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Genetic Diversity Analysis in Bread Wheat (Triticum aestivum L.em.Thell.) for Yield and Physiological Traits

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... Genetic diversity of plants determines their potential for improved efficiency and hence their use for breeding, which eventually may result in enhanced food production. Evaluation of genetic diversity levels among adapted, elite germplasm can provide predictive estimates of genetic variation among segregating progeny for pure-line cultivar development reported (Santosh et al., 2019). Genetic diversity facilitates breeders to develop varieties for specific traits like quality improvement and tolerance to biotic and abiotic stresses. ...
... On the basis of genetic diversity analysis, the maximum percent contribution towards genetic divergence was from grain yield per plot followed by biological yield per plant and minimum by harvest index and number of spikelet which confirms the findings of [24] and of [25] The remaining characters did not show contribution towards genetic divergence reported by [26] . The other is reported by [20] that the studded wheat character showed moderate to high variability and Considerable genetic divergence was also present among the in recombinant inbreed lines. ...
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Wheat (Triticum aestivum L.) is the second major food crop of the world in its importance next to rice. In Ethiopia the crop ranks third in terms of total production next to teff and maize. It is largely grown in the highlands of the country and constitutes roughly 20-30 % of the annual cereal production and plays an appreciable role of supplying the production with carbohydrates, proteins and minerals. Genetic diversity and variability are essential to meet the diversified goals of plant breeding such as breeding for increasing yield, wider adaptation, desirable quality, and pest and disease resistance. Genetic divergence analysis estimates the extent of diversity existed among selected genotypes. In common parlance, genetic variability and genetic diversity are considered synonym to each other which is erroneous. Genetic variability is the variation in alleles of genes or variation in DNA/RNA sequences in the gene pool of a species or population which expresses itself in terms of alternate forms in phenotype. Genetic diversity, on the other hand, is a broad term encompassing all the variability occurring among different genotypes with respect to total genetic make-up of genotypes related to single species or between species. Genetic similarity or dissimilarity can be compared by genetic distance between different individuals. Genetic distance can be used to measure the genetic divergence between different sub-species or different varieties of a species. Different characters of bread wheat show different contribution towards genetic variability and divergence. Estimate of heritability is very important to know the attribute to genetic variance not only in bread wheat but also in any crop plants.
... Kandel et al. (2018) identified superior genotypes after clustering them based on their genetic diversity in performance. Santosh et al. (2019) revealed that the genotypes bearing the desired traits from different clusters can be exploited in future breeding wheat program for the improving yield. Cluster analysis results showed that the cultivars were genetic different from each other could gave the farmers a wider range to choice from it Motlatsi and Mothibeli (2020). ...
... Cluster 3 had high days to heading, plant height, 1000 grain weight and grain yield. Santosh et al. (2019) revealed that cluster-II had maximum number of genotypes and clusters IV, V and VI each had single genotype only. Cluster-I exhibited highest cluster means for grains weight/spike and grain yield/plot. ...
... Cluster I consisted of 8 genotypes whereas Cluster II consisted of 17 genotypes, cluster III of 20 genotypes and cluster IV with 18 genotypes. Similarly, thirty two genotypes were clustered into 6 clusters reported by Santosh et al., 2019. The cophenetic correlation coefficient of the clusters is r= 0.578, which indicate agreement of graphical representation of the distance and correlation matrix, supporting the visual inference in the form of dendrogram (Fig. 2). ...
... The principal component analysis (PCA) transforms the large complex data into smaller correlated number of variables (PCs) with contribution to the total variance. The maximum percent contribution towards total divergence was also reported from grain yield per plot followed by canopy temperature, biological yield per plant and minimum by harvest index (Santosh et al., 2019). These variables are less than or equal to original variables. ...
Article
The study aims to evaluate the yield potential of 60 bread wheat varieties along with three mega-varieties. The experiment was laid down in an augmented design at Banda Agriculture University, Banda, U.P. during rabi 2017-18. The data was recorded on days to 75% heading, days to maturity, plant height and grain yield. The recorded observation were analysed to identify the best performing varieties. Analysis of variance exhibited highly significant differences (P<0.001) among the test varieties for all traits except days to maturity indicating the presence of variability in the experimental material. The highest variance was found for grain yield followed by plant height and 75% flowering while, it was recorded lowest for days to maturity. The less significant difference were found for days to maturity among all lines. The varieties HI 1544 (5773 kg/ha), PBW 621(5773 kg/ha), MP 1203 (5583 kg/ha), UP 2526 (5545 kg/ha) and UP 2554 (5545 kg/ha) recorded highest grain yield. UP 2382 was early matured variety (109 days) among all tested varieties. The PCA and biplot analysis was also undertaken to understand the comparative performance of genotypes in terms of their yield and contributing traits. The first two PCs contributed 79.37% of total variance with 42.03 (PC1) and 37.35 (PC2) eigenvalues. The PC1 and PC2 were significantly influenced by days to 75% heading and seed yield.
