Predicting the characteristics, or traits, and behaviors of an individual organism from its DNA sequence is proving to be a very challenging and complex problem. One complicating factor is that the connection between DNA and traits is not consistent, and identical DNA sequences may not produce identical traits even when the environment is the same. This occurs because certain genes influence how stable or variable (robust) a trait is, with some genes producing traits that are more or less variable. Therefore, the identity of the genes that influence robustness is needed to build models to predict traits from DNA sequences. This knowledge is crucial to accelerate crop breeding, to meet growing global demands for food, and provide economic security to American producers. The current bottleneck in discovering genes that control robustness in plants is the inefficient process of measuring plant traits. To address this problem, the Donald Danforth Plant Science Center in St. Louis, Missouri has built state-of-the-art facilities and developed computer programs to automatically measure the characteristics of thousands of plants. Through extensive collaborative visits, the PI and a student trainee will use these facilities and learn methods for processing this data and how to build low-cost versions of these automated systems with the intent to bring this infrastructure to Montana. This support will enable the identification of DNA sequences that influence robustness and the relationships in robustness among characteristics.
Predicting phenotypes from genotypes is proving to be challenging and complex. One complicating factor is that the relationship between genotype and phenotype is problematic. While the entire genome of an organism, rather than single alleles, determines the phenotype and degree of phenotypic variability, a poor understanding of how robustness is controlled at the molecular and cellular levels remains. This knowledge gap exists because to measure robustness for any given trait, a large number of individuals with the same genetic make-up must be measured. Therefore, the objective of this fellowship is high-throughput phenotyping at the Donald Danforth Plant Science Center for the plant Arabidopsis thaliana. In addition, this fellowship supports the comprehensive training of the PI and a trainee in high-throughput data collection and analyses. The data and skills gained will enable testing of the central hypothesis: genetics influences the robustness of traits in predictable ways. Specifically, the data will support identifying 1) genes modulating robustness (phenotypic variability) and 2) patterns of robustness throughout plant development. We expect that with extensive data from a model species, we will identify patterns that describe robustness for comparison across plant species, that include crops. These data will facilitate crop breeding, crucial to the NSF's mission of national security through its impacts on food and economic security. Furthermore, this fellowship will benefit Montana State University's research infrastructure through supporting the development of a low-cost automated phenotyping platform that can be utilized across crops bred in Montana. Further, hands-on demonstrations of automated phenotyping for plants will expose rural girls to computer science/engineering, fields in which women are traditionally underrepresented.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
