High-throughput evolutionary systems biology for expanded genotype-phenotype mapping

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To what extent can we predict quantitative phenotypes based on genotype? Recent work in genome-wide association studies has proposed that genetic traits are often highly polygenic and influenced by numerous small-effect polymorphisms. This hypothesis has profound consequences for the fields of evolution and genetics. Namely, it suggests that searches for quantitative-trait loci (QTLs) can be placed into a broader explanatory framework where phenotypes are manifestations of the cumulative effects of several biological processes connected by the causal mutations affecting them. Thus, to understand the effect of mutations on phenotypes, we must consider how changes in genes and regulatory sequences affect function in context. Getting at the statistics of this mapping function, for example the extent to which a cellular process can influence a phenotype and be selected on by evolution, is the central question of my research program. In my proposal, I describe short-term objectives that addresses this question for transcriptional processes with the eventual goal of expanding this strategy to other molecular mechanisms such as protein regulation. First, getting at the statistics of any high-dimensional process requires thousands of comprehensive measurements. Next-generation sequencing and high-throughput liquid handling robotics now allow rapid sequencing of many genomes at very low cost. However, the knowledge of mutations at the DNA level do not immediately translate to knowledge of how the underlying cellular architecture has been perturbed. Unfortunately, high-throughput technologies are still lagging for other genomics tools such as transcriptomics and proteomics. Using our expertise in automation and reaction miniaturization, we will develop new technologies to interrogate cellular networks at scale. Second, we apply novel and existing statistical methods for new genomics data to model the cellular network between genotype and phenotype. Previously, similar approaches have been used to infer whether phenotypes had genetic components regardless of the molecular basis of the trait. Here, we use these statistical techniques to partition the variation in underlying cellular architecture that is responsible for the observed phenotypes. Finally, we use these approaches to test hypotheses about quantitative genetics and evolution directly in the lab by interrogating the systems biology of the cell between 1) individuals in a genetically diverse population, 2) between cell lineages over thousands of generations of evolution, and 3) between individuals drifting under no natural selection. This project provides a comprehensive description of the molecular processes that form phenotypes and connects these to phenotypic evolution. We employ interdisciplinary techniques, leveraging robotic liquid-handling and next-generation sequencing to bridge the demand for large datasets in statistical models of the cell and population genetics.