Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers' traditional knowledge.

In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers' appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker-trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers' traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.

SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding.

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding.

Comprehensive genome-wide analysis of wheat xylanase inhibitor protein (XIP) genes: unveiling their role in Fusarium head blight resistance and plant immune mechanisms.

In this comprehensive genome-wide study, we identified and classified 83 Xylanase Inhibitor Protein (XIP) genes in wheat, grouped into five distinct categories, to enhance understanding of wheat's resistance to Fusarium head blight (FHB), a significant fungal threat to global wheat production. Our analysis reveals the unique distribution of XIP genes across wheat chromosomes, particularly at terminal regions, suggesting their role in the evolutionary expansion of the gene family. Several XIP genes lack signal peptides, indicating potential alternative secretion pathways that could be pivotal in plant defense against FHB. The study also uncovers the sequence homology between XIPs and chitinases, hinting at a functional diversification within the XIP gene family. Additionally, the research explores the association of XIP genes with plant immune mechanisms, particularly their linkage with plant hormone signaling pathways like abscisic acid and jasmonic acid. XIP-7A3, in particular, demonstrates a significant increase in expression upon FHB infection, highlighting its potential as a key candidate gene for enhancing wheat's resistance to this disease. This research not only enriches our understanding of the XIP gene family in wheat but also provides a foundation for future investigations into their role in developing FHB-resistant wheat cultivars. The findings offer significant implications for wheat genomics and breeding, contributing to the development of more resilient crops against fungal diseases.

Haploid rhapsody: the molecular and cellular orchestra of in vivo haploid induction in plants.

In planta haploid induction (HI), which reduces the chromosome number in the progeny after fertilization, has garnered increasing attention for its significant potential in crop breeding and genetic research. Despite the identification of several natural and synthetic HI systems in different plant species, the molecular and cellular mechanisms underlying these HI systems remain largely unknown. This review synthesizes the current understanding of HI systems in plants (with a focus on genes and molecular mechanisms involved), including the molecular and cellular interactions which orchestrate the HI process. As most HI systems can function across taxonomic boundaries, we particularly discuss the evidence for conserved mechanisms underlying the process. These include mechanisms involved in preserving chromosomal integrity, centromere function, gamete communication and/or fusion, and maintenance of karyogamy. While significant discoveries and advances on haploid inducer systems have arisen over the past decades, we underscore gaps in understanding and deliberate on directions for further research for a more comprehensive understanding of in vivo HI processes in plants.

Dissection of rhizosphere microbiome and exploiting strategies for sustainable agriculture.

The rhizosphere microbiome plays critical roles in plant growth and provides promising solutions for sustainable agriculture. While the rhizosphere microbiome frequently fluctuates with the soil environment, recent studies have demonstrated that a small proportion of the microbiome is consistently assembled in the rhizosphere of a specific plant genotype regardless of the soil condition, which is determined by host genetics. Based on these breakthroughs, which involved exploiting the plant-beneficial function of the rhizosphere microbiome, we propose to divide the rhizosphere microbiome into environment-dominated and plant genetic-dominated components based on their different assembly mechanisms. Subsequently, two strategies to explore the different rhizosphere microbiome components for agricultural production are suggested, that is, the precise management of the environment-dominated rhizosphere microbiome by agronomic practices, and the elucidation of the plant genetic basis of the plant genetic-dominated rhizosphere microbiome for breeding microbiome-assisted crop varieties. We finally present the major challenges that need to be overcome to implement strategies for modulating these two components of the rhizosphere microbiome.

Recent progress in understanding the cellular and genetic basis of plant responses to low oxygen holds promise for developing flood-resilient crops.

