NSF Postdoctoral Fellowship in Biology FY 2021: Mapping the Relationship Between Genetic Pleiotropy, Gene Function, and Adaptation

This action funds an NSF Plant Genome Postdoctoral Research Fellowship in Biology for FY 2021. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Megan Ruffley is "Mapping the Relationship Between Genetic Pleiotropy, Gene Function, and Adaptation" The host institution for the fellowship is the Carnegie Institution for Science and the sponsoring scientists are Drs. Sue Rhee and Moises Exposito-Alonso.

The impact of climate change and human consumption on ecosystems has been, and is predicted to be, the greatest threat to an inhabitable earth. Society relies on plants not only for food, material goods, and health products, but also for sequestration of a majority of carbon in the atmosphere and for the oxygen needed to sustain life. One major question arising from these concerns is how will plants respond to the changing climate.

Will they migrate to a new suitable habitat, adapt to the changing climate, or face extinction? Genetic and trait data have increasingly been used to predict how natural populations of plants may respond to the changing climate. In this project, modern genomic approaches and the most extensive genetic and trait dataset to date for plants will be used to measure the relationship between mutational effect and adaptation in different environments.

Evolutionary theory predicts that mutations affecting multiple traits will most likely slow down adaptation because physiological and genetic constraints create trade-offs between traits. Identifying when large effect mutations are constraining adaptation by affecting multiple traits will enable the success breeding of plant traits from genome editing technology without unintended consequences that could lead to maladapted organisms in future climates.

Additionally, a better understanding of mutational effects will enhance the power to predict adaptation, which has broad implications for agriculture and conservation biology. Training objectives include acquiring new skills in comparative functional genomics and quantitative genetics at the Carnegie Institution for Science. Support through this fellowship will provide new opportunities for the Fellow to educate the public on why plant science is so important, especially in the face of climate change, and what society can all do to reduce its carbon footprint.

Pleiotropy, the phenomenon that a genetic mutation can affect multiple traits, is often considered a critical barrier to adaptation. The origin of this idea dates back to the early 20th century and Fisher’s Geometric Model which is still used by geneticists to explain the cost of complexity of organisms or aging. However, recent advances in constructing a genotype-to-phenotype map from experimental data in Saccharomyces cerevisiae suggest that pleiotropy is limited within modules of functionally related phenotypes and may not always be detrimental to adaptation.

Little is known about the role of pleiotropy in complex organisms such as plants in nature. This project will study pleiotropy with the 1001 Genomes (1001genomes.org) and Phenomes (arapheno.1001genomes.org) of Arabidopsis thaliana to understand its functional causes and ultimate consequences to adaptation in natural environments. Using an unprecedented publicly available dataset of 1850 phenotypes spanning over 10 million SNPs for up to 1135 individuals of the plant Arabidopsis thaliana, the Fellow will: 1) devise metrics to quantify pleiotropic effects and their modularity across the genome and phenome; 2) study the functional context of pleiotropic mutations using public genome-wide histone modification and cytosine methylation data along with curated datasets of gene annotations in cellular processes, molecular function, and localization; and, 3) test whether pleiotropic variants contribute to or constrain adaptation in semi-natural outdoor experiments with direct measures of fitness.

All data and computational tools and resources generated during the course of this study will be freely available to the broader research community through public repositories. Keywords: Arabidopsis thaliana, machine learning, sequence analysis, pleiotropy, gene function, gene annotation

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.