Search for: All records

Abstract Plant architecture is a major determinant of planting density, which enhances productivity potential for crops per unit area. Genomic prediction is well positioned to expedite genetic gain of plant architectural traits since they are typically highly heritable. Additionally, the adaptation of genomic prediction models to query predictive abilities of markers tagging certain genomic regions could shed light on the genetic architecture of these traits. Here, we leveraged transcriptional networks from a prior study that contextually described developmental progression during tassel and leaf organogenesis in maize (Zea mays) to inform genomic prediction models for architectural traits. Since these developmental processes underlie tassel branching and leaf angle, 2 important agronomic architectural traits, we tested whether genes prioritized from these networks quantitatively contribute to the genetic architecture of these traits. We used genomic prediction models to evaluate the ability of markers in the vicinity of prioritized network genes to predict breeding values of tassel branching and leaf angle traits for 2 diversity panels in maize and diversity panels from sorghum (Sorghum bicolor) and rice (Oryza sativa). Predictive abilities of markers near these prioritized network genes were similar to those using whole-genome marker sets. Notably, markers near highly connected transcription factors from core network motifs in maize yielded predictive abilities that were significantly greater than expected by chance in not only maize but also closely related sorghum. We expect that these highly connected regulators are key drivers of architectural variation that are conserved across closely related cereal crop species. 

The datasets (PSGFS_compiled_data_2022.xlsx, PSGFS_compiled_data_2023 and PSGFS_compiled_data_2024.xlsx) were collected by undergraduate students during the time they participated in the Plant Science for Global Food Security (PSGFS) program in summers 2022, 2023 and 2024 at the International Rice Research Institute (IRRI; Los Baños, Philippines). The PSGFS program is an initiative funded by the National Science Foundation (Grant: NSF IRES #2106718) and led by Diane Wang and Gary Burniske of Purdue University and Amelia Henry and Anilyn Maningas of IRRI. Purdue University PhD student, To-Chia Ting, assisted in compiling these datasets.  The explanation of each worksheet in a excel file could be found in the associated word files (PSGFS_README_2022.doc, PSGFS_README_2023.doc and PSGFS_README_2024.doc). PDF files of the presentations given by the students are also provided and compressed in the Student_presentation_2022.zip, Student_presentation_2023.zip and Student_presentation_2024.zip file. File names of the presentations are composed of worksheet names and students’ last names. 

PSGFS_compiled_data_2022.xlsx contains datasets collected by eight undergraduate students during the time they participated in the Plant Science for Global Food Security (PSGFS) program in Summer 2022 at the International Rice Research Institute (IRRI; Los Baños, Philippines). The PSFGS program is an initiative funded by the National Science Foundation (Grant: NSF IRES #2106718) and led by Diane Wang and Gary Burniske of Purdue University and Amelia Henry and Anilyn Maningas of IRRI. Purdue University PhD student, To-Chia Ting, assisted in compiling these datasets. </p>  </p> The explanation of each worksheet in PSGFS_compiled_data_2022.xlsx could be found at the README.doc. PDF files of the presentations given by the eight students are also provided and compressed in the Student_presentation_PDFs.zip file. File names of the presentations are composed of worksheet names and students’ last names. </p> Grants: NSF IRES, grant number: 2106718