Progress 09/01/09 to 02/28/13
Outputs
OUTPUTS: The project consists of three major spheres of activity: Activity 1) The 600K SNP rice genotyping array was designed during 2010 from a pool of ~16 million SNPs discovered based on the re-sequencing of 125 diverse wild and cultivated rice genomes (funded by the International Rice SNP Consortium). Activity 2: High quality DNA samples were extracted from ~650 diverse O. sativa and ~100 O. rufipogon accessions and used for genotyping with the 600K chip, along with ~50 samples used as controls to optimize the chemistry. An in-house allele-calling algorithm, ALCHEMY, was developed to call the SNPs on the array. The SNP dataset was used as the basis for a genome wide association study (GWAS), in combination with phenotypic data for 37 agronomic and grain quality traits previously generated for the same lines. Through collaboration with scientists at IRRI, in Japan, in India and in China, we provided access to the 600K SNP chip and the ALCHEMY allele-calling algorithm. With scientists from the NIAS in Japan, we genotyped an additional 96 DNA samples representing a mini-core collection from their Gene Bank. With IRRI, we genotyped 1,200 DNA samples, representing an expanded rice diversity panel for O. sativa, and recently imported seeds of these 1,200 purified lines into the US for distribution to the US research community. The combined set of samples from Cornell and from IRRI represents a diversity panel consisting of almost 2,000 diverse accessions, all of which have been genotyped using the SNP chip developed on this project. The genotypic dataset is of high quality and represents a core deliverable under the Global Rice Science Partnership (GRiSP), an international research consortium organized by IRRI, and in which the McCouch lab participates. The large rice diversity panel is currently being phenotyped for numerous traits by scientists at IRRI, CIAT, Cornell, Duke, Penn. State, Ohio State, and many other institutions around the world. In collaboration with researchers in China, we have phenotyped our panel for 6 different isolates of the rice blast fungus and identified genes associated with resistance to the disease. Our collaboration with India consists of making the rice SNP chip available to researchers through the Department of Biotechnology for genotyping within India. Activity 3: We contributed to in a 1 1/2-day workshop in Stuttgart, AR on February 8-9, 2011 that was run in collaboration with the USDA-ARS rice community, and to a 3 day mini-course on March 8-10, 2011 at IRRI, Philippines. The software component and case studies used for the training modules were developed by co-PI Edyth Paul. The software tool supports several key analysis functions- identifying the minimal set of SNP markers that will reveal uniformly distributed polymorphism for a selected set of parents; visualizing and comparing the genomic distribution of a complete set of polymorphic or monomorphic markers for some set of lines. The tool supports the importation and integration of outside datasets, and hosts several publicly available SNP diversity studies. PARTICIPANTS: Nothing significant to report during this reporting period. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
This project aims to generate one of the largest and most useful databases of diversity information for any crop in the world. It has generated a large repository of information about the precise location of 600,000 single nucleotide polymorphisms (SNPs) in rice, as well as the occurrence of particular SNP alleles in 850 diverse rice samples. This information enables breeders to readily select sub-sets of informative SNPs for use on smaller, in-house platforms for immediate applications in marker-assisted or genomic selection. Seven 384-SNP "breeder's chips" were developed from this dataset and are widely used throughout the world. Geneticists can access the information to characterize population structure, to identify genes, QTLs and haplotypes of interest, to identify regions of the genome that are common by descent in different varieties or species of rice, to identify selective sweeps, to undertake genome-wide association mapping, and as the starting point for gene discovery. The availability of a high density SNP chip for rice makes it possible to generate very high quality datasets on large numbers of lines, and greatly facilitates the informatics involved in calling and databasing the SNPs. Use of this high resolution SNP chip facilitates high-throughput germplasm characterization and can enhance the value of the genetic resources available in the world's major germplasm repositories. This, in turn, enhances our ability to more efficiently utilize germplasm resources in our quest to improve the productivity and sustainability of US agriculture. Analysis of the data generated on this project and the availability of the SNP chip to genotype additional rice samples in future experiments is helping the rice community identify both common and rare alleles that underlie agronomically important phenotypes, pointing the way toward a more rational utilization of existing germplasm resources. Finally, the ability to link sequence and diversity information to morphological and agronomic traits, physiological functions, and developmental phenotypes in rice will greatly expand the foundation for comparative genomics using rice as a pivotal reference genome. As a result, this project lays the groundwork for related applications in other major crop species. The development of training materials and workshop presentations aims to ensure that project results are delivered in an appropriate, timely and comprehensible way to the plant breeding community. Our collaborative initiatives have expanded the utilization of the rice SNP chip and associated datasets, enabling us to leverage new information about phenotypes that were not measured as part of our project. Further, this project has motivated the development of an enlarged rice diversity germplasm panel that is now available internationally, and it has kept the rice community working together to develop a robust rice diversity database that will allow plant breeders and geneticists to query the information we have generated and to use it to enhance the productivity and efficiency of applied plant improvement.
