Plant Breeding Research Articles

Integrating Genomics and Phenomics in Agricultural Breeding: A Comprehensive Review

One of the key roles of plant breeders is to improve crop productivity through development of varieties with desirable traits to feed the growing population. The merger of genomics and phenomics - where genomics refer to the study of an organism’s entire DNA sequence and phenomonics is the full explanation of observable characteristics has given a new face to breeding strategies. This paper provides information about this technique from beginning up to now, which implicates high-throughput phenotyping, genomic selection, artificial intelligence platform for crop improvement. It seems that coronal genomics and phenotypical imaging results in transgenic or super climate-resilient plants, therefore improving yield under sustainable conditions. Despite its bright future there are certain issues like data standardization, ethical concerns, and resource restraints that need considering. Development later on gets people thinking about technical fields such as inter-disciplinary researches as well as policy supports that have ability to bring these powerful technologies into assurance of food security together with sustainable agriculture initially collaboration doesn’t need manufacturing centres of technology including genome and phenome data can help breeders achieve wrists precisions in crops development which will result in having robust agricultural systems able to overcome environmental stressors.

A computational analysis revealed BES1 transcription factor and β-amylase as crosstalk elements in Upland cotton species (Gossypium sp.)

Cotton is a resilient and multipurpose crop, meeting major of the world’s textile needs while also yielding byproducts like edible oil and animal feed. Starch plays a crucial role in cotton fabric production. It enhances fabric strength by forming a protective film around cotton fibers, making them more resistant to wear and tear. BES1 (brassinosteroid insensitive 1) is a key regulator in brassinosteroid signaling. It controls thousands of target genes involved in development processes. Interestingly, two β-amylase proteins (BAM7 and BAM8) are part of the BES1 family, despite their primary function as β-amylases. β-Amylase (BAM) and BES1 are two gene families with functional and regulatory roles in controlling shoot growth and development by mediating brassinosteroid effects. They share similar domains and participate in various biological processes, tolerance and responses to stresses like salt and drought. In a computational analysis comparing Arabidopsis and Gossypium species, BAM and BES1 were characterized. BES1 genes were grouped into four clusters based on the comparison of the two species. Two clusters corresponded to BAM7 and BAM8, while the other two clusters were associated with BES1. The conserved nucleotide domain sequence is GCTGGATGG. Short tandem repeats include TG and TTG, which can serve as molecular markers. BES1 is specifically linked to cellulose and fiber production and holds promise as a candidate for plant selection and breeding in Gossypium (cotton).

Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program.

The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

Factor-Analytic Variance-Covariance Structures for Prediction Into a Target Population of Environments.

Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.

Root Anatomical Imaging and Phenotyping in Maize.

Root anatomy plays a crucial role in regulating essential processes such as the absorption and movement of water and nutrients in plants. Root anatomy also impacts the energy costs of building and sustaining root tissues, tissue mechanics, and interactions with other organisms. Although several studies in maize have confirmed the functional utility of numerous root anatomical traits, such as that of cortical cell size and number for stress adaptation, there have been significant obstacles in measuring and analyzing root anatomical characteristics. This has resulted in gaps in our understanding of the genetic control and range of phenotypic variations among different cultivars, and how this diversity relates to overall fitness. Here, we review root anatomical phenotypes in maize and their function in stress adaptation, and briefly discuss phenotyping methods available for root anatomy. We further introduce a simple and accessible phenotyping approach that enables a comprehensive investigation of maize root anatomy. Detailed characterization of root traits and the implementation of robust methods for root anatomical phenotyping could have wide-ranging benefits across various areas of plant science, from fundamental research to enhancing crop breeding efforts.

Genotypic variation in blueberry flower morphology and nectar reward content affects pollinator attraction in a diverse breeding population.

