Advances in Fruit Crop Breeding: Conventional and Molecular Approaches

Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder’s equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.

Global agriculture is exposed to tremendous challenges due to climate change, exceeding population, and fast depleting natural resources. Conventional breeding is robust and reliable but often requires 8 to 12 years for varietal development. These considerations demand the rapid development of varieties that keep up with current climate scenarios. Speed breeding has emerged as one such innovative approach where controlled environmental conditions aid in reducing the generation cycle. The photosensitive nature of pigeonpea limits the generation advancement during off-seasons, thereby extending generation time and slow pacing the genetic improvement. To overcome this temporal, constrain and to accelerate the breeding progress, establishing a speed breeding protocol tailored to pigeonpea is critical. This chapter delves into the framework, protocol, establishment, and upscaling of speed breeding facility for pigeonpea. Technical aspects such as optimization of spectral quality, photoperiod duration, sowing density, and sequential planting regimes were emphasized. The protocol strongly suggests that speed breeding has substantially reduced the generation time in different pigeonpea maturity groups. Feasibility and potential of integrating this approach with several advanced genomic tools like marker-assisted selection, genomic selection and high-throughput was discussed. The operational limitations like energy efficiency and scalability concerns of speed breeding in pigeon pea were reflected. The chapter concludes by addressing prospects of integrating this approach with several traits of future promise to reach the target markets. Additionally, investing in capacity building and sustainability measures will ensure the long-term success of speed breeding endeavors, delivering improved pigeonpea.

Ornamental plants exhibit the capacity to augment visual appeal through their diverse morphological features, encompassing floral forms, hues, foliage, and numerous other attributes. These botanical specimens find application in landscaping, floral arrangements, container gardening, and various commercial endeavors. As a result, there exists a persistent need for the creation of novel cultivars to satisfy market demands. Traditional plant breeding is slow and takes a lot of work. This has led to the use of biotechnology, especially molecular breeding. Molecular breeding lets breeders target specific, difficult-to-change traits like flower color, scent, and plant shape. It uses advanced methods like marker-assisted selection and genome-wide selection to speed up improvements and reduce the time it takes to develop new varieties. Molecular breeding may revolutionize agriculture in the 21st century by making genetic improvements faster and more efficient. Thanks to advances in genomics, gene editing, and molecular plant breeding, we now understand molecular markers much better. This has improved breeding strategies and given us a deeper understanding of crop diversity. This review elucidates the importance of various commercially valuable ornamental traits and delineates the research endeavors and accomplishments in enhancing floral characteristics through molecular breeding techniques.

Next-generation molecular tools with AI integration can accelerate genetic gain in sugarcane by enhancing variation, accuracy, and efficiency, enabling rapid development of high-yielding, high-quality, and climate-resilient varieties. Enhancing genetic gain is essential for sustainable sugar and bioenergy production, especially amid growing global reliance on renewable energy sources. Sugarcane and its byproducts serve as important feedstocks for both first and second-generation biofuels, and face several breeding challenges due to its genetic complexity, extended breeding cycles, and strong environmental interactions. The breeder's equation offers a quantitative framework to accelerate genetic improvement by optimizing four key components: additive genetic variation (σa), heritability (h2), selection intensity (i), and breeding cycle length (L). Additive genetic variation can be enhanced through genome-wide exploration, including genome, pan-genome, and super pangenome analyses, gene discovery, characterization, and the induction of novel variations. Precise estimation of heritability in sugarcane can be achieved through the large-scale characterization of germplasm, high-throughput phenotyping, and detailed genotype × environment interaction (G × E) studies. Selection intensity can be increased by expanding population sizes via genotypic, genomic, and invitro selection, leveraging the law of large numbers, and adopting technologies that provide greater throughput, precision, and cost efficiency. Breeding cycle time can be significantly reduced using tools, such as marker-assisted selection, genomic selection, emerging doubled haploid strategies (though still challenging in polyploid crops like sugarcane), speed breeding, transgenic approaches, and genome editing technologies like CRISPR/Cas9 (including base and prime editing), TALENs. This review provides a comprehensive overview of each component of the breeder's equation in sugarcane breeding and highlights next-generation molecular strategies and tools aligned to these components. The integration of these advanced tools with artificial intelligence holds immense potential to enhance genetic gain and accelerate the development of high-yielding, high-quality, and climate-resilient sugarcane varieties.

