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      A High-Density SNP Map of Sunflower Derived from RAD-Sequencing Facilitating Fine-Mapping of the Rust Resistance Gene R 12

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          Abstract

          A high-resolution genetic map of sunflower was constructed by integrating SNP data from three F 2 mapping populations (HA 89/RHA 464, B-line/RHA 464, and CR 29/RHA 468). The consensus map spanned a total length of 1443.84 cM, and consisted of 5,019 SNP markers derived from RAD tag sequencing and 118 publicly available SSR markers distributed in 17 linkage groups, corresponding to the haploid chromosome number of sunflower. The maximum interval between markers in the consensus map is 12.37 cM and the average distance is 0.28 cM between adjacent markers. Despite a few short-distance inversions in marker order, the consensus map showed high levels of collinearity among individual maps with an average Spearman's rank correlation coefficient of 0.972 across the genome. The order of the SSR markers on the consensus map was also in agreement with the order of the individual map and with previously published sunflower maps. Three individual and one consensus maps revealed the uneven distribution of markers across the genome. Additionally, we performed fine mapping and marker validation of the rust resistance gene R 12 , providing closely linked SNP markers for marker-assisted selection of this gene in sunflower breeding programs. This high resolution consensus map will serve as a valuable tool to the sunflower community for studying marker-trait association of important agronomic traits, marker assisted breeding, map-based gene cloning, and comparative mapping.

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          SNP identification in crop plants.

          Martin W. Ganal, Thomas Altmann, Marion S. Röder (2009)
          In many plants, single nucleotide polymorphism (SNP) markers are increasingly becoming the marker system of choice. However, for many crop plants there are surprisingly low numbers of validated SNP markers available although they are needed in large numbers for studies regarding genetic variation, linkage mapping, population structure analysis, association genetics, map-based gene isolation, and plant breeding. This review summarizes the current status of SNP marker development technologies for major crop plants. It will also provide an outlook into the future regarding possible SNP identification approaches in crop plants on the basis of current development in model systems such as Arabidopsis which will become available with the full sequencing of more plant genomes, genome resequencing, and in conjunction with the next-generation sequencing technologies.
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            Development of a Large SNP Genotyping Array and Generation of High-Density Genetic Maps in Tomato

            Sung-Chur Sim, Gregor Durstewitz, Jörg Plieske … (2012)
            The concurrent development of high-throughput genotyping platforms and next generation sequencing (NGS) has increased the number and density of genetic markers, the efficiency of constructing detailed linkage maps, and our ability to overlay recombination and physical maps of the genome. We developed an array for tomato with 8,784 Single Nucleotide Polymorphisms (SNPs) mainly discovered based on NGS-derived transcriptome sequences. Of the SNPs, 7,720 (88%) passed manufacturing quality control and could be scored in tomato germplasm. The array was used to generate high-density linkage maps for three interspecific F2 populations: EXPEN 2000 (Solanum lycopersicum LA0925 x S. pennellii LA0716, 79 individuals), EXPEN 2012 (S. lycopersicum Moneymaker x S. pennellii LA0716, 160 individuals), and EXPIM 2012 (S. lycopersicum Moneymaker x S. pimpinellifolium LA0121, 183 individuals). The EXPEN 2000-SNP and EXPEN 2012 maps consisted of 3,503 and 3,687 markers representing 1,076 and 1,229 unique map positions (genetic bins), respectively. The EXPEN 2000-SNP map had an average marker bin interval of 1.6 cM, while the EXPEN 2012 map had an average bin interval of 0.9 cM. The EXPIM 2012 map was constructed with 4,491 markers (1,358 bins) and an average bin interval of 0.8 cM. All three linkage maps revealed an uneven distribution of markers across the genome. The dense EXPEN 2012 and EXPIM 2012 maps showed high levels of colinearity across all 12 chromosomes, and also revealed evidence of small inversions between LA0716 and LA0121. Physical positions of 7,666 SNPs were identified relative to the tomato genome sequence. The genetic and physical positions were mostly consistent. Exceptions were observed for chromosomes 3, 10 and 12. Comparing genetic positions relative to physical positions revealed that genomic regions with high recombination rates were consistent with the known distribution of euchromatin across the 12 chromosomes, while very low recombination rates were observed in the heterochromatic regions.
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              A consensus genetic map of sorghum that integrates multiple component maps and high-throughput Diversity Array Technology (DArT) markers