... They noticed that the different genotypes were distributed into main, secondary, and sub groups that determined different degrees of genetic similarity or variation. From working on the triticale crop, [10], reported that fifty nine triticale cultivars were grouped into two major groups. The first group contained all winter triticale varieties and in the second cluster were included all spring triticale varieties, and they indicated that the estimation of genetic diversity of triticale genotypes leading to their identification. ...
... Table (10) shows the means of the thirteen groups of the clustered analysis at the first date of planting and fourteen groups at each of second and third dates only for the two traits, biological yield and grain yield per plant. From it, it is noticed that the highest significant performance of the two traits was achieved in the fourth group, which includes the genotype G16 at the first and second planting dates (46,370, 20,807, 40,187 and 21,487 g.plant -1 for the two traits in the two dates respectively), with significant difference from most of the genotypes in the other groups. ...
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The study included twenty genotypes of triticale, whose seeds were sown during 2018-2019 season at the Research Station of the Faculty of Agriculture, University of Kirkuk in the Sayyadah region on three dates (5 November, 20 November and 5 December) using randomized complete block design according to split plot system with three replications. The data were recorded for traits: first, second and third developmental stages, number of days to 50% flowering, plant height, flag leaf area, number of tillers per plant, number of spikes per plant, length and weight of spike, number of spikelet’s per spike, number of grains per spike, 1000 grains weight, biological yield, grain yield per plant, harvest index, protein percent, specific weight, gluten percent, flour strength, moisture percent and ash percentage, The data were analyzed to identify the nature of the differences between genotypes and planting dates. Because of the significant (genotypes x planting dates) interaction, a cluster analysis was conducted with the aim of grouping similar genotypes into homogeneous groups and estimating the degree of genetic diversity between them through the use of hierarchical clustering technology to estimate distances between groups of genotypes formed for each planting date separately. The results showed that the mean squares of genotypes' was highly significant 1% for all traits except harvest index, with a highly significant interaction with dates for all traits except number of spikelet’s and protein percent. The stages of the cluster analysis showed that the genotypes were distributed into 13 groups for the first date and 14 groups for the second and third dates. Some groups included one genotype, indicating the difference of these genotypes from other due to the difference in their genetic origin, which was consequently reflected on their performance, while other groups includes two genotypes. It is concluded from the results of the clustering analysis that there is a strong convergence between the genotypes of stage 18 with the genotype LIRON at the first date and with POLLMER in the second and third dates because they have the lowest euclidean distances, and this requires avoiding crosses between these pairs, while the highest distance was between CMH80 and CMH82 in the first and third dates and CENT/1715 and POPP-CAAL in the second date indicated high genetic variation between them and other genotypes, which may be due to the variation in their genetic origin or to having preferred main genes, other genotypes devoid of them, which encourages their introduction into hybridization with genotypes that showed distinct genetic variation to take advantage of the phenomenon of heterosis and its segregations.
... Cluster I consisted of 8 genotypes whereas Cluster II consisted of 17 genotypes, cluster III of 20 genotypes and cluster IV with 18 genotypes. Similarly, thirty two genotypes were clustered into 6 clusters reported by Santosh et al., 2019. The cophenetic correlation coefficient of the clusters is r= 0.578, which indicate agreement of graphical representation of the distance and correlation matrix, supporting the visual inference in the form of dendrogram (Fig. 2). ...
... The principal component analysis (PCA) transforms the large complex data into smaller correlated number of variables (PCs) with contribution to the total variance. The maximum percent contribution towards total divergence was also reported from grain yield per plot followed by canopy temperature, biological yield per plant and minimum by harvest index (Santosh et al., 2019). These variables are less than or equal to original variables. ...
