Traditional Breeding Programs Research Articles

Speed Breeding in Cereal Crops: Accelerating Genetic Improvement for Rapid Agricultural Advancement

The global demand for food is rising due to population growth, climate change, and evolving consumer preferences. Traditional plant breeding programs typically require a decade or more to develop and release new crop varieties. Speed breeding is a cutting-edge technique designed to expedite genetic improvement by significantly reducing the seed-to-seed cycle. This review explores the principles, applications, and potential of this method in accelerating agricultural advancement. Originating from NASA's experiments for food production in space, the approach has evolved into a highly efficient strategy for speeding up breeding cycles in cereal crops. By manipulating light, photoperiodic regimes, temperature, and humidity within controlled environments, it is possible to achieve up to six generations of photo-insensitive crops and two to three generations of other crops per year. This rapid generation turnover provides a unique opportunity for accelerated genetic mapping, trait stacking, and enhanced genomic selection. Speed breeding complements modern breeding technologies such as genome editing, high-throughput genotyping, and CRISPR, facilitating quicker development of crop varieties with improved traits. Its applications extend to boosting transgenic pipelines and understanding critical physiological traits in crops. Furthermore, it aligns with breeding methods like single plant selection and single seed descent, offering a more efficient path to desirable outcomes. However, challenges remain, including the need for specialized infrastructure, the impact of genotypic variations, and potential stress responses due to accelerated conditions. Despite these hurdles, speed breeding represents a promising tool in the quest for food security and resilient agricultural systems. This review examines its potential to transform plant breeding, reduce cultivar development times, and contribute to rapid agricultural advancement in a changing world.

Deciphering the differential expression patterns of yield-related negative regulators in hexaploid wheat cultivars and hybrids at different growth stages.

Hexaploid bread wheat underwent a series of polyploidization events through interspecific hybridizations that conferred adaptive plasticity and resulted in duplication and neofunctionalization of major agronomic genes. The genetic architecture of polyploid wheat not only confers adaptive plasticity but also offers huge genetic diversity. However, the contribution of different gene copies (homeologs) encoded from different subgenomes (A, B, D) at different growth stages remained unexplored. In this study, hybrid of elite cultivars of wheat were developed via reciprocal crosses (cytoplasm swapping) and phenotypically evaluated. We assessed differential expression profiles of yield-related negative regulators in these cultivars and their F1 hybrids and identified various cis-regulatory signatures by employing bioinformatics tools. Furthermore, the preferential expression patterns of the syntenic triads encoded from A, B, and D subgenomes were assessed to decipher their functional redundancy at six different growth stages. Hybrid progenies showed better heterosis such as up to 17% increase in the average number of grains and up to 50% increase in average thousand grains weight as compared to mid-parents. Based on the expression profiling, our results indicated significant dynamic transcriptional expression patterns, portraying the different homeolog-dominance at the same stage in the different cultivars and their hybrids. Albeit belonging to same syntenic triads, a dynamic trend was observed in the regulatory signatures of these genes that might be influencing their expression profiles. These findings can substantially contribute and provide insights for the selective introduction of better cultivars into traditional and hybrid breeding programs which can be harnessed for the improvement of future wheat.

A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.

To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, Polynomial Regression, DBN, and Autoencoder outperformed other models and could obtain a better prediction accuracy when they were used for phenotype prediction. In the assessment of deep learning approaches, we exemplified the SVR model, conducting analyses on feature importance and gene ontology (GO) enrichment to provide comprehensive support. After comprehensively comparing four feature importance algorithms, no notable distinction was observed in the feature importance ranking scores across the four algorithms, namely Variable Ranking, Permutation, SHAP, and Correlation Matrix, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The results of this study offer valuable insights into AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL (quantitative trait loci) mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.

The improvement of the CAPS-marker for St, J and V subgenome identification in Triticeae tribe plants using the 5S non-transcribed spacer polymorphism

