversity in different genotypes of okra (Abelmoschus esculentus L. Moench) for varietal improvement

Accurate assessment of genetic diversity facilitates the strategic identification of superior genotypes, enabling the development of high-yielding, climate-resilient cultivars and promoting effective crop improvement strategies. The present study was carried out with 48 different genotypes of Okra (Abelmoschus esculentus (L.) Moench) during (Jan-April 2022) to investigate their genetic diversity. The analysis of genetic divergence using D2 statistics revealed substantial variation among genotypes for the twelve traits studied. The 48 genotypes were grouped into nine clusters, with cluster IV having the highest representation of 24 genotypes, followed by cluster II with 12 genotypes, cluster I with 4 genotypes, cluster III with 3 genotypes, and the remaining clusters with one genotype each. The intra and inter-cluster D2 values ranged from 0 to 95.29 and 103.00 to 588.71, respectively. The highest intra-cluster distance was observed in Cluster III (95.29), and highest inter-cluster distance was observed between cluster V and IX (588.71). This range clearly demonstrated that the inter-cluster distance was greater than the intra-cluster distance indicating wide diversity across the groups. Cluster VII showed a high mean for traits plant height (112.53), peduncle length (3.14), fruit length (20.03), and number of locules (7.87). Cluster V showed the highest mean for the number of fruits (39.33) and yield per plant (1.18). The analysis of all characters' relative contributions showed that the number of fruits per plant and the number of locules per fruit contributed most to genetic divergence. High heterotic effects and desired transgressive segregants are anticipated when different genotypes from clusters with the greatest inter-cluster distance are used for hybridization.

Study of Genetic Diversity in Indian Barnyard Millet Genetic Resources [Echinochloa frumentacea (L.)

Sixtyfour Indian barnyard millet genetic resources were studied to assess the extent of genetic divergence for 18 quantitative traits using Mahalanobis D2 Statistic. Sixtyfour genetic resources of Indian barnyard millet were evaluated during kharif, 2020 in a lattice square design, replicated twice at Regional Agricultural Research. Based on Tocher’s method, the entire genetic materials were separated into eight distinct and non-overlapping clusters suggesting availability of genetic diversity. Cluster I has highest number of accessions (57) and the remaining were mono genotypic clusters II, III, IV, V, VI, VII and VIII containing only one accession each indicating high degree of heterogeneity among the genotypes. The widest inter-cluster distance was recorded between clusters IV and VII. Therefore, it is concluded that the genotypes to these clusters has to be intercrossed in order to create more diversity and improving grain yield in barnyard millet. The traits, grain yield per plant and panicle weight contributed maximum towards total divergence has scope of crop improvement through those traits. Diversity of parent paly main role for the successful of any breeding programme. So the crosses can be made among the parents having widest divergence would more likely to yield desirable recombination in segregating generations.

DCOM-GNN: A Deep Clustering Optimization Method for Graph Neural Networks

Deep clustering plays an important role in data analysis, and with the prevalence of graph data nowadays, various deep clustering models on graph are constantly proposed. However, due to the lack of more adequate clustering guidance, the discriminability of feature representation learned from these models for the clustering task is limited. Therefore, for the purpose of enabling the output of these models to be more cluster-oriented, we propose a Deep Clustering Optimization Method for Graph Neural Networks (DCOM-GNN), which can be attached to the original model architecture conveniently. For DCOM-GNN, it contains two components, one is the inter-cluster distance optimization module, whose role is to further adjust the distance between clusters of the original model output rationally. Another one is the intra-cluster distance optimization module, which aims to improve the cohesiveness of the original model output. Comprehensive experiments show that the performance of various deep clustering models on graph can be significantly improved after adding DCOM-GNN.

Genetic divergence analysis for qualitative and quantitative traits in groundnut (Arachis hypogaea L.)

