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      Empirical phenotyping and genome-wide association study reveal the association of panicle architecture with yield in Chenopodium quinoa

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          Abstract

          Chenopodium quinoa manifests adaptability to grow under varying agro-climatic scenarios. Assessing quinoa germplasm’s phenotypic and genetic variability is a prerequisite for introducing it as a potential candidate in cropping systems. Adaptability is the basic outcome of ecological genomics of crop plants. Adaptive variation predicted with a genome-wide association study provides a valuable basis for marker-assisted breeding. Hence, a panel of 72 quinoa plants was phenotyped for agro morphological attributes and association-mapping for distinct imperative agronomic traits. Inter simple sequence repeat (ISSR) markers were employed to assess genetic relatedness and population structure. Heatmap analysis showed three genotypes were early maturing, and six genotypes were attributed for highest yield. The SD-121-07 exhibited highest yield per plant possessing green, glomerulate shaped, compact density panicle with less leaves. However, SJrecm-03 yielded less exhibiting pink, intermediate shape, intermediate density panicles with less leaves. The phenotyping revealed strong correlation of panicle architecture with yield in quinoa. A genome-wide association study unraveled the associations between ISSR makers and agro-morphological traits. Mixed linear modes analysis yielded nine markers associated with eight traits at p ≤ 0.01. Moreover, ISSR markers significantly associated with panicle shape and leafiness were also associated with yield per plant. These findings contribute to the provision of authenticity for marker-assisted selection that ultimately would support quinoa breeding programs.

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          Most cited references72

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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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            TASSEL: software for association mapping of complex traits in diverse samples.

            Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.
              • Record: found
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              Genetic Distance between Populations

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                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                18 March 2024
                2024
                : 15
                : 1349239
                Affiliations
                [1] 1Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture , Faisalabad, Pakistan
                [2] 2Department of Plant Pathology, University of Agriculture , Faisalabad, Pakistan
                [3] 3Botany and Microbiology Department, College of Science, King Saud University , Riyadh, Saudi Arabia
                [4] 4Facultad de Ciencias Agrotecnológicas, Universidad Autónoma de Chihuahua , Chihuahua, Mexico
                [5] 5Plant Production Department, College of Food and Agricultural Sciences, King Saud University , Riyadh, Saudi Arabia
                Author notes

                Edited by: Guanghai Ji, Yunnan Agricultural University, China

                Reviewed by: Muhammad Aamir Manzoor, Shanghai Jiao Tong University, China

                Jinhao Zhang, Yunnan Agricultural University, China

                These authors have contributed equally to this work and share first authorship

                Article
                10.3389/fmicb.2024.1349239
                10982352
                38562468
                b9dae08e-eefd-467d-9be6-8519d6094632
                Copyright © 2024 Habib, Ijaz, Haq, Hashem, Avila-Quezada, Abd_Allah and Khan.

                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
                : 04 December 2023
                : 27 February 2024
                Page count
                Figures: 16, Tables: 3, Equations: 0, References: 72, Pages: 23, Words: 12944
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors would like to extend their sincere appreciation to the Researchers Supporting Project Number (RSP2024R356), King Saud University, Riyadh, Saudi Arabia for publication.
                Categories
                Microbiology
                Original Research
                Custom metadata
                Microbe and Virus Interactions with Plants

                Microbiology & Virology
                heterogeneous germplasm,phenotypic characterization,ecological genomics,gwas,phenomics

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