With recent progress in active research on flooding and hypoxia/anoxia tolerance in native and agricultural crop plants, vast knowledge has been gained on both individual tolerance mechanisms and the general mechanisms of flooding tolerance in plants. Research on carbohydrate consumption, ethanolic and lactic acid fermentation, and their regulation under stress conditions has been accompanied by investigations on aerenchyma development and the emergence of the radial oxygen loss barrier in some plant species under flooded conditions. The discovery of the oxygen-sensing mechanism in plants and unravelling the intricacies of this mechanism have boosted this very international research effort. Recent studies have highlighted the importance of oxygen availability as a signalling component during plant development. The latest developments in determining actual oxygen concentrations using minute probes and molecular sensors in tissues and even within cells have provided new insights into the intracellular effects of flooding. The information amassed during recent years has been used in the breeding of new flood-tolerant crop cultivars. With the wealth of metabolic, anatomical, and genetic information, novel holistic approaches can be used to enhance crop species and their productivity under increasing stress conditions due to climate change and the subsequent changes in the environment.

Multi-omics assists genomic prediction of maize yield with machine learning approaches.

With the improvement of high-throughput technologies in recent years, large multi-dimensional plant omics data have been produced, and big-data-driven yield prediction research has received increasing attention. Machine learning offers promising computational and analytical solutions to interpret the biological meaning of large amounts of data in crops. In this study, we utilized multi-omics datasets from 156 maize recombinant inbred lines, containing 2496 single nucleotide polymorphisms (SNPs), 46 image traits (i-traits) from 16 developmental stages obtained through an automatic phenotyping platform, and 133 primary metabolites. Based on benchmark tests with different types of prediction models, some machine learning methods, such as Partial Least Squares (PLS), Random Forest (RF), and Gaussian process with Radial basis function kernel (GaussprRadial), achieved better prediction for maize yield, albeit slight difference for method preferences among i-traits, genomic, and metabolic data. We found that better yield prediction may be caused by various capabilities in ranking and filtering data features, which is found to be linked with biological meaning such as photosynthesis-related or kernel development-related regulations. Finally, by integrating multiple omics data with the RF machine learning approach, we can further improve the prediction accuracy of grain yield from 0.32 to 0.43. Our research provides new ideas for the application of plant omics data and artificial intelligence approaches to facilitate crop genetic improvements. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01454-z.

Application and prospect of BSA method based on bibliometrics in crop breeding.

In order to understand the progress and frontier in the application of BSA(bulked segregant analysis) method in crop breeding and to reflect objectively the contribution of different countries, institutions and researchers in this field at home and abroad, this study analyzed 2111 items in the WOS (Web of Science) database from 2000 to 2023 and 446 items in the CNKI (China National through Knowledge Infrastructure) database from 2003 to 2023, regarding the researches of the application of BSA in crop breeding, basing on bibliometric analysis methods using CiteSpace software including keyword co-occurrence analysis, highlight word analysis, keyword clustering analysis, clustering timeline analysis and author co-citation. The results showed that there was an consistent increasing trend in the publication number of the application of BSA in crop breeding both in the domestic and foreign journals year by year. Ranking of the top countries according to the number of publications was China, the United States and India. The Huazhong Agricultural University displayed the highest number of publications in the CNKI database, while the Chinese Academy of Agricultural Sciences was found to have the highest number of publications in the WOS database. The published articles related to the application of BSA in crop breeding abroad mainly focused on the disciplines such as plant science, agronomy, horticulture and genetics, while those in China mainly concentrated on such disciplines as plant science, plant protection, horticulture and biology. The top three authors in terms of influence in the field of appling BSA in crop breeding were Michelmore RW, Kosambi DD and Li H, while Michelmore RW, Lander ES and Li H had closer cooperations with other authors. The top three crops relating to the studies of BSA were rice(Oryza sativa), soybean(Glycine max), corn(Zea mays L.) with the hot spot traits of disease resistance and plant height domestically. The top three crops involving the studies of BSA were rice, Arabidopsis thaliana and wheat(Triticum aestivum L.) with hot spot traits of disease resistance abroad. Up to now, BSA was mainly used to localize and functionally verify the candidate genes linking target traits and the mutated genes in crops in the domestical documents, while the foreign published studies based on BSA were mainly focused on the fine mapping and functional verification of target trait genes aiming at the revelation of genetic mechanisms in crops. Research frontier analysis indicated that rice, peanuts(Arachis hypogaea L.), upland cotton(Gossypium hirsutum L.) would be the main objects of studies concerning application of BSA in crop breeding with the hot topics of crop mutants and crop metabolites in the future.

