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United States Department of Agriculture

Agricultural Research Service


Location: Plant, Soil and Nutrition Research

2012 Annual Report

1a.Objectives (from AD-416):
This project will develop a genetic platform to identify useful variation in a high throughput fashion, and then use this platform to identify those genes and alleles that control kernel quality and tolerance to abiotic stresses. Bioinformatic tools will be developed in conjunction with the above to allow for the rapid analysis of plant germplasm diversity. Although the direct application of these approaches will be in maize and biofuel grasses, many of these genetic, statistical, and bioinformatic approaches could have broad implications for both the plant and animal genetics community at large. Objective 1: Develop statistical, genetic, and genomic approaches for dissecting complex traits in crop plants. Objective 2: Identify key genes and natural allelic variation for improving abiotic stress tolerance, nitrogen use efficiency, nutritional quality, and biofuel potential for maize and related grasses. Objective 3: Development of bioinformatic tools to mine and present functional allelic variation.

1b.Approach (from AD-416):
This project will use the natural variation inherent in the maize and biofuel grass genomes for the dissection of complex traits and for the identification of superior alleles. Such discovery is important to the development of improved breeding strategies for maize, the number one production crop in the world. First, this project will develop genetic resources that allow for the rapid dissection of any complex trait to the gene level. These resources will involve the creation of germplasm, the genotyping of this germplasm, and the development of statistical analyses. Using this platform, the project will then dissect the quantitative traits of nutritional quality, nitrogen use, biofuel productivity, and aluminum tolerance. The identification of advantageous alleles could allow for marker-assisted improvement of maize’s nutrition profile for humans and animals, increased processing efficiency, lower fertilizer requirements, and better adaptation to acidic soils. Finally, this project will improve access to diversity data and analysis tools for plant breeders and geneticists. We will facilitate the use of these materials by creating analysis tools, user friendly websites, and breeder decision making tools.

3.Progress Report:
After discovering 55 million genetic variants across diverse maize, we have used this information to identify what regions of the genome contribute to flowering, nitrogen use efficiency, starch production, plant height, pro-vitamin A and vitamin E content. These results are allowing researchers to identify the key genes for further studies, and we are determining the features of genome that control this natural variation. Several years of yield trials from across the country are being completed, and these are now being used to determine the limits with which we can predict yield with quantitative genetics models.

Together with collaborators around the world, we have applied novel DNA genotyping approaches to evaluate genetic diversity in 30,000 varieties of maize, including genetic mapping stocks, landraces, and breeding lines from the US, CIMMYT, and China. This genotyping is allowing geneticists and breeders to begin to unite research and breeding efforts across the globe. In the US, we are leading the phenotypic characterization of the USDA-ARS collected germplasm in field trials, while global collaborators are field characterizing their germplasm. The incredibly rapid increase in the ability to sequence DNA has resulted in massive challenges in managing and analyzing trillions of data points. We have developed software to both deal with genotyping by DNA sequencing, and statistical analysis software to relate trait variation to DNA variation. This software is used by thousands of scientists every year.

We have initiated research to develop perennial maize. Our group is leading the effort to identify the genes need for overwintering in the US. We have initiated crosses and field trials among maize’s closest cold tolerant perennial relative, Tripsacum dactyloides, to identify these genes.

1. Maize genetic diversity revealed across the entire genome. Maize is the most diverse crop in the world, but it was nearly impossible to characterize that diversity across the genome without recently developed DNA sequencing technology. This project characterized diversity across the genome for over 100 maize lines that are key parents for maize genetic studies and breeding efforts, and characterized over 55 million variable regions in the genome. This analysis conducted by ARS researchers at Ithaca, New York yielded insights into the basis of complex traits and the key genes involved in the domestication of maize. Additionally, it provided thousands of researchers and companies the molecular tools to discover key genes controlling agronomics traits, which will accelerate corn improvement.

2. Developed powerful genotyping approaches for uniting germplasm collections and breeding. In the last five years, DNA sequencing technology has dropped 50,000-fold in cost. This technology has generally been used to understand a few varieties with tremendous genetic detail. However, breeders and germplasm researchers generally work with tens of thousands of samples to find the best diversity for agriculture. ARS researchers at Ithaca, New York developed novel molecular and computational approaches to cost effectively survey diversity from tens of thousands of individuals from any species. These approaches have dropped the costs of diversity surveys by 10 to 100-fold. The costs of these surveys are now lower than the cost of growing plants in field trials, which is transforming how breeding and germplasm surveys are conducted.

