Maydis leaf blight (MLB) is a major foliar fungal disease of maize causing yield losses of up to 40

Aluminium (Al) toxicity is a potential constraint to maize productivity in acidic soils, primarily due to its inhibitory effect on root growth during its early establishment. In the present study, a hydroponic screening protocol was standardized using Modified Magnavaca-II solution at the seedling stage and applied to 250 tropical maize inbred lines. Five root traits-total root length (TRL), root surface area (RSA), root volume (RV), average root diameter (AD), and number of root tips (NRT)-were quantified using WinRHIZO. To assess differential tolerance, the Relative Root Tolerance Index (RRTI)-a ratio-based metric comparing root performance under stress versus control-was calculated along with percent reduction for all traits. Protocol optimization with seven elite inbreds exposed to graded AlCl₃ concentrations (0-1500 µM) identified 300 µM AlCl₃ at 11 days post-germination as optimal for differentiating genotypic responses. Under this optimized condition, the 250 inbreds showed highly significant genotypic variation and genotype × treatment interactions. Stress significantly reduced most root traits by 10-40%, while improving the average root diameter, indicating compensatory thickening. Substantial variability was observed for both RRTI and percent reduction indices, ranging from 3.83 to 533.88. Principal component analysis and composite indices identified IMR292, IMR592, IMR463, IMR621, IMR546, IMR534, IMR629 and IMR395 as tolerant due to high TRL, RSA and NRT under stress, while IMR388, IMR33, IMR58, IMR349 and IMR446 were highly susceptible. The tolerant inbreds offer promising genetic resources for breeding Al-tolerant maize, while the optimized hydroponic system provides a robust, scalable framework for future phenotyping and genetic dissection studies.

Waterlogging poses a major constraint to maize productivity by inducing hypoxia and perturbing metabolic homeostasis. To elucidate the molecular basis of tolerance, we performed comparative RNA-seq profiling of leaf and root tissues from a waterlogging-tolerant (IML 7-308) and a susceptible (IML 7-312) genotype. Furthermore, qRT-PCR was used to validate the expression of key genes. Waterlogging triggered pronounced tissue- and genotype-dependent transcriptional reprogramming, with tolerant roots exhibiting the largest DEG set, underscoring their central role in stress adaptation. Furthermore, GO and KEGG enrichment revealed that glycolysis/fermentation, starch and sucrose metabolism, plant hormone signalling cascades, and secondary metabolite biosynthesis pathways are key components of the adaptive response. Tolerant plants showed marked upregulation of cell wall remodelling genes (XTH; xyloglucan endotransglucosylase), aquaporins (PI2PA), and ion transporters, supporting enhanced aerenchyma and adventitious root formation, hydraulic conductance, and ion homeostasis under waterlogging conditions. Network analysis (WGCNA) identified PIP2A, ACCO20, PIN3, IAA2, XTH, and ALDH2 as key hub genes orchestrating stress-responsive modules in the tolerant genotype. Together, these findings resolve the transcriptional architecture underpinning waterlogging tolerance in maize and offer molecular targets for engineering stress-resilient cultivars.

The fall armyworm is the most destructive invasive insect pest affecting maize in India. Among the various control measures, host plant resistance is regarded as the most economical and environmentally sustainable strategy in the long term. The present study aims to standardise an ear-damage-based screening technique by introducing FAW larval loads of 2, 5, and 10 into each ear, determining key ear traits associated with resistance, and characterising antibiosis resistance using FAW survival and developmental parameters across diverse maize lines. The proposed screening technique involves assessing ear damage ratings (EDR) on a scale of 1 to 9 by comparing cob damage levels among maize genotypes. The results indicated that exposing maize genotypes at the milking stage to five-day-old FAW larvae at a rate of five larvae per ear would enable differentiation among the genotypes as resistant, moderately resistant, susceptible, and highly susceptible based on the EDR. The present study successfully identified resistant-CML336, and moderately resistant genotypes-CML337, CML141, and CML71, based on EDR evaluations. Key husk traits, including the number of husk layers, husk extension from the tip of the ear, and husk tightness, collectively contribute to resistance against ear damage caused by FAW in maize. The resistant and moderately resistant genotypes exhibited the highest levels of antibiosis resistance, leading to detrimental effects on key biological parameters of FAW. The findings of this study substantially contribute to the selection of genotypes for breeding resistance to FAW in maize.