Climate change, rising global food demand, and shrinking resources require transformative innovations in crop breeding. This review outlines recent advances in new breeding technologies (NBTs), including molecular markers, genome-wide association studies (GWAS), genomic selection (GS), next-generation sequencing (NGS), and gene editing (GE) tools such as the clustered regularly interspaced short palindromic repeat (CRISPR/Cas), base editing, and prime editing. These methods enable the accurate improvement of traits, thereby accelerating the development of crops resistant to both abiotic and biotic stresses. The integration of multi-omics platforms, including genomics, transcriptomics, proteomics, metabolomics, and phenomics, provides a comprehensive framework for deciphering and manipulating complex trait architectures. Artificial intelligence (AI) and machine learning (ML) enhance precision breeding by providing data-driven insights and enabling the forecasting of traits. Emphasis is also placed on combining gene editing with other strategies, such as speed breeding, to accelerate the development of traits. This review underscores the importance of an integrated systems biology approach that combines multi-omics, gene editing, AI, and speed breeding to accelerate the development of climate-resilient, high-yielding, and nutritionally enhanced crops. The integration of these innovative technologies holds great promise for addressing global food security, environmental sustainability, and agricultural resilience in the face of climate change. A strategic framework for the future of plant breeding is outlined, emphasizing the importance of interdisciplinary collaboration in building a sustainable agricultural future.

Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.

New Technologies, Tools and Approaches for Improving Crop Breeding

Production of high-yield and high-quality fruits is always the long-term objective of fruit crop cultivation, which, however, is challenged by various abiotic stresses such as drought, extreme temperatures and high salinity, and the adverse impacts of abiotic stresses on fruit crops are exacerbated by climate change in recent years. To cope with these environmental stressors, fruit crops have evolved adaptative strategies involving physiological changes and molecular regulation. In this review, we summarise the relevent changes in photosynthesis, osmotic and reactive oxygen species (ROS) equilibrium, metabolism and protein homeostasis in response to abiotic stresses. Moreover, perception of environmental stimuli as well as recent progress of underlying regulatory mechanisms is also discussed. Based on our current knowledge, possible strategies for stress resilience improvement in fruit crops are accordingly proposed. In addition, we also discuss the challenges in identification of key nodes in plant responses to multiple stresses and development of stress-resilient fruit crops, and addressing these issues in the future would advance our understanding of how fruit crops combat abiotic stresses and facilitate the breeding of superior fruit crops that can adapt to and thrive in the changing environments.

The article presents the results of a long-term study of the world collection of pears, which is one of the largest in the RIPB system. It is kept at the Maykop Experimental Station of the Federal Research Center of the All-Russian Institute of Plant Genetic Resources named after N.I. Vavilov» branch and has 875 varieties, 248 species, forms and varieties, and is constantly updated with new samples. The collection includes wild species, as well as Russian and foreign varieties from Europe, America, Asia. The research was carried out in the soil and climatic conditions of the foothill zone of the North Caucasus in 2005–2015. The aim of the research is to study the dependence of biological traits and properties on the origin of samples and to isolate genotypes with the maximum expression of traits for use in breeding. In the research the methodological instructions of the All-Russian Research Institute of Breeding of Fruit Crops and the North Caucasus Center for the Breeding of Fruit, Berry, Flower and Ornamental Crops and Grapes have been used. The analysis of weather conditions has been carried out using meteorological data of the MES of the RIPB Branch. The regularities of the manifestation of signs and biological properties of the studied samples depending on their geographical origin have been established. The varieties that combine a fairly good quality of fruits and other, valuable characteristics have been identified: late flowering for protection from frost – Obilnaya Tuza, Uspenka; resistance to fungal diseases – Vodnik, Utrennyaya Svezhest; early maturity – Borup, Napoca and high regular yield – P. regelii x P. pyrifolia № 2 interspecific hybrid. As a result of many years of studying the world collection of pears the list of isolated genotypes of valuable traits has been added, the use of which contributes to a significant increase in the efficiency of breeding work.

