RESUMEN
Sheath blight (ShB) is the most serious disease of rice (Oryza sativa L.), caused by the soil-borne fungus Rhizoctonia solani Kühn (R. solani). It poses a significant threat to global rice productivity, resulting in approximately 50% annual yield loss. Managing ShB is particularly challenging due to the broad host range of the pathogen, its necrotrophic nature, the emergence of new races, and the limited availability of highly resistant germplasm. In this study, we conducted QTL mapping using an F2 population derived from a cross between a partially resistant accession (IRGC81941A) of Oryza nivara and the susceptible rice cultivar Punjab rice 121 (PR121). Our analysis identified 29 QTLs for ShB resistance, collectively explaining a phenotypic variance ranging from 4.70 to 48.05%. Notably, a cluster of four QTLs (qRLH1.1, qRLH1.2, qRLH1.5, and qRLH1.8) on chromosome 1 consistently exhibit a resistant response against R. solani. These QTLs span from 0.096 to 420.1 Kb on the rice reference genome and contain several important genes, including Ser/Thr protein kinase, auxin-responsive protein, protease inhibitor/seed storage/LTP family protein, MLO domain-containing protein, disease-responsive protein, thaumatin-like protein, Avr9/Cf9-eliciting protein, and various transcription factors. Additionally, simple sequence repeats (SSR) markers RM212 and RM246 linked to these QTLs effectively distinguish resistant and susceptible rice cultivars, showing great promise for marker-assisted selection programs. Furthermore, our study identified pre-breeding lines in the advanced backcrossed population that exhibited superior agronomic traits and sheath blight resistance compared to the recurrent parent. These promising lines hold significant potential for enhancing the sheath blight resistance in elite cultivars through targeted improvement efforts.
Asunto(s)
Mapeo Cromosómico , Resistencia a la Enfermedad , Oryza , Enfermedades de las Plantas , Sitios de Carácter Cuantitativo , Rhizoctonia , Oryza/genética , Oryza/microbiología , Oryza/inmunología , Resistencia a la Enfermedad/genética , Enfermedades de las Plantas/genética , Enfermedades de las Plantas/microbiología , Enfermedades de las Plantas/inmunología , Rhizoctonia/patogenicidad , Mapeo Cromosómico/métodos , Cromosomas de las Plantas/genética , Fenotipo , Fitomejoramiento/métodosRESUMEN
Advances in sequencing technologies and bioinformatics tools have fueled a renewed interest in whole genome sequencing efforts in many organisms. The growing availability of multiple genome sequences has advanced our understanding of the within-species diversity, in the form of a pangenome. Pangenomics has opened new avenues for future research such as allowing dissection of complex molecular mechanisms and increased confidence in genome mapping. To comprehensively capture the genetic diversity for improving plant performance, the pangenome concept is further extended from species to genus level by the inclusion of wild species, constituting a super-pangenome. Characterization of pangenome has implications for both basic and applied research. The concept of pangenome has transformed the way biological questions are addressed. From understanding evolution and adaptation to elucidating host-pathogen interactions, finding novel genes or breeding targets to aid crop improvement to design effective vaccines for human prophylaxis, the increasing availability of the pangenome has revolutionized several aspects of biological research. The future availability of high-resolution pangenomes based on reference-level near-complete genome assemblies would greatly improve our ability to address complex biological problems.
Asunto(s)
Fitomejoramiento , Plantas , Mapeo Cromosómico , Humanos , Plantas/genéticaRESUMEN
In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of 'Inception-v3' network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.