2.1.1 Rice(Oryza sativa)

Rice is an important cereal and model plant.Rice genomics research can be divided into three approaches: structural genomics, functional genomics and quantitative genomics [2]. Due to the complexity of genomics research, two approaches are commonly used in combination. The combination of structural genomics and functional genomics can comprehensively analyze the composition, structure and function of genes. Yang et al. [11] used the rice seedling HuizhanNano as the experimental material and found that Pi2, Pi2, Pi2, and Pi2 genes play a key role in the strong disease resistance of huizhan using nanopore long-read technology. Using functional molecular markers, Xuan et al. [8] found a significant correlation between the blast resistance gene Pita and blast resistance, and proposed to extend the lifespan of resistant varieties by designing reasonable distribution and rotation of blast resistance genes.

The key advantage of integrating structural and quantitative genomics lies in precisely analyzing complex trait-genome associations through genomic physical structure and quantitative variation. Das et al. [9] has enriched the genomics of flower organs in rice crops by using high-throughput whole genome sequencing technology, pre-processing and mapping of reads, and variation analysis. Liu et al. [10] used 180 rice germplasms with large yield variations as experimental materials to perform association analysis of genes related to grain weight by target sequence capture sequencing, and found that TGW-HC4 was the best haplotype combination. By using genome resequencing technology, Yu et al. [3] identified a large number of genes and genomic regions or QTL controlling green traits, which enabled green super rice to be breeding and promoted.

The integration of functional and quantitative genomics enables systematic gene function analysis and precise quantification of genetic effects. Lee et al. [6] combined QTL mapping and GWAS analysis techniques to analyze heading date genes and rice nutrition-related genes, respectively, and showed that heading date genes had pleiotropic effects on grain, and accelerated heading and nutrient-rich rice breeding could be achieved by locating mutations and mutagenesis. The application of genomics has improved the nutritional content of rice[6, 7].

Beyond integrating these approaches, alternative avenues for investigation exist.By reviewing the origin and domestication history of African rice, Wambugu et al. [12] clarified that the progress of genomics could continuously change the previous understanding of rice domestication, so as to had a more accurate understanding of rice domestication process from the genomic level, which can eventually guide crop improvement. Ahmad [13] reviewed the use of several genomic and transcriptomic approaches to understand the ability of rice to withstand abiotic stress. Zhang et al. [14] provided the eRice database as an online research platform for the rice research community. To provide its epigenomics and genomics resources and will continue to enrich epigenetics data. In summary, genomics advances rice breeding through enhanced nutrition, accelerated heading, and improved blast resistance.

2.1.2 Wheat(Triticum aestivum L)

Wheat is the world's largest cultivated and most widely distributed food crop [15]. Modern biological techniques have been widely used in wheat breeding [16]. Firstly, molecular marker technology has been widely used in wheat breeding. Kumar et al. [17] and Shewry et al. [18] used molecular marker technology to predict genotype and improve nutrient content of Indian wheat. They introduced the trait by identifying QTLS for dietary fiber content and created wild and weed relatives of wheat. Babu et al. [19] performed a cis-genic approach by molecular marker-assisted selection (MAS) to identify multiple anti-rust genes simultaneously.

Secondly, the technology related to sequencing is more and more emerging. Kayess et al. [20] used next-generation genome sequencing to conduct extensive gene screening for heat-tolerant wheat varieties, and combined phenomics and genomics to develop new varieties with high temperature tolerance. Li et al. [16] proposed that the IWGSC RefSeq v2.0 (the second version of the wheat reference sequence) had a nearly complete wheat genome sequence, which could isolate and characterized the genes for agronomic traits important for wheat molecular breeding in the future. Following this, Wang et al. [21] focused on understanding the origin and evolutionary history of wheat, applying resistance gene enrichment sequencing and k-mer-based association mapping markers to clone disease-causing genes and accumulate genomic data. The evolutionary history of wheat was revealed by genome analysis of wild and domesticated wheat [22]. In 2023, Sun et al. [23] reviewed the extensive application of MAS, QTL mapping, and GWAS in genomics-assisted breeding(GAB) in breeding programs. Thereafter, Roychowdhury et al. [24] reviewed methods to improve key agronomics traits such as yield, disease resistance, drought tolerance, and nutritional quality of wheat through precise genetic modification. Subedi et al. [25] performed a translational genomics study of grain size regulatory genes in wheat using the rice candidate gene OsGS3 through the candidate gene approach. Boehm et al. [26] focused on foreign introgression during wheat improvement, developing a genomics-based chromosome engineering pipeline to improve the efficacy and throughput of foreign introgression. Rhodes et al. [27] put forward the urgent need to use the global genomic surveillance system to continuously track and monitor wheat blast and prevent the global spread of rice blast. Finally, several important wheat genomics-related platforms are proposed,including WheatOmics[28], Online database of single grain wheat [29]. Sehgal et al. [30] developed WheatOmics, a multi-omics platform enabling wheat functional genomics research through gene identification, transcript analysis, regulatory mapping, gene networks, mutant/variant analysis, optimized for non-bioinformaticians.