Publications
- Thomson MJ, Zhao K, Wright M, McNally KJ, Rey J, Tung C-W, Reynolds A, Scheffler B, Eizenga G, McClung A, Kim H, Ismail AM, de Ocampo M, Mojica C, Reveche M.Y, Dilla-Ermita CJ, Mauleon R, Leung H, Bustamante C, McCouch SR (2012) High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Mol Breeding. 29:875-886
- McCouch et al., (2011) Development of a high resolution SNP chip (1M) and genotyping of diverse rice (Oryza sp.) accessions. Poster presented at PAG 2011.
- Wright, Tung and McCouch (2011) PANATI: Highly sensitive and computationally efficient analysis of next-generation sequencing data in Oryza sativa for SNP discovery, RNA-seq, CNV detection, genotyping by sequencing, and population genetic analysis, Poster at PAG, Jan. 2011.
- McCouch et al. (2010) Development of a high resolution SNP chip (600K) and genotyping of 850 diverse rice (Oryza sp.) accessions, Poster presented at PAG 2010.
- Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR (2011) Genome-wide association mapping reveals rich genetic architecture of complex traits in Oryza sativa. Nature Communications 2:467
- Famoso AN, Zhao K, Clark RT, Tung C-W, Wright MH, Bustamante C, Kochian LV, McCouch SR (2011) Genetic architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS Genetics 7(8):e1002221. doi:10.1371/journal.pgen.
- McCouch SR, McNally KL, Wang W, Sackville-Hamilton R (2011) Genomics of gene banks: A case study in rice. American Journal of Botany 99(2):407-423
- Xu X, Liu X, Ge S, Jensen JD, Hu F, Li X,Dong Y, Gutenkunst RN, Fang L, Huang L, Li J, He W, Zhang G, Zheng X, Zhang F, Li Y, Yu C, Kristiansen K, Zhang X, Wang J, Wright M, McCouch S, Nielsen R, Wang J, Wang W (2011) Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes. Nature Biotechnology doi:10.1038/nbt.2050
- McCouch S, Tung C-W, Zhao K, Wright M, Kimball J, Reynolds A, Tyagi W, Wang D, DeClerck G, McClung A, Bustamante C (2010) Development of genome-wide SNP assays for rice. Breeding Science 60(5:524-535
- Wright MH, Tung CW, Zhao K, Reynolds A, McCouch SR, Bustamante C (2010) ALCHEMY: a reliable method for automated SNP genotype calling for small batch sizes and highly homozygous populations. Bioinformatics 26(23):2952-2960
- Wright, M and McCouch, SR. (2013) Deep Analysis of 125 Cultivated and Wild Rice Genomes: Distinguishing Recent Gene Flow from Common Ancestry. Rice Functional Genomics Workshop, PAG XX1, San Diego, CA. Jan. 13, 2013
- McCouch, SR, Cobb JN, Crowell, S, DeClerck, G, Greenberg A, Clark R, Falcao, A, Kochian L, Mezey J. (2013) Linking Genome-Wide Association Studies (GWAS) and Plant Breeding. Genomics-Assisted Breeding Workshop, PAG XX1, San Diego, CA. Jan. 15, 2013.
- McCouch, SR. Linking Genome Wide Association Studies (GWAS) and Plant Breeding to Better Utilize Natural Variation in Rice. (2012) Seminar at IRRI, Philippines. May 2012.
