<div>Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement. 
 
Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity. 
 
Result: Principal component analysis revealed that first five principal components possessed Eigen value &gt; 1, cumulatively contributed &gt; 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.</div>

<div>Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis</div>

Background: Based on D2 analysis 53 chickpea genotypes were grouped into seven clusters. Among the VII clusters, cluster I consists of 30 genotypes forming the largest cluster followed by cluster II with 16 genotypes, cluster VII with three genotypes and cluster III, IV, V and VI with one genotype each. Inter cluster distance were found to higher than intra cluster distance which depicted wide genetic diversity among the chickpea genotypes under study. Contribution of various characters towards expression of total genetic divergence indicated that seed yield (27.77%) contributed maximum (24.74%) followed by biological yield and number of pods per plant (20.43%). The clustering pattern of genotype demonstrated that clustering does not follow any particular cluster pattern with respect to origin. The pattern of cluster analysis of the first principal component had the largest eigenroot 4.18 per cent of total variation followed by 3.53, 2.91, 2.02, 1.45, 1.26 and 1.15 from second to seven principal components. The eigenroot of first principal component accounted for 19.91 per cent of total variation followed by second to seven principal components which accounted for 16.82, 13.84, 9.62, 6.90, 5.99 and 5.48 per cent of total variations present in the genotypes. 
 
Methods: The study was carried out at Pulses Research Sub-Station (PRSS), Samba and Advanced Center for Rain-fed Agriculture (ACRA), Rakh Dhiansar during Rabi seasons 2017-18 and 2018-19. The experimental material comprising of 48 chickpea (Cicer arietinum L.) genotypes along with 5 checks were evaluated in augmented block design. 
 
Result: The genotypes of cluster II, V and VII had highest inter-cluster distances as well as higher cluster means for most of the yield component traits indicating that these genotypes were most diverse can be used as parents in hybridization programme of chickpea breeding. The variation present in the chickpea genotypes under study can be exploited through selection and hybridization among identified genotypes for breeding high yielding chickpea genotypes.

Genetic Diversity Analysis of Chickpea Genotypes (Cicer arietinum L.) for Seed Yield and Morpho-genetic Traits

<div style="text-align:justify">Background: Seventy breeding lines of mung bean were evaluated for 20 different characters and mean values were worked for genetic diversity by Mahalanobis D2 statistic. 
 
Methods: The experiments included 70 mung bean breeding lines which were collected from Plant Breeding Unit, Agricultural Research Station, Badnapur. They were grown during Kharif 2016 at experimental research farm Badnapur of Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani. The data were recorded for 20 different characters on 10 randomly selected plants. The statistical analysis were done by Mahalanobis D2 statistics. 
 
Result: The results of D2 analysis indicated the presence of considerable genetic divergence among these breeding lines. In the present study, inter-cluster distances were worked out considering 20 characters and these distances ranged from 240.96 (between cluster V and cluster VII) to 1080.72 (between cluster V and cluster VIII). The inter-specific derivatives were grouped into eight clusters. The maximum inter-cluster distance was between cluster V and cluster VIII. The maximum inter-cluster distance was between cluster V and cluster VIII (1080.72), followed by cluster II and cluster III (932.25), cluster IV and cluster VIII (910.11), cluster VII and cluster VIII (738.30), cluster I and cluster VIII (732.61), cluster VI and cluster V (660.49) and cluster II and cluster VI (494.93). This suggested that there is wide genetic diversity between these clusters. Based on these studies, crosses can be made between breeding lines of these clusters to obtain desirable results either in transgressive breeding or in heterosis breeding. Cluster VIII and cluster IV showed high mean values for most of the yield contributing traits like100-seed weight, shelling %, harvest index, pod length, primary branches per plant, days to 50% flowering, days to maturity, leaf width and days to shattering. So the lines from cluster IV and cluster VIII can be used for mung bean yield improvement programme.</div>

<div>Multivariate Analysis using D2 and Principal Component Analysis in Mung bean [Vigna radiata (L.) Wilczek] for Study of Genetic Diversity</div>

