This Growing Convergence Research project is seeking to explain the resilience of genotype-phenotype maps through functional epistasis via a convergence of three overlapping fields of investigation: molecular evolution, data science and statistical physics. The project is motivated by an unanticipated amount of variation in populations, gene families, and phylogenetic groups revealed by DNA sequencing of genes, individuals, and species. These voluminous data on variation are posing unexpected challenges to the (nearly) neutral theory of molecular evolution that explains the fate of new mutations in a population through random genetic drift and purifying selection. The research team will employ mechanistic models of epistasis, co-evolutionary processes, and deep learning. Deep integration across disciplines will be key for exploring the pivotal role of epistasis and, ultimately, for interrogating the most fundamental rules of life.

The research team proposes a transdisciplinary project to set the foundations for a novel neutral-by-epistasis theory of molecular evolution. This team hypothesizes that a vast majority of population variation and species differences are due to random genetic drift of mutations that are neutral by epistasis, even though each may be individually detrimental. The new neutral-by-epistasis theory unites both neutral and nearly-neutral theories of molecular evolution and has the potential for a paradigm shift in which epistatic interactions between positions, rather than any individual position are the primary unit of comparative analysis.

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.

Agency
National Science Foundation (NSF)
Institute
Division of Integrative Organismal Systems (IOS)
Application #
1934860
Program Officer
Gerald Schoenknecht
Project Start
Project End
Budget Start
2019-10-01
Budget End
2024-09-30
Support Year
Fiscal Year
2019
Total Cost
$153,802
Indirect Cost
Name
Yale University
Department
Type
DUNS #
City
New Haven
State
CT
Country
United States
Zip Code
06520