Progress 06/03/05 to 02/29/08
Outputs
Progress Report Objectives (from AD-416) Objective 1: Develop a joint linkage-association mapping platform that can facilitate discovery of genes and novel alleles controlling complex quantitative traits in maize. Objective 2: Using this platform, identify those genes and alleles that can be used to improve kernel quality and tolerance to soil-related abiotic stresses in maize. Objective 3: Develop bioinformatic tools that integrate agronomic, biochemical, and genotypic data, allowing rapid analysis of plant germplasm diversity. Approach (from AD-416) Linkage mapping and candidate association approaches will be used to dissect these traits. Diverse profiling and high throughput phenotyping approaches will be used to characterize these populations, and relate molecular aspects with agronomic phenotypes. Key to this process will be the development of bioinformatic solutions to relate these genomic and agronomic datasets. Significant Activities that Support Special Target Populations In collaboration with ARS researchers at Raleigh, NC and Columbia, MO, we have produced the largest set of mapping lines for complex trait dissection in any species. 5000 diverse maize inbred lines have been produced. They have been initially genotyped to produce basic maps with a total of 18 million data points. The seed have been increased for deposit with the ARS Maize Stock Center within the coming year. Initial phenotyping for basic developmental and agronomic trait has been accomplished in 6 environments. This project will provide unparalled understanding of the number, location, and positive alleles of genes contributing to crop improvement. This genetic resource will also provide the anchor for future molecular diversity characterization in maize. NP301 Plant, Microbial, and Insect Genetic Resources, Genomics and Genetic Improvement. Specifically, this project contributes to Component 1 (Genetic Resource Management) by characterizing phenotypic variation in diverse maize germplasm. The main focus of this research corresponds with Component 2 (Crop Informatics, Genomics, and Genetic Analyses), where resources to dissect complex traits are developed and applied. The bioinformatic tools for the facilitation of breeding decisions also support Component 3 (Genetic Improvement of Crops).
Impacts
(N/A)
Publications
- Zhang, Z., Todhunter, R.J., Buckler Iv, E.S., Van Vleck, L.D. 2007. Technical note: Use of marker-based relationships with multiple-trait derivative-free restricted maximal likelihood. Journal of Animal Science. 85:881-885.
- Bradbury, P., Zhang, Z., Kroon, D., Casstevens, T., Ramdoss, Y., Buckler Iv, E.S. 2007. Tassel: software for association mapping of complex traits in diverse samples. Bioinformatics. 23:2633-2635.
- Gore, M., Bradbury, P., Hogers, R., Kirst, M., Verstege, E., Van Oeveren, J., Peleman, J., Buckler Iv, E.S., Van Eijk, M. 2007. Evaluation of Target Preparation Methods for Single Feature Polymorphism Detection in Large Complex Plant Genomes. Crop Science. 47:S135-S148.
- Buckler Iv, E.S., Yu, J., Holland, J.B., Mcmullen, M.D. 2008. Genome-wide complex trait dissection through nested association mapping. Genetics. 178:539-551.
- Buckler Iv, E.S., Harjes, C., Rocheford, T., Bai, L., Brutnell, T., Kandianis, C.B., Sowinski, S.G., Stapleton, A.E., Vallabhaneni, R., Williams, M., Wurtzel, E., Jianbing, Y. 2008. Natural genetic variation in the lycopene epsilon cyclase gene can enhance provitamin A biofortification of maize. Science. 319(5861):330-333.