In the Triticeae tribe, several subgenomes (St, J, V and others) were described, and a subgenome-specific molecular marker development is an actual branch of studies. Non-transcribed spacers (NTS) of 5S rDNA are perspective to create species-specific or genome-specific molecular markers in closely related organisms. Early, the 5S rDNA NTS based CAPS marker (StJV-f/StJV-r primers with the SmiM1 enzyme digestion) was designed for identification of the St, J and V subgenomes. The V subgenome amplicons were differed from the St and J amplicons in length. The St amplicons were distinguishable from the J amplicons after the SmiM1 enzyme digestion (St fragments were digested, J fragments were not digested, which is a disadvantage, because a failure with the digestion of St amplicons can be mistaken for J amplicons). This article presents the results of the StJV marker improvement. Comparative analysis of restriction enzyme maps revealed that the Hpy166 II enzyme cuts the StJV amplicons of all studied subgenomes in such a way that different product patterns are obtained. Experimental testing confirmed this finding. Thus, the improved CAPS marker can serve as a promising tool for verifying samples in Triticeae plant collections, screening herbarium materials, as well as in the breeding process aimed at improving wheat by involving its wild relatives in traditional breeding programs efficiently.

Review of sheep breeding and genetic research in Türkiye

The livestock industry in Türkiye is vital to the country’s agricultural sector and economy. In particular, sheep products are an important source of income and livelihood for many Turkish smallholder farmers in semi-arid and highland areas. Türkiye is one of the largest sheep producers in the world and its sheep production system is heavily dependent on indigenous breeds. Given the importance of the sheep industry in Türkiye, a systematic literature review on sheep breeding and genetic improvement in the country is needed for the development and optimization of sheep breeding programs using modern approaches, such as genomic selection. Therefore, we conducted a comprehensive literature review on the current characteristics of sheep populations and farms based on the most up-to-date census data and breeding and genetic studies obtained from scientific articles. The number of sheep has increased in recent years, mainly due to the state’s policy of supporting livestock farming and the increase in consumer demand for sheep dairy products with high nutritional and health benefits. Most of the genetic studies on indigenous Turkish sheep have been limited to specific traits and breeds. The use of genomics was found to be incipient, with genomic analysis applied to only two major breeds for heritability or genome-wide association studies. The scope of heritability and genome-wide association studies should be expanded to include traits and breeds that have received little or no attention. It is also worth revisiting genetic diversity studies using genome-wide single nucleotide polymorphism markers. Although there was no report of genomic selection in Turkish sheep to date, genomics could contribute to overcoming the difficulties of implementing traditional pedigree-based breeding programs that require accurate pedigree recording. As indigenous sheep breeds are better adapted to the local environmental conditions, the proper use of breeding strategies will contribute to increased income, food security, and reduced environmental footprint in a sustainable manner.

Селекция медоносной пчелы: достижения, проблемы и перспективы

The honey bee (Apis mellifera L.) is one of the most important managed insect pollinators worldwide. In recent years, negative processes have been occurring in honey bee populations all over the world, Russia included. Of particular concern are the mass mortality of bees due to decreased adaptation of bee colonies to environmental factors and the hybridization of bees due to replacement of native populations by gentle and productive commercial lines. Nowadays, honey bee selection is a promising area of world beekeeping which is aimed at improving and breeding new lines and breeds that are highly productive, resistant to diseases, and adapted to local climate and honey flow conditions. The review is devoted to the issues of honeybee selection and includes an analysis of the problems and achievements of classical selection, and an assessment of prospects for the introduction of molecular selection into beekeeping. In most selection and breeding programs, economic and behavioral traits, primarily honey productivity, colony strength, gentle temper, and low swarming tendency have been of predominant importance. Other selective traits, such as viability, disease resistance, and local adaptation are considered less significant since their insufficient manifestation can be compensated by caring for the bees, for example, pharmaceuticals, artificial feeding, and other beekeeping methods. Latterly, due to the growing problem of Varroa infestation of bee colonies, breeding programs also consider traits such as hygienic behavior, the growth of Varroa infestation, etc. In Russia, the selection of bees is aimed at obtaining strong colonies that are highly productive in honey, winter-hardy, resistant to diseases, possessing high egg-laying queens, etc. Recently, the genotype-environment interaction and its influence on honey bee health have been of particular interest. These studies demonstrate the relevance of breeding sustainable bee populations and lines adapted to local conditions. This will preserve the diversity of bees, prevent their death, and ensure sustainable productivity and adaptation of bee colonies to environmental changes. In traditional breeding programs, animals are selected and evaluated based on their phenotypic traits and using pedigrees. Such analysis is very labor-intensive, subjective, and considerably time-consuming. Bee selection is complicated by polyandry, male parthenogenesis, free and random mating of queens, death of mated drones, etc. Although the potential of classic breeding methods has not been fully realized yet, modern breeding programs require the use of molecular genetics methods and genomic technologies. The introduction of molecular markers (SSRs, SNP, etc.), sequencing, microarray, and information technology makes it possible to incorporate genetic information into breeding programs and provides more accurate and efficient breeding results. Despite the sequencing of the Apis mellifera genome in 2006, specific genetic markers that could be used in bee selection have not yet been proposed. At the same time, 'Omics' technologies have made it possible to identify various molecular markers (QTL, SNP, RNA, and proteins), which theoretically can be used for the selection and improvement of bee breeds. Quantitative trait loci (QTL) have now been identified, which is associated with queen fertility, disease resistance, and various types of behavior, including hygienic behavior and Varroa-sensitive hygiene in bees. Considerable research interest is aimed at creating bee populations that are resistant to diseases, primarily var-roosis as the biggest threat to global beekeeping, and developing methods for assessing the complex behavior of bees and their effective selection. To assess associations with honey production, gentleness, hygienic behavior, and resistance to the Varroa mite, a high-density SNP chip was developed. It can be used in genomic selection of honeybees and for Genome-Wide Association Studies (GWAS). The successful use of Marker-Associated Selection (MAS) in beekeeping has been demonstrated, namely, DNA markers associated with royal jelly productivity (genes of the mrjp family) have been developed. In 2023, the first study was conducted to estimate the breeding value of a bee reference population based on queen genotyping and it showed that genomic selection can be successfully applied to bees. The use of bee gut microbiome data as markers of colony health and viability in breeding programs is discussed. Thus, honey bee breeding, including molecular selection, provides an increase in the genetic and adaptive potential of existing breeds and the creation of new lines and ecotypes of bees, highly productive and adapted to certain natural and climatic conditions. Selection of honeybees contributes to the development and intensification of beekeeping. Even though the development of DNA markers of productivity, adaptability and resistance to diseases will open up new prospects for genetic selection and provide a significant advantage in time, issues related to the improvement of the classical system of bee selection currently remain relevant. The article contains 4 Figures, 1 Tables, 105 References. The Author declares no conflict of interest.