Pan-genome and multi-parental framework for high-resolution trait dissection in melon (Cucumis melo).

Linking genotype with phenotype is a fundamental goal in biology and requires robust data for both. Recent advances in plant-genome sequencing have expedited comparisons among multiple-related individuals. The abundance of structural genomic within-species variation that has been discovered indicates that a single reference genome cannot represent the complete sequence diversity of a species, leading to the expansion of the pan-genome concept. For high-resolution forward genetics, this unprecedented access to genomic variation should be paralleled and integrated with phenotypic characterization of genetic diversity. We developed a multi-parental framework for trait dissection in melon (Cucumis melo), leveraging a novel pan-genome constructed for this highly variable cucurbit crop. A core subset of 25 diverse founders (MelonCore25), consisting of 24 accessions from the two widely cultivated subspecies of C. melo, encompassing 12 horticultural groups, and 1 feral accession was sequenced using a combination of short- and long-read technologies, and their genomes were assembled de novo. The construction of this melon pan-genome exposed substantial variation in genome size and structure, including detection of ~300 000 structural variants and ~9 million SNPs. A half-diallel derived set of 300 F2 populations, representing all possible MelonCore25 parental combinations, was constructed as a framework for trait dissection through integration with the pan-genome. We demonstrate the potential of this unified framework for genetic analysis of various melon traits, including rind color intensity and pattern, fruit sugar content, and resistance to fungal diseases. We anticipate that utilization of this integrated resource will enhance genetic dissection of important traits and accelerate melon breeding.

Development of an SNP marker set for marker-assisted backcrossing using genotyping-by-sequencing in tetraploid perilla.

High-quality molecular markers are essential for marker-assisted selection to accelerate breeding progress. Compared with diploid species, recently diverged polyploid crop species tend to have highly similar homeologous subgenomes, which is expected to limit the development of broadly applicable locus-specific single-nucleotide polymorphism (SNP) assays. Furthermore, it is particularly challenging to make genome-wide marker sets for species that lack a reference genome. Here, we report the development of a genome-wide set of kompetitive allele specific PCR (KASP) markers for marker-assisted recurrent selection (MARS) in the tetraploid minor crop perilla. To find locus-specific SNP markers across the perilla genome, we used genotyping-by-sequencing (GBS) to construct linkage maps of two F2 populations. The two resulting high-resolution linkage maps comprised 2326 and 2454 SNP markers that spanned a total genetic distance of 2133 cM across 16 linkage groups and 2169 cM across 21 linkage groups, respectively. We then obtained a final genetic map consisting of 22 linkage groups with 1123 common markers from the two genetic maps. We selected 96 genome-wide markers for MARS and confirmed the accuracy of markers in the two F2 populations using a high-throughput Fluidigm system. We confirmed that 91.8% of the SNP genotyping results from the Fluidigm assay were the same as the results obtained through GBS. These results provide a foundation for marker-assisted backcrossing and the development of new varieties of perilla.

Water use efficiency across scales: from genes to landscapes.