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Progress 10/01/06 to 09/30/07
Outputs
Progress Report Objectives (from AD-416) Objective 1: Develop a joint linkage-association mapping platform that can facilitate discovery of genes and novel alleles controlling complex quantitative traits in maize. Objective 2: Using this platform, identify those genes and alleles that can be used to improve kernel quality and tolerance to soil-related abiotic stresses in maize. Objective 3: Develop bioinformatic tools that integrate agronomic, biochemical, and genotypic data, allowing rapid analysis of plant germplasm diversity. Approach (from AD-416) Linkage mapping and candidate association approaches will be used to dissect these traits. Diverse profiling and high throughput phenotyping approaches will be used to characterize these populations, and relate molecular aspects with agronomic phenotypes. Key to this process will be the development of bioinformatic solutions to relate these genomic and agronomic datasets. Significant Activities that Support Special Target Populations This report documents research conducted under a specific cooperative agreement between ARS and Cornell University. Researchers at Cornell have identified the molecular basis of genes controlling carotenoid content. They have also continued a survey of genes involved in nitrogen transport. Cornell provided bioinformatic support and extensive phenotyping efforts to start characterizing kernel quality in 5000 diverse maize lines. Additionally, they have collaborated extensively in the integration of the mixed model, the creation of TASSEL into a pipeline, and increased its ability to deal with pedigrees in the TASSEL software package. This project is monitored through weekly meetings. Accomplishments Created Genetic Resources for Mining Maize Diversity In collaboration with ARS researchers at Raleigh, NC and Columbia, MO, we have produced the largest set of mapping lines for complex trait dissection in any species. 5000 diverse maize inbred lines have been produced. They have been initially genotyped to produce basic maps with a total of 18 million data points. The seed have been increased for deposit with the ARS Maize Stock Center within the coming year. Initial phenotyping for basic developmental and agronomic trait has been accomplished in 6 environments. This project will provide unparalled understanding of the number, location, and positive alleles of genes of gene contributing to crop improvement. This genetic resources will also provide the anchor for future molecular diversity characterization in maize. Developed an approach for mapping controllers for recombination. Recombination is a crucial component of evolution and breeding, producing new genetic combinations on which selection can act. By examining recombination events captured in recombinant inbred mapping populations previously created for maize, wheat, Arabidopsis, and mouse, we demonstrate that substantial variation exists for genome-wide crossover rates in both outcrossed and inbred plant and animal species. We also identify quantitative trait loci (QTL) that control this variation. The method that we developed and employed here holds promise for elucidating factors that regulate meiotic recombination and for creation of hyper- recombinogenic lines, which can help overcome limited recombination that hampers breeding progress. Diversity Database Created For years, groups have been collecting molecular and agronomic data on a wide range of germplasm for a wide range of diversity studies. However, most of this data has never ended up in a database to be made available for reanalysis by other researchers. After several years of development and curation, the Gramene database released a diversity module that contains millions of phenotypic and genotypic data points on diverse maize, wheat, and rice. This research will help genetics identify key genes controlling traits, and it will allow breeders to start developing marker assisted selection schemes. Technology Transfer Number of Web Sites managed: 3 Number of Non-Peer Reviewed Presentations and Proceedings: 12
Impacts
(N/A)
Publications
- Jaiswal, P., Ni, J., Yap, I., Ware, D., Spooner, W., Youens-Clark, K., Ren, L., Liang, C., Zhao, W., Ratnapu, K., Faga, B., Canaran, P., Fogleman, M., Hebbard, C., Avraham, S., Schmidt, S., Casstevens, T., Buckler Iv, E.S., Stein, L., Mccouch, S. 2007. Gramene: a bird's eye view of cereal genomes. Nucleic Acids Research. 34:D717-D723. Available: http://nar.oxfordjournals. org/
- Zhao, W., Canaran, P., Jurkuta, R., Fulton, T., Glaubitz, J., Buckler Iv, E.S., Doebley, J., Gaut, B., Goodman, M., Holland, J.B., Kresovich, S., Mcmullen, M.D., Stein, L., Ware, D. 2007. Panzea: A Database and Resource for Molecular and Functional Diversity in the Maize Genome. Nucleic Acids Research. 34:D752-D757.