Abstract Maize ( Zea mays L.) is a major cereal crop globally, and its productivity has significantly improved over the decades due to the adoption of single‐cross hybrids and targeted breeding strategies. This study is the first in India to quantify genetic gains in maize over time by evaluating hybrids released between 2005 and 2014 from both public and private sectors. Thirty maize hybrids were tested for grain yield (GY) and harvest index (HI) across multiple seasons. The results showed significantly modest genetic improvements for GY and HI, with newer/modern hybrids outperforming older ones. Annual genetic gains were estimated at 1.3% for GY (56.34 kg ha − 1 year − 1 ) and 1.02% for HI (0.004 units year − 1 ). GY was closely correlated with HI, indicating simultaneous improvement and correlated response in yield and HI gains. While private‐sector hybrids performed slightly better, public‐sector hybrids remained competitive. Overall, the findings highlight the success of India's maize breeding efforts and point to the importance of selecting for GY and HI to achieve further progress. This work lays a strong foundation for developing the next generation of high‐yielding maize hybrids.

BACKGROUND:Maize is a globally important cereal crop that supports food and nutritional security and sustains livelihoods through its use as food, feed, and industrial raw material. However, maize productivity is severely constrained by destructive diseases and insect pests. Breeding for durable resistance is challenging due to the quantitative, polygenic, and environment-sensitive nature of these traits. To refine the genomic basis of resistance and identify robust breeding targets, a comprehensive meta-quantitative trait loci (M-QTL) analysis was conducted by integrating 528 quantitative trait loci (QTLs), comprising 368 disease-resistant and 160 insect-resistance QTLs. RESULTS:The collected QTLs were consolidated into 74 stable M-QTLs, including 31 disease-specific (DI-MQTLs), 23 insect-specific (IN-MQTLs), and 20 co-localized M-QTLs (PL-MQTLs) conferring combined resistance to both stresses. Confidence intervals (CIs) were reduced by an average of 70.6% for disease-related and 51.2% for insect-related loci, with the identified M-QTLs showing a mean phenotypic variance explained (PVE) of 14.6%. Several PL-MQTLs, including PL-MQTL4.1 (CI = 2.91 cM; PVE = 23.0%) and PL-MQTL4.2 (CI = 0.84 cM; PVE = 23.1%), emerged as highly stable resistance hotspots. A total of 1884 candidate genes were identified, including those encoding NBS-LRR receptors, receptor-like kinases, transcription factors (WRKY, MYB, NAC, and AP2/ERF), peroxidases, cytochrome P450s, and benzoxazinoid-pathway genes. Key components of the salicylic acid (SA) and jasmonic acid (JA) signaling pathways co-localized within PL-MQTL regions, suggesting a mechanistic basis for broad-spectrum resistance. CONCLUSION:The identified stable M-QTLs and prioritized candidate genes provide robust genomic resources for marker-assisted breeding, genomic prediction, and genome-editing approaches, thereby accelerating the development of durable, broad-spectrum disease- and insect-resistant maize cultivars. © 2026 Society of Chemical Industry.

