BIOINFORMATIC METHODS AND TOOLS TO PREDICT SMALL GRAIN FIELD PERFORMANCE
Location: Plant, Soil and Nutrition Research
Project Number: 1907-21000-031-00
Start Date: Apr 01, 2008
End Date: Mar 31, 2013
Genomic information, particularly information about DNA sequence polymorphism, has great potential to increase the rate of improvement in small grains breeding programs. As genotyping costs fall and genotyping services are made available through the USDA small grains genotyping labs, breeders will need new methods to apply those resources effectively to their selection programs. The overall objectives of this program are to develop effective methods for identification of quantitative trait loci (QTL) and marker-assisted selection (MAS), and to deliver those methods to breeders and geneticists in publications and software.
1: Develop methods for identifying plant breeding quantitative trait loci.
2: Integrate methods for QTL identification into strategies that enable geneticists and breeders to design more efficient experiments and make better selection decisions.
3: Develop breeder-friendly tools for genomic and genetic data access and analysis, with a specific focus on optimum analysis and use of molecular marker and agronomic data for small grains breeders and geneticists.
Objective 1: Develop methods for identifying plant breeding quantitative trait loci.
Experimental Design. Populations under a Wright-Fisher neutral model will be simulated using a standard coalescent approach using different parameter values to compare three analysis methods: 1. Single-marker regression, 2. A random-effect haplotype method, and 3. A coalescent-based haplotype method. These methods will be applied using different haplotype block identification methods.
Objective 2: Integrate methods for QTL identification into strategies that enable geneticists and breeders to design more efficient experiments and make better selection decisions.
Experimental Design. To predict specific untested haplotype-environment effects, the covariance matrix of haplotype-within-environment effects will be modeled in two ways. First, the covariance of haplotype main effects can be modeled on the basis of the sequence similarity of the haplotypes. Second, the covariance of haplotype effects across environments can be modeled much as the covariance of genotype effects in multi-environment trials can be modeled. We will also explore a combination of these two options. Simulations of MAS will be applied to data from the Barley CAP for spring, six-row barley. The form of the distribution of QTL effects obtained from the real data will be maintained. Mixed model and whole-genome selection methods will be applied.
Objective 3: Develop breeder-friendly tools for genomic and genetic data access and analysis, with a specific focus on optimum analysis and use of molecular marker and agronomic data for small grains breeders and geneticists.
Experimental Design. In collaboration with GrainGenes, displays currently available in TASSEL and Haploview will be scoped, resource requirements estimated, and priorities established. In addition, this project will provide association analyses based on diversity data stored in the GrainGenes database, with significant markers to be displayed on a genetic map. Methods developed in the preceding two objectives will be implemented as plugins to the TASSEL software package. TASSEL already handles most of the data input, data management, and output functions. Connections will be established between GrainGenes, The Hordeum Toolbox (THT), and USDA small grains genotyping labs by implementing a GDPC (Genomic Diversity and Phenotype Connection) web-service for each database.