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Progress 10/01/05 to 09/30/06
Outputs
Progress Report 1. What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? Why does it matter? While the genomics revolution is making tremendous strides in identifying genes, and is even beginning to pinpoint the function of some genes, it has yet to connect large numbers of genes and alleles to agronomic traits. In maize, mutagenesis approaches have successfully identified individual genes that influence a trait, but most of the resultant alleles are deleterious to the agronomic phenotype. In this context, using natural diversity to identify genes and alleles of agronomic importance is the most direct approach to bridging the gap between genomics and plant breeding. Maize is an ideal candidate for this approach, as its genome boasts tremendous natural diversity. Thus, in summary, the main objective of this project is to exploit the natural variation inherent in the maize genome
for the dissection of complex traits and for the identification of superior alleles. Two obstacles, however, currently prevent the efficient use of naturally occurring genetic variation in maize: (1) most of the useful genetic variation needed for maize improvement is found in non-elite maize lines (e.g. US and tropical landraces), and (2) most agronomic traits are complex (involve multiple genes) and quantitative. Traditional breeding techniques can take 10 years or more to dissect such complex variation and then apply it to elite lines; a strategy that is not only costly, but one that delays or even prevents needed improvements. To ensure a more rapid analysis of maize germplasm, this project is utilizing powerful approaches now available through genomics and molecular quantitative genetics to identify important genes and alleles. These new approaches allow the movement of alleles from unadapted germplasm into elite germplasm in only 3 to 4 years. More specifically, this project
will first develop a joint linkage- association mapping platform that can facilitate discovery of genes and novel alleles controlling complex quantitative traits in maize. Using this platform, the project will then identify those genes and alleles that can be used to improve maize kernel quality and tolerance to soil- related abiotic stresses, including nitrogen use and aluminum tolerance. The identification of these advantageous alleles could allow for marker- assisted improvement of maizes nutrition profile for humans and animals, increased processing efficiency, lower fertilizer requirements, and better adaptation to acidic soils. Finally, this project will develop bioinformatics tools that integrate agronomic and genotypic data, providing geneticists with user-friendly software for molecular quantitative genetic analyses and plant breeders with decision-making help. As the number one production crop in the world, it is extremely important that advantageous alleles in maize are
first identified and then employed to improve breeding strategies. Current US maize production is almost four times wheat and rice production combined (FAO Stats, 2002); totaling more than half a trillion pounds a year. Such extraordinary agronomic value, however, comes at a price. US corn production requires roughly 80 million acres of land and over18 billion pounds of chemical fertilizer. These environmental concerns, coupled with an escalating global population, make it necessary to improve maize productivity, increase cost efficiency, and reduce land impact. Efforts to genetically manipulate maize comprise some of the best methods to achieve these goals. This CRIS falls within Program 301 (Plant, Microbial & Insect Germplasm Conservation, & Development) and National Program 302 (Plant Biological and Molecular Processes). The characterization of diverse germplasm using genomic association methods is key to the germplasm enhancement and manipulation goals of program 301. The
maize traits we are trying to modify directly relate to program 302 goals on plant productivity, development, and nutrient utilization efficiency. 2. List by year the currently approved milestones (indicators of research progress) Year 1 (FY2005) Continue the development of 25 maize linkage mapping populations. Evaluate candidate genes for flowering time and initiate surveys for kernel quality. Initiate physiological assay of kernel quality, aluminum tolerance, and nitrogen use efficiency in diverse maize inbred lines. Develop diversity schema and diversity upload tool. Year 2 (FY2006) Simulate and initiate development of statistical approaches for joint linkage-association mapping. Initiate genotyping of the 25 maize linkage mapping populations. Evaluate candidate genes for kernel quality and initiate surveys for nitrogen use efficiency and aluminum. Complete physiological assay of kernel quality, aluminum tolerance, and nitrogen use efficiency in diverse inbred lines. Expand the
capabilities of the software package TASSEL (Trait Analysis by Association, Evolution and Linkage) to include mixed models for quantitative association analysis. Year 3 (FY2007) Complete genotyping of the 25 maize linkage mapping populations. Complete candidate gene surveys for aluminum tolerance and nitrogen use efficiency. Initiate agronomic phenotypic evaluation of the 25 maize linkage mapping populations. Train curators of key grain diversity datasets in the use of the diversity database. Complete the development of TASSEL. Year 4 (FY2008) Continue agronomic phenotyping and initiate QTL mapping of the 25 maize linkage mapping populations. Initiate physiological QTL mapping of aluminum tolerance and nitrogen use efficiency on select linkage populations. Conduct joint linkage-association analysis for flowering time and kernel quality traits. Initiate software development of breeding decision-making tool. Year 5 (FY2009) Continue agronomic phenotyping and QTL mapping of the 25 maize