Pollination is crucial to obtaining optimal blueberry yield and fruit quality. Despite substantial investments in seasonal beekeeping services, blueberry producers consistently report suboptimal pollinator visitation and fruit set in some cultivars. Flower morphology and floral rewards are among the key factors that have shown to contribute to pollinator attraction, however little is known about their relative importance for improving yield in the context of plant breeding. Clarifying the relationships between flower morphology, nectar reward content, pollinator recruitment, and pollination outcomes, as well as their genetic components, can inform breeding priorities for enhancing blueberry production. In the present study, we measured ten flower and nectar traits and indices of successful pollination, including fruit set, seed count, and fruit weight in 38 southern highbush blueberry genotypes. Additionally, we assessed pollinator visitation frequency and foraging behavior over two growing seasons. Several statistical models were tested to optimize the prediction of pollinator visitation and pollination success, including partial least squares, BayesB, ridge-regression, and random forest. Random forest models obtained high predictive abilities for pollinator visitation frequency, with values of 0.54, 0.52, and 0.66 for honey bee, bumble bee, and total pollinator visits, respectively. The BayesB model provided the most consistent prediction of fruit set, fruit weight, and seed set, with predictive abilities of 0.07, -0.08, and 0.42, respectively. Variable importance analysis revealed that genotypic differences in nectar volume had the greatest impact on honey bee and bumble bee visitation, although preferences for flower morphological traits varied depending on the foraging task. Flower density was a major driving factor attracting nectar-foraging honey bees and bumble bees, while pollen-foraging bumble bees were most influenced by flower accessibility, specifically corolla length and the length-to-width ratio. Honey bees comprised the majority of pollinator visits, and were primarily influenced by nectar volume and flower density. Corolla length and the length-to-width ratio were also identified as the main predictors of fruit set, fruit weight, seed count, as well as pollen-foraging bumble bee visits, suggesting that these bees and their foraging preferences may play a pivotal role in fruit production. Moderate to high narrow-sense heritability values (ranging from 0.30 to 0.77) were obtained for all floral traits, indicating that selective breeding efforts may enhance cultivar attractiveness to pollinators.

Inclusive Innovation Governance for Just Transitions: Insights from the Bean Agri-Food System in the Brunca Region of Costa Rica

This study explores how an inclusive governance of “niche” innovations with family farmers enables or limits the realization of just transitions in family farming at different scales, from the local to the national levels. The research focuses on a case study in the Brunca region of Costa Rica, which has pioneered innovations for bean research with family farmers. The primary source of information for the study was in-depth interviews with relevant stakeholders. Secondary information comes from a desk review of the relevant policies and strategic documents from the relevant authorities. Lastly, a semi-scoping review was conducted to explore gender and intergenerational gaps in plant breeding, as well as potential conflicts between plant breeding and on-farm agro-diversity. The inclusive governance of bean research contributes to the realization of just-transition dimensions of justice, such as recognitional, procedural, and distributional justice, as a result of farmers’ engagement in participatory plant-breeding processes, and due to the coordination between different institutions at the local and national levels. Progress has been made in terms of the recognition and participation of farmers in research and in decision-making locally. The findings show interactions between the local and national scales, in terms of the commercialization of farmers’ seeds. However, the Costa Rican program for bean research struggles to engage family farmers in national decision-making processes and falls short of addressing equality and intergenerational issues. It also faces challenges in reaching a balance between short-term income generation and long-term conservation of agro-biodiversity on the farm. Fostering just transitions in family farming would require a greater inclusion of women and youth by the Costa Rican bean system. This implies allocating resources to increase their capacities and participation in bean research, as well as in decision-making at all levels. This is imperative and could be achieved if the government and relevant stakeholders align their strategic objectives and priorities to shorten gender and intergenerational gaps within the bean production system.

Improving genomic prediction of rhizomania resistance in sugar beet (Beta vulgaris L.) by implementing epistatic effects and feature selection

\textbf{Background:}Rhizomania counts as the most important disease in sugar beet (\textit{Beta vulgaris} L.) for which no plant protection is available, leaving plant breeding as the only defence strategy at the moment. Five resistance genes have been detected on the same chromosome and further studies suggested that these might be different alleles at two resistance clusters. Nevertheless, it was postulated that rhizomania resistance might be a quantitative trait with multiple unknown minor resistance genes. Here, we present a first attempt at genomic prediction of rhizomania resistance in a population that was genotyped using single nucleotide polymorphism (SNP) markers. \newline \textbf{Methods:} First, genomic prediction was performed using all SNPs. Next, we calculated the variable importance for each SNP using machine learning and performed genomic prediction by including the SNPs incrementally in the prediction model based on their variable importance. Using this method, we selected the optimal number of SNPs that maximised the prediction accuracy. Furthermore, we performed genomic prediction with SNP pairs. We also performed feature selection with SNP pairs using the information about the variable importance of the single SNPs. \newline \textbf{Results:} From the four methods under investigation, the latter led to the highest prediction accuracy. These results lead to the following conclusions: (I) The genotypes that were resistant at all known resistance genes, provided the highest possible variation of virus concentrations that the machine can measure. Thus, it can be assumed that more genes must be involved in the resistance towards rhizomania. (II) We show that prediction models that include SNP interactions increased the prediction accuracy. \newline \textbf{Conclusions:} Altogether, our findings suggest that rhizomania resistance is a complex quantitative trait that is affected by multiple genes as well as their interaction.

