Alberta Innovates Health Solutions - Research Chair in Bioinformatics and Computational Biology
My research chair position provides generous funding to pursue a range of projects focused on understanding how complex traits evolve and how we can develop and refine the genomic methods we use to study them.
Project #1: Do clusters of functionally-related loci evolve by genomic rearrangements, as an adaptive response to heterogeneous environments?
The threespine stickleback has become a model system for studying the genetic basis of complex morphological traits. Stickleback have repeatedly colonized freshwater environments as glaciers retreated after the last ice age, and have adapted to the novel habitats they found there. Loci associated with changes in morphology and behaviour have been mapped in the genome, and often seem to cluster together in "genomic islands". The aim of this project is to ask whether these clusters evolved by genomic rearrangements over long periods of time (many millions of years). To address this question, we will sequence the genomes of other closely related species and employ a comparative genomic approach to date if and when rearrangements have contributed to the evolution of genomic islands. This will help understand whether the loci responsible for adaptation are randomly scattered throughout the genome, which has important implications for the methods we use to search for them. Our current genomic association tests typically assume randomness, so if clustering is common, we need to take this into account. This project will also involve searching for signatures of clustering and comparative genomics in other species, such as arabidopsis, sunflower, drosophila, and more...
Project #2: How can we identify characteristic signatures of local adaptation using long-read sequence data, haplotypes, and patterns of linkage disequilibrium? How much power do these methods have to distinguish selection from demography?
Disentangling how the interplay between evolutionary processes gives rise to complex patterns in the genome is a tricky, exciting, and highly relevant problem in the age of cheap(er) genomes. The aim of this project is to use a combination of analytical theory and individual-based simulations to explore how evolution works and what kinds of signals it is expected to leave in the genome. This will be combined with empirical exploration of patterns in population genomic data from plants and animals as a way of testing new methods to try to find these signatures.
Other ideas and back-burner projects:
- Reconciling quantitative genetic observations with genomic data: standing variation, deleterious mutations, and evolvability
- Convergence and parallelism as ways to study genetic redundancy and the map from genotype to phenotype
- Visualizing complex patterns in high-dimensionality data
- Machine learning as a way to retire once the AI's take over
- Cultural evolution and the maintenance of misinformation