Article
The study aims to evaluate the yield potential of 60 bread wheat varieties along with three mega-varieties. The experiment was laid down in an augmented design at Banda Agriculture University, Banda, U.P. during rabi 2017-18. The data was recorded on days to 75% heading, days to maturity, plant height and grain yield. The recorded observation were analysed to identify the best performing varieties. Analysis of variance exhibited highly significant differences (P<0.001) among the test varieties for all traits except days to maturity indicating the presence of variability in the experimental material. The highest variance was found for grain yield followed by plant height and 75% flowering while, it was recorded lowest for days to maturity. The less significant difference were found for days to maturity among all lines. The varieties HI 1544 (5773 kg/ha), PBW 621(5773 kg/ha), MP 1203 (5583 kg/ha), UP 2526 (5545 kg/ha) and UP 2554 (5545 kg/ha) recorded highest grain yield. UP 2382 was early matured variety (109 days) among all tested varieties. The PCA and biplot analysis was also undertaken to understand the comparative performance of genotypes in terms of their yield and contributing traits. The first two PCs contributed 79.37% of total variance with 42.03 (PC1) and 37.35 (PC2) eigenvalues. The PC1 and PC2 were significantly influenced by days to 75% heading and seed yield.
... Kandel et al. (2018) identified superior genotypes after clustering them based on their genetic diversity in performance. Santosh et al. (2019) revealed that the genotypes bearing the desired traits from different clusters can be exploited in future breeding wheat program for the improving yield. Cluster analysis results showed that the cultivars were genetic different from each other could gave the farmers a wider range to choice from it Motlatsi and Mothibeli (2020). ...
... Cluster 3 had high days to heading, plant height, 1000 grain weight and grain yield. Santosh et al. (2019) revealed that cluster-II had maximum number of genotypes and clusters IV, V and VI each had single genotype only. Cluster-I exhibited highest cluster means for grains weight/spike and grain yield/plot. ...
... The results showed that cluster 1 had the highest number of diversified genotypes (10 genotypes), followed by seven genotypes in each of clusters 2 and 4, and cluster 5 contains six genotypes. It was comparable to the findings of Alam et al., [11] and Santosh et al., [12] that intra-cluster distance was often smaller than inter-cluster distance. As a result, there was a tendency for the genotypes included in the cluster to be less different from one another. ...
Article
Drought is one of the major environmental factors affecting the yield and plant architecture of durum wheat. Days to heading, days to maturity, number of grains per spike, spike length, peduncle length, number of effective tillers per meter, plant height, protein content, sedimentation value, chlorophyll content, canopy temperature after 5 days and 15 days of anthesis like morphological, biochemical, physiological traits were measure in this study. In the current study, ten clusters of forty different durum wheat genotypes were formed, with the highest distance between clusters 6 and 10 (D 2 = 373.85) followed by clusters 9 and 10 (D 2 = 331.09). A total of around 73.82 percent of the overall variation seen across the forty durum wheat genotypes was explained by five main components, each having an eigenvalue ranging from 1.21 to 4.93. Different clusters 6, 9, and 10 include genotypes with particular characteristics. These genotypes may produce desirable genetic recombinants that help breeders create drought-tolerant cultivars. The PC1, PC2, PC3, PC4, and PC5 involve characters of major economic traits viz., plant height, number of grains per spike, spike length, peduncle length, 1000 grain weight, chlorophyll content, biological yield per plot, grain yield per plot, canopy temperature after 5 and 15 days of anthesis. The results of the entire experiment showed that the genotypes of durum wheat evaluated under limited irrigation circumstances had a sufficient amount of variability and diversity.
... number of seeds per spike (9.55%), thousand seed weight (8.22%), number of tillers per plant (7.7%), number of spikelets per spike (6%), plant height (5%), spike length (4.5%), days to maturity (3.0%), peduncle length (3.0%) and the least divergence was shown by days to flowering (2%). This finding is in agreement with the report Jaiswal., et al. [9] and Singh., et al. [4]. Peduncle length (cm) 3 37 ...
... The genetic diversity of breeding materials is critical for increasing wheat nutritional quality, yield, and yield stability. Evaluating the extent of the genetic diversity among adapted, elite germplasm may be useful for estimating the genetic variability among segregating progeny [7]. Elite varieties are recurrently used for the subsequent breeding aimed at accumulating the optimal combination of alleles. ...
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Background The genetic diversity and gene pool characteristics must be clarified for efficient genome-wide association studies, genomic selection, and hybrid breeding. The aim of this study was to evaluate the genetic structure of 509 wheat accessions representing registered varieties and advanced breeding lines via the high-density genotyping-by-sequencing approach. Results More than 30% of 13,499 SNP markers representing 2162 clusters were mapped to genes, whereas 22.50% of 26,369 silicoDArT markers overlapped with coding sequences and were linked in 3527 blocks. Regarding hexaploidy, perfect sequence matches following BLAST searches were not sufficient for the unequivocal mapping to unique loci. Moreover, allelic variations in homeologous loci interfered with heterozygosity calculations for some markers. Analyses of the major genetic changes over the last 27 years revealed the selection pressure on orthologs of the gibberellin biosynthesis-related GA2 gene and the senescence-associated SAG12 gene. A core collection representing the wheat population was generated for preserving germplasm and optimizing breeding programs. Conclusions Our results confirmed considerable differences among wheat subgenomes A, B and D, with D characterized by the lowest diversity but the highest LD. They revealed genomic regions that have been targeted by breeding.