Water scarcity is already set to be one of the main issues of the 21st century, because of competing needs between civil, industrial, and agricultural use. Agriculture is currently the largest user of water, but its share is bound to decrease as societies develop and clearly it needs to become more water efficient. Improving water use efficiency (WUE) at the plant level is important, but translating this at the farm/landscape level presents considerable challenges. As we move up from the scale of cells, organs, and plants to more integrated scales such as plots, fields, farm systems, and landscapes, other factors such as trade-offs need to be considered to try to improve WUE. These include choices of crop variety/species, farm management practices, landscape design, infrastructure development, and ecosystem functions, where human decisions matter. This review is a cross-disciplinary attempt to analyse approaches to addressing WUE at these different scales, including definitions of the metrics of analysis and consideration of trade-offs. The equations we present in this perspectives paper use similar metrics across scales to make them easier to connect and are developed to highlight which levers, at different scales, can improve WUE. We also refer to models operating at these different scales to assess WUE. While our entry point is plants and crops, we scale up the analysis of WUE to farm systems and landscapes.

CRISPR-Based Genome Editing Tools: An Accelerator in Crop Breeding for a Changing Future.

Genome editing is an important strategy to maintain global food security and achieve sustainable agricultural development. Among all genome editing tools, CRISPR-Cas is currently the most prevalent and offers the most promise. In this review, we summarize the development of CRISPR-Cas systems, outline their classification and distinctive features, delineate their natural mechanisms in plant genome editing and exemplify the applications in plant research. Both classical and recently discovered CRISPR-Cas systems are included, detailing the class, type, structures and functions of each. We conclude by highlighting the challenges that come with CRISPR-Cas and offer suggestions on how to tackle them. We believe the gene editing toolbox will be greatly enriched, providing new avenues for a more efficient and precise breeding of climate-resilient crops.

Basic Helix-Loop-Helix Transcription Factors: Regulators for Plant Growth Development and Abiotic Stress Responses.

Plant basic helix-loop-helix (bHLH) transcription factors are involved in many physiological processes, and they play important roles in the abiotic stress responses. The literature related to genome sequences has increased, with genome-wide studies on the bHLH transcription factors in plants. Researchers have detailed the functionally characterized bHLH transcription factors from different aspects in the model plant Arabidopsis thaliana, such as iron homeostasis and abiotic stresses; however, other important economic crops, such as rice, have not been summarized and highlighted. The bHLH members in the same subfamily have similar functions; therefore, unraveling their regulatory mechanisms will help us to identify and understand the roles of some of the unknown bHLH transcription factors in the same subfamily. In this review, we summarize the available knowledge on functionally characterized bHLH transcription factors according to four categories: plant growth and development; metabolism synthesis; plant signaling, and abiotic stress responses. We also highlight the roles of the bHLH transcription factors in some economic crops, especially in rice, and discuss future research directions for possible genetic applications in crop breeding.

Connecting data for consumer preferences, food quality, and breeding in support of market-oriented breeding of root, tuber, and banana crops.

The 5-year project 'Breeding roots, tubers and banana products for end user preferences' (RTBfoods) focused on collecting consumers' preferences on 12 food products to guide breeding programmes. It involved multidisciplinary teams from Africa, Latin America, and Europe. Diverse data types were generated on preferred qualities of users (farmers, family and entrepreneurial processors, traders or retailers, and consumers). Country-based target product profiles were produced with a comprehensive market analysis, disaggregating gender's role and preferences, providing prioritised lists of traits for the development of new plant varieties. We describe the approach taken to create, in the roots, tubers, and banana breeding databases, a centralised and meaningful open access to sensory information on food products and genotypes. Biochemical, instrumental textural, and sensory analysis data are then directly connected to the specific plant record while user survey data, bearing personal information, were analysed, anonymised, and uploaded in a repository. Names and descriptions of food quality traits were added into the Crop Ontology for labelling data in the databases, along with the various methods of measurement used by the project. The development and application of standard operating procedures, data templates, and adapted trait ontologies improved the data quality and its format, enabling the linking of these to the plant material studied when uploaded in the breeding databases or in repositories. Some modifications to the database model were necessary to accommodate the food sensory traits and sensory panel trials. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Plant genome editing: ever more precise and wide reaching.