Will Genomic Information Facilitate Forest Tree Breeding for Disease and Pest Resistance?

Forest trees are beleaguered by the ever-increasing onslaught of invasive pests and pathogens, with some species in danger of functional extinction. Recent successes in developing resistant populations using traditional tree breeding assures that some of the affected species will persist in future forests. However, the sheer number of threatened species requires increases in breeding efficiency. The time is right to consider how the use of genomic resources might aid breeding efforts in the next 20 years. Any operational benefit of genomic resources will be minimal without closer collaboration between tree breeders, forest managers, and genomic researchers. We reflect here on what attributes were responsible for the success of traditional resistance breeding programs and whether advances in genomics can realistically accelerate breeding. We conclude that the use of genomics to directly advance resistance breeding efforts in the next 20 years will be limited. Major obstacles will include factors such as the undomesticated nature of most tree species, the quantitative genetic nature of resistance in many species, and the lack of adequate funding to accelerate and more fully develop genomic resources. Despite these limitations, genomic tools have potential to help increase our understanding of the nature of resistance, and the genetic variability in the host, which can aid in the deployment of resistant populations and may assist in marker-assisted selection, particularly for major gene resistance.

Speed Breeding- An Emerging Trend in Crop Improvement

During the past century, traditional breeding programs resulted in several significantly better types in a variety of unique breeding programs around the world. The plant develops slowly because of the lengthy breeding cycle, which can take ten to fifteen years from the cross to cultivar release. That stated it is quite difficult to combine multiple polygenic traits using traditional breeding methods. Speed breeding is a technique that reduces the length of the breeding cycle by lengthening the photoperiod and adjusting other glasshouse growth factors like temperature, soil type, spacing, etc. Rapid generational advancement is made possible by this strategy. Speed breeding yields 3 to 9 generations annually as opposed to 1 to 2 generations annually using standard selection processes. Instead of 2-3 generations per year under typical glasshouse conditions, speed breeding can produce up to 6 generations per year for spring wheat, durum wheat, barley, chickpea, and pea, and 4 generations for canola. Speed breeding, thus, permits the rapid generation of stable and homozygous genotypes.

Genome-wide association study for reproduction traits in Colombian Creole Blanco Orejinegro cattle.

