Abstract
The astonishing biodiversity we observe today is the result of speciation. Reproductive isolation between species results from the interaction of multiple processes that decrease the fitness of hybrids. There is an increasing availability of large-scale sequencing data from non-model organisms. However, we currently lack the methodological tools to make use of it to decipher the genetic basis of reproductive isolation. I propose to develop computationally efficient, model-based inference of speciation by leveraging the recent advances in ancestral recombination graphs (ARGs). The use of ARGs enables more precise and efficient modeling, allowing for the disentangling of demographic effects from selection against gene flow. While the potential applications of ARGs in speciation research are promising, this approach remains unexplored. In this project, I aim to fill this gap in the literature by adapting the ARG framework for the study of speciation. I will develop novel ARG-based methods to model selection against gene flow and apply these tools to the iconic study system of the Malawi cichlid adaptive radiation. In particular, I will investigate the genetic basis of reproductive isolation and whether incipient disruptive selection is occurring as a result of intense fishing pressure.
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