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MIA Talks

Predictable patterns in phenotypic evolution

January 27, 2021
NSF-Simons Center for Mathematical and Statistical Analysis of Biology; Harvard University

Epistasis between mutations can make adaptation contingent on evolutionary history. Yet despite idiosyncratic epistasis between the mutations involved, microbial evolution experiments show consistent patterns of fitness increase between replicate lines. Recent work shows that this consistency is driven in part by patterns of diminishing-returns and increasing-costs epistasis, which make mutations systematically less beneficial (or more deleterious) on fitter genetic backgrounds. In this talk, we will argue that these predictable patterns emerge generically due to widespread, idiosyncratic epistasis. Extending this idea, we develop a new Fourier analysis-based framework to quantify how macroscopic features of the genotype-phenotype map impact the dynamics of phenotypic evolution. Using this framework, we show that the distribution of fitness effects takes on a universal form when epistasis is widespread and introduce a novel fitness landscape model to rationalize why phenotypic evolution can be repeatable despite sequence-level stochasticity.