Explore the vast range of titles available.

Conventional pre-genomics breeding methodologies have significantly improved crop yields since the mid-twentieth century. Genomics provides breeders with advanced tools for whole-genome study, enabling a direct genotype–phenotype analysis. This shift has led to precise and efficient crop development through genomics-based approaches, including molecular markers, genomic selection, and genome editing. Molecular markers, such as SNPs, are crucial for identifying genomic regions linked to important traits, enhancing breeding accuracy and efficiency. Genomic resources viz. genetic markers, reference genomes, sequence and protein databases, transcriptomes, and gene expression profiles, are vital in plant breeding and aid in the identification of key traits, understanding genetic diversity, assist in genomic mapping, support marker-assisted selection and speeding up breeding programs. Advanced techniques like CRISPR/Cas9 allow precise gene modification, accelerating breeding processes. Key techniques like Genome-Wide Association study (GWAS), Marker-Assisted Selection (MAS), and Genomic Selection (GS) enable precise trait selection and prediction of breeding outcomes, improving crop yield, disease resistance, and stress tolerance. These tools are handy for complex traits influenced by multiple genes and environmental factors. This paper explores new genomic technologies like molecular markers, genomic selection, and genome editing for plant breeding showcasing their impact on developing new plant varieties.

Salinity stress adversely affects plant growth and causes considerable losses in cereal crops. Salinity stress tolerance is a complex phenomenon, imparted by the interaction of compounds involved in various biochemical and physiological processes. Conventional breeding for salt stress tolerance has had limited success. However, the availability of molecular marker-based high-density linkage maps in the last two decades boosted genomics-based quantitative trait loci (QTL) mapping and QTL-seq approaches for fine mapping important major QTL for salinity stress tolerance in rice, wheat, and maize. For example, in rice, ‘Saltol’ QTL was successfully introgressed for tolerance to salt stress, particularly at the seedling stage. Transcriptomics, proteomics and metabolomics also offer opportunities to decipher and understand the molecular basis of stress tolerance. The use of proteomics and metabolomics-based metabolite markers can serve as an efficient selection tool as a substitute for phenotype-based selection. This review covers the molecular mechanisms for salinity stress tolerance, recent progress in mapping and introgressing major gene/QTL (genomics), transcriptomics, proteomics, and metabolomics in major cereals, viz., rice, wheat and maize.

Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1 , PDIL , SAUR , and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight.

Key message ‘Sikkim Primitive’ maize landrace, unique for prolificacy (7–9 ears per plant) possesses unique genomic architecture in branching and inflorescence-related gene(s), and locus Zm00001eb365210 encoding glycosyltransferases was identified as the putative candidate gene underlying QTL ( qProl-SP-8.05 ) for prolificacy. The genotype possesses immense usage in breeding high-yielding baby-corn genotypes. ‘Sikkim Primitive’ is a native landrace of North Eastern Himalayas, and is characterized by having 7–9 ears per plant compared to 1–2 ears in normal maize. Though ‘Sikkim Primitive’ was identified in the 1960s, it has not been characterized at a whole-genome scale. Here, we sequenced the entire genome of an inbred (MGUSP101) derived from ‘Sikkim Primitive’ along with three non-prolific (HKI1128, UMI1200, and HKI1105) and three prolific (CM150Q, CM151Q and HKI323) inbreds. A total of 942,417 SNPs, 24,160 insertions, and 27,600 deletions were identified in ‘Sikkim Primitive’. The gene-specific functional mutations in ‘Sikkim Primitive’ were classified as 10,847 missense (54.36%), 402 non-sense (2.015%), and 8,705 silent (43.625%) mutations. The number of transitions and transversions specific to ‘Sikkim Primitive’ were 666,021 and 279,950, respectively. Among all base changes, (G to A) was the most frequent (215,772), while (C to G) was the rarest (22,520). Polygalacturonate 4-α-galacturonosyltransferase enzyme involved in pectin biosynthesis, cell-wall organization, nucleotide sugar, and amino-sugar metabolism was found to have unique alleles in ‘Sikkim Primitive’. The analysis further revealed the Zm00001eb365210 gene encoding glycosyltransferases as the putative candidate underlying QTL ( qProl-SP-8.05 ) for prolificacy in ‘Sikkim Primitive’. High-impact nucleotide variations were found in ramosa3 ( Zm00001eb327910 ) and zeaxanthin epoxidase1 ( Zm00001eb081460 ) genes having a role in branching and inflorescence development in ‘Sikkim Primitive’. The information generated unraveled the genetic architecture and identified key genes/alleles unique to the ‘Sikkim Primitive’ genome. This is the first report of whole-genome characterization of the ‘Sikkim Primitive’ landrace unique for its high prolificacy.

Prediction of heterotic combinations from parental dissimilarity will improve the efficiency of heterosis breeding. This study was conducted to determine genetic divergence among 15 local germplasm lines of yellow sarson using 11 morphological traits and to study the correlation between parental genetic distance, combining ability and heterosis for the prediction of better heterotic combination. The 15 germplasm lines, 44 F1s (11 lines × 4 tester) along with check variety Pant Shweta were evaluated at Pantnagar, India. The principal component analysis revealed seven significant principal components accounting for 99.99% of the total variation. The Euclidean dissimilarity values ranged from 1.912 to 8.087 and genotypes were grouped into three clusters. Relationships between a group of five dissimilarity coefficients (usual Euclidean, mean Euclidean, Manhattan, range Manhattan and exponential range Manhattan) with heterosis, sum of parental combining ability (SGCA) and specific combining ability (SCA) were calculated using Pearson correlation coefficient. Standard heterosis for plant height and mid-parent heterosis for oil content and number of seed per silique have positive correlation with all dissimilarity coefficients. SGCA exhibited a positive correlation for plant height and silique density, while SCA did not associate significantly for any of the traits studied. Further, the correlation between combining ability and heterosis was also studied and strong significant correlation between combing ability and heterosis was observed for most of the traits studied. Owing to the strong correlation between SGCA and heterosis for most yield attributing trait, information of SGCA to predict heterosis appears to be a better method in Brassica rapa var. yellow sarson.