linkage mapping populations. Continue physiological QTL mapping of aluminum tolerance and nitrogen use efficiency on select linkage populations. Conduct joint linkage-association analysis for aluminum tolerance and nitrogen use efficiency. Complete software development of breeding decision-making tool. 4a List the single most significant research accomplishment during FY 2006. Dissection of complex traits to gene level: We developed a genetic design for dissecting complex traits down to the gene level, while surveying the entire genome. The approach called Nested Association Mapping, uses a large number of linkage populations combined with extensive genotyping of population parents. This methodology will allow traits to be resolved down to the gene level regularly. The statistical approaches were developed in conjunction with Cornell University researchers. With the appropriate collection of molecular and phenotypic data on 5000 maize lines, high resolution-genome scans will be
possible in maize. 4b List other significant research accomplishment(s), if any. We identified several maize genes with alleles that modify pro-vitamin A content. These genes and alleles could be used engineer maize for higher pro-vitamin A content, benefiting both human and animal nutrition. This research was conducted via a state cooperative agreement with Cornell University and in collaboration with researchers at the University of Illinois. This allele will facilitate marker assisted selection, which would be several hundred fold less expensive doing selection using expensive biochemical assays. 4d Progress report. This report serves to document research conducted under a Specific Cooperative Agreement between ARS and the Department of Plant Breeding at Cornell University entitled, "Dissection of maize kernel quality, aluminum and nitrogen tolerance" CRIS 1907-21000-021-01S. Researchers at Cornell have identified a series of genes that modify level carotenoid contents beyond
the first major gene identified. They have also initiated the survey of genes involved in nitrogen transport. Additionally, they have collaborated extensively in the integration of the mixed model in the TASSEL software package. 5. Describe the major accomplishments to date and their predicted or actual impact. Standard quantitative mapping approaches used to examine quantitative traits result in low resolution, making crop improvement by molecular means a slow process. However, if correlations between neighboring polymorphisms (linkage disequilibrium) are properly accounted for, high resolution approaches can be used. Through high-throughput sequencing and the development of new statistical approaches, we have designed candidate gene association approaches with very high resolutionwithin 1000 to 3000bp. This resolution is 5000-fold higher than standard mapping approaches, and thus will allow for a better understanding of the basis of complex traits, as well as a more rapid means
for the genetic improvement of maize. In addition, benefits of this research will extend beyond the confines of maize, as the analysis approaches we develop will be useful for dissecting traits in any species. 6. What science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end- user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? Results concerning linkage disequilibrium, flowering time, and kernel quality associations were presented at several scientific meetings this year, allowing other scientists and the seed industry to see the usefulness of high resolution association approaches. Most seed companies are now using similar approaches in their own research. We implemented and released a unified mixed model approach for association analysis in the TASSEL software package, provided sample data sets and documentation,
and have made available SAS code for performing the same analysis. This approach makes a significant improvement to available association mapping methods. The work described here makes that method readily accessible to the community of plant and animal geneticists. Software for evaluating and integrating candidate gene associations, linkage disequilibrium, and nucleotide diversity has been developed by this lab, and versions of these tools are being used by several major seed companies. The Genomic Diversity and Phenotype Data Model (GDPDM), the database model that we developed for diversity data, was implemented by the Gramene project (www.gramene.org) as the Genetic Diversity Database, which makes genomic and trait data for maize, wheat, and rice publicly available on the internet. In addition, we provided the maize data in that database. 7. List your most important publications in the popular press and presentations to organizations and articles written about your work. (NOTE:
List your peer reviewed publications below). Dr. Buckler won an Arthur S. Flemming Award, and there were several press releases associated with these awards.
Impacts
(N/A)
Publications
- Buckler Iv, E.S., Stevens, N.M. 2006. Darwin's harvest: new approaches to the origins, evolution, and conservation of crops. In: Motley, T.J., Zerega, N., Cross, H. (eds.) Maize Origins, Domestication, and Selection. New York: Columbia University Press. p. 67-90.
- Buckler Iv, E.S., Goodman, M.M., Holtsford, T.P., Doebley, J.F., G, J.S. 2006. Phylogeography of the wild subspecies of zea mays. Maydica. 51:123- 134.
