Advances in genomics and high throughput technologies have the potential to transform breeding such that desired traits can be quickly sourced from wild germplasm and integrated into elite lines. By combining the latest developments in quantitative genetics with bioinformatics we hope to optimize breeding through prediction of crop performance, diversity and crossability.
Sequencing technologies have made tremendous progress over recent years and have allowed the assembly of very large genomes including wheat. The recent release of the first reference genome of Chinese Spring has been a milestone and enables the integration of marker, QTL and SNP information with gene expression and gene functional annotation.
Within the program our goals are to develop novel bioinformatics and biometrics methods for wheat genetics and breeding. This includes the development of a bioinformatics approach to mine genomic resources and identify allelic variation underlying loci. Specifically for the NAM, gene sequences will be obtained. This will allow us to explore the allelic diversity across the founders, develop perfect markers from SNP information, quickly identify regions of interest, and correlate GWAS results with gene regions thus narrow down on candidate genes. Other aspect include the development of new statistical models to improve the power of prediction of association mapping in wheat and the implementation of the Breeding Management System to manage the Hub datasets from research to breeding program.
The International Wheat Genome Sequencing Consortium (2018) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361, DOI: 10.1126/science.aar7191
Watson-Haigh NS, Suchecki R, Kalashyan E, Garcia M, Baumann U (2018) DAWN: a resource for yielding insights into the diversity among wheat genomes. BMC Genomics 19:941. doi: 10.1186/s12864-018-5228-2