With the development of new technologies in recent years, researchers have made significant progress in crop breeding. Modern breeding differs from traditional breeding because of great changes in technical means and breeding concepts. Whereas traditional breeding initially focused on high yields, modern breeding focuses on breeding orientations based on different crops’ audiences or by-products. The process of modern breeding starts from the creation of material populations, which can be constructed by natural mutagenesis, chemical mutagenesis, physical mutagenesis transfer DNA (T-DNA), Tos17 (endogenous retrotransposon), etc. Then, gene function can be mined through QTL mapping, Bulked-segregant analysis (BSA), Genome-wide association studies (GWASs), RNA interference (RNAi), and gene editing. Then, at the transcriptional, post-transcriptional, and translational levels, the functions of genes are described in terms of post-translational aspects. This article mainly discusses the application of the above modern scientific and technological methods of breeding and the advantages and limitations of crop breeding and diversity. In particular, the development of gene editing technology has contributed to modern breeding research.

Safflower is an important oilseed crop mainly grown in the arid and semi-arid regions of the world. The aim of this study was to explore phenotypic and genetic diversity, population structure, and marker-trait association for 100-seed weight in 94 safflower accessions originating from 26 countries using silicoDArT markers. Analysis of variance revealed statistically significant genotypic effects (p < 0.01), while Turkey samples resulted in higher 100-seed weight compared to Pakistan samples. A Constellation plot divided the studied germplasm into two populations on the basis of their 100-seed weight. Various mean genetic diversity parameters including observed number of alleles (1.99), effective number of alleles (1.54), Shannon’s information index (0.48), expected heterozygosity (0.32), and unbiased expected heterozygosity (0.32) for the entire population exhibited sufficient genetic diversity using 12232 silicoDArT markers. Analysis of molecular variance (AMOVA) revealed that most of the variations (91%) in world safflower panel are due to differences within country groups. A model-based structure grouped the 94 safflower accessions into populations A, B, C and an admixture population upon membership coefficient. Neighbor joining analysis grouped the safflower accessions into two populations (A and B). Principal coordinate analysis (PCoA) also clustered the safflower accessions on the basis of geographical origin. Three accessions; Egypt-5, Egypt-2, and India-2 revealed the highest genetic distance and hence might be recommended as candidate parental lines for safflower breeding programs. The mixed linear model i.e., the Q + K model, demonstrated that two DArTseq markers (DArT-45483051 and DArT-15672391) had significant association (p < 0.01) for 100-seed weight. We envisage that identified DArTseq markers associated with 100-seed weight will be helpful to develop high-yielding cultivars of safflower through marker-assisted breeding in the near future.

A number of consequences of climate change, notably salinity, put global food security at risk by impacting the development and production of lentils. Salinity-induced stress alters lentil genetics, resulting in severe developmental issues and eventual phenotypic damage. Lentils have evolved sophisticated signaling networks to combat salinity stress. Lentil genomics and transcriptomics have discovered key genes and pathways that play an important role in mitigating salinity stress. The development of saline-smart cultivars can be further revolutionized by implementing proteomics, metabolomics, miRNAomics, epigenomics, phenomics, ionomics, machine learning, and speed breeding approaches. All these cutting-edge approaches represent a viable path toward creating saline-tolerant lentil cultivars that can withstand climate change and meet the growing demand for high-quality food worldwide. The review emphasizes the gaps that must be filled for future food security in a changing climate while also highlighting the significant discoveries and insights made possible by omics and other state-of-the-art biotechnological techniques.

