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      Spatial Structure and Climatic Adaptation in African Maize Revealed by Surveying SNP Diversity in Relation to Global Breeding and Landrace Panels

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          Climate change threatens maize productivity in sub-Saharan Africa. To ensure food security, access to locally adapted genetic resources and varieties is an important adaptation measure. Most of the maize grown in Africa is a genetic mix of varieties introduced at different historic times following the birth of the trans-Atlantic economy, and knowledge about geographic structure and local adaptations is limited.


          A panel of 48 accessions of maize representing various introduction routes and sources of historic and recent germplasm introductions in Africa was genotyped with the MaizeSNP50 array. Spatial genetic structure and genetic relationships in the African panel were analysed separately and in the context of a panel of 265 inbred lines representing global breeding material (based on 26,900 SNPs) and a panel of 1127 landraces from the Americas (270 SNPs). Environmental association analysis was used to detect SNPs associated with three climatic variables based on the full 43,963 SNP dataset.


          The genetic structure is consistent between subsets of the data and the markers are well suited for resolving relationships and admixture among the accessions. The African accessions are structured in three clusters reflecting historical and current patterns of gene flow from the New World and within Africa. The Sahelian cluster reflects original introductions of Meso-American landraces via Europe and a modern introduction of temperate breeding material. The Western cluster reflects introduction of Coastal Brazilian landraces, as well as a Northeast-West spread of maize through Arabic trade routes across the continent. The Eastern cluster most strongly reflects gene flow from modern introduced tropical varieties. Controlling for population history in a linear model, we identify 79 SNPs associated with maximum temperature during the growing season. The associations located in genes of known importance for abiotic stress tolerance are interesting candidates for local adaptations.

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          Most cited references 18

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          Prioritizing climate change adaptation needs for food security in 2030.

          Investments aimed at improving agricultural adaptation to climate change inevitably favor some crops and regions over others. An analysis of climate risks for crops in 12 food-insecure regions was conducted to identify adaptation priorities, based on statistical crop models and climate projections for 2030 from 20 general circulation models. Results indicate South Asia and Southern Africa as two regions that, without sufficient adaptation measures, will likely suffer negative impacts on several crops that are important to large food-insecure human populations. We also find that uncertainties vary widely by crop, and therefore priorities will depend on the risk attitudes of investment institutions.
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            A single domestication for maize shown by multilocus microsatellite genotyping.

            There exists extraordinary morphological and genetic diversity among the maize landraces that have been developed by pre-Columbian cultivators. To explain this high level of diversity in maize, several authors have proposed that maize landraces were the products of multiple independent domestications from their wild relative (teosinte). We present phylogenetic analyses based on 264 individual plants, each genotyped at 99 microsatellites, that challenge the multiple-origins hypothesis. Instead, our results indicate that all maize arose from a single domestication in southern Mexico about 9,000 years ago. Our analyses also indicate that the oldest surviving maize types are those of the Mexican highlands with maize spreading from this region over the Americas along two major paths. Our phylogenetic work is consistent with a model based on the archaeological record suggesting that maize diversified in the highlands of Mexico before spreading to the lowlands. We also found only modest evidence for postdomestication gene flow from teosinte into maize.
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              A Large Maize (Zea mays L.) SNP Genotyping Array: Development and Germplasm Genotyping, and Genetic Mapping to Compare with the B73 Reference Genome

              SNP genotyping arrays have been useful for many applications that require a large number of molecular markers such as high-density genetic mapping, genome-wide association studies (GWAS), and genomic selection. We report the establishment of a large maize SNP array and its use for diversity analysis and high density linkage mapping. The markers, taken from more than 800,000 SNPs, were selected to be preferentially located in genes and evenly distributed across the genome. The array was tested with a set of maize germplasm including North American and European inbred lines, parent/F1 combinations, and distantly related teosinte material. A total of 49,585 markers, including 33,417 within 17,520 different genes and 16,168 outside genes, were of good quality for genotyping, with an average failure rate of 4% and rates up to 8% in specific germplasm. To demonstrate this array's use in genetic mapping and for the independent validation of the B73 sequence assembly, two intermated maize recombinant inbred line populations – IBM (B73×Mo17) and LHRF (F2×F252) – were genotyped to establish two high density linkage maps with 20,913 and 14,524 markers respectively. 172 mapped markers were absent in the current B73 assembly and their placement can be used for future improvements of the B73 reference sequence. Colinearity of the genetic and physical maps was mostly conserved with some exceptions that suggest errors in the B73 assembly. Five major regions containing non-colinearities were identified on chromosomes 2, 3, 6, 7 and 9, and are supported by both independent genetic maps. Four additional non-colinear regions were found on the LHRF map only; they may be due to a lower density of IBM markers in those regions or to true structural rearrangements between lines. Given the array's high quality, it will be a valuable resource for maize genetics and many aspects of maize breeding.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                16 October 2012
                : 7
                : 10
                [1 ]Centre for Development and the Environment (SUM), University of Oslo, Oslo, Norway
                [2 ]Nordic Genetic Resource Centre, Alnarp, Sweden
                [3 ]Department of Biology, Centre for Ecological and Evolutionary Synthesis (CEES), University of Oslo, Oslo, Norway
                [4 ]Department of Animal and Aquacultural Sciences, Centre for Integrative Genetics (CIGENE), Norwegian University of Life Sciences, Aas, Norway
                New York State Museum, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: OTW AKB. Performed the experiments: OTW MPK. Analyzed the data: OTW. Contributed reagents/materials/analysis tools: MPK PRB AKB. Wrote the paper: OTW PRB MPK AKB.


                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 11
                This research was supported by the Centre for Development and the Environment, University of Oslo. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Evolutionary Biology
                Evolutionary Processes
                Genome Analysis Tools
                Genome Scans
                Model Organisms
                Plant and Algal Models
                Population Biology
                Population Genetics
                Gene Flow
                Gene Pool



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