Genetic Interactions and Synthetic Population Biology Cellular behavior is determined by circuits of interacting genes and proteins. In outbred populations, the components of these circuits are diverse: individual organisms contain different sets of alleles causing variation in phenotypic traits. Relating genotypic and phenotypic variation is challenging because of the possibility of complex interactions, or epistasis, between the alleles. That is, the allele present at one locus can change the relative effect of an allele at another locus. These interactions can, and do, have large effects on phenotypes ranging from acquisition of drug resistance by viruses to autism and other complex disorders. However, little is known about the quantitative distribution of strength of epistasis between genetic elements and what consequences epistatic interactions have for evolution, especially in sexually reproducing populations. These questions are challenging because they require working with genetically diverse, rather than clonal, populations;depend on having a reliable map of genotypes and phenotypes and a mathematical framework for quantitative interpretation of evolutionary dynamics. Proposed work therefore will involve a combination of theoretical, computational and experimental work. The experimental platform will be based on S. cerevisiae and use the synthetic biology approach to create defined interacting genetic circuits, allow quantitative characterization and manipulation of the nature and strength of epistasis, and enable direct real time measurements of population dynamics. Our collaborative research plan tightly integrates theoretical and experimental approaches and the interdisciplinary expertise of the Elowitz and Shraiman laboratories. More specifically: (1) We will develop theoretical models describing the combined effect of epistatic interactions and recombination on the dynamics of alleles and genotypes in populations. These models will be used to analyze key population genetic phenomena, including maintenance of genetic variation, linkage disequilibrium, and outbreeding depression. (2) We will construct a set of synthetic genetic circuits with programmable epistatic interactions between alternative alleles for each component. This system will be based on zinc finger transcriptional regulation modules in S. cerevisiae, and will allow real-time monitoring of the genotype distribution within a genetically diverse population using high-throughput single-cell fluorescence microscopy. (3) We shall use this synthetic system to test specific theoretical predictions concerning genetic variation, outbreeding depression and linkage disequilibrium. In these experiments, genetically diverse yeast populations (with designed interacting alleles) will be subjected to cycles of growth under controlled selection pressure followed by mating and recombination. We will use single cell fluorescence measurements to follow the dynamics of the genotype distribution, providing quantitative data for comparison with model predictions. Together, these aims will provide a foundation for both manipulating epistatic interactions in a simplified outbred laboratory population, and understanding the consequences of epistatic interactions in natural outbred populations.

Public Health Relevance

Proposed work will bridge systems biology and population genetics. The results will provide quantitative insight into the effect of epistatic interactions on the genetic structure of outbred populations. These fundamental issues are critical for understanding the link between population genetics and the observed complex disease phenotypes, and are thus central to the development of genomic medicine.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Lyster, Peter
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California Institute of Technology
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Hormoz, Sahand; Singer, Zakary S; Linton, James M et al. (2016) Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements. Cell Syst 3:419-433.e8
Hormoz, Sahand; Desprat, Nicolas; Shraiman, Boris I (2015) Inferring epigenetic dynamics from kin correlations. Proc Natl Acad Sci U S A 112:E2281-9
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