Prediction of additive genetic variances of descendants for complex families based on Mendelian sampling variances.

The ability to predict the outcome of selection and mating decisions enables breeders to make strategically better selection decisions. To improve genetic progress, those individuals need to be selected whose offspring can be expected to show high genetic variance next to high breeding values. Previously published approaches enable to predict the variance of descendants of two future generations for up to 4 founding haplotypes, or 2 outbred individuals, based on phased genotypes, allele effects and recombination frequencies. The purpose of this study was to develop a general approach for the analytical calculation of the genetic variance in any future generation. The core development is an equation for the prediction of the variance of double haploid lines, under the assumption of no selection and negligible drift, stemming from an arbitrary number of founder haplotypes. This double haploid variance can be decomposed into gametic Mendelian sampling variances (MSV) of ancestors of the double haploid lines allowing usage for non-double haploid genotypes which enables application in animal breeding programs as well as in plant breeding programs. Together with the breeding values of the founders, the gametic MSV may be used in new selection criteria. We present our idea of such a criterion that describes the genetic level of selected individuals in four generations. Since breeding programs do select, the assumption made for predicting variances is clearly violated which decreases the accuracy of predicted gametic MSV caused by changes in allele frequency and linkage disequilibrium. Despite violating the assumption, we found high predictive correlations of our criterion to the true genetic level which was obtained by means of simulation for the "corn" and "cattle" genome models tested in this study (0.90 and 0.97). In practice, the genotype phases, genetic map and allele effects all need to be estimated meaning inaccuracies in their estimation will lead to inaccurate variance prediction. Investigation of variance prediction accuracy when input parameters are estimated was not part of this study.

Assessment of Heterosis in Inter-varietal Hybrids of Mangalore Melon (Cucumismelo var. acidulus) for Yield and Quality Traits

Mangalore melon is one of the distinct class of Cucurbitaceae family widely grown in southern part of India. Despite of having many nutritional values, there is scope for systematic hybridization study to improve the many qualitative and quantitative traits. An investigation was undertaken to develop inter-varietal hybrids and their evaluation to assess the extent of heterosis in newly developed hybrids between selected genotypes of Mangalore melon during kharif, 2019 and 2020 at College of Horticulture, Sirsi, Karnataka, India. A total of 16 intervarietal hybrids were developed by crossing 15 parental lines. Hybrids were developed using hand emasculation and hand pollination technique and the developed hybrids were evaluated using RCBD design with two replications. The results showed significant positive heterosis over mid parent and better parent for all the hybrids. The hybrids Soubhagya×RNMS-1 exhibited highest and significant heterosis over both mid parent (88.05%) and better parent (72.69%) followed by MS-19×MS-5 over mid parent (73.09%) and better parent (61.10%). From this study, it was concluded that hybrid Soubhagya X RNMS-1 was superior over their parents for most of the traits with the total yield of 8.44 kg plant-1. These best performing hybrids are need to be studied for their stability and commercial exploitation, resistance of pest and diseases, keeping quality of hybrids (storability) and experimentation on hybrid seed production technologies as well as storability of seeds should be done.

Mapping taste and flavour traits to genetic markers in lettuce Lactuca sativa

Lettuce is the most highly consumed raw leafy vegetable crop eaten worldwide, making it nutritionally important in spite of its comparatively low nutrient density in relation to other vegetables. However, the perception of bitterness caused by high levels of sesquiterpenoid lactones and comparatively low levels of sweet tasting sugars limits palatability. To assess variation in nutritional and taste-related metabolites we assessed 104 members of a Lactuca sativa cv. Salinas x L. serriola (accession UC96US23) mapping population. Plants were grown in three distinct environments, and untargeted NMR and HPLC were used as a rapid chemotyping method, from which 63 unique Quantitative Trait Loci (QTL) were identified. We were able to identify putative regulatory candidate genes underlying the QTL for fructose on linkage group 9, which accounted for up to 36 % of our population variation, and which was stable across all three growing environments; and for 15-p-hydroxyyphenylacetyllactucin-8-sulfate on linkage group 5 which has previously been identified for its low bitterness, while retaining anti-herbivory field effects. We also identified a candidate gene for flavonoid 3′,5′- hydroxylase underlying a polyphenol QTL on linkage group 5, and two further candidate genes in sugar biosynthesis on linkage groups 2 and 5. Collectively these candidate genes and their associated markers can inform a route for plant breeders to improve the palatability and nutritional value of lettuce in their breeding programmes.