              Emma S. Mace, Jean-François Rami, Sophie Bouchet … (2009)
              Background Sorghum genome mapping based on DNA markers began in the early 1990s and numerous genetic linkage maps of sorghum have been published in the last decade, based initially on RFLP markers with more recent maps including AFLPs and SSRs and very recently, Diversity Array Technology (DArT) markers. It is essential to integrate the rapidly growing body of genetic linkage data produced through DArT with the multiple genetic linkage maps for sorghum generated through other marker technologies. Here, we report on the colinearity of six independent sorghum component maps and on the integration of these component maps into a single reference resource that contains commonly utilized SSRs, AFLPs, and high-throughput DArT markers. Results The six component maps were constructed using the MultiPoint software. The lengths of the resulting maps varied between 910 and 1528 cM. The order of the 498 markers that segregated in more than one population was highly consistent between the six individual mapping data sets. The framework consensus map was constructed using a "Neighbours" approach and contained 251 integrated bridge markers on the 10 sorghum chromosomes spanning 1355.4 cM with an average density of one marker every 5.4 cM, and were used for the projection of the remaining markers. In total, the sorghum consensus map consisted of a total of 1997 markers mapped to 2029 unique loci (1190 DArT loci and 839 other loci) spanning 1603.5 cM and with an average marker density of 1 marker/0.79 cM. In addition, 35 multicopy markers were identified. On average, each chromosome on the consensus map contained 203 markers of which 58.6% were DArT markers. Non-random patterns of DNA marker distribution were observed, with some clear marker-dense regions and some marker-rare regions. Conclusion The final consensus map has allowed us to map a larger number of markers than possible in any individual map, to obtain a more complete coverage of the sorghum genome and to fill a number of gaps on individual maps. In addition to overall general consistency of marker order across individual component maps, good agreement in overall distances between common marker pairs across the component maps used in this study was determined, using a difference ratio calculation. The obtained consensus map can be used as a reference resource for genetic studies in different genetic backgrounds, in addition to providing a framework for transferring genetic information between different marker technologies and for integrating DArT markers with other genomic resources. DArT markers represent an affordable, high throughput marker system with great utility in molecular breeding programs, especially in crops such as sorghum where SNP arrays are not publicly available.
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                Author and article information

                Contributors
                Tongming Yin: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2014
                Publication date (Electronic): 11 July 2014
                Volume: 9
                Issue: 7
                Electronic Location Identifier: e98628
                Affiliations
                [1 ]Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, United States of America
                [2 ]Department of Plant Pathology, North Dakota State University, Fargo, North Dakota, United States of America
                [3 ]Northern Crop Science Laboratory, USDA- Agricultural Research Service, Fargo, North Dakota, United States of America
                [4 ]BioDiagnostics Inc., River Falls, Wisconsin, United States of America
                [5 ]Soybean Genomics and Improvement Lab, USDA- Agricultural Research Service, Beltsville, Maryland, United States of America
                Nanjing Forestry University, China
                Author notes

                Competing Interests: Venkatramana Pegadaraju and Quentin Schultz have competing commercial interests as employees of BioDiagnostics, Inc, a for-profit company that performs molecular marker analysis with the sunflower Golden Gate chip containing the SNPs mentioned in this paper. BioDiagnostics also has a commercial interest in the data generated with the chip because it increases the value of their services to their customers. BioDiagnostics does not have exclusive rights to the SNP markers mentioned in this paper. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: LLQ BSH VP. Performed the experiments: LG VP QS. Analyzed the data: ZIT LG QJS LLQ. Contributed reagents/materials/analysis tools: LLQ BSH LG. Wrote the paper: ZIT LLQ VP BSH QJS.

                Article
                Publisher ID: PONE-D-13-42811
                DOI: 10.1371/journal.pone.0098628
                PMC ID: 4094432
                PubMed ID: 25014030
                SO-VID: a86e491e-4351-4749-bd41-eb82e96e6b7c
                Copyright statement: Copyright @ 2014
                License:

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                Date received : 18 October 2013
                Date accepted : 6 May 2014
                Page count
                Pages: 14
                Funding
                This project was supported by the National Sunflower Association-SNP Consortium, a public-private partnership between the non-profit National Sunflower Association, public researchers at United States Department of Agriculture (USDA), and private seed companies, and the USDA-ARS CRIS Project No. 5442-21000-039-00D. Venkatramana Pegadaraju and Quentin Schultz have competing commercial interests as employees of BioDiagnostics, Inc., a for-profit company that has provided some financial assistance to this project and also has a commercial interest in the data generated with the chip because it increases the value of their services to their customers. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Agriculture
                Subject: Agricultural Biotechnology
                Subject: Marker-Assisted Selection
                Subject: Crops
                Subject: Crop Diseases
                Subject: Crop Management
                Subject: Agrochemicals
                Subject: Computational Biology
                Subject: Evolutionary Biology
                Subject: Population Genetics
                Subject: Genetic Polymorphism
                Subject: Genetics
                Subject: Plant Genetics
                Subject: Crop Genetics
                Subject: Genetics of Disease
                Subject: Population Biology

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