Page Banner

United States Department of Agriculture

Agricultural Research Service


Location: Plant, Soil and Nutrition Research

2009 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.

3. Progress Report
Work on barley: In FY 2009, we measured key genetic parameters in US elite barley populations to determine best practices for using DNA markers to identify genes affecting important agronomic traits, and to accelerate the improvement of those traits. We also evaluated three novel methods for using DNA marker data for barley improvement: 1. Marker imputation that infers missing marker data on the basis of multiple flanking markers; 2 Haplotype block methods that group adjacent and correlated markers across the germplasm; and 3 Genomic selection that uses all markers, rather than selected “significant” markers, to predict germplasm performance. Our analysis identified the existence of seven germplasm pools, or subpopulations, within the Barley Coordinated Agricultural Project (CAP) breeding lines. We also showed that these subpopulations were divergent enough that, with current marker densities, it is best to combine for analysis only data from the most closely related subpopulations. Marker imputation uses high density marker data from a reference or core panel and low density marker data from an experimental panel to predict alleles of all (high density) markers on the experimental panel. Our research showed that marker imputation can provide better predictions of unscored markers than standard tagging methods. This suggests that further development of a core barley panel typed at high density is warranted. Within Barley CAP breeding lines we have systematically identified blocks of adjacent and correlated markers (so-called haplotype blocks). We have developed statistical methods to include haplotype blocks in association mapping analyses. We are determining the power of these methods and applying them to real data. Our research showed that genomic selection can predict agronomic performance with accuracies close to those for phenotypic selection on traits with relatively low heritability. Because of the decrease in breeding cycle time afforded by genomic selection, such accuracies will lead to more rapid gains from genomic than phenotypic selection. We conducted simulations to investigate in detail optimal methods using single markers to map QTL directly in the Barley CAP breeding lines. Two methods were evaluated to account for the complex genetic relationships among the lines and we identified one that had slightly but consistently higher power than the other. Results of this work will provide guidance for barley breeders seeking to use association analysis to find QTL that are useful for variety development. Work on oat: DNA marker data from lines from the last decade of oat breeding in North America have been analyzed to identify regions of the oat genome that harbor genes associated with beta-glucan content. Breeding programs for high beta-glucan content have been initiated using phenotypic, marker-assisted, and genomic selection. DNA from the oat collection of the National Plant Germplasm System has been obtained for accessions divergent in beta-glucan content. This DNA has also been scored for about 1000 markers. This population was grown in the field in 2009, has been harvested and is currently being processed.

4. Accomplishments
1. Genomic selection in barley will accelerate the improvement of agronomic traits. Marker assisted selection methods have failed to accelerate the improvement of complex quantitative traits. The promise of using information from DNA markers to achieve this goal remains to be fulfilled. Using simulations based on DNA marker data from reference panels, we showed that a method called genomic selection could predict agronomic performance of barley lines accurately enough that selection on the basis of marker genotype alone could accelerate barley improvement. This acceleration occurs in part through accurate prediction and in part through reducing the breeding cycle time by avoiding the time and cost of phenotyping. This research resulted in two publications, a research paper specific to barley and a review paper discussing the method and its implications for plant breeding methods in general. Our findings have already spurred research into the application of genomic selection in breeding programs for barley, wheat, oat, and maize, and feasibility studies in rice and sugarcane. As genotyping costs continue to decline, we anticipate genomic selection becoming common in crop breeding leading to many future research efforts having their origins in our work.

2. Marker imputation methods provide a low cost means to achieve high marker coverage. Association genetics methods of gene identification work directly with lines from breeding programs without requiring slow and expensive development of experimental lines. These methods require the use of large germplasm panels that are genotyped with a large number of markers. These data are both costly and require careful quality control. Marker imputation methods can reduce the genotyping burden and improve QC. These methods have been developed for association analyses in humans and had never been applied to plants. Because human population history is quite divergent from that of crops, the efficacy of imputation methods needed to be studied in a crop. We have done this with genomic data from barley, finding the imputation methods to perform well. Marker imputation methods work by modeling patterns of marker information in a reference panel genotyped at high density and then applying those patterns to experimental panels that have been genotyped at lower density. In this way marker data missing from the experimental panels are inferred. Quality control is achieved by detecting cases in which unexpected patterns arise in the experimental panel. As association analyses become more frequent and routine in crops, marker imputation methods will contribute to reducing cost of data collection and automate processes of combining data and evaluating its quality.

Review Publications
Jannink, J., Moreau, L., Charcosset, A., Charmet, G. 2008. Overview of QTL detection in plants and tests for synergistic epistatic interactions. Genetica. 136:225-236.

Jannink, J., Iwata, H., Bhat, P.R., Chao, S., Wenzl, P., Muehlbauer, G.J. 2009. Marker imputation in barley association studies. The Plant Genome. 2:11-22.

Boddhireddy, P., Jannink, J., Nelson, J. 2009. Selective Advance for Accelerated Development of Recombinant Inbred QTL Mapping Populations. Crop Science. 49:1284-1294.

Gutierrez, L., Nason, J.D., Jannink, J. 2009. Morphological Genetic Diversity of Worldwide Barley and Mega-Targets of Selection. Crop Science. 49:483-497.

Yao, N., Jannink, J., White, P.J., Alavi, S. 2008. Impact of Dry Solids and Bile Acid Concentrations on Bile Acid Binding Capacity of Extruded Oat Cereals. Journal of Agricultural and Food Chemistry. 56:8672-8679.

Heffner, E.L., Sorrells, M.E., Jannink, J. 2009. Genomic Selection for Crop Improvement. Crop Science. 49:1-12.

Tinker, N.A., Kilian, A., Rines, H.W., Bjornstad, A., Howarth, C.J., Jannink, J., Anderson, J.M., Rossnagel, B.G., Wight, C.P., Stuthman, D.D., Sorrells, M.E., Scoles, G.J., Eckstein, P.E., Ohm, H.W., Jackson, E.W., Tuvesson, S., Kolb, F.L., Molnar, S.J., Olsson, O., Carson, M.L., Ceplitis, A., Bonman, J.M., Federizzi, L., Langdon, T. 2009. New DArT markers for oat provide enhanced map coverage and global germplasm characterization. Biomed Central (BMC) Genomics. 10(39):1471-2164.

Waugh, R., Muehlbauer, G.J., Jannink, J., Ramsay, L. 2009. Association genetics in barley. Current Opinion in Plant Biology. 12(2):218-222.

Iwata, H., Ebana, K., Fukuoka, S., Hayashi, T., Jannink, J. 2009. Bayesian multilocus association mapping on ordinal and censored traits and its application to the analysis of genetic variation among Oryza sativa L. germplasms. Theoretical and Applied Genetics. 118(5):865-880.

Zhong, S., Dekkers, J., Jannink, J. 2009. Association-Based Genomic Selection in Cultivated Barley. Genetics. 182:355-364.

Iwata, H., Ebana, K., Uga, Y., Hayashi, T., Jannink, J. 2009. Whole genome association mapping of grain shape variation among Oryza sativa L. germplasms based on elliptic Fourier analysis. Theoretical and Applied Genetics. 114(8):1437-1449.

Last Modified: 10/17/2017
Footer Content Back to Top of Page