Functions and Regulatory Mechanisms of bHLH Transcription Factors during the Responses to Biotic and Abiotic Stresses in Woody Plants.

Environmental stresses, including abiotic and biotic stresses, have complex and diverse effects on the growth and development of woody plants, which have become a matter of contention due to concerns about the outcomes of climate change on plant resources, genetic diversity, and world food safety. Plant basic helix-loop-helix (bHLH) transcription factors (TFs) are involved in a variety of physiological processes and play an important role in biotic and abiotic stress responses of woody plants. In recent years, an increasing body of studies have been conducted on the bHLH TFs in woody plants, and the roles of bHLH TFs in response to various stresses are increasingly clear and precise. Therefore, it is necessary to conduct a systematic and comprehensive review of the progress of the research of woody plants. In this review, the structural characteristics, research history and roles in the plant growth process of bHLH TFs are summarized, the gene families of bHLH TFs in woody plants are summarized, and the roles of bHLH TFs in biotic and abiotic stresses in woody plants are highlighted. Numerous studies mentioned in this review have shown that bHLH transcription factors play a crucial role in the response of woody plants to biotic and abiotic stresses. This review serves as a reference for further studies about enhancing the stress resistance and breeding of woody plants. Also, the future possible research directions of bHLH TFs in response to various stresses in woody plants will be discussed.

Lentil adaptation to drought stress: response, tolerance, and breeding approaches.

Lentil (Lens culinaris Medik.) is a cool season legume crop that plays vital roles in food and nutritional security, mostly in the least developed countries. Lentil is often cultivated in dry and semi-dry regions, where the primary abiotic factor is drought, which negatively impacts lentil growth and development, resulting in a reduction of yield. To withstand drought-induced multiple negative effects, lentil plants evolved a variety of adaptation strategies that can be classified within three broad categories of drought tolerance mechanisms (i.e., escape, avoidance, and tolerance). Lentil adapts to drought by the modulation of various traits in the root system, leaf architecture, canopy structure, branching, anatomical features, and flowering process. Furthermore, the activation of certain defensive biochemical pathways as well as the regulation of gene functions contributes to lentil drought tolerance. Plant breeders typically employ conventional and mutational breeding approaches to develop lentil varieties that can withstand drought effects; however, little progress has been made in developing drought-tolerant lentil varieties using genomics-assisted technologies. This review highlights the current understanding of morpho-physiological, biochemical, and molecular mechanisms of lentil adaptation to drought stress. We also discuss the potential application of omics-assisted breeding approaches to develop lentil varieties with superior drought tolerance traits.

ANALISIS LINTAS KARAKTER AGRONOMI TERHADAP KOMPONEN PRODUKSI KACANG TANAH (Arachis hypogaea L.)

The peanut has been one of the most important commercial crops in Indonesia and its yield still needs to be improved. Peanut yield can be increased genetically through plant breeding programs. Indirect selection in peanut plant breeding programs requires information related to the relationship between its characters, especially the relationship between growth and yield characters through correlation and path analysis. This study aims to determine the relationship between growth and yield characteristics of peanuts based on correlation values, direct effects, and indirect effects using path analysis, so that selection criteria for high-yielding peanuts are obtained. This study was conducted using a survey method. 50 peanut plant samples were taken randomly as observation units, then path analysis was carried out. The results showed that the 12 DAP canopy width was positively and significantly correlated with peanut yield characters, such as fresh pod weight and number of pods. The 12 DAP canopy width also had the highest direct and indirect influence on the character of fresh and dry pod weight, and the number of pods. Indirect selection to increase peanut yield can be done using the 12 DAP canopy width character as selection criteria.

Using Dynamic Laser Speckle Imaging for Plant Breeding: A Case Study of Water Stress in Sunflowers.