Thesis
The present investigation was carried out to study the “Variability, character association and diversity studies on qualitative and quantitative traits in bread wheat (Triticum aestivum L.)” among forty-four genotypes of bread wheat for grain yield and its attributing traits. The experiment was arranged in a randomized block design with four replications at the Agronomy Instructional Farm, C. P. College of Agriculture, S. D. Agricultural University, Sardarkrushinagar during rabi 2019-20. The observations for this investigation were recorded for various thirteen traits viz., days to heading (N), days to maturity (N), plant height (cm), number of tiller per meter (N), number of grain per spike (N), spike length (cm), peduncle length (cm), grain yield per plant (g), 1000 grain weight (g), leaf area per plant (cm2), protein content (%), sedimentation value and harvest index (%). The analysis of variance revealed significant differences among the genotypes for all the characters studied indicating presence of adequate amount of variability among forty-four genotypes. High genotypic and phenotypic coefficient of variations were exhibited by peduncle length followed by leaf area per plant, grain yield per plant, number of grains per spike, number of effective tillers per meter, spike length, plant height, days to heading and 1000 grain weight, which suggests the possibility of improving these traits through simple selection. The highest heritability was recorded for leaf area per plant followed by 1000 grain weight, peduncle length and number of effective tillers per meter. The highest genetic advance as per cent of mean was recorded for peduncle length followed by leaf area per plant, number of grains per spike and number of effective tillers per meter. High heritability associated with high genetic advance as per cent of mean was found for the characters viz., number of effective tillers per meter, number of grains per spike, grain yield per plant, leaf area per plant and peduncle length, which indicates that the traits were simply inherited in nature and controlled by few major genes or possessed additive gene effects. High values of genotypic correlations than their corresponding phenotypic correlations were recorded for the all the characters under study. This indicated that, there was high amount of relationship between two variables at genotypic level and its phenotypic expression was let down by the influence of environmental factors and also indicated the importance of these characters in improvement of grain yield in wheat. Grain yield per plant showed highly significant and positive association with number of grain per spike, harvest index, 1000 grain weight, leaf area per plant and spike length both at genotypic and phenotypic levels suggesting utility as selection indices in grain yield improvement. The path coefficient analysis revealed that harvest index recorded the highest direct effect towards grain yield per plant, followed by plant height, number of grain per spike and protein content. However, some characters viz. days to maturity, 1000 grain weight, peduncle length, leaf area per plant, sedimentation value and number of effective tillers per meter shows negative direct effect on grain yield per plant, it can be emphasized that for the improving grain yield in wheat more attention should be given to harvest index, plant height, grain yield per spike, protein content, days to heading and spike length while making selection for developing high yielding wheat genotypes. The genetic divergence measured by Mahalanobis D2 statistic, clustered forty-four genotypes studied for seed yield were grouped into eight clusters. The maximum inter cluster distance was observed between clusters VIII and VII followed by VII and IV. The attributes, viz., spike length, leaf area per plant, peduncle length, plant height and number of grains per spike would be useful for generating transgressive sergeants if commercially practicable as these five traits contributed maximum towards total genetic divergence. On the source of all the above studies, it can be concluded that more emphasis should be given to number of grains per spike, spike length, peduncle length, grain yield per plant, leaf area per plant, number of effective tillers per meter and plant height while doing selection for genetic improvement in bread wheat. Based on the mean performance of grain yield per plant, genotypes viz., GW-451, GW-173, GW-496, HI-1544, HI-1620 and C-306 were categorized as high yielding genotypes. So, for the improvement of yield and their components traits more emphasis could be given to these genotypes and with planning of research for more number of seasons and locations to get more precise results.
