Graduate Student Lightning Talks:

Session 1 - Big Picture

Aimee Schulz, Cornell University

Talk Title: "Declining fitness and high genetic load point to inbreeding depression in fragmented wild maize populations"

Aimee Schulz is a first-year Plant Breeding and Genetics PhD student in the Buckler Lab at Cornell University. Combining her interests in plant breeding, ecology, and evolutionary genetics, Aimee is currently studying the genomic architecture of competitive ability in maize. Aimee graduated from Iowa State University with a BS in Genetics and a BS in Agronomy in 2019, where she was recognized for her research, leadership, and scholarship by the College of Agriculture and Life Sciences as the recipient of the Academic Achievement Award. Additionally, she was an NSF REU student at the Donald Danforth Plant Science Center in the Topp Lab where she studied the genetics of maize root architecture across the genus Zea. During her four years at Iowa State, Aimee conducted undergraduate research with Dr. Matthew Hufford on a variety of topics involving maize evolutionary genetics, with her final project focused on inbreeding depression in wild teosinte populations. She has continued her work with these populations during the start of her graduate career.

Abstract: Crop wild relatives can serve as a source of variation for the genetic improvement of modern varieties. However, the realization of this genetic potential depends critically on the conservation of wild populations. In this study, five populations of Zea mays ssp. parviglumis collected in Jalisco, Mexico were planted in a common garden. Eleven traits correlated with plant fitness were measured and compared with genetic diversity and genetic load. Plants whose seed were sourced from larger populations had greater genetic diversity, lower genetic load, and possessed phenotypes associated with higher fitness, while plants sourced from smaller populations had traits characteristic of lower fitness and increased genetic load. For example, plants from larger populations germinated more quickly, reached anthesis sooner, demonstrated a higher level of photosynthetic activity, and produced more biomass, suggesting a direct correlation between the fitness of a population, genetic diversity, and genetic load. These results emphasize the importance of preserving large populations of Zea mays ssp. parviglumis to limit inbreeding depression and maintain the genetic diversity, habitat, and adaptive potential of this germplasm.

Manfred Mayer, Technical University of Munich, Freising Germany

Talk Title: "What’s new? - Landrace haplotypes for elite germplasm improvement"

Manfred Mayer is a PhD student in the group of Chris-Carolin Schön at the Chair of Plant Breeding at the Technical University of Munich (TUM). He completed his Master’s degree in Agricultural Science in 2015 at TUM, conducting population genetic analyses in maize landraces. He continued to work on maize landraces during a two-year research stay in the lab of Natalia de Leon at the Department of Agronomy at the University of Wisconsin Madison. Manfred’s current work focuses on strategies for making the rich diversity of genetic resources accessible for improving quantitative traits with limited genetic variation in elite germplasm.

Abstract: Maize landraces are expected to harbor novel favorable alleles for agronomic traits. However, for quantitative traits discovering and utilizing this variation for breeding is challenging due to the heterogeneous and heterozygous nature of landraces and their inherent genetic load. We propose a genome-based strategy for accessing the native diversity of landraces in a targeted way. We generated libraries of doubled-haploid (DH) lines derived from three pre-selected European maize landraces. Almost 1,000 DH lines were genotyped with 600k SNP markers and phenotyped in up to eleven environments. We performed genome-wide association studies (GWAS) and identified haplotypes associated with important quantitative traits. Trait-associated haplotypes were categorized as favorable, unfavorable or interacting, based on the significance and sign of their effects across environments. To evaluate the novelty of landrace haplotypes, we assessed their frequency in a panel of 65 breeding lines, representing the diversity of the European Flint elite breeding pool. On average, the frequency of favorable haplotypes detected in landraces was increased in the breeding lines compared to randomly sampled haplotypes, presumably due to positive selection. However, part of the favorable landrace haplotypes were absent in the breeding lines, pointing to useful novel variation. We hypothesize that targeted introgression of the associated landrace haplotypes will be instrumental in elite germplasm improvement.