2.1.3 Corn(Zea mays)

To overcome the challenges and ensure the sustainable increase of maize yield, genomics-based breeding is required [31]. First, gene sequencing has made a major contribution to the elucidation of various mechanisms. By combining PacBio Sequel and Illumina HiSeq sequencing, Wang et al. [32] assembled a more continuous genome of Bipolaris maydis, providing a valuable resource for studying leaf dieback disease in southern maize and helping to elucidate the molecular mechanisms of its pathogenicity. After further high-throughput sequencing(NGS), Farooqi et al. [33] aimed at maize abiotic stress tolerance traits, combined with phenotypic analysis, and compared gene expression and function under extreme conditions to understand the molecular mechanism of abiotic stress in maize. QTLS for cold tolerance, heat stress, salt stress, and drought were also mapped. Subsequently, based on long-read sequencing technology, Liu et al. [34] used maize as a model to achieve gap-free genome assembly. This can eliminate uncertainty between genes and their regulators and understand the internal structure of repetitive domains, thus laying the foundation for epigenome analysis and ultimately creating seamless telomere assemblies of complex genomes in the future. Yang et al. [35] used maize HapMap and genomewide CRISPR mutation libraries to provide insight into population structural variation and the fact that a subset of transposable elements are transcriptionally active and can be induced under stress conditions, and developed an editing platform for high-throughput gene targeting in maize. This will revolutionize maize functional genomics research. Hu et al. [36] used genome assembly and population genome analysis to identify candidate genes associated with sweet maize traits. Dang et al. [37] has conducted GWAS and genomic prediction analyses for plant structural traits. It is concluded that the common origin of modern sweet corn in the United States was northern Mexico, and the plant configuration characters of sweet corn and waxy corn were understood.

For the GWAS technique, Waqas et al. [38] combined it with QTL mapping to explore important chromosomal regions and genes in maize under drought and heat stress. Farooqi et al. [33] reviewed the progress of maize multi-omics in abiotic stress resistance and breeding, highlighting the application and potential of genomics to metabolomics in stress resistance research. Using GWAS and co-expression analyses of root traits, Wu et al. [39] revealed genetic differences between aboveground agrtural traits and RSA traits and provided an important candidate gene resource for future maize root genetic improvement. Talukder et al. [40] targeted breeding of waxy maize by recessive wx1 introspection using three superior self-bred maize cultivars HKI323, HKI1105, and HKI1128. This maize has a shorter reproductive cycle, is rich in amylopectin, and can be used as a wx1 gene donor in breeding programs. Muntean et al. [31] outlined the application of approaches such as cis-genetics and epigenetics to enable maize breeding to move from domestication to precision breeding through assisted selection processes or precise breeding programs, and enable the development of new maize crops with high yield and quality performance as quickly as possible. McMillen et al. [41] targeted maize in the African region and used an ecophysiological model to disentangle the relationship between genes and the environment by integrating genomic information with physiology.

Finally, Woodhouse et al. [42] proposed MaizeGDB, a genomic database that can manage pan-genomic data, so that researchers can easily understand the structural and functional differences between maize and its direct relatives. Recently, Zhou et al. [43] proposed a method to dwarf maize. Based on the geneticrelationship between d024, indel-4 and ZmCYP90D1, they proposed to breed a new type of maize dwarf germplasm by crossing the corn-teosinte - Gramineum allopolyploid.

