Selection Of Superior Genotypes Research Articles

DESERT ROSE DIALLEL CROSSES: SEED PRODUCTION AND PHYSIOLOGY

The desert rose, known for its striking sculptural appearance and diverse flowers, holds a prominent place in the market. Despite its ornamental potential, there is limited information on the genetic variability and selection of superior genotypes and their seeds. This study aimed to assess the general (GCA) and specific (SCA) combining ability among desert rose accessions, estimate genetic parameters, and evaluate seed characteristics' correlation. Additionally, we examined the quality of seeds resulting from crosses involving five desert rose genotypes based on floral color. The evaluation included various seed-related traits such as percentage, days until harvesting, number of fruits, seeds per fruit, water content, weight of 100 seeds, seed dimensions, germination, speed of germination index (SGI) in sand, germination, and SGI in paper, seedling length, and dry seedling mass. Genotype 1 displayed positive GCA for all traits, indicating a higher number of favorable alleles for seed production. Hybrid 1X3 exhibited positive SCA estimates, suggesting significant production potential. Notably, 2X5 and 1X5 hybrids demonstrated superior seed quality. Seeds per fruit, germination in sand, seedling length, and dry seedling mass were key contributors to genetic diversity. The study revealed high heritability and positive correlations.

The Role of Transcriptomics in Crop Improvement: A Review

Traditional breeding methods have been practiced for years, while more contemporary initiatives using markers-assisted breeding have made significant advancements that have helped us meet the world's food demands. With the depletion of natural resources, the threat of population growth, and the effects of climatic change, the demand for food will be extremely high in the future. But with urgent needs for food and other farm related produce, there is an extreme need to utilize genomic resources for precise crop breeding fully. Plant breeding can benefit in integrating transcriptome studies to improve crops as well as enables the exploration of regulatory networks and whole genome expression patterns, which help in revealing novel stress tolerance associated genes in crop plants. Transcriptome studies can effectively link the phenotype with the genotypes of the observed traits, regardless of the genome availability and/or complexity of the crops facilitating the practical selection of superior genotypes for us.

Simultaneous cloning and selection of Psidium genotypes resistant to Meloidogyne enterolobii

One of the obstacles to arboreal plant breeding is the time required between the selection of superior genotypes and their multiplication. This study investigates Psidium (guava) hybrids developed to obtain rootstock or new scions resistant to the nematode Meloidogyne enterolobii. The use of half-siblings or hybrid seeds of these genetic materials does not preserve the genetic profile of resistant individuals, making destructive selection methods unfeasible. Propagating juvenile Psidium material by minicutting produces a high rooting percentage, facilitating the cloning of segregating families and reducing the time required to produce replicas. In this study, segregating families for resistance to M. enterolobii were cloned by minicutting, with replicas maintained in clonal minigardens while the mother plants were inoculated and evaluated for nematode reproduction in the root system. The results indicate resistance segregation both among and within families. Early cloning by minicutting demonstrated 100% efficiency, allowing the identification of 30 resistant individuals to occur simultaneously with the first multiplication cycle of these individuals, reducing the time and uncertainty involved in recovering superior materials. The methodology adopted is an effective strategy, allowing advances in guava breeding programs. Additionally, individuals resistant to M. enterolobii were observed in the hybrids P. guajava x P. cattleianum; P. cattleianum x P. guineense and P. guineense x P. cattleianum.

Correlation and Path Analysis in Cowpea (Vigna unguiculata (L.) Walp.)

This study, conducted at Horticultural Research Farm of Babasaheb Bhimrao Ambedkar University, Lucknow, India, over 2020-21 and 2021-22, aimed to examine coefficient of correlation and direct and indirect effects of yield contributing traits on economic yield among 30 diverse cowpea genotypes. The research employed a Randomized Block Design (R.B.D.) with three replications, assessing twenty-six quantitative traits. Genotypic correlations exceeded phenotypic correlations, underscoring genetic influence over environmental factors. Key findings included plant height's positive correlation with branches per plant and days to first flowering, while negatively correlating with nodes on main branches and pod diameter. Traits such as number of pods per plant and average pod weight showed strong positive correlations with pod yield, highlighting their importance in breeding programs. Path coefficient analysis revealed significant positive direct effects on pod yield per plant from traits including plant height, branches per plant, and average pod weight. Indirect effects through traits like days to first flowering and non-reducing sugars also contributed to pod yield. These insights into trait correlations and effects are crucial for developing superior cowpea genotypes with enhanced yield and agronomic traits. The findings emphasize the importance of genetic variability in breeding programs, enabling the selection of superior genotypes to improve cowpea productivity.