Session 2 - Genomic Selection

Patrick Sweet, University of Minnesota

Talk Title: "Genomewide selection utilizing historic datasets improves early stage selection accuracy and selection stability"

I grew up in Aurora, IL with no background in science or agriculture. During my first year as an undergrad at Saint Mary’s University of Minnesota I had the opportunity to be the greenhouse caretaker where I found an appreciation for plants. After graduating with a B.S. in biochemistry and a math minor, I pursued a graduate degree in plant breeding and genetics from Purdue. Upon completion of a master’s degree I took a job within seeds development at Syngenta. While maintaining my role with Syngenta, I soon started in the Applied Plant Science PhD. program at the University of Minnesota in Spring 2019. Here, I am studying corn genetics and breeding under the supervision of Dr. Rex Bernardo.

Abstract: Over the past decade, commercial maize (Zea mays L.) breeding programs have generated a large quantity of complimentary phenotypic and genotypic datasets on their recurrent breeding populations. As many genetic subgroups and alleles are recycled over the years, these data can be a valuable resource for predicting the performance of future breeding cycles. Our objective was to test the efficacy of using historic breeding data within a genomewide selection (GWS) framework to improve plant breeding selections in early stage testing of maize lines. Across a multiyear maize breeding dataset propriety of Syngenta Seeds, LLC., we compared the selection accuracy of GWS and phenotypic selection (PS). Genomewide selection improved selection accuracy relative to PS by 15% for grain yield, 21% for grain moisture, 3% for test weight, and 58% for ear height. Additionally, we demonstrate the stability of GWS for plant selections through a stratified sampling procedure in which loss of testing environments was simulated. Plant selections with GWS were robust and stable with the simulated loss of up to four testing environments (out of seven). Specifically, under this scenario, where four early stage testing environments were lost, the average GWS selection accuracies only decreased relatively by 10, 8 and 5% for grain yield, grain moisture, and test weight, respectively, compared with an average relative reduction for PS by 35, 13, and 12% for grain yield, grain moisture, and test weight, respectively. These results indicate that an abundance of historic genotypic and phenotypic data can compensate for a lack of preliminary yield trials

Guilherme De Jong, Federal University of Lavras, Brazil

Talk Title: "Comparison of genomic prediction strategies for general combining ability in early stages of hybrid breeding programs"

Guilherme de Jong is a PhD student at Federal University of Lavras (UFLA) in Genetics and Plant Breeding and currently is a visiting PhD student at the Roslin Institute, the University of Edinburgh. He received his B.S from State University of Ponta Grossa (UEPG) in Agronomy and MS from Federal University of Lavras (UFLA). Guilherme’s research interests are ways to improve the efficiency of breeding programs using genomic selection strategies and other genomic tools.

Abstract: Genomic prediction studies in maize have primarily focus on prediction of hybrids in late stages of the breeding program and have largely ignored selection of inbred lines for use as parents in subsequent breeding cycles. This study evaluated the performance of genomic prediction models for selection of parents in subsequent breeding cycles using estimated general combining ability. Five genomic prediction models were evaluated under different SNP marker densities or with true QTL genotypes using stochastic simulations of entire maize breeding programs. The simulated maize breeding programs modelled over 20 years of breeding using AlphaSimR. The performance of the genomic prediction models was measured at the double haploid stage by tracking genetic gain for hybrids formed by crossing inbred lines from different heterotic pools. The results showed that the performance of the genomic prediction models depends on the marker density. Under low-density, the models that included pool-specific effects showed higher genetic gain than the models that have a common additive effect for both heterotic pools. In contrast, the performance of the models was similar when high-density markers were used. Using the true QTL genotypes showed the superiority of the models that included dominance effects. For heterosis, under low-density, the genomic prediction models showed a different performance. While using high-density markers and true QTL genotypes, the models that included dominance effects showed higher heterosis than the models that included only additive effects. In conclusion, the performance of the genomic prediction models is dependent on the marker density, genomic prediction accuracy increases with high-density, and the performance of models using SNP markers is different from what was expected using true QTL genotypes.