- Buckler Iv, E.S., Gaut, B.S., Mcmullen, M.D. 2006. Molecular and functional diversity of maize. Current Opinion in Plant Biology. 9:172-176.
- Doebley, J., Buckler Iv, E.S., Fulton, T., Gaut, B., Goodman, M., Holland, J.B., Kresovich, S., Mcmullen, M., Stein, L., Ware, D., Zhao, W. 2005. Molecular and functional diversity in the maize genome. Plant and Animal Genome XIII. [Abstracts].
- Yu, J., Buckler Iv, E.S. 2006. Genetic association mapping and genome organization of maize. Current Opinion in Biotechnology. 17:155-160.
- Vollbrecht, E., Springer, P.S., Goh, L., Buckler Iv, E.S., Martienssen, R. 2005. Architecture of floral branch systems in maize and related grasses. Nature. 436:1119-1126.
- Yu, J., Pressoir, G., Holland, J.B., Kresovich, S., Buckler Iv, E.S. 2005. Bridging molecular diversity and functional diversity: complex traits dissection in maize. Agronomy Abstracts.
- Fukunaga, K., Hill, J., Vigouroux, Y., Matsuoka, Y., G, J.S., Liu, K., Buckler Iv, E.S., Doebley, J. 2005. Genetic diversity and population structure of teosinte. Genetics. 169:2241-2254.
- Flint Garcia, S.A., Thuillet, A., Yu, J., Pressoir, G., Romero, S.M., Mitchell, S.E., Doebley, J., Kresovich, S., Goodman, M.M., Buckler Iv, E.S. 2005. Maize association population: a high resolution platform for quantitative trait locus dissection. Plant Journal. 44:1054-1064.
- Yu, J., Pressoir, G., Briggs, W., Vroh, B., Yamasaki, M., Doebley, J.F., Mcmullen, M.D., Gaut, B.S., Holland, J.B., Kresovich, S., Buckler Iv, E.S. 2006. A unified mixed-model method for association mapping accounting for multiple levels of relatedness. Nature Genetics. 38:203-208.
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Progress 10/01/04 to 09/30/05
Outputs
1. What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? What does it matter? While the genomics revolution is making tremendous strides in identifying genes, and is even beginning to pinpoint the function of some genes, it has yet to connect large numbers of genes and alleles to agronomic traits. In maize, mutagenesis approaches have successfully identified individual genes that influence a trait, but most of the resultant alleles are deleterious to the agronomic phenotype. In this context, using natural diversity to identify genes and alleles of agronomic importance is the most direct approach to bridging the gap between genomics and plant breeding. Maize is an ideal candidate for this approach, as its genome boasts tremendous natural diversity. Thus, in summary, the main objective of this project is to exploit the natural variation inherent in the maize genome for the
dissection of complex traits and for the identification of superior alleles. Two obstacles, however, currently prevent the efficient use of naturally occurring genetic variation in maize: (1) most of the useful genetic variation needed for maize improvement is found in non-elite maize lines (e.g. US and tropical landraces), and (2) most agronomic traits are complex (involve multiple genes) and quantitative. Traditional breeding techniques can take 10 years or more to dissect such complex variation and then apply it to elite lines; a strategy that is not only costly, but one that delays or even prevents needed improvements. To ensure a more rapid analysis of maize germplasm, this project is utilizing powerful approaches now available through genomics and molecular quantitative genetics to identify important genes and alleles. These new approaches allow the movement of alleles from unadapted germplasm into elite germplasm in only 3 to 4 years. More specifically, this project will
first develop a joint linkage- association mapping platform that can facilitate discovery of genes and novel alleles controlling complex quantitative traits in maize. Using this platform, the project will then identify those genes and alleles that can be used to improve maize kernel quality and tolerance to soil- related abiotic stresses, including nitrogen use and aluminum tolerance. The identification of these advantageous alleles could allow for marker- assisted improvement of maizes nutrition profile for humans and animals, increased processing efficiency, lower fertilizer requirements, and better adaptation to acidic soils. Finally, this project will develop bioinformatics tools that integrate agronomic and genotypic data, providing geneticists with user-friendly software for molecular quantitative genetic analyses and plant breeders with decision-making help. As the number one production crop in the world, it is extremely important that advantageous alleles in maize are first
