33
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genome-Wide Association Mapping and Genomic Prediction Analyses Reveal the Genetic Architecture of Grain Yield and Flowering Time Under Drought and Heat Stress Conditions in Maize

      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

          Drought stress (DS) is a major constraint to maize yield production. Heat stress (HS) alone and in combination with DS are likely to become the increasing constraints. Association mapping and genomic prediction (GP) analyses were conducted in a collection of 300 tropical and subtropical maize inbred lines to reveal the genetic architecture of grain yield and flowering time under well-watered (WW), DS, HS, and combined DS and HS conditions. Out of the 381,165 genotyping-by-sequencing SNPs, 1549 SNPs were significantly associated with all the 12 trait-environment combinations, the average PVE (phenotypic variation explained) by these SNPs was 4.33%, and 541 of them had a PVE value greater than 5%. These significant associations were clustered into 446 genomic regions with a window size of 20 Mb per region, and 673 candidate genes containing the significantly associated SNPs were identified. In addition, 33 hotspots were identified for 12 trait-environment combinations and most were located on chromosomes 1 and 8. Compared with single SNP-based association mapping, the haplotype-based associated mapping detected fewer number of significant associations and candidate genes with higher PVE values. All the 688 candidate genes were enriched into 15 gene ontology terms, and 46 candidate genes showed significant differential expression under the WW and DS conditions. Association mapping results identified few overlapped significant markers and candidate genes for the same traits evaluated under different managements, indicating the genetic divergence between the individual stress tolerance and the combined drought and HS tolerance. The GP accuracies obtained from the marker-trait associated SNPs were relatively higher than those obtained from the genome-wide SNPs for most of the target traits. The genetic architecture information of the grain yield and flowering time revealed in this study, and the genomic regions identified for the different trait-environment combinations are useful in accelerating the efforts on rapid development of the stress-tolerant maize germplasm through marker-assisted selection and/or genomic selection.

          Related collections

          Most cited references34

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

          Modern maize varieties going local in the semi-arid zone in Tanzania

          Background Maize is the most produced crop in Sub-Saharan Africa, but yields are low and climate change is projected to further constrain smallholder production. The current efforts to breed and disseminate new high yielding and climate ready maize varieties are implemented through the formal seed system; the chain of public and private sector activities and institutions that produce and release certified seeds. These efforts are taking place in contexts currently dominated by informal seed systems; local and informal seed management and exchange channels with a long history of adapting crops to local conditions. We here present a case study of the genetic effects of both formal and informal seed management from the semi-arid zone in Tanzania. Results Two open pollinated varieties (OPVs), Staha and TMV1, first released by the formal seed system in the 1980s are cultivated on two-thirds of the maize fields among the surveyed households. Farmer-recycling of improved varieties and seed selection are common on-farm seed management practices. Drought tolerance and high yield are the most important characteristics reported as reason for cultivating the current varieties as well as the most important criteria for farmers’ seed selection. Bayesian cluster analysis, PCA and FST analyses based on 131 SNPs clearly distinguish between the two OPVs, and despite considerable heterogeneity between and within seed lots, there is insignificant differentiation between breeder’s seeds and commercial seeds in both OPVs. Genetic separation increases as the formal system varieties enter the informal system and both hybridization with unrelated varieties and directional selection probably play a role in the differentiation. Using a Bayesian association approach we identify three loci putatively under selection in the informal seed system. Conclusions Our results suggest that the formal seed system in the study area distributes seed lots that are true to type. We suggest that hybridization and directional selection differentiate farmer recycled seed lots from the original varieties and potentially lead to beneficial creolization. Access to drought tolerant OPVs in combination with farmer seed selection is likely to enhance seed system security and farmers’ adaptive capacity in the face of climate change.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction

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

              A SUPER Powerful Method for Genome Wide Association Study

              Genome-Wide Association Studies shed light on the identification of genes underlying human diseases and agriculturally important traits. This potential has been shadowed by false positive findings. The Mixed Linear Model (MLM) method is flexible enough to simultaneously incorporate population structure and cryptic relationships to reduce false positives. However, its intensive computational burden is prohibitive in practice, especially for large samples. The newly developed algorithm, FaST-LMM, solved the computational problem, but requires that the number of SNPs be less than the number of individuals to derive a rank-reduced relationship. This restriction potentially leads to less statistical power when compared to using all SNPs. We developed a method to extract a small subset of SNPs and use them in FaST-LMM. This method not only retains the computational advantage of FaST-LMM, but also remarkably increases statistical power even when compared to using the entire set of SNPs. We named the method SUPER (Settlement of MLM Under Progressively Exclusive Relationship) and made it available within an implementation of the GAPIT software package.
                Bookmark

                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
                30 January 2019
                2018
                : 9
                : 1919
                Affiliations
                [1] 1Maize Research Institute, Sichuan Agricultural University , Wenjiang, China
                [2] 2Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Ministry of Agriculture , Chengdu, China
                [3] 3International Maize and Wheat Improvement Center , Texcoco, Mexico
                [4] 4International Maize and Wheat Improvement Center , Harare, Zimbabwe
                [5] 5International Maize and Wheat Improvement Center , Nairobi, Kenya
                [6] 6College of Bioscience and Biotechnology, Shenyang Agricultural University , Shenyang, China
                [7] 7Institute of Crop Sciences, Chinese Academy of Agricultural Sciences , Beijing, China
                Author notes

                Edited by: Rodomiro Ortiz, Swedish University of Agricultural Sciences, Sweden

                Reviewed by: Yinglong Chen, University of Western Australia, Australia; Elisabetta Frascaroli, University of Bologna, Italy

                *Correspondence: Yanli Lu, yanli.lu82@ 123456hotmail.com Xuecai Zhang, xc.zhang@ 123456cgiar.org

                Present address: Raman Babu, DowDupont, Hyderabad, India

                This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2018.01919
                6363715
                30761177
                7a32c16c-9342-4a7c-a4bc-65b0d995a865
                Copyright © 2019 Yuan, Cairns, Babu, Gowda, Makumbi, Magorokosho, Zhang, Liu, Wang, Hao, San Vicente, Olsen, Prasanna, Lu and Zhang.

                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
                : 21 September 2018
                : 10 December 2018
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 51, Pages: 15, Words: 0
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                maize,association mapping,genomic prediction,drought stress,heat stress,combined drought and heat stress

                Comments

                Comment on this article