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Progress 09/01/11 to 08/31/12
Outputs
OUTPUTS: The project consists of three major spheres of activity: 1) design and manufacture of a 600,000 SNP chip for rice; 2) preparation, genotyping and analysis of 850 diverse rice DNA samples; 3) development of training/educational materials to facilitate the use of SNPs in US and IRRI breeding programs.. Activity 1) The 600K SNP rice genotyping array was designed during 2010 from a pool of ~24 million SNPs discovered based on the re-sequencing of 125 diverse wild and cultivated rice genomes (funded by the International Rice SNP Consortium). Activity 2: High quality DNA samples were extracted from ~650 diverse O. sativa and ~100 O. rufipogon accessions and used for genotyping with the 600K chip, along with ~50 samples used as controls to optimize the chemistry. An in-house allele-calling algorithm, ALCHEMY, was developed to call the SNPs on the array. The SNP dataset was used as the basis for a genome wide association study (GWAS), in combination with phenotypic data for 37 agronomic and grain quality traits previously generated for the same lines. Through collaboration with scientists at IRRI, in Japan, in India and in China, we provided access to the 600K SNP chip and the ALCHEMY allele-calling algorithm. With scientists from the NIAS in Japan, we genotyped an additional 96 DNA samples representing a mini-core collection from their Gene Bank. With IRRI, we genotyped 1,200 DNA samples, representing an expanded rice diversity panel for O. sativa. The combined set of samples from Cornell and from IRRI represents a diversity panel consisting of almost 2,000 diverse accessions, all of which have been genotyped using the SNP chip developed on this project. The genotypic dataset is of high quality and represents a core deliverable under the Global Rice Science Partnership (GRiSP), an international research consortium organized by IRRI, and in which the McCouch lab participates. The large rice diversity panel is currently being phenotyped for numerous traits by scientists at IRRI, Cornell and many other institutions around the world. In collaboration with researchers in China, we have phenotyped our panel for 6 different isolates of the rice blast fungus and identified genes associated with resistance to the disease. Our collaboration with India consists of making the rice SNP chip available to researcher through the Department of Biotechnology for genotyping within India. Activity 3: We contributed to in a 1 1/2-day workshop in Stuttgart, AR on February 8-9, 2011 that was run in collaboration with the USDA-ARS rice community, and to a 3 day mini-course on March 8-10, 2011 at IRRI, Philippines. The software component and case studies used for the training modules were developed by co-PI Edyth Paul. The software tool supports several key analysis functions- identifying the minimal set of SNP markers that will reveal uniformly distributed polymorphism for a selected set of parents; visualizing and comparing the genomic distribution of a complete set of polymorphic or monomorphic markers for some set of lines. The tool supports the importation and integration of outside datasets, and hosts several publicly available SNP diversity studies. PARTICIPANTS: Nothing significant to report during this reporting period. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
This project aims to generate one of the largest and most useful databases of diversity information for any crop in the world. It has generated a large repository of information about the precise location of 600,000 single nucleotide polymorphisms (SNPs) in rice, as well as the occurrence of particular SNP alleles in 850 diverse rice samples. This information enables breeders to readily select sub-sets of informative SNPs for use on smaller, in-house platforms for immediate applications in marker-assisted or genomic selection. Geneticists can access the information to characterize population structure, to identify genes, QTLs and haplotypes of interest, to identify regions of the genome that are common by descent in different varieties or species of rice, to identify selective sweeps, to undertake genome-wide association mapping, and as the starting point for gene discovery. The availability of a high density SNP chip for rice makes it possible to generate very high quality datasets on large numbers of lines, and greatly facilitates the informatics involved in calling and databasing the SNPs. Use of this high resolution SNP chip facilitates high-throughput germplasm characterization and can enhance the value of the genetic resources available in the world's major germplasm repositories. This, in turn, enhances our ability to more efficiently utilize germplasm resources in our quest to improve the productivity and sustainability of US agriculture. Analysis of the data generated on this project and the availability of the SNP chip to genotype additional rice samples in future experiments is helping the rice community identify both common and rare alleles that underlie agronomically important phenotypes, pointing the way toward a more rational utilization of existing germplasm resources. Finally, the ability to link sequence and diversity information to morphological and agronomic traits, physiological functions, and developmental phenotypes in rice will greatly expand the foundation for comparative genomics using rice as a pivotal reference genome. As a result, this project will lay the groundwork for related applications in other major crop species. The development of training materials and workshop presentations aims to ensure that project results are delivered in an appropriate, timely and comprehensible way to the plant breeding community. Our collaborative initiatives have expanded the utilization of the rice SNP chip and associated datasets, enabling us to leverage new information about phenotypes that were not measured as part of our project. Further, this project has motivated the development of an enlarged rice diversity germplasm panel that is now available internationally, and it has kept the rice community working together to develop a robust rice diversity database that will allow plant breeders and geneticists to query the information we have generated and to use it to enhance the productivity and efficiency of applied plant improvement.