identified and then employed to improve breeding strategies. Current US maize production is almost four times wheat and rice production combined (FAO Stats, 2002); totaling more than half a trillion pounds a year. Such extraordinary agronomic value, however, comes at a price. US corn production requires roughly 80 million acres of land and over18 billion pounds of chemical fertilizer. These environmental concerns, coupled with an escalating global population, make it necessary to improve maize productivity, increase cost efficiency, and reduce land impact. Efforts to genetically manipulate maize comprise some of the best methods to achieve these goals. This CRIS falls within Program 301 (Plant, Microbial & Insect Germplasm Conservation, & Development) and National Program 302 (Plant Biological and Molecular Processes). The characterization of diverse germplasm using genomic association methods is key to the component 2 goals (Genomic characterizationNBand genetic improvement) of
program 301. The maize traits we are trying to modify directly relate to program 302 component 2 goals (Biological Processes that Improve Crop Productivity and Quality). 2. List the milestones (indicators of progress) from your Project Plan. Year 1 (FY 2005) 1. Continue the development of 25 maize linkage mapping populations. 2. Evaluate candidate genes for flowering time and initiate surveys for kernel quality. 3. Initiate physiological assay of kernel quality, aluminum tolerance, and nitrogen use efficiency in diverse maize inbred lines. 4. Develop diversity schema and diversity upload tool. Year 2 (FY 2006) 1. Simulate and initiate development of statistical approaches for joint linkage-association mapping. 2. Initiate genotyping of the 25 maize linkage mapping populations. 3. Evaluate candidate genes for kernel quality and initiate surveys for nitrogen use efficiency and aluminum. 4. Complete physiological assay of kernel quality, aluminum tolerance, and nitrogen use
efficiency in diverse inbred lines. 5. Expand the capabilities of the software package TASSEL (Trait Analysis by Association, Evolution and Linkage) to include mixed models for quantitative association analysis. Year 3 (FY 2007) 1. Complete genotyping of the 25 maize linkage mapping populations. 2. Complete candidate gene surveys for aluminum tolerance and nitrogen use efficiency. 3. Initiate agronomic phenotypic evaluation of the 25 maize linkage mapping populations. 4. Train curators of key grain diversity datasets in the use of the diversity database. Complete the development of TASSEL. Year 4 (FY 2008) 1. Continue agronomic phenotyping and initiate QTL mapping of the 25 maize linkage mapping populations. 2. Initiate physiological QTL mapping of aluminum tolerance and nitrogen use efficiency on select linkage populations. 3. Conduct joint linkage-association analysis for flowering time and kernel quality traits. 4. Initiate software development of breeding decision-making
tool. Year 5 (FY 2009) 1. Continue agronomic phenotyping and QTL mapping of the 25 maize linkage mapping populations. 2. Continue physiological QTL mapping of aluminum tolerance and nitrogen use efficiency on select linkage populations. 3. Conduct joint linkage-association analysis for aluminum tolerance and nitrogen use efficiency. 4. Complete software development of breeding decision-making tool. 3a List the milestones that were scheduled to be addressed in FY 2005. For each milestone, indicate the status: fully met, substantially met, or not met. If not met, why. 1. These FY 2005 goals were shared with the old project CRIS 21000-013-00D. Continue the development of 25 maize linkage mapping populations. Milestone Fully Met 2. Evaluate candidate genes for flowering time and initiate surveys for kernel quality. Milestone Fully Met 3. Initiate physiological assay of kernel quality, aluminum tolerance, and nitrogen use efficiency in diverse maize inbred lines. Milestone Fully Met
4. Develop diversity schema and diversity upload tool. Milestone Substantially Met 3b List the milestones that you expect to address over the next 3 years (FY 2006, 2007, and 2008). What do you expect to accomplish, year by year, over the next 3 years under each milestone? FY 2006: We will simulate and initiate the development of statistical approaches for joint linkage-association mapping. Genotyping of the 25 maize linkage mapping populations will be initiated using SNPs. The completed candidate gene survey for kernel quality will be evaluated, with the initiation of new surveys for aluminum tolerance and nitrogen use efficiency. Physiological assays of kernel quality, aluminum tolerance, and nitrogen use efficiency in diverse maize inbreds will be completed. We will expand the capabilities of TASSEL to include mixed models for quantitative association analysis. FY 2007: Year 3 will result in complete genotyping of the 25 maize linkage mapping populations, including the