Salinity stress impairs maize growth by inducing osmotic stress, pigment degradation, and ionic imbalance, particularly during early seedling development. This study investigated the morpho-physiological and ionic responses of different maize genotypes exposed to increasing salinity levels (control, 3, 6, and 9 dS/m) at the seedling stage. Salinity caused a reduction in biomass accumulation (shoot fresh weight and shoot dry weight), plant height, and K+/Na+ ratio, with pronounced effects under severe stress. Significant genotypic variability was detected for photosynthetic pigments (chlorophyll a, chlorophyll b, total chlorophyll and carotenoids) growth traits, and ionic regulation, indicating diverse physiological adaptation strategies. Stress tolerance indices and multivariate analysis revealed that chlorophyll stability, carotenoid accumulation, and maintenance of ionic homeostasis (K+/Na+ ratio) were the dominant physiological determinants of salinity tolerance. Additionally, principal component analysis showed a shift from biomass-driven variation under non-stress conditions to pigment- and ion-driven variation under higher salinity. Based on the results, genotypes BML 6 and HKI 163 maintained higher pigment content and improved K+/Na+ balance, enabling better growth under saline conditions. These findings highlight key physiological traits underlying salinity tolerance and provide insight into early-stage adaptive mechanisms in maize.

Aluminum (Al) toxicity is a major production constraint to tropical maize in acidic soils, primarily impairing root growth. This study aimed to dissect the genetic basis of seedling-stage root architectural traits conferring Al tolerance in tropical maize using genome-wide association study (GWAS). A hydroponic protocol was standardized by evaluating seven inbred lines under different Al concentrations, and 300μM AlCl3 at day 11 was found optimal for phenotyping. Significant reductions in root length, surface area, volume and tips ranged 24-38%, while diameter increased by 13% under stress in 250 diverse maize inbred lines. Principal component and correlation analyses indicated strong association of root elongation and branching traits, while thickening was largely independent. Genotyping with 60,227 SNPs revealed three subpopulations with moderate linkage disequilibrium decay (65.4 kb), supporting high-resolution GWAS. Mixed Linear Model analysis detected 44 significant SNPs across eight chromosomes, explaining 9.68-18.43% of phenotypic variance. Eight QTL clusters were identified, including two major hotspots on chromosomes 8 and 3 designated as “QTL hot spot A” and “QTL hot spot B” with seven and five major QTLs respectively. Candidate gene analysis highlighted 33 functionally relevant genes linked to stress tolerance, including glutathione S-transferases, Dehydroascorbate reductases, MATE transporters, and regulators of STOP1 stability and activity. In-Silico expression analysis confirmed stress-responsive regulation of several promising genes. Collectively, this study provides genomic regions and candidate genes underpinning root-based Al tolerance, offering valuable targets for marker-assisted breeding and genomic prediction in tropical maize.

IntroductionBaby corn (Zea mays L.), harvested before silking, offers a climate-smart, dual-purpose solution by delivering both early-market fresh cobs and tender green biomass suitable for ruminant forage. This study aimed to enhance resilience and sustainability in rainfed agricultural systems by evaluating the performance and yield stability of 61 hybrids developed at ICAR-IARI, Jharkhand, across four contrasting agro-climatic zones of India-Hazaribag, Ludhiana, Karimnagar, and Srinagar.MethodsKey traits analyzed included days to first picking, baby corn weight without husk (BCWoH), total green husk weight (TGHW), and fodder weight (FW), with biomass data recorded from two representative locations. Genotype × environment interaction (GEI) was assessed, and advanced multivariate analyses using AMMI and GGE biplots were performed to identify stable and high-yielding hybrids.Results and discussionSignificant GEI was observed for BCWoH, indicating strong environmental influence on yield performance, whereas biomass traits were largely determined by location-specific factors. Hybrids CR71, CR82, and CR70 were identified as both high-yielding and phenotypically stable across environments for BCWoH and FW, outperforming the standard checks (AH7043 and CMVLBC-2). Genotypes CR7, CR44, and CR50 displayed consistent forage yields, supporting their role in crop-livestock integrated systems. The positive correlation between cob yield and fodder traits highlights opportunities for multi-trait selection, thereby improving land and input-use efficiency. These dual-purpose hybrids demonstrate robust yield resilience under low-input, rainfed conditions, making them suitable for sustainable intensification in peri-urban and resource-constrained systems. Their potential contribution aligns with SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Additionally, promising parental inbred lines offer scope for developing next-generation resilient hybrids through genomic and combining ability-based strategies.