This study focuses on the promising use of biospeckle technology to detect water stress in plants, a complex physiological mechanism. This involves monitoring the temporal activity of biospeckle pattern to study the occurrence of stress within the leaf. The effects of water stress in plants can involve physical and biochemical changes. Some of these changes may alter the optical scattering properties of leaves. The present study therefore proposes to test the potential of a biospeckle measurement to observe the temporal evolution in different varieties of sunflower plants under water stress. An experiment applying controlled water stress with osmotic shock using polyethylene glycol 6000 (PEG) was conducted on two sunflower varieties: one sensitive, and the other more tolerant to water stress. Temporal monitoring of biospeckle activity in these plants was performed using the average value of difference (AVD) indicator. Results indicate that AVD highlights the difference in biospeckle activity between day and night, with lower activity at night for both varieties. The addition of PEG entailed a gradual decrease in values throughout the experiment, particularly for the sensitive variety. The results obtained are consistent with the behaviour of the varieties submitted to water stress. Indeed, a few days after the introduction of PEG, a stronger decrease in AVD indicator values was observed for the sensitive variety than for the resistant variety. This study highlights the dynamics of biospeckle activity for different sunflower varieties undergoing water stress and can be considered as a promising phenotyping tool.

Genetic improvement of phosphate-limited photosynthesis for high yield in rice

Low phosphate (Pi) availability decreases photosynthesis, with phosphate limitation of photosynthesis occurring particularly during grain filling of cereal crops; however, effective genetic solutions remain to be established. We previously discovered that rice phosphate transporter OsPHO1;2 controls seed (sink) development through Pi reallocation during grain filling. Here, we find that OsPHO1;2 regulates Pi homeostasis and thus photosynthesis in leaves (source). Loss-of-function of OsPHO1;2 decreased Pi levels in leaves, leading to decreased photosynthetic electron transport activity, CO2 assimilation rate, and early occurrence of phosphate-limited photosynthesis. Interestingly, ectopic expression of OsPHO1;2 greatly increased Pi availability, and thereby, increased photosynthetic rate in leaves during grain filling, contributing to increased yield. This was supported by the effect of foliar Pi application. Moreover, analysis of core rice germplasm resources revealed that higher OsPHO1;2 expression was associated with enhanced photosynthesis and yield potential compared to those with lower expression. These findings reveal that phosphate-limitation of photosynthesis can be relieved via a genetic approach, and the OsPHO1;2 gene can be employed to reinforce crop breeding strategies for achieving higher photosynthetic efficiency.

Cross-species transcriptomics reveals differential regulation of essential photosynthesis genes in Hirschfeldia incana.

Photosynthesis is the only yield-related trait not yet substantially improved by plant breeding. Previously, we have established Hirschfeldia incana as the model plant for high photosynthetic light-use efficiency (LUE). Now we aim to unravel the genetic basis of this trait in H. incana, potentially contributing to the improvement of photosynthetic LUE in other species. Here, we compare its transcriptomic response to high light with that of Arabidopsis thaliana, Brassica rapa, and Brassica nigra, three fellow Brassicaceae members with lower photosynthetic LUE. We built a high-light, high-uniformity growing environment, in which the plants developed normally without signs of stress. We compared gene expression in contrasting light conditions across species, utilizing a panproteome to identify orthologous proteins. In-depth analysis of three key photosynthetic pathways showed a general trend of lower gene expression under high-light conditions for all four species. However, several photosynthesis-related genes in H. incana break this trend. We observed cases of constitutive higher expression (like antenna protein LHCB8), treatment-dependent differential expression (as for PSBE), and cumulative higher expression through simultaneous expression of multiple gene copies (like LHCA6). Thus, Hirschfeldia incana shows differential regulation of essential photosynthesis genes, with the light-harvesting complex as the first point of deviation. The effect of these expression differences on protein abundance and turnover, and ultimately the high photosynthetic LUE phenotype is relevant for further investigation. Furthermore, this transcriptomic resource of plants fully grown under, rather than briefly exposed to, a very high irradiance, will support the development of highly efficient photosynthesis in crops.

Genotype-environment interaction for grain yield in maize (Zea mays L.) using the additive main effects and multiplicative interaction (AMMI) model.