In the present investigation, 60 genotypes of groundnut were grouped into 12 different clusters. D2analysis indicated existence of wider genetic variability in the population of sixty genotypes which were grouped in twelve clusters, based on their inter clusters distance. The maximum inter-cluster distance was found between cluster III and X carrying one and two genotypes from each cluster respectively followed by that between V and X and cluster III and XII. The minimum inter-cluster distance was observed between cluster VII and XI. The minimum intra-cluster distance (D) was found in cluster II which includes eight genotypes. Cluster III showed high genetic divergence with cluster X followed by cluster V. Therefore it is advisable to make crossing between the genotypes from cluster I with cluster X, cluster III, cluster V and cluster VIII for the generation of wide spectrum genetic variability and isolation of transgressive segregants for enhancement of pod yield in groundnut.

Multivariate analysis for yield and yield-related traits of oat (Avena sativa L.) genotypes in Ethiopia

The study aimed to generate information on agro-morphological variability among the 120 oat genotypes in the central highlands of Ethiopia. The result showed that the hierarchical clustering grouped the genotypes into four clusters. The highest number of genotypes grouped into the first cluster (38) followed by the second cluster (35) and the fourth cluster (33) while the lowest genotypes (14) were recorded in the third cluster. High forage yield-producing genotypes were grouped into the second cluster while high grain-producing genotypes were grouped into the third cluster. The intra-cluster genetic distance values ranged from 4.3 (C3) to 2.3 (C1). The minimum inter-cluster genetic distance was recorded at 11.7 (between C1 and C2), while the maximum inter-cluster distance was 63.2 (between C3 and C4). Principal component analysis showed that the first four principal components with eigenvalues greater than one contributed 78.3% of the variability among genotypes. The highest contributing traits in the first component were forage yield production rate, biological yield production rate, panicle length at grain harvest, forage dry matter yield, and number of spikelets per panicle. The traits with the greatest weight on the second component were harvest index, grain yield, number of leaves at forage harvest, and plant height at grain harvest. Proper selection of best yield performing genotypes coupled with high yield contributing traits is very important to yield improvement and future oat breeding program. Therefore, selection based on the traits that had a high contribution to the first two principal components would be effective for yield improvement in oat genotypes.

Exploring Genetic Distinctiveness among Radish (Raphanus sativus L.) Strains

The field experiment was carried out using the twenty genotypes of radish and evaluated during the Rabi season, 2021–2022, at the experimental unit of the Department of Horticulture, Tilak Dhari PG College, Jaunpur (U.P.). The objective was to estimate the genetic diversity available in the genotypes. The D2 analysis showed broad diversity among the genotypes, which was grouped into five clusters on the basis of various horticultural traits. The clustering pattern showed that the highest genotypes were recorded in cluster III (8 genotypes), followed by cluster V (5 genotypes), cluster IV (3 genotypes), cluster II (2 genotypes), and cluster I (2 genotypes). The greatest intra-cluster distance was recorded in Cluster IV (3.380), followed by Cluster III (3.219), Cluster II (3.104), and Cluster V (3.079). Cluster I have the smallest intra-cluster distance (2.182). The highest inter-cluster distance was 6.851 observed between Cluster IV and II, while the lowest was 3.139 between Cluster V and III. By the divergence analysis, the parents for hybridization for diverse clusters could be selected to harness the heterotic vigour’s and also to minimize the genetic erosion. The selection of the best genotype for breeding programs for the development of high-yielding varieties.

MULTIVARIATE ANALYSIS FOR EVALUATING DIVERSITY IN AVENA SATIVA GERMPLASM

Avena sativa L. is a quick-growing, highly nutritious fodder of cool climates, grown for various purposes, and can meet feed demands in scarce periods. The study aimed to identify potential genotypes that offer higher fodder yields. Over three years (2018–2020), 225 genotypes collected from the USDA and FRI were analyzed for genetic diversity based on morphological and yield-related characteristics. The experiments followed an alpha lattice design, with data recorded before panicle emergence. The assessment revealed significant genetic variability among the accessions for the studied traits. The principal component analysis demonstrated that three primary components explained the bulk of the total variability each year. Genotypes with high green fodder yield, tillers per plant, leaves per plant, and plant height acquired positions in the right quadrants of the biplots for 2018 and 2020. Positive correlations observed between tillers per plant and leaves per plant and among plant height, tillers per plant, leaves per plant, and green fodder yield. Exotic and local genotypes were widely distributed across all four quadrants, indicating substantial genetic diversity. The cluster analysis classified 225 oat genotypes into 10 groups based on phenotypic characteristics. Clusters II, VIII, IX, and X displayed higher mean values for most studied traits. Clusters with maximum inter-cluster distances, such as Clusters II and X in 2018, I and X in 2019, and III and VIII in 2020, could be useful in future hybridization programs. Genotypes 198 (Mustang) and 219 (Boppy) provided superior fodder yield than standard checks. These genotypes need further evaluation in different locations for sustainable performance and recommendations for general cultivation.