The profitability of the beef cattle production system relies heavily on reproductive traits. Unfortunately, certain traits, such as age at first calving (AFC), calving interval (CI), and gestation length (GL), can pose challenges in traditional breeding programs because of their low heritability (0.01-0.12) and sex-limited characteristics. Another important aspect is the conservation of the genetic resources of animals adapted to the Colombian regions, which implies the preservation and rational use of the creole breeds in the country market. Therefore, this study aimed to identify genomic regions in the creole cattle breed Blanco Orejinegro (BON) that influence the reproductive traits in females. The dataset comprised 439 animals and 118,116 single-nucleotide polymorphisms' (SNPs) markers. The GS3 program was used to identify the SNP effects employing the BAYES Cπ methodology. The number of SNPs with effect for AFC was 25, 1527 for CI, and 23 for GL. Some of the genes found associated with reproductive and growth traits as well as immune response and environmental adaptation ECE1, EPH, EPHB2, SMARCAL1, IGFBP5, IGFBP2, FCGRT, EGFR, MUL1, PINK1, STPG1, CNGB1, TGFB1, OXTR, IL22RA1, MYOM3, OXTR, CNR2, HIVEP3, CTPS1, CXCL8, FCGRT, MREG, TMEM169, PECR, and MC1R. Our results evidenced a high contribution of the genetic architecture of the Colombian creole cattle breed Blanco Orejinegro that may impact should be included in implementing genetic improvement and conservation programs.

Perspectives for the use of genomic selection for genetic improvement of dairy cattle in Ukraine

An important problem in modern dairy cattle breeding is the achievement of a high level of genetic progress in economically important traits through the implementation of effective breeding programs. For this purpose, genomic selection is currently used in many countries of the world. The aim of the study was to investigate possibilities of use of genomic selection in dairy cattle breeding in Ukraine. On the basis of analysis of “Catalogue of sires of dairy and dual-purpose breeds for reproduction of cows in 2020” (sperm of these sires was used in Ukraine) two methods of breeding value estimation were compared: 1) traditional method based on pedigree and performance of progeny; 2) genomic method based on effects of SNPs. Considerable advantage of sires with genomic evaluations was proved. These sires excel sires with traditional evaluation for milk yield by 1.6 times, for fat percentage by 2.2 times, for fat yield by 1.7 times, for protein percentage by 2.1 times and for protein yield by 1.7 times. Using estimates of breeding values of sires pare-wise genetic correlations between main genetic traits were computed. The negative genetic relationship between milk yield and fat and protein percentages was revealed. Values of energy corrected milk (ECM) of daughters and dams of sires across breeds and countries of origin were calculated. It was shown that dams of sires of Holstein and Jersey breeds had highest values of energy corrected milk (9,132.0 kg and 8,041 kg, respectively) while dams of sires of Ukrainian Black-and-White dairy breed had lowest values of this trait (5,848.1 kg). According to country-of-origin daughters of sire’s form USA, Canada and the Netherlands had highest values of energy corrected milk. Values of response to selection using traditional breeding program and genomic selection were compared. It was proved that by means of shortening generation intervals on pathways of genetic improvement “sires of bulls”, “sires of cows” and “dams of bulls” using genomic selection it is possible to increase rate of genetic progress for milk yield from 100.1 kg to 180.0 kg that is by 80%

Novel SNP Markers and Their Application in Low-Cyanide Cassava (Manihot esculenta crantz) Breeding Program

The major flaw of cassava is its indigenous cyanide. Traditional breeding program for diminishing cyanide in cassava take a long waiting time for harvesting cassava tubers to assess cyanide content. Genetic markers are useful for selection of good agricultural traits, which also applied in low-cyanide cassava breeding programs by detecting gene associate with cyanide content in cassava. Therefore, the objective of this study was to develop SNP markers to detect low cyanide trait in cassava. Genotype analysis was carried out using Genotyping by Sequencing (GBS) technology assembling with phenotype analysis. Then, the SNP markers associated with cyanide content were found. Afterward, the primers of SNP marker associated with cyanide content 2 positions were developed using tetra-primer ARMS-PCR techniques. S16_640082 and S16_735381 SNP markers on chromosome 16 showed potential ability of the selection of cassava varieties with cyanide content less than 250 mg HCN/kg fresh weight. The selection accuracy of S16_640082 and S16_735381 SNP markers was 73.33 and 76.64 %, respectively. These markers could be used in marker assisted selection (MAS) in cassava seedling. It substantially reduces cassava breeding programs cost and time. HIGHLIGHTS The major flaw of cassava is its indigenous cyanide. Up to our knowledge, there is rarely report on SNP marker related to cyanide content in Thai cassava population By performing genome wide association mapping (GWAS) to characterize the genome position related to HCN, 2 novel SNP markers associated with cyanide content were found The primers to detect the SNP markers were developed using tetra-primer ARMS-PCR techniques The markers showed ability of the selection of cassava varieties with cyanide content less than 250 mg HCN/kg fresh weight GRAPHICAL ABSTRACT