examination of recombination events and segregation distortion. We will also complete candidate gene surveys for aluminum tolerance and nitrogen use efficiency. First replications of agronomic phenotypic evaluation of the 25 maize linkage populations will be conducted. Curators of key genomic datasets will be trained to upload and maintain the diversity databases. We will expand TASSEL development to integrate the novel statistics for joint linkage-association. FY 2008: Second replications of agronomic phenotypic evaluation of the 25 maize linkage populations will be conducted. We will initiate physiological QTL mapping of aluminum tolerance and nitrogen use efficiency on select linkage populations. High resolution joint linkage-association analysis will be conducted for flowering time and kernel quality traits. We will initiate software development of breeding decision-making tool. 4a What was the single most significant accomplishment this past year? High resolution trait
mapping has been difficult to accomplish in germplasm with complex breeding relationships. We in collaboration with Cornell University researchers developed new, generalized statistical approaches for high resolution association mapping in any population structure. This methodology not only expands the range of species and samples that can be evaluated with high resolution association mapping approaches, but also resolves problems with false positive results. With the appropriate collection of molecular and phenotypic data, high resolution mapping of genes is possible in virtually all agronomic species. 4b List other significant accomplishments, if any. We identified a maize gene variant that modifies pro-vitamin A content. This allele could be used to breed for maize with 2 to 3 fold higher pro- vitamin A content, benefiting both human and animal nutrition. This research was conducted via a state cooperative agreement with Cornell University and in collaboration with researchers
at the University of Illinois. This allele will facilitate marker assisted selection, which would be several hundred fold less expensive doing selection using expensive biochemical assays. 4d Progress report. This report serves to document research conducted under a Specific Cooperative Agreement between ARS and the Department of Plant Breeding at Cornell University entitled, Dissection of maize kernel quality, aluminum and nitrogen tolerance CRIS 1907-21000-021-01S. Researchers at Cornell have established a hydroponic system for evaluating seedling growth under differing aluminum levels, and have successfully implemented a fertigation system for scoring nitrogen use efficiency in diverse inbred lines. Physiologists were consulted to develop high throughput protocols for these experimental systems. Substantial increases in both nitrogen use efficiency and aluminum tolerance have been identified for diverse lines when compared to the typical lines used in the US. 5. Describe the
major accomplishments over the life of the project, including their predicted or actual impact. Standard quantitative mapping approaches used to examine quantitative traits result in low resolution, making crop improvement by molecular means a slow process. However, if correlations between neighboring polymorphisms (linkage disequilibrium) are properly accounted for, high resolution approaches can be used. Through high-throughput sequencing and the development of new statistical approaches, we have designed candidate gene association approaches with very high resolutionwithin 1000 to 3000bp. This resolution is 5000-fold higher than standard mapping approaches, and thus will allow for a better understanding of the basis of complex traits, as well as a more rapid means for the genetic improvement of maize. In addition, benefits of this research will extend beyond the confines of maize, as the analysis approaches we develop will be useful for dissecting traits in any species. 6. What
science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end- user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? Results concerning linkage disequilibrium, flowering time, and kernel quality associations were presented at several scientific meetings this year, allowing other scientists and the seed industry to see the usefulness of high resolution association approaches. Most seed companies are now using similar approaches in their own research. We developed software for sample tracking and robot assisted reaction setup this year. This software has been transferred to a robotic company, and will be collaboratively developed over the next couple of years for eventual release to the research community. Software for evaluating and integrating candidate gene associations, linkage disequilibrium, and nucleotide diversity has
been developed by this lab, and versions of these tools are being used by several major seed companies. 7. List your most important publications in the popular press and presentations to organizations and articles written about your work. (NOTE: List your peer reviewed publications below). Dr. Buckler won the ARS Early Career Scientist of the Year Award and won a Presidential Early Career Award. There were several press releases associated with these awards.
Impacts
(N/A)
Publications
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