Anaerobic germination (also known as germination stage oxygen deficiency) tolerance is an important trait for tailoring direct-seeded rice varieties. In the present study, a meta-analysis of QTLs governing anaerobic germination we identified 21 Meta-QTLs with < 1 cM (~ 250 kb) confidence interval, 10 with 1–4 cM (~ 250–1000 kb), and 15 with > 4 cM (> 1000 kb). Gene ontology (GO) analysis identified trehalose biosynthetic process (GO: 0005992), negative regulation of translation (GO: 0017148), protein and amino acid phosphorylation (GO: 0006468), cation transport activity (GO: 0006812), ATP binding (GO: 0005524), inorganic cation transmembrane transporter activity (GO: 0022890), protein serine/threonine kinase activity (GO: 0004674), rRNA N-glycosylase activity (GO: 0030598), and nucleoside-triphosphatase activity (GO: 0017111) as significant. We identified 56 differentially expressed genes (21 Meta-QTLs) and designated 13 candidates based on molecular functions and gene ontology. Genes Os02g0304900 , Os01g0568400 , and Os01g0566500 encode proteins in abscisic acid (ABA) metabolism and signaling pathway which is essential in signaling-, response-, and management of hypoxic stress during anaerobic germination. Another candidate Auxin-responsive SAUR protein influences the auxin distribution within tissues to regulate the coleoptile elongation, possibly in coordination with another gene OsNAC024 maintaining ROS activity in anaerobic germination response. Other probable genes act as a positive regulator of amylase ( Os03g0665200 ), enhance proline content, lower hydrogen peroxide levels, and increase antioxidant enzyme activities ( Os01g0568400 ), lipid transfer, metabolism, and storage ( Os04g0554800; Os08g0131300 ), and in sugar signaling. These candidates can be validated in independent populations and targeted for haplotype-based genomic and marker-assisted breeding for direct-seeded rice.

Soil acidity-associated toxicities of aluminum (Al), cadmium (Cd), and manganese (Mn) severely constrain rice productivity in upland ecosystems. To investigate the genomic basis of adaptation to acidic soil-related metal stress, we conducted an integrated meta-QTL (M-QTL) and functional genomics analysis in rice. Meta-analysis of 681 QTLs and MTAs from 53 QTL mapping and GWAS studies identified 79 robust M-QTLs, including ten overlapping regions associated with Al-, Cd-, and Mn-responsive traits. A multi-criteria prioritization framework identified 98 candidate genes supported by positional overlap, transcriptomic recurrence, and functional annotation, enriched for ion transport, detoxification, and redox regulation pathways. M-QTL10.9 emerged as a major hotspot enriched for glutathione-S-transferase genes, whereas M-QTL9.5 contained the highest density of prioritized candidates linked to Al and Cd responses. Comparative physiological & biochemical analyses of the contrasting rice genotypes Sahasarang and IR64 revealed genotype-dependent differences in antioxidant responses, metal partitioning, metabolic regulation, and cell wall remodeling under individual and combined metal stresses. Expression profiling of prioritized candidate genes, including OsACO family genes, OsZIP10, and OsGSTU10, further revealed genotype-dependent transcriptional divergence under combined stress. The identification of overlapping M-QTLs across Al, Cd, and Mn datasets suggests both shared and stress-specific adaptive responses to acidic soil-associated metal stress in rice.

Plant phenology regulates ecosystem functions at diverse scales but is impacted by micro and macro climatic variations, and climate change. In India, precise estimations of pheno-phase transition dates remain scarce at different spatial and temporal scales, necessitating comprehensive research efforts. This study aims to gather continuous intra-day ground data about vegetation and climate conditions using PhenoCam (optical RGB and IR images) along with meteorological sensors, at Dalma Wildlife Sanctuary (DWS), Jharkhand. To derive phenological metrics, different indices were computed from images captured by PhenoCam sensors and Satellite derived Normalized Difference Vegetation Index (NDVI). Since the PhenoCam covers diverse vegetation species in the frame, the analysis was performed over three specific subset Region of Interests (ROI): Bombax ceiba (Semal) tree, background cluster of vegetation and a sample tree. MODIS NDVI data revealed that most of the area is highly deciduous with major greening in the 1st half of April and senescence during 2nd half of March. The study found that Green Chromatic Coordinate Index (GCC) and Blue Chromatic Coordinate Index (BCC) results could reveal greening and senescence phases correctly. The timing of start of leaf flush (SOLF), end of leaf flush (EOLF) and end of leaf maturity (EOLM) estimated based on inflection point method from Pheno-Cam images are: for Semal tree: 5th April, 2nd May, 10th June, 2022; for background vegetation: 15th March, 28th March and 2nd May, 2022; and for sample tree: 15th March, 28th March and 25th April, 2022, respectively. The dates of SOLF differed in 2023 and it occurred twice for Semal and background vegetation: for Semal tree: 20th February and 3rd April 2023, and for background vegetation: 20th January and 8th March, 2023, respectively. The rate of leaf flush and rate of leaf maturity was not similar in different years as the rates were much higher in 2023 than in 2022. The temperature and rainfall during winter and spring played an important role in greening, senescence, and its sustenance. These findings revealed the micro-climatic effect on plant phenology in the Dalma Wildlife Sanctuary, as well as the importance of integrating PhenoCam and satellite data in accurate monitoring of phenological phases.