Evaluation of Genetic Diversity through D2 Statistic in Chickpea (Cicer arietinum L.)

The present investigation was conducted at the Agriculture Research Farm, Department of Genetics and Plant Breeding, College of Agriculture, Rajmata Vijyaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, Madhya Pradesh, India. The objective of the investigation was to assess the genetic diversity among 71 different chickpea genotypes in relation to their yield and its attributing traits. The experimental design employed was a complete randomized block design with two replications. A comprehensive set of observations was made on twelve distinct yield accrediting traits from five randomly selected plants within each genotype. Based on D2 Statistics analysis, the 71 chickpea genotypes were classified into 26 distinct clusters. Conspicuously, the cluster with the highest numbers of genotypes was designated as cluster 1. A remarkable finding emerged from the analysis of intra-cluster distances, with cluster 16 displaying the greatest distance within its constituents. The evaluation of inter-cluster distances revealed significant dissimilarity between clusters 22 and 26, suggesting presence of considerable genetic variation between these clusters. Conversely, the inter-cluster distance was minimal between clusters 2 and 4, indicating a closer genetic relationship between genotypes ICCV 201109 and SAGL- 162387. In terms of the genetic diversity analysis, it became evident that the yield related traits exerting the most substantial influence on the overall genetic divergence among the 71 chickpea genotypes were biological yield per plant, 100-seed weight, and the numbers of pods per plant. In contrast, the numbers of effective pods per plant contributed minimally to the overall genetic divergence. Based on the findings from both inter-cluster distances and individual performance assessments (per se performance), two specific genotypes viz., ICCV 201207 and SAGL 22-121, were short out as promising for inclusion in a hybridization programme. These findings contribute to a deeper understanding of chickpea genetic variability and lay the groundwork for further breeding programmes aimed to enhance chickpea crop productivity.

Genetic Variability, Heritability, Genetic Advance and Cluster Analysis in Indian Mustard (Brassica juncea L. Czern & Coss.) Under Timely and Late Sown Conditions

Two sets of field experiments were conducted at Genetics and Plant Breeding Research Farm of Narendra Deva University of Agriculture and Technology Kumarganj, Faizabad(U.P.), India during Rabi 2014-15 with 30 diverse genotypes of Indian mustard including three checks (Kranti, Vardan and RGN-73) to assess the genetic variability, heritability and genetic advance under timely (TS) and late sown (LS) conditions. The genotypes were evaluated for thirteen quantitative characters. The analysis of variance showed highly significant differences among the genotypes for all the characters. The maximum phenotypic and genotypic coefficient of variability was observed for biological yield plant-1 in timely sown condition while in late sown condition the maximum phenotypic and genotypic coefficient of variability was observed for seed yield plant-1. High heritability coupled with high genetic advance was observed for biological yield plant-1 and seed yield plant-1 in both TS and LS conditions indicating the influence of additive gene effects. D2 analysis grouped the 30 genotypes into six clusters. The distribution of genotypes in both the environments was different. Maximum genotypes (11) were present in cluster II in TS while maximum genotypes (12) were present in cluster IV in LS. The maximum inter cluster distance was observed between clusters IV and VI in TS while in LS the clusters II and VI showed maximum inter cluster distance. Hybridization among the genotypes separated by high inter-cluster distance will result in most heterotic crosses.

