Predicting Phenotypes for an Unknown Future
High throughput phenotyping is shifting plant genetics towards high(er) dimensional trait data sets. Once a set of images, point clouds, or hyperspectral reflectance measurements have been collected, the marginal cost of quantifying additional plant traits from the same sensor data is low. In an example of the utility of multidimensional trait, 3D reconstructions from 2D images and organ-instance segmentation can be used to map genes controlling leaf angle variation on a leaf-by-leaf level in sorghum, incorporating data collected from the same plants at multiple time points. Community association populations which have been widely adopted by multiple research groups also produce high dimensional trait data, and these data can be used to identify pleiotropic effects of both known mutants and previously uncharacterized loci. Curated datasets of more than 200 hundred traits were assembled in both maize and sorghum, and analyzed using both GPWAS and multivariate adaptive shrinkage. In maize, GPWAS predicts pleiotropic effects for loci which are consistent with loss of function phenotypes. In sorghum, MASHR identifies previously unknown effects of a classical dwarfing gene on root architecture. Given the value of high dimensional trait data from community association panels there is substantial value in structuring the collection of high throughput sensor data from association populations in ways that enable recycling and reuse.
Bio
James Schnable is the Gardner Professor of Maize Quantitative Genetics at the University of Nebraska - Lincoln. With over a decade of experience in plant genetics, he maintains a diverse relationship between multiple sciences within his lab and research. Schnable leads and mentors a team of postdoc, graduate students, and technicians in his position in the Department of Agronomy and Horticulture and the Center for Plant Science Innovation at the University of Nebraska - Lincoln. His research is currently supported by the US Department of Agriculture, the National Science Foundation, the Department of Energy, and the Nebraska Corn Growers. He is especially interested in harnessing new technologies from engineering and computer sciences and integrated them into maize and sorghum genetic and genomic research. As a founding partner in three successful startups in his field, Schnable understands the importance of innovative pathways combined with academic research. Data2Bio, Dryland Genetics, and EnGeniousAg continue to break new ground in the agriculture and genomic sectors.
He holds a BA in Biology from Cornell University and a Ph.D. in Plant Biology from UC-Berkeley. He was NSF Plant Genome Fellowship supported postdoctoral scholar at the Danforth Center in St. Louis and the Chinese Academy of Agricultural Sciences in Beijing, China. He received the Marcus Rhoades early career award for maize genetics in 2018, the North American Plant Phenotyping Network Early Career award, and the American Society of Plant Biologists Early Career Award in 2019.
Join us at 3:30 in the Marley lobby for refreshments.
The presentation will begin at 4:00 p.m. in Marley 230
A live broadcast is available via zoom: https://arizona.zoom.us/j/88614287572 Password: spls2023
