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United States Department of Agriculture

Agricultural Research Service


Location: Plant, Soil and Nutrition Research

2013 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:
Geneticist, breeders, and farmers have all created maize varieties with a wide range of adaptations. With our colleagues, we have now genotyped over 43,000 varieties. While these genotyping approaches are quite powerful, they also produce large amounts of missing data. We have developed robust approaches for accurately estimating the states of the missing data, which allows landraces of maize to be compared to elite breeding materials of the US. For the first time, we and our collaborators have been able to compare the adaptations important in US breeding lines with the adaptation important to landraces from throughout the Americas. Over the coming years, we will use this approach to identify useful genetic variation that are rare in applied breeding germplasm, which can then be applied to accelerating breeding programs.

The genome of maize is complex with large stretches of DNA between each gene. In fact, genes only make up 5% of the genome. Using powerful genetic and genomic studies, we have estimated the relative importance of the genes versus the regions between the genes. For common genetic variation, these regions between the genes control nearly 75% of the variability that we see in the field. It is likely that these inter-gene regions are determining how much, when, and where in the plant each gene produces proteins. This research highlights the importance of needing to understand the complete genome of many varieties to fully design the maize of the future.

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. Our genotyping-by-sequencing software has now been applied to more than 100 species of plants and animals.

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

1. Developed analysis tools for genotyping-by-sequencing technology. In the last five years, DNA sequencing technology has dropped 50,000-fold in cost. Previously, ARS researchers at Ithaca, New York developed the molecular biology approaches that reduced the costs diversity surveys by 10 to 100-fold. However, this produced a situation where it was cheaper to produce the data than to analyze the data. We have created open source software tools that process this data efficiently and scales to extremely large datasets. We have accompanied this software development with three hands-on workshops over the last year. Overall, more than 100 species of plants and animals have been analyzed using this software, and it is making genetic diversity surveys are reality in numerous agricultural and conservation biology communities.

2. Maize inter-genic diversity revealed to control maize field variation. The maize genome is made of genes (5% of the genome) and large inter-genic regions between the genes (95% of the genome). The inter-genic regions have been very difficult to characterize, because they are extremely variable between maize varieties. Using varieties of approaches to score these regions with next generation sequencing technology, ARS researchers at Ithaca, New York, have been able to show that 75% of common maize field variation is controlled by these inter-genic regions. It is likely that these inter-gene regions are determining how much, when, and where in the plant each gene produces proteins. This research is directing what parts of the genome must be modeled in order to develop the next generation of maize varieties.

Review Publications
Ersoz, E.S., Wright, M.H., Pangilinan, J.L., Sheehan, M.J., Tobias, C.M., Casler, M.D., Buckler IV, E.S., Costich, D. 2012. SNP discovery with EST and NextGen sequencing in switchgrass (panicum virgatum L.). PLoS One. 7(9):e44112.

Lu, F., Lipka, A.E., Glaubitz, J.C., Elshire, R., Cherney, J.H., Casler, M.D., Buckler IV, E.S., Costich, D. 2013. Switchgrass genomic diversity, ploidy and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genetics. 9(1):e1003215. DOI:10.1371/journal.pgen.1003215.

Larson, S., Lipka, A.E., Buckler IV, E.S. 2013. Lessons from dwarf8 on the strengths and weaknesses of structured association mapping. PLoS Genetics. 9(2):e1003246.

Peiffer, J.A., Flint Garcia, S.A., De Leon, N.N., McMullen, M.D., Kaeppler, S.M., Buckler IV, E.S. 2013. The genetic architecture of maize stalk strength. PLoS One. 8(6):e67066. Available:

Peiffer, J.A., Spor, A., Koren, O., Jin, Z., Tringe, S., Dangl, J.L., Buckler IV, E.S., Ley, R.E. 2013. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proceedings of the National Academy of Sciences. 110(16):6548-6553.

Lipka, A.E., Gore, M., Magallanes-Lundback, M., Mesberg, A., Lin, H., Tiede, T., Chen, C., Buell, R.C., Buckler IV, E.S., Rocheford, T., Dellapenna, D. 2013. Genome-wide association study and pathway level analysis of tocochromanol levels in maize grain. Genes, Genomes, Genetics. DOI: 10.1534/g3.113.006148.

Romay, M.C., Millard, M.J., Glaubitz, J.C., Peiffer, J.A., Swarts, K.L., Casstevens, T.M., Elshire, R.J., Acharya, C.B., Mitchell, S.E., Flint Garcia, S.A., McMullen, M.D., Holland, J.B., Buckler IV, E.S., Gardner, C.A. 2013. Comprehensive genotyping of the US national maize inbred seed bank. Genome Biology. 14(6):1-18.

Last Modified: 4/17/2014
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