HISTORY AND EVALUATION OF EMMER WHEAT

Grains are the most important source of calories and protein in the world. It is known that 52% of the daily calorie intake per capita in the world is taken from proteins and 47% from grain products. Wheat grain, which is one of the most important cereal products, has been the main food source for most countries for many years due to its suitable nutritional value, transportation, storage, easy cultivation, and wide adaptation limits.
 Undoubtedly, wheat comes first among the cultivated plants that are of great importance in different aspects today. Wheat is an indispensable food item for human nutrition in Turkey as well as all over the world. Productive and high-quality wheat varieties developed with traditional plant breeding programs have met the nutritional needs of people so far, and studies continue rapidly today. However, world agriculture is facing serious problems due to changing biotic and abiotic environmental pressures. The cultivars have become more homogeneous in terms of their genetic structures and contain less genetic diversity than their village varieties, transitional forms, and wild relatives. Wild species, transitional forms, and village varieties are gene stores that have a wide genetic base and constitute an important resource for the elimination of future problems of cultivated plants or for the acquisition of new characteristics in plants.

Characterization of guava (Psidium guajava L.) germplasm through morphological traits and SCoT markers

ABSTRACT Traditional breeding programmes have been limited to the selection and introduction of genotypes with promising agronomic characteristics, but studies focused on genetic diversity in the low hills of the North West(NW) Himalayas have not been conducted, which is very important for the identification of potential parents for breeding programmes in guava. In the present study, six best performing guava cultivars/hybrids were characterised using morphological descriptors and SCoT markers. Broad phenotypic variability among the guava cultivars/hybrids was detected using morphological descriptors. A set of 36 SCoT markers were used for polymorphism, out of which 31(86.1%) markers showed polymorphism, indicating high genetic variability in the guava cultivars/hybrids. During the analysis, 291 polymorphic amplicons were obtained, ranging from four to 19, with an average of 9.4 amplicons per primer and average polymorphic information content of 0.47.The UPGMA classified hybrids and cultivars into two groups. Based on morphological and molecular performance, L-49(Sardar) was ranked as the most promising cultivar for the low hills of NW Himalayan conditions. Morphological descriptors along with SCoT markers proved efficient in detecting the levels of genetic variability among the collections maintained in the field. These results can be used as an additional source of exploitation in guava breeding programmes.

UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies.

Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m2 microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity.

Harnessing heterosis and male sterility in soybean [Glycine max (L.) Merrill]: A critical revisit.

Soybean is a predominantly self-pollinated crop. It is also one of the important oilseed legumes. Soybean is an excellent crop having industrial, traditional, culinary, feeding, and cultural roles. Genetic diversity in breeding programs is of prime importance as it ensures the success of any breeding by enhancing the outcomes and results of the plants. The phenomenon wherein the progeny exhibits greater biomass (yield) and a faster rate of development and fertility than its parents is referred to as heterosis. As of now, heterosis is mainly limited to the trait of seed yield and is considered the basis for the development of better (superior) varieties. Male sterility (MS) is extensively used for the production of seeds and the improvement of crops coupled with the traditional breeding programs and molecular technology. Therefore, deployment of MS and heterosis in breeding soybean could yield better outcomes. This review aims to focus on two aspects, namely, MS and heterosis in soybean with its scope for crop improvement.

Marker density and statistical model designs to increase accuracy of genomic selection for wool traits in Angora rabbits.

