Long-Term Goals:
- Developing a suite of genomic/genetic/analytical tools and training materials that integrate genomic information into applied polyploid plant breeding programs.
- Accelerating the genetic gain and phenotypic outcomes in polyploid plant breeding.
- Improving the yield, stress tolerance, quality, and profitability of polyploid crops.
Major Computational Needs Identified:
- Multi-SNP haplotype discovery and population genotyping using next-generation sequencing.
- Linkage mapping with multi-allelic markers and genotype quality scores.
- GWAS and genomic selection in mixed ploidy populations and with multi-allelic markers.
- QTL mapping in interconnected F1 populations.
- Fine mapping, haplotype visualization, and efficient assembly of QTL alleles across multiple loci.
Project Objectives:
Objective 1: Develop computational tools for genomics-assisted breeding in polyploid crops
Recently significant progress has been made in developing computational tools to facilitate breeding and genetics research in polyploidy crops. The major groups responsible for these advances are involved in this project. Nonetheless, these tools need to be extended to address the complex genetics and breeding systems that exist in polyploid specialty crops. This work will leverage the existing datasets and enhance the productivity of current genomic projects in rose, blackberry, tart cherry, strawberry, blueberry, potato, sweetpotato among others. The software developed will meet the five needs identified during the planning grant: (a) multi-SNP haplotype discovery and population genotyping using next-generation sequencing; (b) linkage mapping with multi-allelic markers and genotype quality scores; (c) GWAS and genomic selection in mixed ploidy populations and with multi-allelic markers; (d) QTL mapping in interconnected F1 populations; (e) fine mapping, haplotype visualization, and efficient assembly of QTL alleles across multiple loci.
Objective 2: Develop simulation tools to optimize genetic mapping and genetic gain per unit cost in polyploidy crops.
To have a practical impact on cultivar development, it is not enough to have the right analytical tools. Breeders need to make complex resource allocation decisions, and prior experience with phenotypic selection does not necessarily translate into efficient designs for genomic selection. Software will be developed so the user can explore different designs for genetic mapping projects or breeding programs. Simulation options will include the mating design, genome size, meiotic properties, population size, and costs for genotyping and phenotyping.
Objective 3: Apply computational tools for genomics-assisted breeding in public breeding programs for rose, potato, sweetpotato, blueberry, blackberry, and turfgrass.
Research projects involving the new computational tools are planned for six polyploid crops representing a range of ploidy levels, preferential pairing propensity, interspecific diversity among breeding germplasm, and genomic data/resource availability. The participating breeding programs are also at different stages in the implementation of genomics-assisted breeding. For instance, while potato breeders are fine-tuning genomic selection models, blackberry breeders are still working to develop the first dense linkage maps and map loci for major qualitative loci. Each of these programs brings existing datasets (phenotypic and genotypic data) to the project. The interaction among breeding groups and other polyploid breeders facilitated by this project will help breeders in less advanced crops to make major leaps in their use of genomics-assisted breeding. Furthermore, the datasets from the project’s breeding groups will be used to validate the software created and develop training modules for the new software
Objective 4:Train polyploid breeders and geneticists to use new computational tools.
Complete documentation of the syntax and options for each software will be created, as well as example datasets and corresponding workflows. These training materials will be publicly available through a Polyploid Community Resource web page that will be developed and hosted by Washington State University. Graphical user interfaces will be developed for the command-line software developed in Objectives 1 and 2 and made available through the website. Hands-on workshops will showcase the new software and train the polyploid breeding community about polyploid genetics and the use of the analytical toolset.