Genetic diversity of four quail populations, including Korean quails (maroon quails), Peking white quails, Chinese yellow quails and Chinese black quails, were analyzed by microsatellite markers, aiming to provide scientific basis for new breeds of Chinese black quails for egg production as well as the assessment, protection and utilization of Chinese quail&rsquo;s genetic resources. The results showed that 48 alleles were detected by nine microsatellite markers in the four quail populations, with the mean of 5.33 alleles in each locus. The average effective number of alleles marked by the nine microsatellite markers in Chinese black quails, Peking white quails, Chinese yellow quails and maroon quails were 3.5338, 3.6135, 4.0312 and 3.6508 respectively. The average heterozygosity of the four quail populations were 0.6952, 0.7046, 0.7353 and 0.7096 respectively. The average polymorphic information content of nine microsatellite loci in four quail populations were 0.6204, 0.6587, 0.6942 and 0.6639 , respectively, all of which were greater than 0.5, indicating the four populations&rsquo; copious genetic diversity. In this study, the average genetic differentiation coefficient among populations was 0.0349, so the genetic variation among populations accounted for 3.49%, which demonstrated that genetic variation among populations was just a small proportion of the total population genetic variation, and there was little differentiation among the four populations. Cluster analysis indicated that Chinese black quails and Peking white quails were firstly clustered, and then Chinese yellow quails and maroon quails were clustered, and finally the two were clustered together.&nbsp;

Analysis of genetic diversity of four quails by microsatellite markers

<div>Background: Livestock&nbsp;genetic diversity studies focus on their within and diversity, breed history, adaptive variations, ancestral information, site of domestication and parentage testing and assess the genetic uniformity, admixture or subdivision, inbreeding, or introgression in the population which is helpful in breed formation and their sustainable utilization. 
&nbsp;</div>

<div style="text-align:justify">Methods: The present research work was conducted during the year 2016-17 at National Bureau of Animal Genetics Resources, Karnal-132001. STR data of 25 markers on 1237 random samples of 27 goat populations was used for analysis. The genetic diversity analysis of new population &nbsp;viz: Narayanpatna, Raighar, Kalahandi, Malkangiri of Odisha state and Rohilkhandi (UK) and their association studies with other Indian goat breeds was performed. 
 
Result: It was found that used markers are highly polymorphic- and the studied breeds/population showed great diversity and distributed mostly on the basis of physio-geographical condition and type of production but among new populations diversity was least which might be due to exchange of animal for breeding purposes. The studied new goat populations were well differentiated from other goat breeds which might be due to physio-geographical condition and breeding practices, so these may be considered as separate breeds/populations. In conclusion, the results showed high level of conserved genetic diversity in the Indian goat breeds. The smaller and isolated new population showed less diversity and a higher inbreeding level as compared to registered breeds.</div>

<div>Genetic Diversity Analysis among 27 Indian Goat Populations using Microsatellite Markers</div>

<div>Background: Though modern high yielding and synthetics developed through maize heterosis are replacing low-yielding maize populations worldwide, it has shown little impact in Kashmir due to poor adaptability and expensive seed cost. Nevertheless, retaining popularity due to adaptability, more resistance to natural factors, early maturity, good grain quality, great food and fodder value and ability to thrive best under low input conditions, landraces are going out of farmer&rsquo;s domain and becoming extinct owing to low yielding potential, low resilience to some biotic stresses and lower sensitivity to inputs. For developing high yielding, climate-resilient cultivars and broadening of the genetic base, efficient and rapid identification and introgression of novel and favourable alleles dispersed among the landraces are necessary.&nbsp; 
 
Methods: Seventy maize landraces from different parts of Kashmir valley designated as K-L1 to K-L70 were subjected to genetic analysis for thirteen important agronomic traits viz. days to 50% tasselling, days to 50% silking, anthesis-silking interval, plant height, ear height, days to maturity, ear diameter, kernel rows per ear, kernels per row, prolificacy, shelling percentage, yield per hectare and 100-grain weight. The least significance increase was calculated to evaluate best performing landraces and clustering of landraces was done using Mahalanobis D2 analysis through Tocher&rsquo;s method. The experimentation was carried out at Dryland Agriculture Research Station Budgam during the years 2019 and 2020. 
 
