Restriction-site Associated DNA sequencing (RADseq) is a powerful tool for using next-generation sequencing to scan across the genomes of scores to hundreds of individuals from natural populations. The lab is involved both in using this technique to generate data, and in developing statistical and analytical tools for RADseq data. Members of the lab are applying RADseq in collaborative projects involving threespine stickleback, cutthroat trout, disease-vector mosquitoes, wolves, Channel Island foxes, oak-wilt fungi, anoles, and cane toads. The goals of these projects range from determining basic demographic features, like population size and phylogeography, to estimating patterns of genetic diversity, detecting selection and mapping behavioral and morphological traits.
The explosion of population genomic data from non-model organisms, made possible by techniques like RADseq, has outstripped our understanding of genomic evolution and our ability to make sense of the data. To bridge this gap, we are combining experimental evolution in yeast with next-generation sequencing. Here we can control population sizes, migration rates, strengths of selection, recombination rates, and amount and structure of standing genetic variation in replicated experiments, and observe the results of evolution at the genomic sequence level. The goal is to improve our powers of inference about natural populations from population genomic data.
Evolutionary genomic approaches have powerful applications to conservation of species and ecosystems.
We are collaborating with a number of researchers to improve techniques for developing large sets of
genetic markers, assess phylogeographic structure, detect hybridization and introgression, and estimate
patterns of genetic variation in natural populations.
In addition, we are collaborating on a genomic study of the evolution and epidemiology of Tasmanian devil facial tumor disease, a unique transmissible cancer, that poses a serious threat of extinction for this iconic species. Our goal is to use RADseq and other genomic techniques to identify genetic variation in devil populations associated with disease progression, so that devil populations could potentially be managed using genomic techniques to reduce spread of the disease.
Members of the lab are involved in several projects developing novel theory, analytical tools, or simulation approaches to understand evolutionary processes. For example, we are studying the relationship between genetic regulatory networks and the structure of variation in complex multivariate phenotypes (i.e. the M and G matrices of quantitative genetics). The long-term goal of this work is to link models of network evolution to specific regulatory networks in yeast, where we can empirically test the model predictions. Second, we have developed a novel method for estimating the dimensionality of evolution that is widely applicable to mate choice and reproductive isolation, local adaptation, host-parasite coevolution, and other scenarios.