Plant Breeders, Assemble!
How interdisciplinary teams are working together for plant improvement?
Professor, MSU Foundation Professor & William J Beale Distinguished Faculty
Department of Plant Biology
Michigan State University
Bio: Dr. C. Robin Buell received her Ph.D. from Utah State University in 1992 and was a postdoctoral fellow in the DOE Plant Research Laboratory at Michigan State University, followed by a USDA and then a NIH postdoctoral fellowship at the Carnegie Institution of Washington, Department of Plant Biology in Stanford, CA. In 1997, she accepted a position as an Assistant Professor in the Department of Biological Sciences at Louisiana State University. In 1999, she joined the faculty at The Institute for Genomic Research (TIGR) in Rockville, MD where she remained until 2007 when she joined the Department of Plant Biology at MSU. Her research is focused on the genome biology of plants and plant pathogens, including comparative genomics, bioinformatics, and computational biology. Her research involves crop plants (corn, rice, potato, sweetpotato), biofuels (switchgrass), and medicinal/herbal plants (periwinkle, mints, nightshade, ginseng, Camptotheca) while her work with plant pathogens has focused primarily on bacteria and oomycetes. She was a central member of the consortium that generated the rice genome sequence, a crop that feeds 50% of the world’s population every day, and developed a public database for rice researchers that receives over two million visits each year from scientists across the world. From her work on medicinal plants, she developed and maintains the Medicinal Plant Genomics Resource that has enabled discoveries in natural product biosyntheis. She has received funding from NSF, USDA, DOE, NIH, and the Bill & Melinda Gates Foundation and published over 200 papers. Dr. Buell has an active research group composed of postdoctoral research fellows, research assistants, graduate students, undergraduate students and high school interns and collaborates with scientists across the United States and throughout the world. She has served as an editor at Plant Physiology, the Plant Genome, Crop Science, Frontiers in Plant Genetics and Genomics, and Plant Cell. She has served as an advisor to several large plant genome research consortia and the National Academy of Sciences Committee on Genetically Engineered Crops. She is a fellow of the American Association for the Advancement for Science and the American Society of Plant Biologists.
“Evolution of potato: The world’s most important vegetable crop”
Abstract: Cultivated potato is a vegetatively propagated autotetraploid, a unique trait among major crop and model plant species. The assembly of characteristics that define cultivated potato relies on a complex balance of multiallelic loci with frequent epistatic interactions that are lost through sexual reproduction, with the result that most progeny are inferior to either parent as a consequence of inbreeding depression. We have uncovered a high degree of heterozygosity and rampant copy number variation that result in a highly heterogenous genome and a complex transcriptome with additive and non-additive gene expression. Using a panel of wild species, landraces, and cultivars, we have identified introgressions from wild species, loci under selection, and confirmed the single origin of domestication for this important food crop. Collectively, these results provide new foundations to begin to breed potato for improved agronomic traits to meet 21st century food security needs.
Biometrics and Statistics Unit
International Maize and Wheat Improvement Center (CIMMYT)
Bio: Uruguayan national and graduate of the Republic University of Uruguay (BS, Agriculture, 1974) and the University of Nebraska-Lincoln (PhD, Statistics and Quantitative Genetics, 1984), Crossa came to CIMMYT as a postdoctoral fellow in the Maize Research Program in 1984. He has helped define key methodologies for conserving and using the center's maize genetic resources, covering proper regeneration procedures and strategies for forming core subsets of large germplasm collections. Crossa’s theoretical and practical work on genetic resources conservation made him to be selected the best scientist of the CGIAR Centers in 2008. His substantive body of research and publications has addressed many other areas of breeding and agronomy research, including genotype x environment, and QTL x environment interactions, general breeding and experimental design, hybrids and heterotic patterns, and association mapping, to name a few important subjects, and enjoys international acclaim and application. Crossa is a Fellow of the Agronomy Society of America and of the Crop Science Society of America, Member of the Mexican Academy of Science, Member of the National Research System of the National Council of Research and Technology (CONACYT) of Mexico, invited professor at Universities in Mexico and Uruguay, and Adjunct Professor at the University of Nebraska – Department of Statistics and Department of Agronomy and Horticulture. Articles he has co-authored on maize genetic conservation and breeding have on six occasions received recognition for being among the three best published papers of the year by the Genetic Resource Conservation Division of the Crop Science Journal of the Crop Science Society of America. Recently, Crossa and colleges impacted plant breeding by being one of the first researchers in showing genomic-enabled predictions models with high accuracy using pedigree and markers information applied in massive maize (and wheat) field data. Few years later he and colleagues showed how to incorporate genotype × environment interaction into the genomic-enabled prediction models together with marker, pedigree and meteorological and climatic data. Crossa has over 310 scientific articles published in referee journals and more than 30 book chapters. A new book written with a colleague in modern phenotypic and genomic selection indices methods for plant breeding will be published in 2018.