Review Publications
Brown, P.J., Upadyayula, N., Mahone, G.S., Tian, F., Bradbury, P., Myles, S., Holland, J.B., Flint Garcia, S.A., McMullen, M.D., Buckler IV, E.S., Rocheford, T.R. 2011. Distinct genetic architectures for male and female inflorescence traits of maize. PLoS Genetics. 7(11):e1002383.

Morrell, P.L., Buckler IV, E.S., Ross-Ibarra, J. 2011. Crop genomics: advances and applications. Nature Reviews Genetics. 13:85-96.

Costich, D., Friebe, B., Sheehan, M.J., Casler, M.D., Buckler IV, E.S. 2010. Genome-size variation in switchgrass (Panicum virgatum): flow cytometry and cytology reveal rampant aneuploidy. The Plant Genome. 3:130-141.

Ganal, M.W., Durstewitz, G., Polley, A., Berard, A., Buckler IV, E.S., Charcosset, A., Clarke, J.D., Graner, E., Mcmullen, M.D., Falque, M. 2011. A large maize (Zea Mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS One. 6(12):e28334. DOI: 10.1371/journal.pone.0028334.

Pressoir, G., Brown, P.J., Zhu, W., Upadyayula, N., Rocheford, T., Buckler IV, E.S., Kresovich, S. 2009. Natural variation in maize architecture is mediated by allelic differences at the PINOID co-ortholog barren inflorescence2. Plant Journal. 58:618-628.

Li, X., Zhu, C., Yeh, C., Wu, W., Takacs, E.M., Petsch, K.A., Tian, F., Bai, G., Buckler Iv, E.S., Muehlbauer, G.J., Timmermans, M., Scanlon, M.J., Schnable, P.S., Yu, J. 2012. Genic and non-genic contributions to natural variation of quantitative traits in maize. Genome Research. 22:2436-2444.

Zhang, N., Gibon, Y., Gur, A., Chen, C., Lepak, N.K., Hohne, M., Zhang, Z., Kroon, D., Tschoep, H., Sitt, M., Buckler IV, E.S. 2010. Fine quantitative trait loci mapping of carbon and nitrogen metabolism enzyme activities and seedling biomass in the intermated maize IBM mapping population. Plant Physiology. 154:1753-1765.

Hung, H., Browne, C.J., Guill, K.E., Coles, N., Eller, M., Garcia, A., Lepak, N.K., Melia-Hancock, S., Oropeza-Rosas, M., Salvo, S., Upadyayula, N., Buckler IV, E.S., Flint Garcia, S.A., Mcmullen, M.D., Rocheford, T., Holland, J.B. 2012. The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population. Heredity. 108:490-499.

Hufford, M., Xu, X., Van Heerwaarden, J., Pyhajarvi, T., Chia, J., Cartwright, R., Elshire, R., Glaubitz, J., Guill, K.E., Kaeppler, S., Lai, J., Morrell, P., Shannon, L., Song, C., Springer, N., Swanson-Wagner, R., Tiffin, P., Wang, J., Zhang, G., Doebley, J., McMullen, M.D., Ware, D., Buckler IV, E.S., Yang, S., Ross-Ibarra, J. 2012. Comparative population genomics of maize domestication and improvement. Nature Genetics. 44:808-811. DOI: 10.1038/ng.2309.

Huang, X., Sang, T., Zhao, Q., Wei, X., Feng, Q., Zhao, Y., Li, C., Zhu, C., Lu, T., Zhang, Z., Li, M., Fan, D., Guo, Y., Wang, A., Wang, L., Deng, L., Li, W., Lu, Y., Weng, Q., Liu, K., Huang, T., Zhou, T., Jing, Y., Li, W., Zhang, L., Buckler IV, E.S., Qian, Q., Zhang, Q., Li, J., Han, B. 2010. Genome-wide and fine resolution association studies of 14 agronomic traits in rice land races. Nature Genetics. 42:961-967.

Last Modified: 4/21/2014
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