2.1.4 Soybean(Glycine max)

In recent years, significant progress has been made in soybean genomics, revealing the role of epigenetics in soybean regulation, polyploidy and diploidization, and the 3D structure of the soybean genome [44]. Sun et al. [45] reviewed various aspects of soybean genomics, including the application of 3D genomics, which was very extensive and comprehensive. More importantly, the important breeding trait of soybean is the relationship between oil yield and protein content, and many scientists have made great efforts in this regard. Liu et al. [46] used resequencing and GWAS to reveal key loci for soybean protein content and oil production. Kumar et al. [47] applied low-coverage resequencing, genome selection (GS), QTL mapping, and GWAS to seek ways to improve soybean seed oil and protein simultaneously. Kim et al. [48] identified candidate genes associated with protein, oil, and amino acid content in 203 wild soybean germplasm by GWAS and detected Single Nucleotide Polymorphism(SNP) markers for protein and oil content at loci on new chromosomes 15 and 20. Jia et al. [49] used SLAF-seq data for genome-wide association analysis, combined with transcriptome expression data, to identify three potential candidate genes with excellent haplotypes, These included Glyma.03G186200, Glyma.09G099500 and Glyma.18G248900, which facilitated potential MAS and improve oil production. In addition, for other important agrologic traits, Liu et al. [50] conducted individual de novo genome assembly of 26 representative soybean samples, constructed genome maps, and conducted pan-genome analysis, which revealed many new genetic variations and helped to find the correlation between genetic variations and candidate genes. Du et al. [51] proposed that genomics has played an important role in soybean resistance to biological stresses of Staygreen syndrome, soybean Mosaic virus, soybean cystinea, soybean common cutworm, Phytophthora soei root rot and stem rot using GWAS, linkage analysis and QTL analysis. Bhat et al. [52] introduced the application of GAB in soybean improvement. Through QTL analysis, high-throughput genotyping and high-throughput digital phenotyping (HTP) platforms, the utility of GAB in soybean has been greatly increased. By combining CRISPR-Cas9 and cutting-edge sequencing technologies such as RNA-seq and GWAS, Razzaq et al. [53] have made precise alterations of genetic information to greatly improve important traits such as yield, nutritional value, and stress resistance. Johnson et al. [54] reviewed the advances in genomics before and after soybean improvement in the last decade.GWAS and GS were proposed to improve the utilization efficiency of micronutrients in soybean and solve the problem of micronutrient deficiency in global soil.

Finally, the application of database is also indispensable. Razzaq et al. [53] also proposed the Soybean Genetics and Genomics Database (SoyBase) with extensive soybean genomics data. The database updates and new tools and features of SoyBase were detailed later by Brown et al. [55].

2.2.1 Peanut(Arachis hypogaea)

Peanut is an important food, oilseed and feed crop [56]. The development of sequencing technology has brought great progress to peanut genomics. Li et al. [57] used Chinese peanut as experimental material and obtained the first draft of genome sequence of Bacteroides peanutiae strain YY187 isolated from leaves by PacBio Sequel sequencing technology. Contribute to understanding the genetic diversity of fungal pathogens and further comparative genomics. Bhat et al. [56] performed whole-genome resequencing on 179 peanut samples to reveal the genome-wide structural and functional characteristics of SNP. In 2024, Raza et al. [58] proposed that GAB combined with NGS methods, high-throughput trait related markers, HTP, MAB, and GS can be used to design new varieties faster and help de novo domestication. In 2024, using long read sequencing and RNA sequencing, Xue et al. [59] introduced Amon2.0, a higher quality, more continuous, complete and accurate monticola genome assembly method, which was helpful for future molecular-assisted breeding. Ma et al. [60] used tetraploid peanut (Arachis Tifrunner) as experimental material to construct genomic Simple Sequence Repeats(SSRs) and combined with computer e-PCR analysis to enrich the genomic data of peanut SSRs . Tang et al. [61] used qRT-PCR analysis and CRISPR/Cas9 technology to identify a total of 19 AhFatB genes for improving peanut oil quality.

