A meta-QTL analysis was conducted in Indian mustard to identify robust and stable meta-QTLs (MQTLs) by utilizing 1504 available QTLs, which included 891 QTLs for yield-related traits and 613 QTLs for quality traits. For yield-related traits, a total of 57 MQTLs (YRTs_MQTLs) were uncovered from the clustering of 560 projected QTLs, which had a 4.18-fold smaller confidence interval (CI) than that of the initial QTLs, whereas, for quality traits, as many as 51 MQTLs (Quality_MQTLs) were derived from 324 projected QTLs, which had a 2.65-fold smaller CI than that of the initial QTLs. Sixteen YRTs_MQTLs were observed to share chromosomal positions with 16 Quality_MQTLs. Moreover, four most promising YRTs_MQTLs and eight Quality-MQTLs were also selected and recommended for use in breeding programs. Four of these selected MQTLs were also validated with significant SNPs that were identified in previously published genome-wide association studies. Further, in silico functional analysis of some promising MQTLs allowed the detection of as many as 1435 genes, which also involved 15 high-confidence candidate genes (CGs) for yield-related traits and 46 high-confidence CGs for quality traits. After validation, the identified CGs can also be exploited to model the plant architecture and to improve quality traits through marker-assisted breeding, genetic engineering, and genome editing approaches.

Field pea (Pisum sativum L.) is a highly nutritious winter-season pulse crop. It is used as food, feed, and fodder and offers nutritional security to low-income people in developing countries. Different graphical approaches like Principal Component Analysis (PCA) and Genotype + Genotype × Environment (GGE) biplots were used along with the conventional line × tester to identify efficient parents, combining ability effects and distinct heterotic groups in field pea (Pisum sativum L.). The study used a line tester design (9 × 3) for seed yield and its associated traits. In the conventional analysis, lines Aman and HFP 715 and the tester GP02/1108, as well as crosses HFP 715 × GP02/1108, Aman × GP02/1108, and Pant P-243 × HFP 1426 showed the best GCA (General Combining Ability) and SCA (Specific Combining Ability) effects, respectively, for seed yield and its attributes. The ^σ2SCA > ^σ2GCA, and ^σ2D > ^σ2A in almost all the traits indicated control of non-additive gene effects. High manifestations of heterobeltiosis for seed yield were evidenced by the superiority of 24 out of 27 crosses over the better parent. The highest significant heterobeltiosis was observed in the cross HFP 715 × GP02/1108, followed by IPF 14-16 × GP02/1108, IPF 14-16 × HFP 1426, DDR-23 × HFP 1426, DDR-23 × GP02/1108, and Aman × GP02/1108 for yield and its attributes. The biplot techniques were used to analyze data and compare their results with conventional line × tester analysis. Overall, graphical analysis results were very similar to those of traditional analysis. Consequently, it can surely be assumed that these methods could be helpful in presenting data from field pea breeding experiments carried out with line × tester design.

Knowledge of genetic diversity in lentil is imperative for selection of parental genotypes that could yield heterotic combinations. The aim of the present study was to investigate the genetic diversity among 43 diverse lentil genotypes to identify complementary and unique genotypes for breeding programmes. Field experimentation was carried out in two winter seasons (2019–2020 and 2020–2021) in Hisar (29°10′ N, 75°46′ E) using randomized block design (RBD) with three replications. The chi-square test analysis showed significant genotypic variation for qualitative traits. There was substantial genetic variation among the genotypes for most quantitative traits, connoting the need to exploit a high degree of genetic variation through selection. Multiple-trait selection would also be beneficial, as seed yield was positively associated with most quantitative traits. The principal component analysis recognized seed yield (SY), days to 50% flowering (DTF), days to maturity (DTM), number of pods per plant (NPP), number of primary branches (NPB), plant height (PH) and biological yield (BY) as target traits that prominently described variation within lentil genotypes. The cluster analysis discriminated the lentil genotypes into five discrete clusters. Cluster III and V were the most distant groups, implying wider diversity among the genotypes of these groups. Furthermore, cluster analysis identified genotypes IPL 316, LH 17-19, LH 18-04, LH 17-17, IPL 81 and Pant L-8 as high-yielding genotypes, while L 4717 was identified as an early-maturing genotype. Therefore, to obtain a broad spectrum of early-maturing high-yielding segregants, the selected genotypes may serve as superior parental lines for structuring breeding strategies.

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The cotton plant (Gossypium spp.) is widely cultivated for its fibre, yet it represents a significant source of diverse phytochemicals and industrially valuable by-products. This review summarises the major chemical constituents of cotton, including primary metabolites such as cellulose, proteins and fatty acids and secondary metabolites such as gossypol, terpenoids, flavonoids, phenolic acids, tannins and coumarins. Special emphasis is placed on the pharmacologically active substances such as gossypol and various terpenes, highlighting their roles in antimicrobial, anticancer and antioxidant activity. In addition to its primary fibre product, cotton generates a variety of by-products including cottonseed oil, meal, hulls and linters which serve applications in food, feed, pharmaceuticals and biotechnology. The review further examines the impact of genetic variation, transgenic modifications and environmental conditions on the plant's chemical profile. The synthesis of current knowledge demonstrates that cotton is not only a major fibre crop but also a valuable source of bioactive compounds with significant industrial and biomedical potential.