2008 Annual Report
1a.Objectives (from AD-416)
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.
1b.Approach (from AD-416)
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.
The Project Plan for 1907-21000-031-00D, Bioinformatic Methods and Tools to Predict Small Grain Field Performance, received the highest possible score from OSQR review.
We finished theory development and application of a model to detect synergistic epistasis in bi-parental QTL experiments. This form of epistasis occurs when the combined action of deleterious alleles at several loci is greater than predicted, based on the effect of each allele individually. This work was presented at the Third International Conference on Quantitative Genetics in Hangzhou, China and has been accepted by the journal Genetica. These efforts addressed NP 301 Action Plan Component 2, Statement C.
Other research has focused on understanding and using genetic diversity in breeding and conservation. We developed and applied a data-driven method to identify best barley breeding program pairs for germplasm exchange. Related research developed and applied a method to test the hypotheses of diversifying or stabilizing selection on specific traits across multiple breeding populations or natural populations. This progress addressed NP 301 Action Plan Component 3, Statement A.
The cost of marker data is dropping rapidly to the point that industry, and soon public breeders, will routinely genotype all experimental lines. We have researched statistical methods that use data from genome-wide markers to predict barley field performance. We took actual barley genotypic data, used it to simulate phenotypic data, then applied the methods. We found that at current marker densities, the markers provided a large fraction of their benefit by accounting for the genetic relationship between observed individuals, and a smaller fraction by identifying specific gene effects. This progress addressed NP 301 Action Plan Component 3, Statement A.
In human genetics, methods of identifying tagging markers have been proposed to get the most information about genotypic variation while scoring a limited, fixed set of markers. A further refinement may come from using multimarker tags to impute genotypes at markers that were not scored in the experimental panel. We have implemented these methods on barley data. The methods work very well and we hope soon to begin developing methods to apply them to selection and breeding purposes. These efforts addressed NP 301 Action Plan Component 2, Statement C.
An issue related to tag markers, is that of haplotype blocks. In general, all polymorphisms within a block are strongly associated. Because of this association, haplotype blocks may be useful units of analysis to predict the phenotype. We are exploring haplotype block identification methods and comparing haplotype blocks in two- and six-row barley types. We have also begun to compare levels of linkage disequilibrium (LD), that is, the non-random association of different variants of genes, between pairs of markers in two- and six-row barley. The main conclusions are that LD between two- and six-row barley is only weakly related, and that this relationship of LD drops to close to zero between all but the most tightly linked genes. These analyses addressed NP 301 Action Plan Component 3, Statement A.
Using genetic analysis to choose pairs of varieties to cross.
To increase the efficiency of breeding, new methods are needed to optimize the selection of pair of parents, which when crossed to each other, would produce offspring with desired traits. We hypothesized that genetic analysis could indicate which pairs of parents are most complementary and therefore most suitable to be crossed to each other. We developed theory and methods to identify, on the basis of genome-wide QTL (quantitative trait loci) analysis, pairs of individuals that might generate the highest performing offspring. We found that under many circumstances, choosing the best individuals to form crosses will be the best solution, though when selection intensity is high it may be worth identifying specific complementary parents. This research lays an important cornerstone of the methodology needed for the selection of parents and the choice of crosses to maximize genetic gain in populations that have been scored with DNA markers and analyzed for QTL, either through linkage mapping or association methods. This accomplishment addresses NP 301 Action Plan Component 3, Statement A.
|Number of Non-Peer Reviewed Presentations and Proceedings||1|
Jannink, J. 2007. Qtl x genetic background interaction: application to predicting progeny value. Euphytica. 161:61-69.
Zhong, S., Jannink, J. 2007. Using QTL results to discriminate among crosses based on their progeny mean and variance. Genetics. 177:567-576.
Doehlert, D.C., Jannink, J., Mcmullen, M.S. 2008. Size distributions of different orders of kernels within the oat spikelet. Crop Science. 48:298-340