Result: Significant differences for all agronomic traits except anthesis-silking interval and prolificacy depicted the presence of significant genetic variability. The least significant increase calculations allowed the evaluation of best-performing landraces for specific traits and&nbsp; Mahalanobis D2 analysis through Tocher&rsquo;s method led to clustering landraces into 14 clusters with cluster I having the highest 23, followed by cluster VI with 14, cluster II and cluster III each with 9, cluster VIII with 6 genotypes and rest 9 clusters being mono-genotypic. Intra and inter-clustering distance and cluster means suggest utilizing genotypes belonging to clusters with wider statistical distances for crossing in hybridization programme to produce segregates with extensive variation resulting in higher heterosis.</div>

Genetic Diversity Analysis in Maize Landraces under Temperate Ecology

Background: The Kachchhi-Sindhi horse is a native Indian breed, mainly found in the Gujarat and Rajasthan regions of India, with an estimated population of about 4,000 horses. A comprehensive genetic assessment to understand the genetic diversity and population structure of this breed is essential for conservation strategies and for maintaining the health and vitality of the breed. 
&nbsp; 
Methods: To determine the genetic diversity and population structure of the Kachchhi-Sindhi breed, we genotyped 50 unrelated, healthy adult horses using 30 microsatellite DNA loci. The genetic data were analyzed to calculate the number of alleles, observed and expected heterozygosity&nbsp; and the inbreeding coefficient (FIS). We also tested for Hardy-Weinberg equilibrium deviations and marker neutrality. We used Structure analysis to assess the admixture within the breed. Besides, population structure analysis was done with principal coordinates analysis (PCoA). Alongside mode shift tests, as well as Sign, Standardized differences and Wilcoxon sign rank tests, were made for assessing signs of a potential population bottleneck recently. 
&nbsp; 
Result: Our study disclosed substantial genetic diversity in the Kachchhi-Sindhi breed. Key genetic parameters averaged an average number of alleles of 10.10&plusmn;3.18, with an effective number of alleles of 5.15&plusmn;1.88 and observed heterozygosity of 0.90&plusmn;0.11, indicating significant genetic variation in the population. The inbreeding coefficient (FIS) was very low at -0.17&plusmn;0.19, indicating limited inbreeding. While 14 of the 30 microsatellites showed deviations from Hardy-Weinberg equilibrium, most markers were considered neutral, with only four showing non-neutral behaviour, according to the Ewens-Watterson neutrality test. Population analysis by PCoA revealed a single large but fragmented population of Kachchhi-Sindhi horses. Further tests, such as the mode-shift test and M ratio value, showed no evidence of a recent bottleneck in the breed. These are crucial for guiding future breeding and conservation efforts for the Kachchhi-Sindhi breed. 
&nbsp;

Genetic Diversity Analysis of Kachchhi-Sindhi Horses and Repercussions for Conservation in India

<div style="text-align: justify;">This study aimed to understand the genetic diversity and population structure of alfalfa germplasm from the United States. In this study, the population structure and genetic diversity of six alfalfa cultivars of United States origin were investigated by microsatellite analysis with 40 individuals per cultivar. A total of 312 discernible alleles were amplified from the whole genome with an average of 31.2 alleles per locus. The average values of polymorphic information content and Shannon&rsquo;s information index were 0.928 and 0.133, respectively, showing high levels of genetic diversity. Two populations were identified by STRUCTURE software with principal coordinate analysis and neighbour-joining clustering. Analysis of molecular variance analysis (AMOVA) revealed that the majority of genetic variation was within cultivars (96.42%) rather than between cultivars (3.58%). In conclusion, analyses of genetic diversity and population structure may be useful for the genetic analysis and utilization of genetic variation in alfalfa breeding.</div>

<div style="text-align: justify;">Genetic diversity studies of alfalfa germplasm (Medicago sativa L. subsp. sativa) of United States origin using microsatellite analysis</div>

<div>Background: The seed yield of chickpea can be improved by selection of superior genotypes on the basis of different yield and yield component traits. These genotypes exclusively utilize in breeding programs. Yield is a complex trait which is affected by several factors, hence, a well-known technique known as principal component analysis was used to identify and minimize the number of traits for effective selection. To obtain efficient recombinants, the identified component traits need to be combined from diverse parents through recombination breeding followed by selection of transgressive segregants. Hence, the present study is envisaged to measure the genetic diversity among genotypes of chickpea. 
 