Canonical correlations between morphoagronomic and production traits in traditional cowpea varieties based on genotypic values

Obtaining information about the main morphoagronomic and yield components, as well as their interrelationships, is of utmost importance in cowpea breeding programs, aiming at the selection of superior genotypes. The analysis of canonical correlations allows us to predict and describe the relationship between groups of characters, allowing the indirect selection of superior genotypes. The objective of this work was to verify relationships between morphological characters and production components through the analysis of canonical correlations using genotypic values obtained via mixed REML/BLUP models in traditional cowpea varieties. The experiment was conducted in a randomized block design with three replications and 12 treatments. Traditional varieties were characterized and evaluated during the germination, seedling, flowering and harvest stages with data obtained from plants, pods and seeds. Using the genotypic correlation matrix, two sets of characteristics were established, Group I, formed by production components and Group II, formed by morphoagronomic characters. A second correlation analysis was performed to verify associations between morphoagronomic and seed traits (seed length, width and thickness). It was found that dependence between the groups evaluated and intergroup associations can be established, which allows the study of cause-effect and the practice of indirect selection between groups of variables studied. An increase in pod length, hundred-grain mass and seed size is achieved through the selection of early maturing plants, with greater width of the apical leaflet and length of the hypocotyl.

Multi-trait index: selection and recommendation of superior black bean genotypes as new improved varieties

Common bean provides diet rich in vitamins, fiber, minerals, and protein, which could contribute into food security of needy populations in many countries. Developing genotypes that associate favorable agronomic and grain quality traits in the common bean crop could increase the chances of adopting new cultivars black bean. In this context, the present study aimed at selection of superior black bean lines using multi-variate indexes, Smith-Hazel-index, and genotype by yield*trait biplot analysis. These trials were conducted in Campos dos Goytacazes - RJ, in 2020 and 2021. The experimental design used was randomized blocks, with 28 treatments and three replications. The experimental unit consisted of four rows 4.0 m long, spaced at 0.50 m apart, with a sowing density of 15 seeds per meter. The two central rows were used for the evaluations. The selection of superior genotypes was conducted using the multiple trait stability index (MTSI), multi-trait genotype-ideotype distance index (MGIDI), multi-trait index based on factor analysis and genotype-ideotype distance (FAI-BLUP), Smith-Hazel index, and Genotype by Yield*Trait Biplot (GYT). The multivariate indexes efficiently selected the best black bean genotypes, presenting desirable selection gains for most traits. The use of multivariate indexes and GYT enable the selection of early genotypes with higher grain yields. These lines G9, G13, G17, G23, and G27 were selected based on their performance for multiple traits closest to the ideotype and could be recommended as new varieties.

Introspection of discriminate function analysis and MGIDI selection index for selection to improve yield in rice (Oryza sativa L.)

In order to improve the grain yield the different selection indices were constructed based on a discriminate function using 36 rice genotypes. Two types of Smith and Hazel selection methods namely equal (Selection Index I) and differential weightage (Selection Index II) were used to construct the selection index using 17 quantitative traits, resulting in 94.34 and 95.88% genetic advance, respectively. The genotypes M7-1, M7-4, M7-6, M7-8, and M7-11 proved their top rankings based on index scores in both cases. To know better understand the selection and identification of superior genotypes the MGIDI were engaged. Based on 15% selection pressure the genotypes F5-5, M4-2, Anna (R) 4, M4-8 and M4-9 were identified as superior performing genotypes for all the studied variables. However, the ranking of genotypes by MGIDI was not similar to the ranking of Smith Hazel method because this method does not include the variables’ correlation structure. Comparing the classical method of selection index the MGIDI were more advantageous to identify the best performing genotypes based on the factorial analysis of BLUP values and more helpful to identify the traits responsible for selection of superior genotypes.