The impact of external stress on peanut development cannot be ignored. Huang et al. [62] conducted genomic research on peanut biological stress, and summarized the response of peanut to a variety of pests and microbial pathogens and the progress of omics. For example, Marker-Assisted Selection (MAS) and markassisted backcross have been successfully applied in peanut. O-methyltransferases (OMTs) play important roles in biological metabolism and stress tolerance. Cai et al. [63] conducted a genome-wide study of OMTs to provide a theoretical basis for understanding peanut development and stress response. For the Virginia peanut population, Newman et al. [64] used PCR allele competition expansion detection method, GS of high-quality markers and molecular marker assisted selection (MAS) methods to conduct a specific study on its genomic data. Wang et al. [65] reviewed that omics, especially genomic data, plays an important role in understanding the molecular basis of peanut seed development and also contributes to understanding the genetic and epigenetic mechanisms of key agronomic traits.

2.2.2 Cotton(Gossypium)

Cotton is an important fiber crop and model system in the world [66]. Firstly, Manivannan et al. [67] comprehensively reviewed the progress of cotton genomics in recent years, including the introduction of functional genes in cotton organelle genome and methods to improve cotton fiber quality, etc. In addition, the application of NGS, GS, 3D genomics and cotton pan-genome in cotton was introduced. However, Nadeem et al. [68] introduced the application of sequencing technology to cotton leaf rolling virus resistance and construction of mutation libraries induced by EMS (a chemical mutagen in breeding) in cotton, respectively [69]. Comparative genomics, developed by sequencing technology, has also been widely used. Using four Bacillus strains as experimental materials, Liu et al. [70] performed comparative genomic analysis and found that Bacillus velezensis was more abundant in secondary metabolites, which could effectively control verticillium wilt in cotton. Cohen et al. [71] used genome-wide comparative techniques to identify several nematode resistance genes, which in turn could be introduced and stacked into several upland cotton lineages. A more comprehensive understanding can be obtained by combining various genomic technologies, such as QTL, GWAS, RNA interference (RNAi) and virus-induced gene silencing, and 3D genomics. Naoumkina et al. [72] review the application of these techniques to improve both cotton fiber yield and quality. Iram described the application in drought tolerance of cotton [73]. Wen et al. [74] revealed the origin of cotton species and applied to the study of candidate genes related to fiber development, Yang et al. [75] reviewed future plans for breeding "super cotton". Wang et al. [76] used Shixiya No.1 (SXY1), an Asian cotton plant, as experimental material to identify and regulate candidate genes, which promoted the progress of cotton functional genomics. Shen et al. [77] obtained genomic data of 890 upland cotton varieties through genome-wide selective scanning detection, and analyzed that Introgression is affected by both artificial selection and environmental factors, which contributed to the understanding of the mechanism of intraspinal penetration.

Finally, the establishment of a database is very important for the study of cotton's association with genomics technology. In 2020, Yang et al. [78] proposed that a global data exchange platform should be established. After that, Zhang et al. [79] introduced a Gossypean genome database and detailed its functions and usage, such as the ability to search for orthologous gene ids. Liu et al. [80] constructed Fingerprint Finder by short-read sequencing, indicating that GWAS and fingerprint sites complement each other, which can more comprehensively understand the population genomics of cotton.

