Post Doctoral Position in Multiomic Prediction of Oat Seed Composition
School of Integrative Plant Science
College of Agriculture and Life Sciences
Cornell University, Ithaca, New York
The position is in the Plant Breeding and Genetics Section at Cornell University, and is part of a USDA Agriculture and Food Research Initiative grant to breed more nutritious oat. Oat is uniquely valued among grain crops for the health-promoting composition of its seeds. The project leverages extensive genomic, transcriptomic, and metabolomic datasets collected in oat to develop and evaluate methods to improve the health-promoting composition of oat seed effectively. We will accelerate improvement of oat seed composition based on analyses of these datasets.
The Plant Breeding & Genetics Section, within the School of Integrative Plant Science, trains interdisciplinary scientists in the elaboration of new breeding methods, the discovery of genetic mechanisms important for economically important traits, and the creation of genetic stocks, germplasm, and varieties. We promote a collaborative and interactive workspace to improve learning, cross connectivity, and mutual support between basic and applied researchers. Cornell University plant breeders are world leaders in innovative plant breeding research, teaching, and extension, and we collaborate globally.
The Jannink lab works with several crop species (wheat, oat, barley, cassava, and the brown algae sugar kelp) to develop genomic prediction methods and integrate them optimally within breeding schemes. We work together to discover, build on, and share new ideas and tools from across computational disciplines that lead to successful applied breeding outcomes. With the Jannink lab, Dr. Michael Gore and Dr. Mark Sorrells provide leadership on the multiomic oat selection project.
In research for this project, the postdoc will collaborate with oat breeders at Universities in Minnesota, Wisconsin and South Dakota, as well as a postdoctoral associate currently working on the project. We have characterized an oat diversity panel of 384 genotypes with high-density DNA marker data, RNA-seq gene expression data, and non-targeted LC-MS, GC-MS, and targeted fatty acid methyl ester data of mature oat seed. We will analyze these data to identify important genomic drivers of the mature oat seed metabolome. We will test whether results from this analysis can improve prediction accuracy in a series of 18 biparental crosses. In a parallel effort, led by a current postdoc, a population of 1,920 oat TILLING (Targeted Induced Local Lesions In Genomes) lines is being generated from which we will sequence 80 target genomic segments. TILLING lines mutated for putative metabolomic regulators will be evaluated to validate their function.
The candidate will be expected to contribute statistical genetic and machine learning expertise to the project and will conduct and interpret genome- and transcriptome-wide association studies to identify causal factors affecting the seed metabolome. In analyzing the biparental populations, the candidate will leverage those analyses along with identification of haplotype blocks identical by descent between training and test populations.
Additional responsibilities include support for curating and databasing information from the project.
Terms of Appointment
Term is one year renewable to three years contingent on performance.
Anticipated Division of Time
Field work, sample prep, data collection-10%
Statistical & quantitative genetic data analysis and interpretation-40%
Training of lab members and collaborators in analysis methods-15%
Ph.D. in statistical or quantitative genetics with experience or interest in breeding applications, or Ph.D. in plant or animal breeding with emphasis in statistics or bioinformatics. Proven scientific writing ability and communication skills.
Preferred Specific Skills
Genome-wide association studies and genomic prediction methods. High-dimensional data analysis. Programming skills in R or other quantitative / statistical scripting. Bioinformatics skills (sequence alignment, construction of pan-genome graphs). Basic notions of mating designs in breeding.
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