Genotype-environment interaction consists of the different response of individual genotypes resulting from changing environmental conditions. Its significance is a phenomenon that makes the breeding process very difficult. On the one hand, the breeder expects stable genotypes, i.e., yielding similarly regardless of environmental conditions. On the other hand, selecting the best genotypes for each region is one of the key challenges for breeders and farmers. The aim of this study was to evaluate genotype-by-environment interaction for grain yield in new maize hybrids developed by Plant Breeding Smolice Co. Ltd., utilizing the additive main effects and multiplicative interaction (AMMI) model. The investigation involved 69 maize (Zea mays L.) hybrids, tested across five locations in a randomized complete block design with three replications. Grain yield varied from 8.76 t ha-1 (SMH_16417 in Smolice) to 16.89 t ha-1 (SMH_16043 in Płaczkowo), with a mean yield of 13.16 t ha-1. AMMI analysis identified significant effects of genotype, environment, and their interaction on grain yield. Analysis of variance indicated that 25.12% of the total variation in grain yield was due to environment factor, 35.20% to genotypic differences, and 21.18% to genotype by environmental interactions. Hybrids SMH_1706 and SMH_1707 are recommended for further breeding programs due to their high stability and superior average grain yield.

Study of Metroglyph Analysis in Faba Bean (Vicia faba L.) Genotypes for Different Quantitative Characters

The study conducted at the Research Farm of Genetics and Plant Breeding, Naini Agricultural Institute, Sam Higginbottom University of Agriculture, Technology and Sciences during the rabi/winter season of 2022-23 aimed to estimate the genetic diversity and morphological variations among 20 Faba bean (Vicia faba L.) genotypes using Anderson’s metroglyph analysis for different quantitative traits. Analysis of variance revealed significant differences for all the characters studied indicating the presence of considerable amount of variability among the genotypes. The genotypes viz, IVT HB19-14 I, IVT HB 19-11, ABSPS-2-1, AVT I HB16-15, IVT HB18-15, VIKRANT (CHECK), ABSPS-1-5, ABSPS-1-2 recorded significantly superior seed yield and yield contributing traits respectively. Six distinct morphological complexes were identified as a result of the analysis, which showed significant variation among the genotypes for the majority of traits. Complex I included eight genotypes with high seed yield and a high number of seeds per plant, while Complex II comprised five genotypes with low seed yield and a low number of seeds per plant. Complex III: High seed yield with a lower number of seeds per plant including three genotypes. Complex IV: Moderate seed yield with a higher number of seeds per plant, represented by two genotypes, Complex V: Very low seed yield and a very low number of seeds per plant, represented by one genotype and Complex VI: Low plant height with a very low number of spikelet’s per panicle, represented by one genotype. The genotypes IVT HB18-15, IVT HB19-13, IVT HB19-14 I, IVT HB19-11, ABSPS-1-1, ABSPS-1-2, ABSPS-1-4, and VIKRANT were identified as superior due to their high index scores and were suggested for use in breeding programs to enhance yield and other desirable traits. The study concluded that metroglyph analysis is an effective method for assessing genetic diversity and morphologicalvariations, providing valuable insights for breeders to improve Faba bean yield and adaptability through targeted cross-breeding of superior genotypes.

Variation in a Poaceae-conserved fatty acid metabolic gene cluster controls rice yield by regulating male fertility

A wide variety of metabolic gene clusters exist in eukaryotic genomes, but fatty acid metabolic gene clusters have not been discovered. Here, combining with metabolic and phenotypic genome-wide association studies, we identify a major locus containing a six-gene fatty acid metabolic gene cluster on chromosome 3 (FGC3) that controls the cutin monomer hydroxymonoacylglycerols (HMGs) contents and rice yield, possibly through variation in the transcription of FGC3 members. We show that HMGs are sequentially synthesized in the endoplasmic reticulum by OsFAR2, OsKCS11, OsGPAT6, OsCYP704B2 and subsequently transported to the apoplast by OsABCG22 and OsLTPL82. Mutation of FGC3 members reduces HMGs, leading to defective male reproductive development and a significant decrease in yield. OsMADS6 and OsMADS17 directly regulate FGC3 and thus influence male reproduction and yield. FGC3 is conserved in Poaceae and likely formed prior to the divergence of Pharus latifolius. The eukaryotic fatty acid and plant primary metabolic gene cluster we identified show a significant impact on the origin and evolution of Poaceae and has potential for application in hybrid crop breeding.