Phenological growth stages of cardamom (Elettaria cardamomum Maton): Detailed identification and description using the extended BBCH scale

AbstractCardamom (Elettaria cardamomum Maton) is an important spice crop with high market value. In this study, we describe the phenological growth stages of cardamom using a three‐digit extended Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale, which includes eight principal growth stages, comprising four vegetative, two reproductive and two capsule development stages. Additionally, we identified 45 secondary stages within these principal growth stages, providing a comprehensive characterisation of the crop's growth progression, including bud development, leaf emergence, tillering and shoot development, while the reproductive stages included inflorescence emergence and flowering. The capsule development stage details the maturation process of capsules. The study also highlights the parallel occurrence of tillering, shoot development and inflorescence emergence stages during specific growth phases. The knowledge of growth stages facilitates timely interventions, optimising crop management strategies and enhancing overall crop productivity. Additionally, these findings offer valuable insights for breeding programmes and the selection of superior genotypes, contributing to the sustainable cultivation and economic viability of cardamom as an important export‐oriented spice crop.Highlights BBCH scale provides uniform coding for different growth stages of cardamom. Identified and described eight principal growth stages (PGS). PGS comprises of 4 vegetative, 2 reproductive and 2 capsule development stages. 45 secondary stages were identified within 8 principal growth stages. Tillering, shoot development and inflorescence emergence occurs parallelly.

Genetic Variability Studies in Yellow Seeded Linseed

The experimental materials consisted of 40 yellow seeded genotypes of linseed taken from “AICRP on linseed, Department of Genetics and Plant Breeding, IGKV, Raipur”, Chhattisgarh, India along with one check variety Surabhi. Seed yield is an important trait and the cumulative result of a number of component traits controlled by polygenes and influenced greatly by the environmental factors. Hence, yield alone would not be effective for selection of superior genotypes. In order to make the selection effective one has to give emphasis on the yield attributing traits. According to analysis of variance, “significant differences exist for all the parameters investigated from yellow seeded linseed genotypes.” It demonstrates that the yellow seeded genotypes of linseed have sufficient variability in seed yield and contributing characteristics. 1000 Seed weight (7.7g) and seed yield (15g) were the highest in the YLS-31 and YLS-23 genotypes, respectively. The highest GCV was found in number of secondary branches per plant (39.5%) followed by seed yield (32.1%), harvest index (21.6%), and number of primary branches per plant (24.3%). The highest PCV was found in the number of primary branches per plant (47%). Number of secondary branches per plant (43.2%), 1000 seed weight (37.9%), number of capsules per plant (33.9%) and harvest index (22.8%) were the next most important factor.

Review of applications of artificial intelligence (AI) methods in crop research.

Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.

Genetic parameters for host-response to acanthocephaliasis caused by the endoparasite Neoechinorhynchus buttnerae in the Amazon fish Colossoma macropomum

Tambaqui (Colossoma macropomum) is the main native fish produced by Amazon aquaculture. However, the production chain of this fish has been significantly affected by acanthocephaliasis, caused by the endoparasite Neoechinorhynchus buttnerae. The selection of superior genotypes for resistance and resilience to acanthocephaliasis is a fundamental approach to improve production in the context of environmental sustainability and food security. Thus, the objectives of this study were to 1) conduct an experimental challenge against the endoparasite N. buttnerae in tambaqui, and 2) determine the genetic parameters of disease traits caused by N. buttnerae in an experimental breeding population of tambaqui. Estimation of genetic parameters was performed using a genomic relationship matrix (SNPs data) obtained from an experimental challenge performed in 70 full-sib families. The experimental challenge was successfully performed in three replicate tanks, using the parasite eggs in the ichthyoplankton as source for a natural route of infection, which in turn was induced through oral filtration the plankton. The total gain in body weight of all individuals was measured during the infestation period, as a measure of Resilience (RSF) and averaged 42 g (SD = 21.8). The average parasite abundance per animal at the end of the challenge period, indicating Resistance (RTCT), was about 205.2 parasites (SD = 178.6). The heritability estimates for these traits ranged from 0.17 ± 0.04 to 0.20 ± 0.04, indicating the presence of low to moderate heritability levels for host response against N. buttnerae in tambaqui. Genetic correlations between resistance and resilience traits were consistently negative (favorable) and statistically significant, ranging from −0.512 ± 0.134 to −0.252 ± 0.166. These results suggest the possibility of selecting animals for both resistance and resilience simultaneously. The significant genetic variation found for N. buttnerae resistance and favorable genetic correlations with resilience in tambaqui indicated that selecting superior genotypes is a viable approach to reduce the impact of acanthocephaliasis disease in aquaculture.