Genetic diversity analysis of Indian Cassava (Manihot esculenta Crantz) accessions using morphological and molecular markers

Thirty cassava accessions, including released and pre-release varieties in India were selected for morphological, statistical and molecular analysis to evaluate their genetic divergence. Based on ANOVA (Analysis of Variance), significant differences were observed in thirty genotypes for the fifteen characters used in the study. Most of the characters such as plant height, stem weight plant−1, number of leaves plant−1 and mean weight of tuber exhibited high GCV and PCV along with a positive correlation to tuber yield plant−1. Path analysis showed that stem weight plant−1, number of tubers plant−1, number of leaves plant−1, leaf dry matter content and number of nodes plant−1 directly affected tuber yield plant−1. The genotypes were then grouped into nine clusters following Mahalanobis’ D2 statistics with clusters IV and VIII recording the maximum inter-cluster distance, which gave an idea about the diversity prevailing among them. PCA analysis showed that stem girth, number of nodes and tuber yield plant−1 accounted for greater variability among the genotypes. Molecular analysis using SSR markers showed polymorphism for eighteen markers and molecular dendrogram clustering grouped the genotypes into three principal clusters with different subclusters. Overall, the study highlights the genomic diversity of cassava accessions and provides valuable insights into their characteristics and yield potential.

Genetic Diversity Analysis of Oryza glaberrima Derived Introgression Lines under Direct Seeded Condition

The increasing water scarcity and labour wages have led to the search for alternative crop establishment in rice, such as direct seeded rice (DSR). DSR can reduce unproductive water flow, reduce labour requirements, and mitigate greenhouse gas emissions. Genetic diversity was assessed for hundred and three rice introgression lines using Mahalanobis’ D2 analysis and Tochers’ clustering method. Cluster I had the largest number of genotypes (73), tailed by Cluster II with 15 genotypes. Cluster III and IV had the maximum intra-cluster distances, with Cluster IV having a significant number for desirable characters. The results disclosed that days to 50% flowering, tailed by plant height contributed much to diversity. The most significant inter-cluster distances were found in Clusters involving III and IV, II and III, I and IV and II and IV. These introgression lines can be utilised in crosses, resulting in fruitful recombination during the selection process.

Principal Component Analysis and Genetic Divergence Studies for Yield and Yield-Related Trait of Groundnut (Arachis hypogaea L.) Genotypes at Pawe North Western Ethiopia

A study was conducted on sixty-four (64) groundnut genotypes at Pawe northwestern part of Ethiopia. And the analysis of variance revealed the existence of significant variation among the tested groundnut genotypes. Cluster analysis showed the existence of five (5) divergent groups and the maximum inter-cluster distance was observed between cluster (II) and cluster (V) D2=1419.77 and the shortest inter-cluster squared distance was found between cluster (I) and Cluster (III) D2=247.23. The first four principal components for (PCs) accounted for 70.03% of the total variation. Overall, the results of the current study depicted the presence of sufficient genetic diversity existed among tested groundnut genotypes for further use in breeding programs. However, a one-season experiment will not realize variability because quantitative traits are polygenic and highly influenced by the environment. So, further experiments must be conducted over location and seasons.

A cluster analysis of Ricebean (Vigna umbellata (Thumb.) Ohwi and Ohashi) Accessions with Specified NaCl Salt Concentration at Seedling Stage under Controlled Conditions

