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      Genome-wide association study of seed protein, oil and amino acid contents in soybean from maturity groups I to IV

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          Genomic regions associated with seed protein, oil and amino acid contents were identified by genome-wide association analyses. Geographic distributions of haplotypes indicate scope of improvement of these traits.

          Abstract

          Soybean [ Glycine max (L.) Merr.] protein and oil are used worldwide in feed, food and industrial materials. Increasing seed protein and oil contents is important; however, protein content is generally negatively correlated with oil content. We conducted a genome-wide association study using phenotypic data collected from five environments for 621 accessions in maturity groups I–IV and 34,014 markers to identify quantitative trait loci (QTL) for seed content of protein, oil and several essential amino acids. Three and five genomic regions were associated with seed protein and oil contents, respectively. One, three, one and four genomic regions were associated with cysteine, methionine, lysine and threonine content (g kg −1 crude protein), respectively. As previously shown, QTL on chromosomes 15 and 20 were associated with seed protein and oil contents, with both exhibiting opposite effects on the two traits, and the chromosome 20 QTL having the most significant effect. A multi-trait mixed model identified trait-specific QTL. A QTL on chromosome 5 increased oil with no effect on protein content, and a QTL on chromosome 10 increased protein content with little effect on oil content. The chromosome 10 QTL co-localized with maturity gene E2/ GmGIa. Identification of trait-specific QTL indicates feasibility to reduce the negative correlation between protein and oil contents. Haplotype blocks were defined at the QTL identified on chromosomes 5, 10, 15 and 20. Frequencies of positive effect haplotypes varied across maturity groups and geographic regions, providing guidance on which alleles have potential to contribute to soybean improvement for specific regions.

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          The online version of this article (10.1007/s00122-019-03304-5) contains supplementary material, which is available to authorized users.

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          Recovery of inter-block information when block sizes are unequal

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            Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection.

            We report a large-scale analysis of the patterns of genome-wide genetic variation in soybeans. We re-sequenced a total of 17 wild and 14 cultivated soybean genomes to an average of approximately ×5 depth and >90% coverage using the Illumina Genome Analyzer II platform. We compared the patterns of genetic variation between wild and cultivated soybeans and identified higher allelic diversity in wild soybeans. We identified a high level of linkage disequilibrium in the soybean genome, suggesting that marker-assisted breeding of soybean will be less challenging than map-based cloning. We report linkage disequilibrium block location and distribution, and we identified a set of 205,614 tag SNPs that may be useful for QTL mapping and association studies. The data here provide a valuable resource for the analysis of wild soybeans and to facilitate future breeding and quantitative trait analysis.
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              An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

              Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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                Author and article information

                Contributors
                +1-614-292-9003 , mchale.21@osu.edu
                +1-919-513-1917 , rouf.mian@ars.usda.gov
                Journal
                Theor Appl Genet
                Theor. Appl. Genet
                TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0040-5752
                1432-2242
                26 February 2019
                26 February 2019
                2019
                : 132
                : 6
                : 1639-1659
                Affiliations
                [1 ]ISNI 0000 0001 2173 6074, GRID grid.40803.3f, Department of Crop and Soil Sciences, , North Carolina State University, ; Raleigh, NC 27695 USA
                [2 ]ISNI 0000 0001 0722 6377, GRID grid.254230.2, Department of Crop Science, , Chungnam National University, ; Daejeon, 34134 South Korea
                [3 ]ISNI 0000 0001 2285 7943, GRID grid.261331.4, Department of Horticulture and Crop Science, , The Ohio State University, ; Columbus, OH 43210 USA
                [4 ]ISNI 0000 0004 1936 9991, GRID grid.35403.31, Department of Crop Sciences, , University of Illinois and USDA-ARS, ; Urbana, IL 61801 USA
                [5 ]ISNI 0000 0004 0404 0958, GRID grid.463419.d, Corn, Soybean Wheat Quality Research Unit, , USDA-ARS, ; Wooster, OH 44691 USA
                [6 ]ISNI 0000 0001 2285 7943, GRID grid.261331.4, Center for Soybean Research and Center of Applied Plant Sciences, , The Ohio State University, ; Columbus, OH 43210 USA
                [7 ]ISNI 0000 0004 0404 0958, GRID grid.463419.d, Soybean and Nitrogen Fixation Unit, , USDA-ARS, ; Raleigh, NC 27607 USA
                Author notes

                Communicated by Volker Hahn.

                Author information
                http://orcid.org/0000-0003-1028-2315
                Article
                3304
                10.1007/s00122-019-03304-5
                6531425
                30806741
                c8dbce01-a5c4-4545-9bf5-7362c9744b88
                © The Author(s) 2019

                OpenAccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 14 June 2018
                : 5 February 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100012009, United Soybean Board;
                Award ID: 1720-162-0111
                Award ID: 1720-152-0106
                Award Recipient :
                Funded by: USDA National Institute of Food and Agriculture
                Award ID: Hatch project OHO01279
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2019

                Genetics
                Genetics

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