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      Genetic diversity, population structure, and selection signature in Ethiopian sorghum [ Sorghum bicolor L. (Moench)] germplasm

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          Abstract

          Ethiopia, the probable center of origin and diversity for sorghum [ Sorghum bicolor L. (Moench)] and with unique ecogeographic features, possesses a large number of sorghum landraces that have not been well studied. Increased knowledge of this diverse germplasm through large-scale genomic characterization may contribute for understanding of evolutionary biology, and adequate use of these valuable resources from the center of origin. In this study, we characterized genetic diversity, population structure and selection signature in 304 sorghum accessions collected from diverse sorghum growing regions of Ethiopia using genotyping-by-sequencing. We identified a total of 108,107 high-quality single-nucleotide polymorphism (SNPs) markers that were evenly distributed across the sorghum genome. The average gene diversity among accessions was high ( H e = 0.29). We detected a relatively low frequency of rare alleles (26%), highlighting the potential of this germplasm for subsequent allele mining studies through genome-wide association studies. Although we found no evidence of genetic differentiation among administrative regions ( F ST = 0.02, P = 0.12), population structure and cluster analyses showed clear differentiation among six Ethiopian sorghum populations ( F ST = 0.28, P = 0.01) adapting to different environments. Analysis of SNP differentiation between the identified genetic groups revealed a total of 40 genomic regions carrying signatures of selection. These regions harbored candidate genes potentially involved in a variety of biological processes, including abiotic stress tolerance, pathogen defense and reproduction. Overall, a high level of untapped diversity for sorghum improvement remains available in Ethiopia, with patterns of diversity consistent with divergent selection on a range of adaptive characteristics.

          Most cited references72

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          Fast model-based estimation of ancestry in unrelated individuals.

          Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
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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.
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              A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species

              Advances in next generation technologies have driven the costs of DNA sequencing down to the point that genotyping-by-sequencing (GBS) is now feasible for high diversity, large genome species. Here, we report a procedure for constructing GBS libraries based on reducing genome complexity with restriction enzymes (REs). This approach is simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches. By using methylation-sensitive REs, repetitive regions of genomes can be avoided and lower copy regions targeted with two to three fold higher efficiency. This tremendously simplifies computationally challenging alignment problems in species with high levels of genetic diversity. The GBS procedure is demonstrated with maize (IBM) and barley (Oregon Wolfe Barley) recombinant inbred populations where roughly 200,000 and 25,000 sequence tags were mapped, respectively. An advantage in species like barley that lack a complete genome sequence is that a reference map need only be developed around the restriction sites, and this can be done in the process of sample genotyping. In such cases, the consensus of the read clusters across the sequence tagged sites becomes the reference. Alternatively, for kinship analyses in the absence of a reference genome, the sequence tags can simply be treated as dominant markers. Future application of GBS to breeding, conservation, and global species and population surveys may allow plant breeders to conduct genomic selection on a novel germplasm or species without first having to develop any prior molecular tools, or conservation biologists to determine population structure without prior knowledge of the genome or diversity in the species.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press
                2160-1836
                June 2021
                19 April 2021
                19 April 2021
                : 11
                : 6
                : jkab087
                Affiliations
                [1 ] College of Agriculture and Veterinary Medicine, Jimma University , P.O. Box 307, Jimma, Ethiopia
                [2 ] Plant Genome Mapping Laboratory, University of Georgia , Athens, GA 30602, USA
                [3 ] College of Agriculture, Hawassa University , P.O. Box 05, Hawassa, Ethiopia
                Author notes
                Corresponding author: Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, PO Box 307, Jimma, Ethiopia. Tel: +251-917-12 832801, E-mail: kassahunb@ 123456gmail.com
                Article
                jkab087
                10.1093/g3journal/jkab087
                8495740
                33871028
                13936306-ff57-4d70-bcea-cebd49fc8eb5
                © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 March 2021
                : 17 January 2021
                Page count
                Pages: 10
                Funding
                Funded by: United States Agency for International Development, DOI 10.13039/100000200;
                Funded by: Feed the Future Laboratory for Climate Resilient Sorghum;
                Funded by: USAID, DOI 10.13039/100000200;
                Funded by: United States Government;
                Categories
                Investigation
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140
                AcademicSubjects/SCI00010
                AcademicSubjects/SCI00960

                Genetics
                genetic diversity,population structure,selection signature,sorghum
                Genetics
                genetic diversity, population structure, selection signature, sorghum

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