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

Doctor of PhilosophyGenetics Interdepartmental ProgramJesse PolandThe 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

Genomic Analysis and Prediction within a US Public Collaborative Winter Wheat Regional Testing Nursery

The development of inexpensive, whole-genome profiling enables a transition to allele-based breeding using genomic prediction models. These models consider alleles shared between lines to predict phenotypes and select new lines based on estimated breeding values. This approach can leverage highly unbalanced datasets that are common to breeding programs. The Southern Regional Performance Nursery (SRPN) is a public nursery established by the USDA–ARS in 1931 to characterize performance and quality of near-release wheat (Triticum aestivum L.) varieties from breeding programs in the US Central Plains. New entries are submitted annually and can be re-entered only once. The trial is grown at \u3e30 locations each year and lines are evaluated for grain yield, disease resistance, and agronomic traits. Overall genetic gain is measured across years by including common check cultivars for comparison. We have generated whole-genome profiles via genotyping-bysequencing (GBS) for 939 SPRN entries dating back to 1992 to explore the potential use of the nursery as a genomic selection (GS) training population (TP). The GS prediction models across years (average r = 0.33) outperformed year-to-year phenotypic correlation for yield (r = 0.27) for a majority of the years evaluated, suggesting that genomic selection has the potential to outperform low heritability selection on yield in these highly variable environments. We also examined the predictability of programs using both program-specific and whole-set TPs. Generally, the predictability of a program was similar with both approaches. These results suggest that wheat breeding programs can collaboratively leverage the immense datasets that are generated from regional testing networks

A Field-Based Analysis of Genetic Improvement for Grain Yield in Winter Wheat Cultivars Developed in the US Central Plains from 1992 to 2014

Progress in plant breeding programs is the result of creating and selecting new lines with novel allele combinations that perform better than their parents. This year-on-year improvement is known as genetic gain and is a function of genetic diversity, selection accuracy, selection intensity, and selection cycle time. To estimate the gain in wheat (Triticum aestivum L.) breeding in the US Central Plains, lines that were submitted to the collaborative Southern Regional Performance Nursery (SRPN) between 1992 and 2014 were grown in a common nursery for 3 yr at two locations in a single-replicate augmented block design. Moderate to high broad-sense heritability was observed for plant height (H2 = 0.88), heading date (H2 = 0.79), and grain yield (H2 = 0.41). From the common grow-out, genetic gain for yield over the time period was estimated at 1.1% yr−1, whereas individual breeding program genetic gain varied between 0.3 and 1.9% yr−1. Increases in Kansas state on-farm yields during the same period showed a nonsignificant trend of 0.13% yr−1 with large year-to-year variation. These results suggest that although progress is being made in US Central Plains breeding programs, a yield gap remains that could be attributable to genetic progress not being realized in on-farm production

A Field-Based Analysis of Genetic Improvement for Grain Yield in Winter Wheat Cultivars Developed in the US Central Plains from 1992 to 2014

Progress in plant breeding programs is the result of creating and selecting new lines with novel allele combinations that perform better than their parents. This year-on-year improvement is known as genetic gain and is a function of genetic diversity, selection accuracy, selection intensity, and selection cycle time. To estimate the gain in wheat (Triticum aestivum L.) breeding in the US Central Plains, lines that were submitted to the collaborative Southern Regional Performance Nursery (SRPN) between 1992 and 2014 were grown in a common nursery for 3 yr at two locations in a single-replicate augmented block design. Moderate to high broad-sense heritability was observed for plant height (H2 = 0.88), heading date (H2 = 0.79), and grain yield (H2 = 0.41). From the common grow-out, genetic gain for yield over the time period was estimated at 1.1% yr−1, whereas individual breeding program genetic gain varied between 0.3 and 1.9% yr−1. Increases in Kansas state on-farm yields during the same period showed a nonsignificant trend of 0.13% yr−1 with large year-to-year variation. These results suggest that although progress is being made in US Central Plains breeding programs, a yield gap remains that could be attributable to genetic progress not being realized in on-farm production

Genomic Analysis and Prediction within a US Public Collaborative Winter Wheat Regional Testing Nursery

The development of inexpensive, whole-genome profiling enables a transition to allele-based breeding using genomic prediction models. These models consider alleles shared between lines to predict phenotypes and select new lines based on estimated breeding values. This approach can leverage highly unbalanced datasets that are common to breeding programs. The Southern Regional Performance Nursery (SRPN) is a public nursery established by the USDA–ARS in 1931 to characterize performance and quality of near-release wheat (Triticum aestivum L.) varieties from breeding programs in the US Central Plains. New entries are submitted annually and can be re-entered only once. The trial is grown at \u3e30 locations each year and lines are evaluated for grain yield, disease resistance, and agronomic traits. Overall genetic gain is measured across years by including common check cultivars for comparison. We have generated whole-genome profiles via genotyping-bysequencing (GBS) for 939 SPRN entries dating back to 1992 to explore the potential use of the nursery as a genomic selection (GS) training population (TP). The GS prediction models across years (average r = 0.33) outperformed year-to-year phenotypic correlation for yield (r = 0.27) for a majority of the years evaluated, suggesting that genomic selection has the potential to outperform low heritability selection on yield in these highly variable environments. We also examined the predictability of programs using both program-specific and whole-set TPs. Generally, the predictability of a program was similar with both approaches. These results suggest that wheat breeding programs can collaboratively leverage the immense datasets that are generated from regional testing networks

Southern Regional Performance Nursery phenotypic data 1992-2015

Zip file containing the entry data used for the publication Genomic analysis and prediction within a US public collaborative winter wheat regional testing nursery. The ZIP file comprises of a list of all entries used, the raw phenotypes for all entries, a key for location codes, and a key for state codes