Utilizing a historical wheat collection to develop new tools for modern plant breeding

Abstract

The Green Revolution is credited with saving billions of lives by effectively harnessing new genetic resources and breeding strategies to create high-yielding varieties for countries lacking adequate food security. To keep the next billion people in a state of food security, plant breeders will need to rapidly incorporate novel approaches and technologies into their breeding programs. The work presented here describes new genomic and phenomic strategies and tools aimed at accelerating genetic gain in plant breeding. Plant breeders have long relied on regional testing networks to evaluate new breeding lines across many locations. These are an attractive resource for both retrospective and contemporary analysis due to the vast amount of data available. To characterize genetic progress of plant breeding programs in the Central Plains, entries from the Southern Regional Performance Nursery dating back to 1992 were evaluated in field trials. The trend for annual improvement was 1.1% yr⁻¹, matching similar reports for genetic gain. During the same time period, growth of on-farm yields stagnated. Genomic selection, a promising method to increase genetic gain, was tested using historical data from the SRPN. A temporal-based model showed that, on average, yield predictions outperformed a year-to-year phenotypic correlation. A program-based model found that the predictability of a breeding program was similar when using either data from a single program or from the entire regional collection. Modern DNA marker platforms either characterize a small number of loci or profile an entire genome. Spiked genotyping-by-sequencing (sGBS) was developed to address the need in breeding programs for both targeted loci and whole-genome selection. sGBS uses a low-cost, integrated approach that combines targeted amplicons with reduced representation genotyping-by-sequencing. This approach was validated using converted and newly-designed markers targeting known polymorphisms in the leaf rust resistance gene Lr34. Plant breeding programs generate vast quantities of data during evaluation and selection of superior genotypes. Many programs still rely on manual, error-prone methods to collect data. To make this process more robust, we have developed several open-source phenotyping apps with simple, intuitive interfaces. A contemporary Green Revolution will rely on integrating many of these innovative technologies into modern breeding programs.