Charcoal rot, caused by Macrophomina phaseolina, is a destructive soil-borne disease that threatens several oilseed crops. Its persistence in the soil through microsclerotia, wide host range, and strong association with drought and heat stress makes it a formidable challenge for sustainable production. Breeding for resistance is widely recognized as the most effective and environmentally sound management strategy. This review synthesizes current knowledge on the biology and economic impact of charcoal rot in oilseed crops, with a focus on screening methodologies, genetic resistance, and breeding interventions. Field-based techniques such as root and stem severity scoring and colony-forming unit indices remain central to resistance evaluation, although challenges of standardization and reproducibility persist. Advances in molecular and genomic tools, including QTL mapping, genome-wide association studies (GWASs), marker-assisted selection (MAS), and genomic selection (GS), have begun to strengthen the identification and deployment of resistance loci. In addition, speed breeding, high-throughput phenotyping, and gene-editing platforms such as CRISPR/Cas offer novel opportunities to accelerate cultivar development. Integration of these approaches, along with the exploration of wild relatives and pre-breeding materials, is essential for broadening the genetic base of resistance and achieving durable, climate-resilient oilseed production. By linking pathogen biology, screening methods, and advanced genetic strategies, this review provides a comprehensive framework for future breeding programs aimed at mitigating the impact of charcoal rot in oilseed crops.

Burgeoning population, ever changing lifestyles and advancing climate change has made it mandatory to revamp the currently available crop cultivars so as to secure food & nutritional security worldwide and accomplish other market driven traits. Although a lot of appreciable work has been done to produce high yielding and nutrient-rich strains of panoply of food and fiber crops, the pace of breeding superior varieties is yet to match the demand for the same. The duration of the seed-to-seed cycle, which is 10-12 years in case of conventional approaches, is one of the crucial bottlenecks in the progress of modern plant breeding ventures. The concept of Speed Breeding serves as a saviour here by drastically reducing the time required for cultivar development, release and commercialization to nearly half. It is a suite of techniques that involves the manipulation of environmental conditions under which crops are grown, aiming to accelerate flowering & seed set and advance to the next breeding generation as quickly as possible. It encompasses manipulation of day/night temperature, available light spectrum & intensity, photoperiod duration, soil moisture, use of PGRs, adjusting CO2 & O2 levels in air and high-density plantings in order to reduce time to floral initiation, hasten embryo development and seed maturity. Recent research has shown the power of combining emerging techniques, such as gene editing using CRISPR/Cas9, high-throughput phenotyping and genotyping, genomic selection, and MAS, with SB for boosting genetic gain. There are few key challenges limiting the deployment of speed breeding techniques in developing countries, including the high costs of infrastructure, required expertise & skill set and continuous financial support for research and development to maintain this as a sustainable operation. However, the existing constraints can be resolved by further optimization of the SB protocols for critical food crops and their efficient integration in plant breeding pipelines. Collaborative international research endeavours involving multi-disciplinary teams are needed to encourage the integration of SB systems in basic and applied research. Nonetheless the technique of Speed breeding will come out as the next breakthrough of the century and become the part and parcel of modern breeding manoeuvres.

Genomic selection-based breeding programs offer significant advantages over conventional phenotypic selection, particularly in accelerating genetic gains in plant breeding, as demonstrated by simulations focused on combating Fusarium head blight (FHB) in wheat. FHB resistance, a crucial trait, is challenging to breed for due to its quantitative inheritance and environmental influence, leading to slow progress using conventional breeding methods. Stochastic simulations in our study compared various breeding schemes, incorporating genomic selection (GS) and combining it with speed breeding, against conventional phenotypic selection. Two datasets were simulated, reflecting real-life genotypic data (MASBASIS) and a simulated wheat breeding program (EXAMPLE). Initially a 20-year burn-in phase using a conventional phenotypic selection method followed by a 20-year advancement phase with three GS-based breeding programs (GSF2F8, GSF8, and SpeedBreeding + GS) were evaluated alongside over a conventional phenotypic selection method. Results consistently showed significant increases in genetic gain with GS-based programs compared to phenotypic selection, irrespective of the selection strategies employed. Among the GS schemes, SpeedBreeding + GS consistently outperformed others, generating the highest genetic gains. This combination effectively minimized generation intervals within the breeding cycle, enhancing efficiency. This study underscores the advantages of genomic selection in accelerating breeding gains for wheat, particularly in combating FHB. By leveraging genomic information and innovative techniques like speed breeding, breeders can efficiently select for desired traits, significantly reducing testing time and costs associated with conventional phenotypic methods.

