2009 Annual Report
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.
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.
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.
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.
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.
Heffner, E.L., Sorrells, M.E., Jannink, J. 2009. Genomic Selection for Crop Improvement. Crop Science. 49:1-12.
Waugh, R., Muehlbauer, G.J., Jannink, J., Ramsay, L. 2009. Association genetics in barley. Current Opinion in Plant Biology. 12(2):218-222.
Zhong, S., Dekkers, J., Jannink, J. 2009. Association-Based Genomic Selection in Cultivated Barley. Genetics. 182:355-364.
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.
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.
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.