IntroductionDiversification of the rice-wheat (RW) cropping system is indispensable for the development of agriculture due to its adverse impacts on groundwater depletion, environment, and profitability in the north-western Gangetic plains (NWGP) of India. Although policy initiatives encouraging diversification of the RW system, significant evidence, and knowledge gaps remain particularly due to limited farmers’ participatory studies assessing the quantitative scope of crop diversification in NWGP.MethodsA large number of on-farm farmers’ participatory experiments (n = 250)on each of rice and maize were conducted to evaluate the yield, profitability, irrigation water use and water productivity, energy-use and global warming potential (GWP) in nine districts of the Haryana and Punjab states.Results and discussionResults showed significant inter-district variations in all parameters. The mean rice equivalent yield (REY) of maize was 6.6% lower than rice. However, wheat yield after maize was 16.7% higher than after rice. On average, rice required about 10 times more irrigations than maize, resulting in ~1,040% higher irrigation water productivity in maize. Cost of cultivation for rice was markedly higher due to greater energy inputs. Net returns from maize were 46.5 and 32.5% over rice, while it provided 30.3 and 6.2% higher net profit under subsidized electricity in Haryana and Punjab, respectively. Total GWP of maize was ~63% lower, energy use declined by ~271%, labourers use by 38.6%, and diesel consumption by ~37% compared to rice. These findings emphasize maize’s environmental and economic advantages, advocating its substitution for rice in suitable agro-ecologies. Nonetheless, further research, considering rice’s pivotal role in global food security, such diversification should be region-specific and supported by enabling research, extension, and policy interventions to ensure sustainability and livelihood security in northwestern India. Issues are suggested to optimize maize-based diversification in NW India.

Maize (Zea mays L.) is a globally important crop used for food, feed, and biofuels production. High kernel starch content is a key breeding target for yield, bioethanol production and other industrial uses. However, drought and waterlogging stresses in maize severely limit maize production and starch content and quality. A critical gap exists in identifying common genomic regions relevant to high starch, drought and water logging stresses tolerance in maize. Present study addresses this gap by presenting a novel integration of starch and abiotic stressrelated quantitative trait loci (QTLs) to identify stable genomic regions for them in maize. We performed a metaQTL (MQTL) analysis, a method that enhances mapping resolution and identifies stable multi-trait loci to facilitate marker-assisted selection (MAS). This approach aims to enhance breeding efficiency for drought, waterlogging and starch content, thus improving bioethanol production under challenging environmental conditions. Integrating 254 QTLs from 21 studies, MQTL analysis identified 21 stable MQTLs for targeted traits with an average confidence interval (CI) of 6.2 cM, achieving a 73.4 % (3.76-fold) reduction from the initial QTL average CI of 23.3 cM. The phenotypic variance explained (PVE %) for the identified MQTLs ranged from 3.0 % (MQTLST2.1) to 30 % (MQTLST5.3), with an average of 9.2 %. Within the MQTL regions, 680 candidate genes were identified, which includes 414 for starch content, 164 for waterlogging, and 102 drought tolerance. Four MQTL hotspots (overlapping MQTL regions) on chromosomes 1, 3, 4, and 5 harboured key genes associated together with stress tolerance and starch metabolism, making them promising targets for improving bioethanol production under adverse conditions. RNA-seq-based expression profiling using public datasets confirmed the tissue-specific expression of candidate genes. These findings will be useful for the development of maize hybrids with enhanced starch/ethanol yields and stress resilience through MAS, genomic selection, and genetic engineering.