Bombyx mori, the domesticated silkworm, is one of the most economically significant and scientifically tractable insects. Since the publication of its draft genome sequence in 2004 and the subsequent production of a refined, consortium-level assembly in 2008, silkworm genomics has undergone a transformative evolution encompassing whole-genome resequencing, functional genomics, epigenomics, small RNA biology, and comparative and population genomics. These advances have not only illuminated the molecular mechanisms underlying silk production, development, immunity, and domestication, but have also catalysed a wide range of applications spanning biotechnology and biomedicine. This review synthesises current knowledge on silkworm genomics, tracing the progression from early sequencing initiatives to state-of-the-art genome editing strategies including the CRISPR/Cas9 system. A systematic literature search was conducted to identify peer-reviewed publications relevant to silkworm genomics and its applications. The primary databases consulted were Web of Science, Scopus, PubMed, and Google Scholar. The primary date range for inclusion was 2006 to 2026, with an exception for foundational studies published before 2006, which were included when they represented seminal contributions to the field. Special attention is given to the genomic architecture of silk protein genes, the regulatory roles of non-coding RNAs, epigenetic landscapes, and comparative genomics with related Lepidoptera. The biotechnological utility of the silkworm is explored in the context of silk-based biomaterials for tissue engineering, silk as a drug delivery scaffold, the production of chimeric spider silk fibres in transgenic silkworms, and the silkworm as an in vivo model for infectious disease research. The review further addresses the improvement of sericulture through molecular breeding, marker-assisted selection, and precision genome editing, as well as the genomic basis of innate immunity and disease resistance. Future directions in multi-omics integration, single-cell transcriptomics, synthetic biology, and pangenomics are discussed. Together, these developments affirm the central importance of silkworm genomics at the interface of fundamental biology and applied innovation.

Genomic selection (GS) and marker-assisted selection (MAS) rely on marker–trait associations and are both routinely used for breeding purposes. Although similar, these two approaches differ in their applications and how markers are used to estimate breeding values. In this study, GS and MAS were compared in their ability to predict six traits associated with resistance to a destructive wheat disease, Fusarium head blight (FHB). A panel consisting in 273 soft red winter wheat lines from the US Midwestern and Eastern regions was used in this study. The statistical models for MAS were built using Fhb-1, the best-studied quantitative trait loci (QTL) for FHB resistance, and two sets of QTL: one independently identified by other groups and a newer set identified “in house”. In contrast, genomic selection models relied on 19,992 SNPs distributed throughout the genome. For the MAS and GS models, marker effects were estimated with ordinary least square and ridge regression best unbiased linear prediction, respectively. Intermediate to high values of prediction accuracy (0.4–0.9) were observed for most GS models, with lower values (<0.3) found for MAS models. Treating QTL as fixed effects in GS models resulted in higher prediction accuracy when compared with a GS model with only random effects, but overestimated accuracies were obtained with in house QTL. For the same selection intensity, GS resulted in higher selection differentials than MAS for all traits. Our results indicate that GS is a more appropriate strategy than MAS for FHB resistance.

Plant breeding dramatically improved crops performances during human history and will play a pivotal role in shaping the future of agriculture. However, this activity requires large amount of time and resources. The availability of multi-year datasets and abundant DNA information enables new analyses and breeding approaches that can increase the productivity and efficiency of a breeding program. In recent years, the LSU Rice Breeding Program has implemented marker assisted selection (MAS) and genomic selection (GS) to predict performance of rice genotypes before field testing. The goal of this project was to conduct analyses and test molecular approaches to increase the efficiency of the breeding program. The objectives were: 1) investigate the effect of planting date on rice analyzing multi-year planting date experiments; 2) develop and validate a marker set to implement GS; 3) evaluate GS across elite rice populations; 4) evaluate crosses between different rice market classes developed with MAS. The analysis of planting date experiments showed that planting date significantly affects rice key traits. Grain yield, maturity, and milling yield decrease with delayed plantings. Thus, planting date must be considered when developing new improved varieties. A marker set to implement GS for southern US rice germplasm was designed for outsourcing. The validation on experimental data from 2018 to 2020 produced good GS predictive ability across different traits and segments of the breeding program. Consequently, all experimental lines tested in the last five years were genotyped with it. Four bi-parental and two multi-parent elite populations representing southern US rice germplasm were used to test GS across different traits and different training populations. The comparison of genomic and phenotypic response to selection showed that GS has similar efficacy in predicting the future performance of lines for high priority traits like grain and milling yield. The analysis showed that large amount of data improves the accuracy of selection, and that training set optimization can maximize GS accuracy. Finally, MAS made possible to efficiently develop populations from between grain classes crosses. The resulting lines showed improvement only for few traits, while no great advantages were observed for most of the other agronomic traits.