Methods: The experimental material comprised of 40 chickpea genotypes evaluated in randomized block design with three replications. The experimental unit was four rows per plot with 4 m length and spacing between row to row and plant to plant maintained as 30 x 10 cm. NPK (20:40:00) fertilizers was applied as basal doses. The data were recorded for each genotype on nine quantitative traits as per standard methods. Descriptive statistics and PCA analysis was performed by using the statistical package SPSS 16.0 version and cluster analysis was done using the Wards method of hierarchical clustering technique. 
 
Result: Out of nine PCs only three PCs exhibited more than 1.0 Eigen value and showed about 73.4% variability. PC1 contributed 28.6% of the total variation and correlated with days to flowering, days to maturity, plant height, first pod height, seeds per pod and number of pods per plant while PC2 explained 21.00% of the total variation and dominated by plant height, first pod height and seed yield. PC3 explained an additional 13.00% of the total variation and dominated by primary branches per plant. Genotype commonly found in more PC, were BG 4016, IPCB 2015-165, IPC 2011-247, GNG2459 and RKG 19-4. Hierarchical clustering technique grouped 40 genotypes into two main clusters (A and B) and nine sub clusters. The present investigation depicted that the chickpea germplasm displayed considerable genetic diversity for most of the traits under consideration.&nbsp;</div>

<div>Principal Component and Genetic Diversity Analysis for Seed Yield and Its Related Components in the Genotypes of Chickpea (Cicer arietinum L.)</div>

<div>Background: Mungbean is a short duration grain legume widely grown in south and Southeast Asia. The extent of variability through Principal Component Analysis (PCA) and cluster analysis in promising mungbean genotypes should be known for possible yield improvement. A study was undertaken to work out the extent of variability among twenty four mungbean genotypes through cluster analysis and Principal Component Analysis (PCA). 
 
Methods: The experiment was laid out in a randomized block design with three replications during kharif 2018 and 2019 at the experimental field of Agricultural Research Station, Navgaon (Alwar) under rainfed condition.&nbsp; 
 
Result: Principal component analysis revealed that the first three main PCAs amounted 78.80% of the total variation among genotypes for different traits. Out of total principal components, PC1 accounts for maximum variability in the data with respect to succeeding components. Number of branches per plant (28.62%), number of clusters per plant (23.55%) and seed yield (15.58%) showed maximum per cent contribution towards total genetic divergence on pooled basis. Cluster analysis showed that genotypes fall into seven different clusters and their inter and intra cluster distance showed genetic diversity between different genotypes. The maximum number of genotypes i.e., 8 was found in cluster II followed by cluster III comprising of 6 genotypes. Genotypes RMG-1138 and IPM-02-03 representing the mono genotypic cluster signifies that it can be the most diverse variety and it would be the appropriate genotype for hybridization with ones present in other clusters to tailor the agriculturally important traits and ultimately to boost the seed yield in mungbean under rainfed conditions.</div>

Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

Genetic Diversity Analysis of Chickpea Genotypes (Cicer arietinum L.) for Seed Yield and Morpho-genetic Traits

Multivariate Analysis using D² and Principal Component Analysis in Mung bean [Vigna radiata (L.) Wilczek] for Study of Genetic Diversity

Analysis of genetic diversity of four quails by microsatellite markers

Genetic Diversity Analysis among 27 Indian Goat Populations using Microsatellite Markers

Genetic Diversity Analysis in Maize Landraces under Temperate Ecology

Genetic Diversity Analysis of Kachchhi-Sindhi Horses and Repercussions for Conservation in India

Genetic diversity studies of alfalfa germplasm (Medicago sativa L. subsp. sativa) of United States origin using microsatellite analysis

Principal Component and Genetic Diversity Analysis for Seed Yield and Its Related Components in the Genotypes of Chickpea (Cicer arietinum L.)

Genetic Diversity and Principal Component Analysis in Mungbean [Vigna radiata (L.) Wilczek] under Rainfed Condition