Session 3 - Phenomics

Elina Adikhari, Kansas State University

Talk Title: "Eco-geographic Adaptation Signals Improve Heading Date Prediction Accuracy in Wild Emmer Wheat"

I am a final year Ph.D. candidate in Genetics working with Drs. Eduard Akhunov and Allan Fritz at Kansas State University. I received M.S. in Plant Sciences from North Dakota State University and B.Sc. in Agriculture from Tribhuvan University, Nepal. I study the genetic basis of adaptation in wild relatives of wheat and apply this information to identify and introgress drought adaptive genetic variants in hexaploid wheat with an aim to develop predictive models for genomic selection. I also work on fine-mapping and cloning of R genes in wheat. In my free time, I love to play with my 2 year old son.

Abstract: Combination of low precipitation and increased evapotranspiration elevates the drought severity in most of the wheat growing belt, the Central Great Plains and Southwest of Western North America (Cook et al., 2015). Breeding for drought resistance in wheat requires accessing genetic diversity of wild relatives from the extreme drought and heat stressed environment. We identified the accessions of wild emmer wheat, the tetraploid ancestor (AABB genome), carrying alleles conferring adaption to extreme climatic conditions by looking at eco-geographic patterns of genomic variation. We associated the patterns of genomic variation in wild emmer (AABB genome) with climate data from the accessions’ collection sites using a population of 475 georeferenced wild emmer accessions from Israel using 90K iSelect SNP array and sequence - based genotyping. The data was mapped to a wild emmer genome and missing SNPs predicted by imputation. Out of 341,228 SNPs from sequence data and 26,697 genotype calls from 90K SNP array, we retained 26,548 and 9,175 SNPs, respectively. The analysis of population stratification revealed three genetically distinct groups of wild emmer accessions coinciding with their geographic distribution. Pearson correlation among bioclimatic variables identified twenty five unique climatic variables. Partitioning of genomic variance showed that geographic location and climate together explain 44% of SNPs among emmer accessions with 10% of SNPs affected by climatic factors only. Eco-geographic adaptation alleles identified through multiple environmental association scans using historical onsite climatic data improved the prediction accuracy of heading date by nine percent. This targeted breeding pipeline, we tested and validated in wild emmer wheat shows a promising future for making informed selection decision in breeding programs.

Amanda McRae, University of California, Berkeley

Talk Title: "Spray-induced gene silencing of powdery mildew genes reduces plant disease"

Amanda McRae is a graduate student in the Department of Plant and Microbial Biology at the University of California, Berkeley. Her interest in plant-microbe interactions was sparked while studying at the University of Arizona where she received her Bachelor of Science in Microbiology. Currently, she is working to uncover how pathogens manipulate their host plants to provide nutrients using the powdery mildew fungus, Golovinomyces orontii, and the model plant, Arabidopsis thaliana.

Abstract: Spray-induced gene silencing (SIGS) is an emerging method to study gene function in plant pathogens and is being developed to protect crops from pests. It utilizes exogenously applied RNA designed to reduce gene expression in target organisms using endogenous RNA interference machinery. The widespread obligate biotrophic pathogen, powdery mildew, infects plants including wheat, barley, grape, and tomato which require fungicides to control. The goal of this study was to determine if powdery mildews are SIGS candidates and if so, develop a method to reduce powdery mildew growth using SIGS and screen for gene targets that contribute to fungal spore production. For this study we used the Arabidopsis thaliana-Golovinomyces orontii pathosystem. First, we demonstrated that G. orontii can uptake extracellular RNA using Fluorescein-labeled RNA. Next, we developed a SIGS method using the powdery mildew fungicide target CYP51 as a control gene to titrate dosage and timing of spray. Using SIGS, we screened 16 additional targets. Four out of ten metabolic genes tested contributed to fungal growth on Arabidopsis, with spore production reduced up to 50%. Two out of six effector genes targeted reduced fungal spore formation up to 33%. We have also shown these methods are translatable to grape powdery mildew, resulting in reduction of powdery mildew disease of grapevine. SIGS may be the future of controlling powdery mildew growth on crops because of its flexibility, reduced environmental and health risks, and rapid transition from the bench to the field.