Genome-editing technologies consisting of targeted mutagenesis and gene targeting enable us to modify genes of interest rapidly and precisely. The discovery in 2012 of CRISPR/Cas9 systems and their development as sequence-specific nucleases has brought about a paradigm shift in biology. Initially, CRISPR/Cas9 was applied in targeted mutagenesis to knock out a target gene. Thereafter, advances in genome-editing technologies using CRISPR/Cas9 developed rapidly, with base editing systems for transition substitution using a combination of Cas9 nickase and either cytidine or adenosine deaminase being reported in 2016 and 2017, respectively, and later in 2021 bringing reports of transversion substitution using Cas9 nickase, cytidine deaminase and uracil DNA glycosylase. Moreover, technologies for gene targeting and prime editing systems using DNA or RNA as donors have also been developed in recent years. Besides these precise genome-editing strategies, reports of successful chromosome engineering using CRISPR/Cas9 have been published recently. The application of genome editing to crop breeding has advanced in parallel with the development of these technologies. Genome-editing enzymes can be introduced into plant cells, and there are now many examples of crop breeding using genome-editing technologies. At present, it is no exaggeration to say that we are now in a position to be able to modify a gene precisely and rearrange genomes and chromosomes in a predicted way. In this review, we introduce and discuss recent highlights in the field of precise gene editing, chromosome engineering and genome engineering technology in plants.

Metabolomics as a Prospective Tool for Soybean (Glycine max) Crop Improvement.

Global demand for soybean and its products has stimulated research into the production of novel genotypes with higher yields, greater drought and disease tolerance, and shorter growth times. Genetic research may be the most effective way to continue developing high-performing cultivars with desirable agronomic features and improved nutritional content and seed performance. Metabolomics, which predicts the metabolic marker for plant performance under stressful conditions, is rapidly gaining interest in plant breeding and has emerged as a powerful tool for driving crop improvement. The development of increasingly sensitive, automated, and high-throughput analytical technologies, paired with improved bioinformatics and other omics techniques, has paved the way for wide characterization of genetic characteristics for crop improvement. The combination of chromatography (liquid and gas-based) with mass spectrometry has also proven to be an indisputable efficient platform for metabolomic studies, notably plant metabolic fingerprinting investigations. Nevertheless, there has been significant progress in the use of nuclear magnetic resonance (NMR), capillary electrophoresis, and Fourier-transform infrared spectroscopy (FTIR), each with its own set of benefits and drawbacks. Furthermore, utilizing multivariate analysis, principal components analysis (PCA), discriminant analysis, and projection to latent structures (PLS), it is possible to identify and differentiate various groups. The researched soybean varieties may be correctly classified by using the PCA and PLS multivariate analyses. As metabolomics is an effective method for evaluating and selecting wild specimens with desirable features for the breeding of improved new cultivars, plant breeders can benefit from the identification of metabolite biomarkers and key metabolic pathways to develop new genotypes with value-added features.

Genome editing and beyond: what does it mean for the future of plant breeding?

MAIN CONCLUSION: Genome editing offers revolutionized solutions for plant breeding to sustain food production to feed the world by 2050. Therefore, genome-edited products are increasingly recognized via more relaxed legislation and community adoption. The world population and food production are disproportionally growing in a manner that would have never matched each other under the current agricultural practices. The emerging crisis is more evident with the subtle changes in climate and the running-off of natural genetic resources that could be easily used in breeding in conventional ways. Under these circumstances, affordable CRISPR-Cas-based gene-editing technologies have brought hope and charged the old plant breeding machine with the most energetic and powerful fuel to address the challenges involved in feeding the world. What makes CRISPR-Cas the most powerful gene-editing technology? What are the differences between it and the other genetic engineering/breeding techniques? Would its products be labeled as "conventional" or "GMO"? There are so many questions to be answered, or that cannot be answered within the limitations of our current understanding. Therefore, we would like to discuss and answer some of the mentioned questions regarding recent progress in technology development. We hope this review will offer another view on the role of CRISPR-Cas technology in future of plant breeding for food production and beyond.

