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      Exploring the diversity of virulence genes in the Magnaporthe population infecting millets and rice in India

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          Abstract

          Blast pathogen, Magnaporthe spp., that infects ancient millet crops such pearl millet, finger millet, foxtail millet, barnyard millet, and rice was isolated from different locations of blast hotspots in India using single spore isolation technique and 136 pure isolates were established. Numerous growth characteristics were captured via morphogenesis analysis. Among the 10 investigated virulent genes, we could amplify MPS1 (TTK Protein Kinase) and Mlc (Myosin Regulatory Light Chain edc4) in majority of tested isolates, regardless of the crop and region where they were collected, indicating that these may be crucial for their virulence. Additionally, among the four avirulence ( Avr) genes studied, Avr-Pizt had the highest frequency of occurrence, followed by Avr-Pia. It is noteworthy to mention that Avr-Pik was present in the least number of isolates (9) and was completely absent from the blast isolates from finger millet, foxtail millet, and barnyard millet. A comparison at the molecular level between virulent and avirulent isolates indicated observably large variation both across (44%) and within (56%) them. The 136 Magnaporthe spp isolates were divided into four groups using molecular markers. Regardless of their geographic distribution, host plants, or tissues affected, the data indicate that the prevalence of numerous pathotypes and virulence factors at the field level, which may lead to a high degree of pathogenic variation. This research could be used for the strategic deployment of resistant genes to develop blast disease-resistant cultivars in rice, pearl millet, finger millet, foxtail millet, and barnyard millet.

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          Most cited references57

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          Detecting the number of clusters of individuals using the software structure: a simulation study

          The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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            Inference of Population Structure Using Multilocus Genotype Data

            We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
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              STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                09 May 2023
                2023
                : 14
                : 1131315
                Affiliations
                [1] 1 ICAR-All India Coordinated Research Project (ICAR-AICRP) on Small Millets, PC Unit, University of Agricultural Sciences, Gandhi Krishi Vigyana Kendra (GKVK) , Bengaluru, Karnataka, India
                [2] 2 Department of Plant Biotechnology, University of Agricultural Sciences, Gandhi Krishi Vigyana Kendra (GKVK) , Bengaluru, Karnataka, India
                [3] 3 Department of Plant Pathology, Vivekananda Parvatiya Krishi Anusandhan Sansthan , Almora, Uttarakhand, India
                [4] 4 ICAR-National Rice Research Institute , Cuttack, Odisha, India
                [5] 5 ICAR-All India Coordinated Research Project (ICAR-AICRP) on Small Millets Zonal Agril. Research Station, Vishweshwaraiah Canal (V.C.) Farm , Mandya, Karnataka, India
                [6] 6 Regional Agricultural Research Station, Assam Agriculture University , Gossaigaon, Assam, India
                [7] 7 Department of Plant Pathology, Agricultural Research Station, Gajularega , Vizianagaram, Andra Pradesh, India
                [8] 8 Department of Plant Pathology, Uttarakhand University of Hort. and Forestry , Ranichauri, Uttarakhand, India
                [9] 9 Department of Plant Pathology, Center for Excellence in Millets, Athiyandal , Tiruvannamalai, Tamil Nadu, India
                [10] 10 Department of Plant Pathology, Zonal Agricultural Research Station, Kumharwand Farm , Jagdalpur, Chhattisgarh, India
                [11] 11 Indian Council of Agricultural Research ICAR-Indian Institute of Millets Research, Rajendranagar , Hyderabad, Telangana, India
                [12] 12 Hill Millet Research Station, Navasari Agricultural University, Waghai , Dangs, Gujarat, India
                [13] 13 Department of Plant Pathology, College of Agriculture , Rewa, Madhya Pradesh, India
                [14] 14 ICAR-All India Coordinated Research Project (ICAR-AICRP) on Small Millets, Regional Agricultural Research Station , Nandyal, Andhra Pradesh, India
                [15] 15 Institute of Excellence, Vijnana Bhavan, University of Mysuru , Manasagangotri, Karnataka, India
                [16] 16 ICAR-Indian Agricultural Research Institute , New Delhi, India
                Author notes

                Edited by: Salej Sood, ICAR- Central Potato Research Institute, India

                Reviewed by: Pramesh Devanna, University of Agricultural Sciences Raichur, India; Pankaj Kumar Singh, National Agri-Food Biotechnology Institute, India

                *Correspondence: K. B. Palanna, kbpalanna@ 123456gmail.com ; H. Rajashekara, rajaiaripath@ 123456gmail.com ; B. N. Devanna, devnova2460@ 123456gmail.com

                †These authors have contributed equally to this work

                Article
                10.3389/fpls.2023.1131315
                10203591
                23dbec96-5acb-4c59-aea7-d2998e81d1d0
                Copyright © 2023 Palanna, Vinaykumar, Prasanna, Rajashekara, Devanna, Anilkumar, Jeevan, Raveendra, Khan, Bhavana, Upadhyay, Patro, Rawat, Rajesh, Saravanan, Netam, Rajesha, Das, Patil, Jain, Saralamma, Nayaka, Prakash and Nagaraja

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 24 December 2022
                : 03 April 2023
                Page count
                Figures: 8, Tables: 4, Equations: 0, References: 59, Pages: 12, Words: 5699
                Funding
                Funded by: Department of Science and Technology, Ministry of Science and Technology, India , doi 10.13039/501100001409;
                This research was funded by DST-Science and Engineering Research Board (SERB-EEQ/2019/000678) Government of India, New Delhi - 110 070.
                Categories
                Plant Science
                Original Research
                Custom metadata
                Technical Advances in Plant Science

                Plant science & Botany
                millet crops,magnaporthe,virulent and avirulent genes,blast pathogen,pcr
                Plant science & Botany
                millet crops, magnaporthe, virulent and avirulent genes, blast pathogen, pcr

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