The bio-fortification of maize presents a promising approach for improving nutritional security. Considering iron and zinc deficiencies as significant global public health challenges, the present study included the multi-location evaluation of maize elite inbred lines (300) and hybrids (31) to see variability for iron and zinc content, quantify the genotypes-by-environment (GEI) interaction, and identify stable genotypes with high Fe and Zn content. The findings demonstrated significant GEI for both Fe and Zn, indicating diversity among genotypes and environments under study. Environmental effects were higher for Fe than on Zn. Ideal genotypes for Fe included hybrids H21 and H27, along with inbred G107 and G114. For Zn content, H27 and inbred G3, G178, and G9 emerged as the best genotypes. The analysis unveiled multiple mega-environments for Fe and Zn content, suggesting genotype adaptation to specific environmental conditions. These promising genotypes identified can be of great potential in breeding for high Fe and Zn in maize. Understanding the specific genetic and physiological mechanisms underlying the performance of hybrids and inbred lines with high as well as stable Fe and Zn content and winning genotypes within each mega-environment are crucial for targeted breeding strategies.

Turcicum leaf blight (TLB), caused by the fungus Setosphaeria turcica, is a serious foliar disease affecting maize production worldwide. Identifying and utilizing TLB-resistant sources and genes is the most effective way to manage this disease. In this study, we conducted genome-wide association mapping (GWAS) to identify key genomic regions and candidate genes (CGs) for TLB resistance. A diverse panel of 384 maize inbred lines was screened for TLB disease at four hot-spot sites (Bajaura, Mandya, Dharwad, & Srinagar) between 2018 and 2022 and genotyped using 60,227 SNPs. The average disease score and percent disease incidence (PDI) ranged from 2.1 to 7.3 and 19.6-77.7 % respectively. Stable genotypes for TLB resistance were identified, including CML 334 W, UMI 1200, DML 112, P72cl x brasil1177-2, DQL259, CML 549 W, DML 16, DML 310, and IML 12-10. Further, 16 significant marker trait associations (MTAs) were identified for TLB resistance on all chromosomes except for chromosome 1, 9, and 10, explaining 23-30 % phenotypic variation. Seven MTAs overlapped with previously reported MTAs/CGs, while nine were novel. Further, 18 CGs and 5 Ortho-CGs were identified in the genomic regions of MTAs. These CGs were mainly associated with cellular anatomical entity (proteins involved in cell wall formation and defense), binding (a function of a protein attaching to other molecules to trigger a defensive response), and cellular processes. Some of the identified CGs, such as Zm00001eb226290, Zm00001eb293670, and Zm00001eb293590 have been found responsible for TLB disease resistance in maize and sorghum. This study offers clear translational value for maize breeding. The identified stable resistant lines are valuable donors for future breeding programs. This research advances TLB resistance understanding relative to recent maize genomics such as modern genomic selection, molecular breeding, and genome editing.

Study assessed nutritive value (fresh and ensiled samples) and silage quality of various maize byproducts: baby corn husk, whole plant post baby corn picking, whole plant post sweet corn harvesting, whole plant with cobs at silage stage, and whole plant post mature cob harvesting. All the samples/genotypes/hybrids were significantly (p <0.01) different for dry matter (DM), ether extract (EE) and crude protein (CP) at the fodder stage. The highest DM (33.6%) was observed in whole plant, including the grain sample and the least in baby corn husk (14.66%). Similarly, the least CP (3.25%) was observed in the whole plant after harvesting the mature cob, and the highest was in the sweet corn plant (9.36%). The silage pH of samples was in the range of 3.3-4. Baby corn husk as green fodder and silage maintained good quality (DM, CP, NDF, ADF and ash contents), while others followed quality sequence IQPMH 18-2>IBCH 1>Sugar 75> LQPMH 1. This study revealed the potential of utilizing various maize crop byproducts as silage.