Epitranscriptomic mRNA modifications governing plant stress responses: underlying mechanism and potential application.

Plants inevitably encounter environmental adversities, including abiotic and biotic stresses, which significantly impede plant growth and reduce crop yield. Thus, fine-tuning the fate and function of stress-responsive RNAs is indispensable for plant survival under such adverse conditions. Recently, post-transcriptional RNA modifications have been studied as a potent route to regulate plant gene expression under stress. Among over 160 mRNA modifications identified to date, N6 -methyladenosine (m6 A) in mRNAs is notable because of its multifaceted roles in plant development and stress response. Recent transcriptome-wide mapping has revealed the distribution and patterns of m6 A in diverse stress-responsive mRNAs in plants, building a foundation for elucidating the molecular link between m6 A and stress response. Moreover, the identification and characterization of m6 A writers, readers and erasers in Arabidopsis and other model crops have offered insights into the biological roles of m6 A in plant abiotic stress responses. Here, we review the recent progress of research on mRNA modifications, particularly m6 A, and their dynamics, distribution, regulation and biological functions in plant stress responses. Further, we posit potential strategies for breeding stress-tolerant crops by engineering mRNA modifications and propose the future direction of research on RNA modifications to gain a much deeper understanding of plant stress biology.

Integrating speed breeding with artificial intelligence for developing climate-smart crops.

INTRODUCTION: In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass production, which is imperative for the maintenance of ecosystem services worldwide. Since conventional breeding technologies for crop improvement are limited, time-consuming, and involve laborious selection processes to foster new and improved crop varieties. An urgent need is to accelerate the plant breeding cycle using artificial intelligence (AI) to depict plant responses to environmental perturbations in real-time. MATERIALS AND METHODS: The review is a collection of authorized information from various sources such as journals, books, book chapters, technical bulletins, conference papers, and verified online contents. CONCLUSIONS: Speed breeding has emerged as an essential strategy for accelerating the breeding cycles of crop plants by growing them under artificial light and temperature conditions. Furthermore, speed breeding can also integrate marker-assisted selection and cutting-edged gene-editing tools for early selection and manipulation of essential crops with superior agronomic traits. Scientists have recently applied next-generation AI to delve deeper into the complex biological and molecular mechanisms that govern plant functions under environmental cues. In addition, AIs can integrate, assimilate, and analyze complex OMICS data sets, an essential prerequisite for successful speed breeding protocol implementation to breed crop plants with superior yield and adaptability.

The Promising Nanovectors for Gene Delivery in Plant Genome Engineering.

Highly efficient gene delivery systems are essential for genetic engineering in plants. Traditional delivery methods have been widely used, such as Agrobacterium-mediated transformation, polyethylene glycol (PEG)-mediated delivery, biolistic particle bombardment, and viral transfection. However, genotype dependence and other drawbacks of these techniques limit the application of genetic engineering, particularly genome editing in many crop plants. There is a great need to develop newer gene delivery vectors or methods. Recently, nanomaterials such as mesoporous silica particles (MSNs), AuNPs, carbon nanotubes (CNTs), and layer double hydroxides (LDHs), have emerged as promising vectors for the delivery of genome engineering tools (DNA, RNA, proteins, and RNPs) to plants in a species-independent manner with high efficiency. Some exciting results have been reported, such as the successful delivery of cargo genes into plants and the generation of genome stable transgenic cotton and maize plants, which have provided some new routines for genome engineering in plants. Thus, in this review, we summarized recent progress in the utilization of nanomaterials for plant genetic transformation and discussed the advantages and limitations of different methods. Furthermore, we emphasized the advantages and potential broad applications of nanomaterials in plant genome editing, which provides guidance for future applications of nanomaterials in plant genetic engineering and crop breeding.