The Angora rabbit, a well-known breed for fiber production, has been undergoing traditional breeding programs relying mainly on phenotypes. Genomic selection (GS) uses genomic information and promises to accelerate genetic gain. Practically, to implement GS in Angora rabbit breeding, it is necessary to evaluate different marker densities and GS models to develop suitable strategies for an optimized breeding pipeline. Considering a lack in microarray, low-coverage sequencing combined with genotype imputation was used to boost the number of SNPs across the rabbit genome. Here, in a population of 629 Angora rabbits, a total of 18,577,154 high-quality SNPs were imputed (imputation accuracy above 98%) based on low-coverage sequencing of 3.84X genomic coverage, and wool traits and body weight were measured at 70, 140 and 210 days of age. From the original markers, 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, 500K, 1M and 2M were randomly selected and evaluated, resulting in 50K markers as the baseline for the heritability estimation and genomic prediction. Comparing to the GS performance of single-trait models, the prediction accuracy of nearly all traits could be improved by multi-trait models, which might because multiple-trait models used information from genetically correlated traits. Furthermore, we observed high significant negative correlation between the increased prediction accuracy from single-trait to multiple-trait models and estimated heritability. The results indicated that low-heritability traits could borrow more information from correlated traits and hence achieve higher prediction accuracy. The research first reported heritability estimation in rabbits by using genome-wide markers, and provided 50K as an optimal marker density for further microarray design, genetic evaluation and genomic selection in Angora rabbits. We expect that the work could provide strategies for GS in early selection, and optimize breeding programs in rabbits.

Basic helix-loop-helix (bHLH) transcription factor MdbHLH3 negatively affects the storage performance of postharvest apple fruit

The storage period of fleshy fruits greatly affects their quality and selection, and is largely controlled by genetic factors. Therefore, it is imperative to elucidate how genetic factors affect fruit ripening and its storage. Here, we evaluated the postharvest storage properties of the basic helix-loop-helix (bHLH) transcription factor MdbHLH3-overexpressing transgenic Royal gala apple fruits. During storage, the contents of starch, malic acid, fructose, glucose, and sucrose in fruits of three MdbHLH3 transgenic lines were always higher than those of the wild-type (WT) control. Interestingly, the sugar-acid ratio also showed the same trend during fruit storage. Additionally, the fruit firmness decreased with increasing storage time, and the contents of cell wall components such as water-soluble pectin and cellulose in transgenic fruits were higher than those in control fruits, while the firmness of transgenic fruits was lower than that in WT control fruits. Though the ethylene release rate in both showed the same trend (firstly increasing, then decreasing, and finally peaking) in 90-day stored fruits, transgenic apples had higher ethylene levels than the WT control throughout storage. Furthermore, the activities of membrane peroxidase, antioxidant enzymes, and fruit ripening enzymes in all transgenic fruits were significantly higher than those in the WT control. Thus, our findings show how MdbHLH3 negatively regulates and reduces apple storage time. This may prove useful for not only developing biotechnological strategies, but also support traditional breeding programs, to help improve the storage time of fleshy fruits.

An Overview on Phenomics Applications in Different Agriculture Disciplines

In the past ten years, ground and aerial platforms with several sensors have been rapidly adopted for phenotyping diverse biotic, abiotic stressors and other characters during the crop plant's growth stages. An increase in yield achieved from traditional breeding programs worldwide are no longer sufficient to meet the projected demand for the three major cereals like Rice, Wheat and Maize. Thus the phenomics application which includes high throughput phenotyping (HTP) i.e artificial intelligence based techniques should be bought over traditional phenotyping in order to meet the needy demands of traditional ones. Also Artificial intelligence (AI)-based data analysis techniques can increase the reliability of diagnoses and, as a result, be included into instruments for effective treatment. These methods find pertinent data for use in plant breeding and pathology activities by using feature extraction, identification, classification, and prediction criteria as well as for precision breeding. This approach has various applications in various agriculture disciplines. The main steps under such techniques includes: Image acquisition, Preprocessing of images, segmentation of images, Image Representation and Description and Image recognition. The use of these helps to speed up genetic progress and lessen the phenotyping drawbacks.

Applications of Molecular Markers in Fruit Crops: A Review

Markers are any trait of an organism that can be identified with confidence and relative ease, and can be followed in a mapping population or they can be defined as heritable entities associated with the economically important trait under the control of polygenes. Molecular markers have diverse applications in fruit crop improvement, particularly in the areas of genetic diversity and varietal identification studies, disease diagnostics, hybrid detection, sex differentiation and marker assisted selection. Molecular markers provide new directions to the efforts of plant breeders particularly in gene localization, taxonomy, phylogenetic analysis and also play an important role to decrease the time required for development of new and excellent cultivars. The most interesting application of molecular markers is marker-assisted selection (MAS). Suitable DNA markers should be polymorphic in the nature and should be expressed in all tissues, organs, at various developmental stages. Compared with traditional breeding programs, molecular markers can increase the efficiency and effectiveness of fruit breeding programs.

A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms.

Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.