Publications
- Wright, M and McCouch, SR (2013) Deep Analysis of 125 Cultivated and Wild Rice Genomes: Distinguishing Recent Gene Flow from Common Ancestry. Rice Functional Genomics Workshop, PAG XX1, San Diego, CA. Jan. 13, 2013
- McCouch, SR, Cobb JN, Crowell, S, DeClerck, G, Greenberg A, Clark R, Falcao, A, Kochian L, Mezey J (2013) Linking Genome-Wide Association Studies (GWAS) and Plant Breeding. Genomics-Assisted Breeding Workshop, PAG XX1, San Diego, CA. Jan. 15, 2013.
- McCouch, SR (2012) Linking Genome Wide Association Studies (GWAS) and Plant Breeding to Better Utilize Natural Variation in Rice. Seminar at IRRI, Philippines. May 2012.
- Michael J. Thomson, Keyan Zhao, Mark Wright, Kenneth L. McNally, Jessica Rey, Chih-Wei Tung, Andy Reynolds, Brian Scheffler, Georgia Eizenga Anna McClung, Hyunjung Kim, Abdelbagi M. Ismail, Marjorie de Ocampo, Chromewell Mojica, Ma. Ymber Reveche, Christine J. Dilla-Ermita, Ramil Mauleon, Hei Leung, Carlos Bustamante, Susan R. McCouch(2012)
- High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Mol Breeding. Volume 29, Number 4 (2012), 875-886, DOI: 10.1007/s11032-011-9663-x
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Progress 09/01/10 to 08/31/11
Outputs
OUTPUTS: Objectives and Accomplishments:The project consists of three major spheres of activity: 1) design and manufacture of a 1M SNP chip for rice; 2) preparation, genotyping and analysis of 850 diverse rice DNA samples; 3) development of training/educational materials. Activity 1) The 1M SNP rice genotyping array was designed during 2010 (as outlined in last year's report), and used to genotype 550 diverse rice accessions during 2011. This dataset provided the basis for a rigorous quality control (QC) and evaluation of accuracy and call rate on the 1M SNP chip using a new version of our in-house allele-calling software ALCHEMY. We confirmed that the 1M SNP array generates high quality rice genotypes with very low amounts of missing data. To magnify the impact of the new chip, we developed a collaboration with the International Rice Research Institute (IRRI) in the Philippines to genotype an additional 1,440 diverse rice samples of interest to the international rice research community, bringing to a total of 2,000 the number of wild and cultivated rice strains to be assayed with the 1M SNP chip. The larger number of chips/samples allowed us to negotiate a better price per chip from Affymetrix. Activity 2: High quality DNA samples were prepared from single plants representing purified genetic stocks of approximately 450 O. sativa and approximately 100 O. rufipogon accessions that were used in the first round of hybridizations with the 1M SNP chips. These were strains of rice that had been previously phenotyped and used for genome wide association mapping on a related project. In addition, our collaborators at IRRI extracted DNA from single plants of 1,440 purified strains; these DNA samples are being genotyped this fall, and will be available early in 2012. To evaluate the methylation status of stressed and control rice seedlings (heat, cold, Al, Cd, As, PEG), we are growing 11 diverse rice varieties under stress and control conditions. Tissue and DNA samples are being harvested from individual plants and we are developing a new methodology based on enzyme digestion with HpaII to enable us to reliably identify changes in methylation status throughout the rice genome in response to stress. Activity 3: We participated in two workshops during 2011. One was a 1 1/2-day workshop in Stuttgart, AR on February 8-9, 2011 that was run in collaboration with the US rice community. The second was a 3 day mini-course at IRRI on March 8-10, 2011 coordinated by Michael Thomson. The software component of the training module was developed by Edyth Paul using PowerBuilder & SQL Anywhere. The tool hosts the 1536-SNP genotyping dataset (385 accessions) published by Zhao et al. (2010) and supports several key analysis functions- it enables users to visualize and compare the genomic distribution of a set of polymorphic or monomorphic markers for a given set of lines; it allows users to identify informative SNP markers uniformly distributed across all 12 chromosomes for a selected set of parents as the basis for designing smaller SNP assays; it searches for varieties containing haplotypes of interest (for example, associated with the Pi-ta gene for blast disease resistance) PARTICIPANTS: Individuals: Susan McCouch (PI): Provides management and coordination of all aspects of the project. Edyth Paul (co-PI): Edyth Paul, CEO of Geneflow, Inc. developed training material for both of the workshops hosted under this project. Mark Wright, Research Associate in the Dept. of Plant Breeding and Genetics is responsible for a) developing the suite of algorithms (PANATI) used to generate the SNP discovery