47
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Soybean ( Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding.

          Results

          To understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits.

          Conclusions

          This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-017-1289-9) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references42

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Yield Trends Are Insufficient to Double Global Crop Production by 2050

          Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops—maize, rice, wheat, and soybean—that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems.

            Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
                Bookmark

                Author and article information

                Contributors
                zhiwu.zhang@wsu.edu
                gdwang@genetics.ac.cn
                bgzhu@genetics.ac.cn
                zxtian@genetics.ac.cn
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                24 August 2017
                24 August 2017
                2017
                : 18
                : 161
                Affiliations
                [1 ]ISNI 0000000119573309, GRID grid.9227.e, State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, , Chinese Academy of Sciences, ; Beijing, 100101 China
                [2 ]ISNI 0000000119573309, GRID grid.9227.e, State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, , Chinese Academy of Sciences, ; Beijing, 100101 China
                [3 ]GRID grid.452609.c, Institute of maize research, , Heilongjiang Academy of Agricultural Sciences, ; Harbin, 150086 China
                [4 ]ISNI 0000 0001 0526 1937, GRID grid.410727.7, Institute of Animal Science, , Chinese Academy of Agricultural Sciences, ; Beijing, 100193 China
                [5 ]GRID grid.452609.c, , Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, ; Mudanjiang, 157041 China
                [6 ]GRID grid.452609.c, Institute of Soybean Research, , Heilongjiang Academy of Agricultural Sciences, ; Harbin, 150086 China
                [7 ]Heihe Branch of Heilongjiang Academy of Agricultural Sciences, Heihe, 164300 China
                [8 ]ISNI 0000 0000 9291 3229, GRID grid.162110.5, School of Computer Science and Technology, , Wuhan University of Technology, ; Wuhan, 430070 China
                [9 ]ISNI 0000000119573309, GRID grid.9227.e, Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, , Chinese Academy of Sciences, ; Harbin, 130102 China
                [10 ]ISNI 0000 0001 2157 6568, GRID grid.30064.31, Department of Crop and Soil Sciences, , Washington State University, ; Pullman, WA 99164 USA
                [11 ]ISNI 0000 0004 1797 8419, GRID grid.410726.6, , University of Chinese Academy of Sciences, ; Beijing, 100039 China
                Author information
                http://orcid.org/0000-0001-6051-9670
                Article
                1289
                10.1186/s13059-017-1289-9
                5571659
                28838319
                98d344af-198c-4293-af0e-422dbd6a2685
                © The Author(s). 2017

                Open AccessThis 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. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 11 May 2017
                : 25 July 2017
                Funding
                Funded by: "Strategic Priority Research Program" of the Chinese Academy of Sciences
                Award ID: XDA08000000
                Award Recipient :
                Funded by: National Natural Science Foundation of China
                Award ID: 31525018 and 91531304
                Award Recipient :
                Funded by: National Key Research and Development Program
                Award ID: 2016YFD0100401
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Genetics
                soybean,agronomic traits,gwas,network
                Genetics
                soybean, agronomic traits, gwas, network

                Comments

                Comment on this article