Maize is one of the most versatile and commercially produced crops used for food, feed, fodder, ethanol, oil, and industrial raw materials. Maize is affected by various diseases, but among these, maydis leaf blight (MLB) is one of the most serious diseases. The disease is caused by Cochliobolus heterostrophus and is responsible for yield losses up to 40%. When developing cultivars for a specific ecology, days to flowering and maturity are important breeding traits to consider. Thus, understanding the genetic basis of MLB resistance, specifically the “O” race of the pathogen, and maturity-related traits is crucial to develop climate-resilient maize hybrids. This study aimed to determine the gene actions and their interactions for MLB resistance and maturity-related traits using a six-parameter model (P1, P2, F1, BC1P1, BC1P2, and F2). Five experimental crosses were attended using resistant (R) (CML269-1 and P72c1Xbrasil1177-2) and susceptible (S) (HKIPC4B and ESM113) lines in R×S (1), S×R (2), R×R (1), and S×S (1) combinations. The susceptible lines belonged to the early (HKIPC4B) and medium (ESM113) maturity groups, while the resistant lines belonged to the medium (CML269-1) and late (P72c1Xbrasil1177-2) maturity groups. These six genetic populations were screened under artificially created epiphytotic conditions at a hot-spot site. In the analysis, MLB resistance showed a dominance genetic effect with significant (P<0.01) additive × additive interactions. Maturity-related traits showed significant dominance genetic effects (P< 0.01), with dominance × dominance interactions, suggesting the suitability of hybrid breeding for these traits. The estimated genes responsible for MLB resistance ranged from 0.002 to 5.78 per cross. In MLB resistance, broad and narrow-sense heritability were found to be 91.9% and 84.3%, respectively, which indicated the possibility of genetic improvement through selection. Disease response and maturity-related traits were negatively correlated, suggesting that long-duration genotypes are more resistant to disease than short-duration. The detailed understating of gene actions can aid in designing breeding strategies to develop resistant cultivars with the required duration for various stress-prone ecologies.

Waxy maize characterized by high amylopectin content resulting from a recessive wx1 gene, is important for both dietary and industrial applications, yet it suffers from low yields and limited breeding options. This study aims to develop a thorough understanding of the underlying genetics for successful hybridization experiments in waxy maize and the identification of potential cross combinations to derive high-yielding waxy maize hybrids in India. Here, we evaluated the kernel starch composition, yield-related traits, molecular diversity, kinship, LD, population structure, and selection signatures in a panel of 11 waxy and 37 non-waxy maize genotypes. The starch content in the panel ranged from 57.85 to 66.96%, while the amylopectin ranged from 70.65% to 96.32%. A significant positive correlation between kernel starch and amylopectin (0.39**) was identified suggesting the potential for simultaneous improvement of both these traits. The 48 maize lines were genotyped with 24,477 highly polymorphic single nucleotide polymorphisms (SNPs). Seventy-eight per cent of the pair-wise relative kinship values were less than or equal to 0, indicating minimal redundancy in the genomic composition of the inbred lines. The range of genetic distance among the pairs of waxy lines was 0.190 to 0.231 as compared to 0.076-0.264 in the non-waxy genotypes suggesting a greater genetic variation among the non-waxy genotypes. The mean LD value across the genome was 0.44. Two to four groups were identified using the model-based population structure, phylogenetic analysis and principal component analysis with no clear pattern of clustering based on the type of corn. Pairwise comparisons using the SNP dataset between waxy and non-waxy maize detected 27 loci under positive selection. The information generated in this study will be useful in the diversification of Indian waxy maize lines and the development of superior waxy maize hybrids.