2.3.1 Cabbage(Brassica oleracea)

Chinese cabbage is a nutritious and economically significant vegetable crop [81]. Chinese cabbage can also be used as a model crop [82]. Ma et al. [82] proposed that high-density Chinese cabbage genetically mutated populations (CCGMPs) could promote the progress of functional genomics and use Chinese cabbage as a model to explore the function of leafy vegetable crops. Recent advances in the search for the cabbage genome, Lv et al. [83], based on High-throughput/resolution chromosome conformation capture (Hi-C) 's technology, assembled a high-quality draft cabbage genome. Xu et al. [84] discovered the importance of genomic SSRs markers for genetic linkage analysis, MAS, and genetic mapping by mining a large number of potentially variable SSNS through genome-wide bioinformatics surveys. As a kind of natural functional pigment, carotenoids are very nutritious. Cao et al. [81] used comparative genomics to study carotenoid biosynthesis genes, which laid a foundation for understanding the pathway of carotenoid biosynthesis and breeding cabbage with high carotenoid content. Yin et al. [85] used genome-wide data and bioinformatics methods to perform gene structure analysis, cis-acting element analysis, and collinearity analysis of the HD-Zip gene family, and found that the HD-Zip gene family has a regulatory role in carotenoid content and heat stress tolerance. In addition, for other agronomic shapes of cabbage, Ji et al. [86] used population comparative genomics, RNA-seq analysis, and qRT-PCR experiments to identify six genes that can be used for MAS and also understand the mechanism of heading traits. Wang et al. [87] combined multiple genomic data to analyze the glucosinolates (GSLs) biosynthetic genes, which helps to understand the synthesis mechanism of GSLs and prepare for the breeding of cabbage with high GSLs. Xing et al. [88] comprehensively analyzed the pseudo-response regulators(PRR) genes of cabbage cultivar 'BoZG17-1' through multiple sequence alignment PRR gene, gene structure analysis and cis-regulatory element analysis. It is beneficial to the cultivation of resistant varieties. Xu et al. [89] used genome sequencing, complete sequence alignment, genome structure comparison and phylogenetic analysis to study the cabbage chloroplast genome, which provided high-quality genetic breeding information. Chen et al. [90] studied the genome of Ogura CMS cabbage by gene sequencing, collinearity analysis and PCR amplification, and based on this, a new non-breeding cabbage, Bel CMS cabbage, was constructed. It has the advantages of less exogenous cytoplasm and no dead buds at the beginning of flowering. Finally, databases can provide rich genomic data, and Li et al. [91] introduced the content and functions of a newly developed database, the non-heading Chinese cabbage database, which will be combined with bioinformatics to facilitate comparative genomic analysis in the future.

2.3.2 Carrot(Brassica oleracea)

Carrot is a nutrient-rich vegetable [92]. First, in order to improve the breeding of carrots, Macko-Podgorni et al. [93] used open-pollinated carrots as experimental materials and found that the expression of DcDCAF1 could regulate storage root development through GWAS. Liu et al. [95] improved hybridization of carrots using MAS, introgression, and third generation sequencing. Duran et al. [94] used sequencing genotyping and probe analysis to construct a genetic map of fertility restoration genes . Second, comparative genomics was also important. Zhao et al. [96] performed NGS and analysis of Aspergillus rhizobium isolate CBR2 (the causative agent of black rot of carrot) and Alternaria dauci (the causative agent of Alternaria leaf blip), respectively. Advances in comparative genomic analysis of carrots have been promoted. Zhe et al. [97] identified candidate genes for the formation of trichomes by resequencing and qRT-PCR analysis, which identified 12 genes associated with trichomes. Hao et al. [92] elucidated that DcNAC genes in carrot respond to abiotic stress by multiple sequence alignment and phylogenetic tree analysis. Finally, Rolling et al. [98] presented the content and functionality of the comprehensive database CarrotOmics, such as its ability to provide a visual carrot reference genome and drive genome-assisted selection.