Parental selection proposal strategy for recurrent selection in tropical wheat breeding

AbstractRecurrent selection is an effective breeding method for improving complex traits in wheat (Triticum aestivum L.) breeding. In this study, we propose a genitor selection strategy, employing a multi‐trait selection approach and genetic diversity analysis. To evaluate our proposed method, we conducted a lattice design trial during the winter seasons of 2020 and 2021 in Viçosa, MG, Brazil, comprising 87 lines and 13 commercial cultivars. It assessed various traits such as plant height, days to heading, wheat leaf rust, tan spot of wheat, a hundred grain mass, hectoliter weight, and grain yield, and the best linear unbiased predictor (BLUP) was obtained for each trait via restricted maximum likelihood. Next, a genotypic distance matrix, derived from principal component analysis using the BLUP values, was computed to perform hierarchical clustering via the unweighted pair group method with arithmetic mean. The selection of superior genotypes was made utilizing the multi‐trait genotype‐ideotype distance index, resulting in the identification of eight lines and two commercial cultivars as candidates. Furthermore, the genetic diversity analysis revealed distinct clusters among the selected genotypes. To assess the divergence of the chosen genitors, this study proposes a complementarity matrix using factor analysis. Our results indicate that this selection strategy effectively directed the identification of the best and divergent genitors. The complementarity matrix developed in this paper to evaluate the crossings proposal can also be used to evaluate combinations in any panel selected via a multi‐trait approach and can be easily applied and interpreted by breeders of various programs.

Improving predictive ability in sparse testing designs in soybean populations

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments.

The Genetic Dissection of Economically Important Traits in Chilli Genotypes (Capsicum annuum L.) through Multivariate Analysis

Chilli (Capsicum annuum L.) cultivar share a variety of different and intriguing growth and yield characteristics, for a plant breeder studies on genotypic and phenotypic relationship for horticulture cum vegetable spicy crop and its traits are useful to design, evaluate and to develop selection criteria for desired traits. With this background information the present study focused on identification of crucial characteristics required for boosting yield and to determine the parameters for the selection of superior genotypes. A total of 30 chilli genotypes were subjected to variability, divergence and mutual correlation studies for yield and yield component traits by using multivariate analysis.
 Results from the analysis of variance and variability revealed highly significant differences at (p≤0.01) and (p≤0.05) for all the studied traits. The highest genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were observed for FL fallowed by FYP, NFP, IFW, WPDM, FD, PH and NSF respectively, except for MC has revealed low GCV and PCV. All the traits were shown the presence of high heritability ( ) in broad sense (>60%) coupled with high GAM (>20%), except for MC, indicating the presence of positive additive gene action and effective transmission of characters to the next generation with selection. Highly significant and positive strong correlation was observed for IFW (r = 0.68**), WPDM (r = 0.54**), TW (r = 0.40*) and FD (r = 0.37*) with FYP, while the path analysis revealed highest direct effect for the traits such as FPT, FL and NFP. Principle component analysis revealed a 70.60% total variation accounted with first four PCAs with Eigen vector values more than one.
 Further cluster analysis grouped the 30 chilli genotypes into four major cluster (Cluster I, II, III and IV) and combined cluster analysis identified genotypes contributing to the maximum variability, viz., cultivars such as LIPSTICK, B.KADDI, B.DABBI, BHOOT JALOKIA, SUM- 17, BCH- 42, PBC- 80, LCA- 310, M- 262, KA2- L, S. SELECTION 1, JCH- 42 and 9608- U, so these genotypes serves as a donor for further breeding programme to develop superior cultivars.

Character Association and Path Co-efficient Analysis Among Inbred Lines of Pearl Millet (Pennisetum glaucum (L.) R. BR.)

