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Describing Evolution with Theoretical, Empirical, and Computational Tools

Final Report Summary - DETECT (Describing Evolution with Theoretical, Empirical, and Computational Tools)

The main goals of this proposal were to i) develop theory and methodology enabling the identification of adaptively evolving genomic regions using polymorphism data, ii) develop theory and methodology for the estimation of genome-wide rates of adaptation, and iii) apply the developed theory in two strategic collaborative applications (in wild mouse, and experimental yeast). Significant progress was made on all fronts owing to our ERC funding, with multiple Bayesian based statistical approaches developed for topics ranging from the co-estimation of per-site selective effects with whole genome demographic effects, to the estimation of the full distribution of fitness effects of new mutations. These statistical and theoretical developments were successfully implemented in the study of the evolution of cryptic coloration in wild mouse populations in North America, having identified and quantified the underlying mutations contributing to this important phenotype. Moving from natural to experimental populations - studying the evolution of salt tolerance in lab populations of yeast - we described the shifts in the distribution of fitness effects associated when populations transition from being near their phenotypic optimum to being far, and have quantified the important role of epistasis in shaping the underlying fitness landscape.
This work has thus touched on fields spanning genetics, evolution, ecology, biochemistry, statistics and computer sciences - and the important knowledge gained during this funding period has sparked many additional research directions and funding opportunities.