Research studies on conservative genetics of endangered plants are very important to establish the management plans for the conservation of biodiversity. Rosewood is an evergreen tree of the Amazon region and its essential oil has great acceptance in the medical and cosmetic industry. The present study aimed to explore the genetic diversity and population structure of 90 rosewood accessions collected from eight localities of Peruvian Amazon territory through DArTseq markers. A total of 7485 informative markers resulted from genotyping by sequencing (GBS) analysis were used for the molecular characterization of rosewood germplasm. Mean values of various calculated diversity parameters like observed number of alleles (1.962), the effective number of alleles (1.669), unbiased expected heterozygosity (0.411), and percent polymorphism (93.51%) over the entire germplasm showed the existence of a good level of genetic variations. Our results showed that the Mairiricay population was more diverse compared to the rest of the populations. Tamshiyacu-2 and Mairiricay-15 accessions were found genetically distinct accessions. The analysis of molecular variance (AMOVA) reflected maximum variations (75%) are due to differences within populations. The implemented clustering algorithms, i.e., STRUCTURE, neighbor-joining analysis and principal coordinate analysis (PCoA) separated the studied germplasm on the basis of their geographical locations. Diversity indices for STRUCTURE-based populations showed that subpopulation A is more diverse population than the rest of the populations, for such reason, individuals belonging to this subpopulation should be used for reintroduction or reinforcement plans of rosewood conservation. We envisage that molecular characterization of Peruvian rosewood germplasm with DArTseq markers will provide a platform for the conservation, management and restoration of endangered rosewood in upcoming years.

Laural (Laurus nobilis) is a Mediterranean plant with reported antibacterial properties, yet the genetic basis of its antibacterial efficacy remains largely unexplored. This study evaluated the antibacterial activity of Laurus nobilis methanolic extracts against Escherichia coli, Staphylococcus aureus, and Bacillus cereus, combined with genome-wide association studies (GWAS) and machine learning (ML) approaches to identify genetic markers and predict antibacterial efficacy in 92 plant samples. Antibacterial tests revealed significant variability in inhibition zones, with E. coli showing the highest inhibition (Canakkale2: 24.5 mm), followed by S. aureus (Aydin2: 26.0 mm). Minimum inhibitory concentration (MIC) analysis demonstrated notable regional differences; extracts from Mersin3 showed the highest efficacy (MIC = 6.25 mg/mL), while Aydin1 exhibited the lowest activity (MIC = 100 mg/mL). Population structure and neighbor joining tree analysis split the germplasm into two groups. GWAS identified significant genetic markers associated with antibacterial traits, including marker 26557159 for EC-MEAN (Escherichia coli-Mean) (p = 1.10 × 10⁻⁴, MarkerR² = 0.1799, genetic variance = 9.41792) and marker 26584774 for BC-MEAN (Bacillus cereus-Mean) (p = 8.89 × 10⁻⁵, MarkerR² = 0.18512, genetic variance = 12.48948). Protein–protein interaction network of loci associated with marker trait association (MTA) marker (26557159) indicated involvement in high-affinity secondary active ammonium transmembrane transporter activity, providing insights into genetic regions influencing antibacterial properties. ML models predicted antibacterial activity with high accuracy. XGBoost achieved the best performance for MIC predictions (R² = 0.999, RMSE = 0.434), while random forest (R² = 0.984) demonstrated robust performance for both MIC and disc diffusion assays. LightGBM performed well for MIC prediction (R² = 0.988) but showed limited accuracy for disc diffusion outcomes (R² = 0.695). This study is the first to combine GWAS and ML for predicting antibacterial efficacy in L. nobilis, identifying specific genetic markers (e.g., 26557159, 26584774) and demonstrating that XGBoost achieves near-perfect MIC prediction (R² = 0.999). These findings provide a genomic and computational foundation for marker-assisted breeding of laurel with enhanced antibacterial properties and support the sustainable use of plant-derived anti-microbials.

The experimental material was planted at the University of Agriculture, Peshawar to estimate genetic parameters of heritability and correlation for yield traits of indigenous Brassica Juncea genotypes. RCB design with three replications was used to sow 20 Brassica genotypes. Highly significant genetic variations were obtained among studied parameters. Days to half flowering revealed positive associations with primary branches per plant (r= 0.337^**). Primary branches per plant was positively correlated with pod length (r= 0.407^**), seed per pod (r= 0.418^**) seed yield per plant (r= 0.479^**). Similarly, pod length showed positive correlation with seed per pod (r= 0.324^*) and seed yield per plant (r= 0.331^**), while seed per pod was positively correlated with seed yield per plant (r= 0.878^**). Highest broad-sense heritability was recorded for seed yield per plant (0.97), while lowest heritability was estimated for primary branches per plant (0.46).