Article
The present investigation conducted on 80 accessions of wheat during 2018-19 at Main Experiment Station (MES) of A.N.D. University of Agriculture and Technology, Kumarganj, Ayodhya (U.P.) to evaluate genetic divergence based on cluster and principal component analyses for yield and its contributing characters for the identification of most diverse and promising genotypes. The experiment was laid out in Augmented Block Design. The observations were recorded on various phenotypic characters. The varieties of wheat were grouped into 08 distinct clusters by using K mean and Euclidean cluster analysis. Maximum inter cluster distance was found between Cluster VII
Article
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Selection for newly promising bread wheat genotypes with high yield potential under arid and semi- arid conditions is the main objective of wheat breeding programs in ACSAD. The present study was carried out to evaluate agronomic traits, genetic diversity and principle component analysis of 168 bread wheat genotypes selected in F5 generation. The genotypes were evaluated for nine different yield contributing characters viz., days to heading, maturity date, plant height, number of spikes/plant, number of spikelets/spike, number of grains/spike, 1000-kernel weight, grain yield/plant and straw yield/plant and were grown in randomized complete block design with three replications under rainfed conditions at Izraa and kafrdan Agricultural Experiment Stations of ACSAD at Daraa governorate, Syria and Bekaa governorate, Lebanon, respectively for two growing seasons (2016/17 and 2017/18). The most important results obtained can be summarized as follows: - The combined analysis of variance indicated the homogeneous of significant differences between years for most studied traits. The genotypes exhibited highly significant (P≤ 0.01) for all traits studied in both and across seasons indicating considerable amount of variation among genotypes for each trait. - For days to heading (50%) and maturity dates the line 14 under Izraa as well as the three lines Line 52, Line 90 and Line 128 under kafrdan were the earliest across seasons. While, for grain yield/plant the four new bred lines; 33, 71, 109 and 147 recorded the highest mean across seasons and under both sites which had values ranged from 27.14g for line 147 under Izraa and29.40 g for line 33 under Kafrdan conditions. Meanwhile, grain yield was positively correlated with each of no. of spikes/plant (0894** and 0.901**), no. of sikelets/spike (0.723** and 0.744**), no. of grains/spike 0.696** and 0.744**) and 1000-kernel (0.681** and 0.556**) across seasons under the two sites, respectively. - Principle component analysis (PCA) showed existence of a high level of variability among the genotypes and allowed the division of the collection of genotypes into two groups (component 1 and 2) which gave an explanation of 84.74% and 84.59% of the total variance in the two sites. The first component of PCA could justify most of the variance among genotypes (58.70% and 60.98% under Izraa and kafrdan conditions, respectively). While, the second component could justify more than 40.52% and 46.32% under Izraa and kafrdan, respectively. - Cluster analysis of euclidean distances, classified the 168 genotypes into four main clusters divided into 15 and 10 intra cluster under Izraa and Kafrdan, respectively. The first (I) and second (II) clusters was the largest and contained 24 and 20 genotypes (14.28% and 11.90% of total genotypes, respectively) under Izraa as well as the first (I), second (II), third (III) and fourth (IV) clusters recorded the highest number of sets; 19, 22, 33 and 25 genotypes (11.31, 13.09, 19.64 % and 14.88% of total genotypes, respectively) under Kafrdan. - These results indicate that the high yielding genotypes 33, 71, 109 and 147 have more adaptability for under rainfed conditions in both sites and could be used in breeding programs to develop high yielding genotypes or distributed to the targeted farmers under arid and semi-arid environments. Key words: Bread wheat, Rained conditions, Phenotypic Correlation, Principle Component Analysis, Cluster analysis.
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Cluster analysis fifteen genotypes were distributed into major, secondary and subgroups, the highest genetic dimension was among the genotypes between (Atlas and GOUMRlA-),(3-DAJAJ and JAWAHIR-2) and reached (1), and the reason for this is that they have a different genetic material, while the lowest genetic distance (0.969) between the two genotypes (JAWAHIR-2 and Tal Afar-3) In the first season, while the genetic dimension was higher reached (1) between the genotypes of the genotype (Tesfa and ALMAZ-19), while it was the lowest between the genotypes (Afar-3 and HUBARA- 5), which reached (0.923) and with remained of the genotypes for the second season ,it show the highest genetic dimension was between the seven parents of parent Terbol with parent Atlas, as it reached (0.997) between the hybrid (Terbol × 5-DAJAJ) with parent (5-DAJAJ) and the hybrid (1ASEEL-× 1 BAOBAB-) with (Terbol × Atlas) and hybrid (BAOBAB-1 × Research-4) with the two hybrids (Atlas ×Research-4) and (1ASEEL-× 5-DAJAJ), which reached (0.999) lower genetic dimension was between parent (GOUMRlA-3) and the parent (BAOBAB-1) reached (0.920), while the hybrids had low genetic distance between the hybrid (Atlas × GOUMRlA-3) and hybrid (Atlas × 1aseEL-) as it reached (0.903). Noticed that the correlation coefficients of the values of average performance and heterosis on the basis of the best parents were positive and significant indicating the strength of the positive relationship between them, and the genetic dimension had a negative insignificant correlation with heterosis on the basis of the parents' average specific combining ability.
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