E&E Seminar: “Using phylogenies to investigate questions in evolutionary and statistical genetics” by Matthew Pennell (University of Southern California)
April 9, 2024
12:30 PM - 1:30 PM
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Please join us on April 9, 2023 at 12:30 in SELE 4289 for an E&E Seminar featuring "Using phylogenies to investigate questions in evolutionary and statistical genetics" by Dr. Matthew Pennell (University of Southern California).
Host: Boris Igic
Abstract: A fundamental question in evolutionary biology is: how do evolutionary processes, operating at different timescales, interact? While the conceptual continuity between micro- and macroevolution is widely appreciated, in practice researchers in population genetics and phylogenetics have developed distinct methodological traditions for asking similar questions, such as whether there is an association between focal variables (including genetic variants), after controlling for shared ancestry. This lack of coherence is increasingly untenable, as pressing questions in evolutionary genetics require simultaneous consideration of processes that occur at different time scales. In my talk, I will discuss ongoing work in my lab group towards deriving a general theoretical foundation for integrating between- and within-species genomic and phenotypic variation that will empower researchers to do whole new types of analyses at the interface of these scales. A specific phenotype that we are interested in is gene expression. Specifically, our group is investigating how gene expression levels reflect the outcome of coevolutionary dynamics between mRNA and protein. Many studies have measured the correlation between mRNA and protein levels (between cells, genes, and species) but it has been difficult to make robust inferences about evolutionary processes from these correlations. I will discuss a theoretical investigation into how different gene regulatory architectures and selective regimes might change these correlations. We have built upon these findings to develop a new inference method that allows us to estimate both mutational input and correlational selection from phylogenetically structured data. We have fit this model to a recently published transcriptome/proteome dataset from mammalian cell lines.
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