Mapping the genomic landscape of gene flow and natural selection

Speciation and local adaptation are fundamental processes underlying biological diversity. Understanding them requires accurate estimates of the strength and timing of gene flow and divergent selection. The increasing amounts of whole-genome data provide an enormous source of information. Yet, demography and other types of selection also affect patterns of genomic diversity and can mimic the signal of the focal processes. Therefore, inferring key parameters of interest is still a major challenge, and so it remains difficult to make meaningful conclusions about the relative importance of gene flow and selection in speciation and local adaptation. The main objectives of this project are to infer and map divergent selection in the face of gene flow, both in discrete as well as in spatially continuous populations, and to develop wild tomatoes (Solanum section Lycopersicon) as a model for speciation and population genomics. These objectives will be pursued in three project parts.In Part 1 we will develop an approach to jointly infer the strength and timing of divergent selection and gene flow with power to delineate alternative speciation scenarios. We will also devise a new generation of genome scans to identify candidate genes underlying local adaptation and reproductive isolation (RI). To do so, we will combine deterministic migration-selection theory with the structured coalescent process, and build a composite-likelihood framework for parameter inference based on whole-genome sequencing (WGS) data. We will validate these methods by applying them to existing data from the yellow monkeyflower (Mimulus guttatus) and Heliconius butterflies, aiming to identify speciation genes and determine how much gene flow occurred during speciation.The aim of Part 2 is to derive a refined theory of effective dispersal in continuous space and to integrate this theory with existing spatial coalescence models to map the genomic landscape of effective gene flow and infer the strength of selection. We will use population genetic simulations to test the robustness of this approach with respect to sampling design, the nature of selection, and the demographic history of the spatial population. We will then apply the new method to existing whole-genome polymorphism and recombination data from Drosophila melanogaster populations sampled along a latitudinal gradient in Eastern North America.In Part 3 we will quantify the amount and timing of gene flow and selection involved in speciation and divergence in the Arcanum and Peruvianum species groups of wild tomatoes. We will analyse how the speciation process corresponds to the contemporary species ranges and to mating system transitions. We further aim at identifying the genetic basis of RI and adaptive divergence by mapping the genomic landscape of effective gene flow between and within pairs of sister species. To achieve these aims, we will generate de novo genome assemblies for S. arcanum and S. peruvianum and then whole-genome resequence a total of 92 plant specimens. Wild tomato seeds will be obtained from the C. M. Rick Tomato Genetics Resource Center (University of California, Davis), and plants grown in the greenhouse. Additional leaf tissue will be collected along three environmental gradients on a collection trip to Peru. We will use the methods developed in Parts 1 and 2 to analyse the sequence data.In this project we shift the current focus on descriptive analyses of patterns of genomic diversity and divergence in speciation and adaptation genomics towards the model-based joint inference of divergent selection and gene flow. In doing so, we explicitly account for random genetic drift and demographic history, as well as the confounding effects of background selection. The generation and analyses of WGS data for wild tomatoes will promote future research on this promising model system. The approaches developed here will be applicable to a wide range of topics, including local adaptation in humans or anticipating the effects of climate change on biological diversity.