The Ricebean is still seen as a crop that is underappreciated and mistreated. The term salinity describes the buildup of soluble salts in soils, which results in saline soils. The Department of Plant Breeding and Genetics, Faculty of Agriculture, West Bengal, India, used a completely randomized block design (CRBD) and a factorial pattern with three replications to conduct this experiment in a growth chamber. In the specified salt concentration of 120 mM of NaCl salt, data were gathered on a variety of seedling growth traits. D2 values should be lower within a cluster than between clusters. The distance between clusters within cluster III (462.54) is the highest, followed by cluster II (294.43) and cluster I (55.88). As a result, these two groups are more diverse, and hybridization between genotypes in these clusters would facilitate gene transfer. The gap between clusters III and II, VI and I, and clusters VI and IV had the largest intra-cluster distance. The shortest inter-cluster distances were between clusters IV & I, V & I, and V & II. Except for leaf fresh weight, shoot dry weight, root dry weight, and leaf dry weight and on germination percentage, relative reduction of dry weights, tolerance index (TI), and salinity susceptibility index (SSI) of various seedling traits, genotypes belonging to cluster I were found to have the least relative reduction for most characters. Therefore, it may be assumed that the genotypes KRB-10, KRB-271, KRB-189, KRB-273, KRB-77, KRB-81, KRB-95, and KRB-70, which belong to this cluster I, will be tolerant to salinity. Similar to this, twelve genotypes from cluster IV—KRB-56, KRB-211, Bidhan-1, KRB-73, KRB-102, KRB-66, KRB-272, KRB-104, KRB-274, KRB-44, KRB-39, and Bidhan-2—had the highest relative reduction for the majority of the characters. The aforementioned results showed that although the majority of the Ricebean genotypes under investigation had a wide range of individual qualities, when the constellation of traits was considered as a whole, they also belonged to different groups.

Understanding the Genetic Basis of Yield-related Traits in Little Millet (Panicum sumatrense Roth. ex. Roem. and Schultz.) Germplasm through Association and Diversity Analysis

Background: Little millet is an important crop grown by tribal farmers in India. Genetic variability can be exploited to develop new varieties with higher yield. However, yield is complex and depends on multiple interconnected component characters. Diversity analyses, such as D2 analysis, are used to evaluate the diversity among genotypes and determine the traits that contribute the most diversity in a given population. These analyses are crucial for achieving the goal of developing new varieties with increased yield. Methods: In this study, 323 little millet genotypes were evaluated using an Augmented RCBD, focusing on ten quantitative traits. The experiment was conducted during the rabi season of 2020-2021, and good agronomic practices were followed. D square cluster analysis and path analysis were used to analyze the data, with the "R" tool and the "biotools" and "agricolae" packages, respectively. Result: In this study, 323 little millet genotypes classified into thirteen distinct clusters based on Mahalanobis's D2 statistics, reflecting differences in their phenotypic characteristics. The largest cluster (cluster I) included 243 genotypes, while the smallest clusters (Cluster IX, Cluster X, Cluster XI, Cluster XII and Cluster XIII) had only 1 genotype. The inter-cluster distance varied, with the largest value (577.7) between cluster V and XII. This analysis can be useful for identifying desirable genotypes and understanding the population's genetic diversity and structure.

Estimation of Genetic Divergence in Garden Pea (Pisum sativum L.) Germplasm

Genetic diversity among thirty garden pea genotypes was worked out using Mahalanobis D2 statistics. Based on genetic distance, genotypes were grouped into ten clusters of the ten clusters, cluster I was the largest, having 11 genotypes, followed by cluster IV, cluster VIII, cluster III, cluster V and clusters II, VI, VII, IX and X, with one genotype in each. The average intra-cluster distance was maximum in cluster VIII (487.04) and minimum in cluster V (125.72). The maximum inter-cluster distance was observed between cluster VIII and cluster X (3439.67), indicating the genotypes in these clusters are more diverged and they can be used as parents in the hybridization program to obtain superior segregants. Cluster means for various traits revealed that cluster VIII was found superior for plant height at 60 DAS, number of branches per plant at 60 DAS, and number of pods per plant. Thus, genotypes from this cluster could be used as divergent parents to improve these particular characters. Pod yield per plant contributed the maximum (38.16 %) to the genetic diversity among the characters studied.

Genetic Divergence Study in Groundnut Genotypes (Arachishypogaea L.)

Employing the Mahalanobis D2 statistic, a divergence study was performed on 36 groundnut genotypes and 13 clusters were identified. Indicating that the genotypes of these groups may be more divergent from one another, the maximum inter-cluster distance (D) was found between cluster II and cluster XIII (315.56), followed by cluster III and cluster IX (289.56), cluster III and cluster XII (289.21), and cluster XII and cluster XIII (297.11). The genotypes in the mentioned clusters showed a significant difference in the means for traits that contribute significantly to yield, indicating that the genotypes in these clusters make ideal parents in a hybridization programme to produce transgressive segregants and improve groundnut.