Assessment of genotype x environment interaction for stability and the performance of yield and its attributes of inbred lines across the environments serve as an important pre-breeding step in maize for selection of parental lines. A set of 70 inbred lines was evaluated for grain yield and other yield traits in multi-location trials under diverse environments to analyze Additive Main Effects and Multiplicative Interaction Effects, genotype and genotype x environment (GGE) biplots and best linear unbiased predictor (BLUP). Ajoint analysis of variance revealed significant differences across environments for the studied traits. The biplot analysis revealed that inbred lines, UMI 1260 and UMI 1266 performed well in Coimbatore (E1) and Bhavanisagar (E2), whereas IMR 108200 and UMI 1257 performed well in Vagarai (E3). GGE and WAASBY biplot analysis showed that Coimbatore and Vagarai environments were found discriminative and may be suitable for selecting genotypes for specific adaptation, while Bhavanisagar is ideal for selecting genotypes with broad adaptability. Multiple stability indices, such as yield stability index (YSI), the harmonic mean of the relative performance of genetic values, and the weighted average of absolute scores of BLUPs (WAASBY) facilitated the selection of stable inbred lines in terms of grain yield, while multi-trait selection index aided in the selection of genotypes based on multiple traits. Positive selection gains were observed for yield and related traits, whereas negative gains were observed for plant height and ear height. The coincidence index at 20% selection intensity revealed a high level of coincidence between WAASBY and HMRPGV, indicating selection efficiency when either of the indices was used. Inbred lines, UMI 1286, UMI 1276, and UMI 1266 ranked consistently across all the stability indices and hence can serve well as potential inbred lines in hybrid breeding. The use of multiple stability indices enhances the accuracy of selecting inbreds or genotypes by compensating for the limitations of individual indices, providing a more balanced and reliable evaluation.

Maize, as a staple crop, contributes significantly to global nutritional security. However, improving its nutritional quality, including grain zinc (GZn), grain iron (GFe), kernel oil (KO), protein quality (PQ), and content (PC), is difficult due to the complex and polygenic nature of these traits. In traditional quantitative trait loci (QTLs) mapping, different populations tested across variable environments have resulted in heterogeneous findings, highlighting the challenge of QTL instability. Therefore, we tested whether Meta-QTL (MQTL) analysis enables the identification of stable QTLs with broader allelic coverage and higher mapping resolution for effective marker-assisted selection (MAS) of complex traits. A comprehensive literature search revealed 29 mapping studies encompassing 308 QTLs for the targeted traits. A total of 34 stable MQTLs were identified, with an average CI of 4.59 cM. These MQTLs were located on all ten maize chromosomes, with phenotypic variance explained (PVE %) ranging from 7.3 % (MQTL1_2) to 49.0 % (MQTL3_2). Furthermore, the analysis revealed six MAS-friendly and five hotspot MQTLs. Besides, 591 CGs were identified underlying these MQTLs, of which 14 have known roles in grain filling, metal homeostasis, and fatty acid biosynthesis in maize. In silico analysis confirmed the tissue-specific expression of these 14 CGs. MQTL analysis effectively refined the genomic regions (4.86 folds) linked with nutritional quality and identified stable MQTLs and CGs. These findings will be useful for developing nutritionally enriched varieties through MAS and genetic engineering.

A lackof key amino acids, including lysine, tryptophan and methionine causes nutritional imbalance in maize (Zea mays L.) grain protein. The present investigation was carried out to determine the genetic variation for kernel methionine, lysine and tryptophan content in 25 promising maize inbred lines. ANOVA revealed significant differences between the genotypes for methionine (1.56-2.96%), lysine (1.90-3.68%), and tryptophan (0.51-0.92 %) content. QIL-4-2831 (2.96%), QIL-4-2829 (2.60%), QIL-4-2830 (2.44%), QIL-4-2311(2.42%) and QIL-4-3080 (2.39%) had the highest mean methionine content. The present findings also indicated that there was no significant correlation between methionine and lysine (r=0.14), nor between methionine and tryptophan (r = 0.09). However, lysine and tryptophan were shown to have a positive correlation (r = 0.84**). The high methionine lines can be used for developing high methionine cultivars in future crop improvement programs.