“MODELS AND METHODS FOR GENOMIC-BASED PREDICTION ”
GENOMIC AND DEEP LEARNING MODELS FOR PREDICTING MULTIPLE TRAITS AND MULTIPLE ENVIRONMENTS
In the last years several genomic-enabled predictions models have been developed for the prediction of large number of unobserved phenotypes in different environments using dense molecular markers. Models include single and multi-traits, and single and multi-environments with the objective of increase the prediction accuracy of the primary trait gran yield on unobserved individuals. Increase in prediction accuracy over the genomic best linear unbiased estimator (GBLUP) are achieved by means of the Gaussian kernel model with genomic × environments interaction. Recently, we have developed models that incorporate other non-linear kernels that increase the prediction accuracy of genomic × environments interaction over the Gaussian kernel by 5-10%. A Bayesian multi-trait multi-environment models was described and used to efficiently exploit correlated traits and environments producing and thus increases the prediction accuracy of about 10-20% over the single trait, single environment model. However due to the nature of the estimations process this Bayesian model requires intense computing resources. In an attempt to speed up the prediction of extensive number of unobserved phenotypes in large sets of multi-environments (big data) we have been developing Deep Machine Learner (DL) models and methods. DL models with densely connected network architecture were compared with GBLUP on nine real genomic data sets. The DL appeared to be competitive, since they were better than GBLUP in 4 of the 9 data sets under a scenario that ignored covariates capturing genomic environment interaction. However, when genomic environment interaction was included, DL was not inferior in terms of prediction accuracy to GBLUP.
GENOMIC PREDICTION MODELS WITH GENOTYPE x ENVIRONMENT INTERACTION FOR PREDICTING GRAIN YIELD USING HYPERSPECTRAL IMAGE DATA
In modern agriculture and plant breeding the use of hyperspectral cameras provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. Since vegetation indices only use some wavelengths (referred to as bands), authors have proposed using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize and wheat breeding trials indicated that using all bands produced accurate prediction. However, until now, these prediction models have not accounted for the effects of genotype × environment (G×E) and band × environment (B×E) interactions incorporating genomic or pedigree information.In this study, we propose Bayesian regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G×E and B×E interaction effects. The data set used comprised wheat lines from the Global Wheat Program of CIMMYT evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nanometers (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands. Models with B×E interaction terms were the most accurate models.
Research Plant Pathologist and Adjunct Associate Professor
USDA-Agricultural Research Service Cereal Disease Laboratory
Bio: Plant pathologist Dr. Matthew Rouse works at the USDA-Agricultural Research Service’s Cereal Disease Laboratory and serves as Adjunct Associate Professor in the Department of Plant Pathology at the University of Minnesota. Matt joined the CDL in 2010 immediately after completing a PhD in plant pathology at the University of Minnesota. He earned an M.S. degree in plant pathology from Kansas State University and a B.S. degree in Zoology from Oklahoma State University. As part of the CDL, Matt’s research mission is to reduce losses in wheat and barley to major diseases including stem rust and leaf rust. Matt has contributed towards solutions to the threat of Ug99 and other emerging virulent races of the stem rust pathogen. In the fight against Ug99, Matt has conducted research in Kenya, Ethiopia, and in a biocontainment facility at the University of Minnesota. Excellent team members at the CDL, University of Minnesota, and international collaborators worked with Matt to make rapid progress in identifying new stem rust resistance genes, establishing new field disease screening nurseries in Ethiopia, and assisting breeders in the release of stem rust resistant wheat varieties across the globe. Matt and his beautiful wife Alicia have two young children.
Regents Professor and the Borlaug-Monsanto Chair in Crop Improvement
Texas A&M University
Bio: Dr. William Rooney is a Regents Professor and the Borlaug-Monsanto Chair in Crop Improvement in the Department of Soil and Crop Sciences at Texas A&M University. He earned his B.S. and M.S. degrees in Agronomy and Plant Breeding, respectively at Texas A&M University. He completed his Ph.D. in Plant Breeding and Genetics from the University of Minnesota in 1992. Since 1995, he has led a sorghum improvement program with the goal of enhancing the productivity and profitability of grain, forage and bioenergy sorghums.
Bio: Mayor completed her bachelor’s degree in agronomy and master of science in genetics at the University of Rosario in Argentina. She earned her doctorate at Iowa State University in plant breeding working on the genetic bases of prolificacy in maize. She started at DuPont Pioneer as a Molecular Breeding Scientist in corn for the southeast United States and moved into sorghum breeding in 2011. Since then, she established a molecular breeding strategy for this crop and took over responsibilities for the sorghum breeding program established in Manhattan, Kansas. Over the last couple of years, her responsibilities were extended to develop breeding strategy and planning for eastern Kansas and the High Plains markets covering the Manhattan, Kansas and Plainview, Texas research stations. The major focus of the evaluation zone are improved yield, stalk strength, sugarcane aphid tolerance and cold tolerance using new phenotyping and molecular technologies available.
“Sorghum hybrid development in a commercial setting ”
Abstract: The Corteva Sorghum research department has been providing elite commercial hybrids to farmers for the last 50 years. To this end, our research and crop platform teams align objectives and breeding targets according to current markets and available resources. This information becomes the framework or blueprint for our commercial breeding programs. During the hybrid development process, the latest phenotyping and genotyping technologies are implemented to efficiently increase our genetic gain by developing superior parental lines and creating the best possible hybrid combinations among those parents. As an example, current efforts in molecular breeding and precision phenotyping provide breeders with more effective tools throughout the creation, evaluation and selection process, thus providing hybrids with higher yield, improved resistance to diseases, better standability and enhanced drought tolerance. An overview of sorghum research and development will be presented, as well as updates on collaborative projects between Corteva and the United Sorghum Checkoff Board.