2.4.1 Apple(Malus pumila Mill)

Apple is a fruit of economic importance [99]. Many attempts had been made to address abiotic stresses. Liu et al. [100] used the 'Baleng Crab' cultivar as the experimental material, and developed two markers, SNP182G and SNP11G/SNP761A, to increase the salt-alkali tolerance of apple rootstock through SNP marker analysis and accurate prediction of salt-alkali stress damage index. Yao et al. [101] used "Golden Delicious" as experimental material to perform genome-wide identification and construct phylogenetic relationships for the HSP20 gene family, and finally identified several HSP20 genes as candidates for heat stress, which could contribute to the development of heat-tolerant apple varieties. Verma et al. [102] reviewed the role of multiple gene families in drought and salt tolerance in apple and introduced some miRNA candidates under investigation. In addition to abiotic stresses, pests and diseases are also a serious threat to apples. Anthrax is the causative agent of apple bitter rot. Khodadadi et al. [103] performed phylogenetic analysis and secondary metabolic gene cluster analysis by whole genome sequencing combined with bioinformatics software such as fastANI, which provided valuable genomic resources of anthrax and was beneficial to the disease management of apple stash. Carneiro et al. [104] completed the draft genome sequence of the pathogen Colletotrichum chrysophilum by sequencing the DNA of the chrysophilum strain M932 using a two-end sequencing system. It helps to develop varieties that can resist renal leaf spot disease and bitter rot disease. In order to select high-quality varieties, Shen et al. [105] proposed a new bioinformatics tool, bulked segregant analysis tool for out-of-crossing species and a genomics aided prediction model, they had used it to predict several QTLS and candidate genes for key agrologic traits in apple. Kumar et al. [106] used Malus domestica and wild Malus germplasm as experimental materials to study the genetic relationship of various nutrients in apple, including ascorbic acid, through apple 20 K SNP array genotyping, combined with GWAS and metabolite epitope analysis. This facilitates targeted breeding to improve different nutrients. Keller et al. [107] used apple REFPOP as experimental material and used FruitPhenoBox for gene phenotype analysis and SNP genotyping to study the genetic basis of apple shape and color, which helps to select the appearance of apple when breeding. Chen et al. [99] reviewed the previous research on red meat apples and proposed that MdMYB10 was an important research direction for the selection of high-quality red meat apples.

Finally, apple genomics data and research methods are also constantly enriching. For The 'Red Fuji' apple, Peng et al. [108] used HiFi long reads and Hi-C reads to analyze the high-quality haplotype 'Red Fuji' apple genome, which was a valuable resource for comparative genomics. In 2021, Minamikawa et al. [109] used 184 apple apple breeding lines as experimental materials to show that combining the data of infinium and the GRAS-Di genotype system, GWAS and genomic prediction(GP) can produce more accurate results. Then in 2024, Minamikawa et al. [110], using seven founders as experimental materials, through SNP genotyping chip system, GP and GWAS methods, the breeding value of the founder haplotype was revealed and the accuracy of these methods was evaluated.

2.4.2 Watermelon(Citrullus lanatus)

Watermelon is the third largest fruit crop in the world [113]. Understanding the genetic changes in watermelon domestication history helped modern watermelon breeding scientists to generate their seamless genomes. Using 27 watermelon and its relatives as experimental materials, and found that watermelon was derived from Citrullus lanatus subsp. Cordophanus , which was helpful for analyzing good shape and introducing it into breeding. Renner et al. [112] used Illumina sequencing and other technologies to perform chromosome level genome assembly of Kordofan melon and found a large number of genome structural variations(SV), which can provide insights into genetic changes in watermelon domestication history and contribute to modern watermelon breeding. Yang et al. [113] used Citrullus lanatus cultivars to study the genetic map of watermelon by Perfect SNP, Target SNP-seq technology and construction of phylogenetic trees, which helps to promote the diversity of watermelon varieties. It helps to promote the diversity of watermelon varieties. In order to breed superior varieties, Yang et al. [114] used two watermelon mother species "1061" and "812" as experimental materials, and found the structural and functional properties of ClPRX gene family, such as its involvement in watermelon skin lignin synthesis, through qRT-PCR analysis and combined with multiple bioinformatics analyses. Yang et al. [113] used bulk segregant analysis Pipelines and RNA-Seq methods to identify important QTLS that could contribute to the development of drought-tolerant watermelon cultivars. Maraga et al. [115] used the hybrid progeny of BIL-53 and IIHR-140-152 as experimental materials, and identified two potential candidate genes for watermelon epidermal traits through QTL mapping and mapping, candidate gene analysis and sequencing, and SNP identification, Named Cla97C09G175150 and Cla97C09G175170. Mashilo et al. [116], for C. lanatus. The comparative analysis of traits, genetic characteristics and genomes was helpful for gene integration breeding.

Finally, some watermelon-related databases have gradually emerged. Deng et al. [111] used four watermelon germplasm as experimental materials to perform Genome de novo assembly and Oxford Nanopore Technology long-read sequencing. High-quality watermelon reference genomes and mutation libraries were obtained. Patel et al. [117] summarized the history and mechanism of watermelon anthracnose and proposed that for sustainable cultivation of watermelon anthracnose resistance, it is necessary to establish resistance gene libraries to continuously improve resistance. The application of genomics in other crops is also summarized in Table 1.