A field experiment with 70 pearl millet inbred lines was carried out for analysis of character association and path coefficient for 21 metric traits. A perusal of character association indicated highly significant positive association of panicle weight, green fodder yield plant-1, dry fodder yield plant-1, number of productive tillers plant-1, spike girth, plant height, leaf blade length, number of nodes plant-1, spike length, harvest index, threshing percentage, leaf blade width and specific leaf area at 45 DAS with grain yield plant-1 as well as among themselves at both phenotypic and genotypic levels. Path coefficient analysis indicated that kernel yield plant-1 exhibited high positive direct effect on panicle weight followed by threshing percentage and green fodder yield plant-1. Critical analysis of results obtained from character association and path analysis indicated that grain yield plant-1 had strong positive association with panicle weight which also had high magnitude of positive direct effect. This reveals the importance of this component trait in selection of superior genotypes for higher yield in genotypes of pearl millet.

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects (p < 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets.

In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.

Forage peanut genetic variability: Multi-trait selection for forage production and ornamental purposes

The selection of superior genotypes considering several traits simultaneously allows the release of more adapted, productive, and nutritive cultivars. To select forage peanut genotypes for use as animal feed and for ornamental purposes, 67 genotypes were evaluated. Twelve agronomic and nutritive value traits were evaluated in three temporally separated trials: a randomized complete block design. The mixed model method (Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP)) was employed for estimating genetic parameters and predicting genotypic values, which were used in the selection indexes. The selection criteria considered vegetative and seed propagation traits corresponding to the use of forage peanut as animal feed and for ornamental purposes. Seed productivity was also evaluated and correlated with other traits. Genetic variability with high heritability in seed production was observed. Plant vigor, ground cover, and dry matter yield were negatively correlated with seed production. Among the genotypes evaluated, there is a possibility of selecting 14 and 16 genotypes corresponding to seed propagation for animal feed and ornamental purposes, respectively. For vegetative propagation, there are 16 and 14 promising genotypes for animal feed and ornamental purposes, respectively. Vegetatively propagated genotypes generally performed better in terms of agronomic traits related to feed production.

Variability and character association studies for yield and yield contributing characters in cucumber (Cucumis sativus) under low hills of Himachal Pradesh

Cucumber is an important member of family Cucurbitaceae having potential applications across many sectors. Twenty genotypes were evaluated at the Department of Vegetable Science, College of Horticulture and Forestry (Dr Yashwant Singh Parmar University of Horticulture and Forestry), Neri, Hamirpur, Himachal Pradesh during June– September 2021 for studying variability in morphology, yield and its contributing traits. In studying genetic parameters, PDI of downy mildew, number of fruits/plant, total yield per plot and PDI of powdery mildew, traits showed high estimates of PCV and GCV, indicating the considerable amount of variation was present in germplasm. Most of the traits under study showed high heritability with high to moderate genetic advance as a per cent of mean, which showed the influence of additive gene action for controlling traits. Thus, selection is effective for improvement in these cucumber genotypes. Correlation association and path analysis showed fruit length, fruit breadth, internodal length, number of fruits per plant, harvest duration and ascorbic acid. These traits were considered during the selection of superior genotypes among the twenty genotypes. Thus, in this study, LC-C-4-21 and LC-C-14-21 were higher yielders and can be used for further breeding programmes.

Whole-genome resequencing of common bean elite breeding lines

The expansion of bean genome technologies has prompted new perspectives on generating resources and knowledge essential to research and implementing biotechnological tools for the practical operations of plant breeding programs. This study aimed to resequence the entire genome (whole genome sequencing—WGS) of 40 bean genotypes selected based on their significance in breeding programs worldwide, with the objective of generating an extensive database for the identification of single nucleotide polymorphisms (SNPs). Over 6 million SNPs were identified, distributed across the 11 bean chromosomes. After quality variant filtering, 420,509 high-quality SNPs were established, with an average of 38,228 SNPs per chromosome. These variants were categorized based on their predicted effects, revealing that the majority exerted a modifier impact on non-coding genome regions (94.68%). Notably, a significant proportion of SNPs occurred in intergenic regions (62.89%) and at least one SNP was identified in 58.63% of the genes annotated in the bean genome. Of particular interest, 7841 SNPs were identified in 85% of the putative plant disease defense-related genes, presenting a valuable resource for crop breeding efforts. These findings provide a foundation for the development of innovative and broadly applicable technologies for the routine selection of superior genotypes in global bean improvement and germplasm characterization programs.