Location: Plant, Soil and Nutrition Research
Project Number: 8062-21000-039-00-D
Project Type: Appropriated
Start Date: Mar 1, 2013
End Date: Feb 28, 2018
1: Evaluate the genetic architecture of maize with special emphasis on mining of rare alleles. 2: Develop methods to facilitate accelerated breeding for adaptive traits and utilization of germplasm diversity. 3: Identify genetic variation controlling perennial characteristics in grasses. 4: Develop bioinformatic resources for genomic and phenomic data handling. 5: Advance the state of the art for data analysis and trait model development to decode the genetic rules governing genotype-to-phenotype (G2P) and genotype x environment (GxE) for crop improvement. 6: Facilitate the use of genomic and genetic data, information, and tools for germplasm improvement, thus empowering ARS scientists and partners to use a new generation of computational tools and resources.
This project will use the natural variation inherent in maize and related 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 evaluate the prevalence and importance of rare alleles for key agronomic traits in maize. Avoiding negative rare alleles and increasing the frequency of positive rare alleles will be keys to accelerating breeding gains in all crop species, including maize. This project will develop mapping approaches to model the effects of rare alleles, and then incorporate these allele effects into estimates of global maize germplasm breeding values. Second, maize belongs to the most productive group of grasses in the world, which includes its highly productive perennial relatives. This project will determine the genetics underlying perenniality and cold tolerance in these maize relatives, in order to lay the groundwork for the development of perennial maize in the future. Finally, crop diversity is now described with trillions of data points, and current bioinformatics analysis tools are insufficient to support high throughput genotyping, quantitative genetics, and genome wide prediction. We will work with the plant genetics community to develop analysis tools that can handle genomic diversity’s “big data.”