pool; b) inventing and implementing the strategy used to design the 1M SNP chip; and c) developing and implementing the algorithm used to call SNPs coming off the 1M array (ALCHEMY). Chih-Wei Tung, Research Associate in the Dept Plant Breeding & Genetics, is responsible for developing and optimizing protocols for genotyping of rice samples. Maria Carrizales, PhD student in the Dept. Plant Biology, Cornell University is managing plant stress experiments to evaluate the role of epigenetic variation in plant response to environmental stress. Partner Organizations: This project has broadened our interactions with the rice research community and forged closer ties with USDA researchers, IRRI and scientists in the public and private sector worldwide. A collaboration with IRRI allowed us to purchase 2,000 1M SNP chips from Affymetrix. We worked with two commercial genotyping service providers, Expression Analysis in North Carolina, US (CEO: Steve McPhail), and DNA Landmarks in Montreal, Canada (CEO: Charles Pick), to standardize the DNA prep protocols and prices charged to the public for genotyping using our rice arrays. We worked closely with researchers at Pioneer Hybrid, RiceTec and Bayer CropSciences during the beta-testing phase of the 44K Affymetrix rice chip, and all have expressed interest in helping to beta-test the 1M SNP array in the near future. Outputs from this project appear as deliverables under Theme 1 of the new Global Rice Science Partnership initiative (http://irri.org/our-science/global-rice-science-partnership-grisp) coordinated by IRRI and McCouch serves on the Scientific Oversight Committee for the GRiSP. Collaborators: We worked closely with scientists at Affymetrix (Julie Montgomery, Anne Ferguson, Michael Christiansen) to optimize the DNA prep and ith Affy's International Marketing group (Rob Henke) to identify high quality genotyping providers in China, India, Thailand and Japan so that rice researchers in Asia can genotype rice samples without shipping DNA or seeds out of their countries. Collaborators Georgia Eizenga, Anna McClung and colleagues at the Dale Bumpers National Rice Research Center in Stuttgart, AR helped to select the 550 diverse accessions of O. sativa and O. rufipogon for genotyping to ensure relevance to the US breeding community. McClung organized the workshop that was held on 02/08/2011 to introduce SNP genotyping to the rice breeding community. Collaborator Michael Thomson at IRRI organized the SNP workshop that was held in the Philippines 03/08/2011. Collaborator Ken McNally at IRRI identified the set of 1440 non-redundant rice accessions for genotyping with the 1M SNP array, oversees the extraction of high quality DNA and shipping of DNA to Expression Analysis. TARGET AUDIENCES: Our target audience includes the rice breeding and genetics communities around the world, with particular emphasis on expanding opportunities for the US rice breeding community to utilize SNP genotyping and genotyping information in their applied breeding programs. The major way in which this project contributes to this goal is by providing a large, publicly available, high quality rice diversity dataset that can be used to identify materials for use as parents in crosses, to identify haplotypes associated with QTLs and traits of interest, to identify subsets of SNPs as the basis for developing lower-resolution (and lower-cost) genotyping strategies for genotypic selection in breeding populations, and to better understand the genetics, physiology and evolution of complex traits. PROJECT MODIFICATIONS: Not relevant to this project.
Impacts
Broad Impacts: The over-arching goal of this proposal is to develop a SNP chip and data resource that will empower the rice community to readily integrate modern molecular breeding technologies with classical breeding practice. By providing a diversity dataset on a large collection of rice germplasm, we aim to enhance the rice community's efforts to immediately apply marker-assisted selection in breeding, as well as to undertake QTL and association mapping, mutant analysis, gene discovery, germplasm characterization, comparative and evolutionary studies. The unanticipated outcomes of the project will likely derive from empowering basic research discoveries that link sequence and epigenetic variation with phenotypic diversity associated with physiological response, plant development and agronomic traits, increasing our understanding of the biological role of genomic sequence.
Publications
- Oral/ Poster Presentations: McCouch et al. (2010) Development of a high resolution SNP chip (600K) and genotyping of 850 diverse rice (Oryza sp.) accessions, Poster presented at PAG 2010.
- McCouch et al., (2011) Development of a high resolution SNP chip (1M) and genotyping of diverse rice (Oryza sp.) accessions. Poster presented at PAG 2011.
- Wright, Tung and McCouch (2011) PANATI: Highly sensitive and computationally efficient analysis of next-generation sequencing data in Oryza sativa for SNP discovery, RNA-seq, CNV detection, genotyping by sequencing, and population genetic analysis, Poster at PAG, Jan. 2011.
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Progress 09/01/09 to 08/31/10
Outputs
OUTPUTS: The project consists of three major spheres of activity: 1)design and manufacture of a 600,000 SNP chip for rice; 2)preparation, genotyping and analysis of 850 diverse rice DNA samples; 3) development of training/educational materials to facilitate SNPs in breeding US and IRRI breeding programs. I review progress on each of these activities below. Activity 1: Over the last 9 months under separate funding, we re-sequenced ~100 diverse O.sativa and O.rufipogon accessions to 6-50x genome coverage using next generation sequencing technology. We aligned the short sequences to the reference Nipponbare genome, called the SNPs, and used the SNP discovery pool as the basis for designing a high density SNP array. The array targets 950,000 SNPs, a 58% increase over the original proposal of 600K SNPs. A deep analysis of genotyping information from our previously designed 44K SNP chip enabled us to modify the algorithm used to select probes and altered the design of the new Affymetrix SNP array. The new design is currently undergoing a rigorous quality control evaluation. SNP selection followed the original proposal to provide: a)comprehensive coverage of the rice genome, with 1 SNP per ~0.5-0.6 kb across the genome, b)multiple SNPs in every annotated, single copy gene; c)a balanced spectrum of polymorphisms within/between sub-populations of O.sativa and its wild relative, O.rufipogon/O.nivara; d)target Copy Number Variation with probes for invariant sites throughout the rice genome, and e)target CCGG methylation sites as the basis for exploring the role of epigenetics in regulating trait expression. The 950K rice chip is expected to be manufactured during Oct-Nov, 2010 by Affymetrix, with genotyping to start in mid-Nov 2010. Activity 2:We are preparing 550 DNA samples for the first run of the 950K SNP chip utilizing protocols and allele calling software (ALCHEMY, www.alchemy.sourceforge.net/) optimized previously using the 44K Affymetrix chip. Samples include ~450 O.sativa and ~100 O.rufipogon samples that were phenotyped on other projects. The ~300 samples that will be used to evaluate the methylation status of stressed and unstressed tissue samples will be prepared once we know the new SNP chip performs as expected. Activity 3:We are preparing training materials that will be used to run a half-day workshop in collaboration with the US rice community in Feb 2011, and a 2-3 day course at IRRI in March 2011. The software component of the training module is under development at this time using PowerBuilder & SQL Anywhere. The tool will support several key analysis functions- identifying the minimal set of SNP markers that will reveal uniformly distributed polymorphism for a selected set of parents; visualizing and comparing the genomic distribution of a complete set of polymorphic or monomorphic markers for some set of lines. This tool will support the importation of outside datasets, allowing it to be used beyond the context of the training module and will host the SNP genotyping dataset recently published by Zhao et al.,(2010)(1536-SNPs x 385 rice accessions). PARTICIPANTS: Nothing significant to report during this reporting period. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
This project aims to generate one of the largest and most useful databases of diversity information for any crop in the world. By providing a large repository of information about the precise location of 950,000 single nucleotide polymorphisms (SNPs) in rice, as well as the occurrence of particular SNP alleles in 850 diverse rice samples, it will enable breeders to readily select sub-sets of informative SNPs for use on smaller, in-house platforms for immediate applications in marker-assisted or genomic selection. Geneticists will be able to access the information to identify sets of genome-wide polymorphisms for use in high throughput quantitative trait locus (QTL) and association mapping experiments or to select highly targeted sets of SNPs for high-resolution haplotype analysis and gene discovery. Scientists will be able to interrogate the data to learn more about the role of methylation in regulating plant response to both internal, developmental signals as well as external, environmental cues, enabling plant breeders to select on epigenetic variation. The availability of a high density SNP chip for rice will make it possible to undertake large-scale, high-throughput germplasm characterization, enhancing the value of the genetic resources available in the world's major germplasm repositories. This, in turn, will enhance our ability to more efficiently utilize germplasm resources for improving the sustainability of US agriculture. The development of training materials and workshop presentations for the plant breeding community will aim to ensure that project results are delivered in an appropriate and comprehensible way, and will lay the foundation for ongoing efforts to expand the utilization of both the SNP chip and the diversity database to enhance the productivity and efficiency of applied plant improvement.
Publications
- No publications reported this period
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