Additional Supervisor(s):
Vicencio Oostra (QMUL)
Project Description:
Elucidating the processes underlying adaptive maintenance of variation within species is a fundamental problem in evolutionary biology. Balancing selection is a key driver of adaptive evolution that maintains variation within and across species, underlying a breadth of biological mechanisms, such as immune defense, metabolic function, intralocus sexual conflict, and host-parasite coevolution. Yet, little is known about recent or fleeting balancing selection because its genomic footprints are difficult to distinguish from those of neutral evolution.
We have recently shown that our new methods allow us to identify recent selection in genomes. Building on this work, the objective of this project is to extend and develop a suite of computational tools, including using machine learning, for studying recent and transient balancing selection from temporally and spatially distributed genomic data across a diverse range of species.
The developed computational tools will be deployed to genomes of African Bicyclus butterflies. Some species inhabit strongly seasonal savannahs (with large selection differences between dry and wet seasons) and other species inhabit aseasonal rainforests (with constant selection pressures). Invasion of savannahs by rainforest species has independently occurred several times in the last 15 million years, allowing replicated tests of the effect of seasonally fluctuating balancing selection on diversity patterns.
Depending on the student’s interests, additional data sets of Anopheles mosquitoes from central Africa and Drosophila melanogaster fruit flies globally distributed can be analysed to dissect the role of balancing selection in rapid adaptation under different conditions of data collection.
Policy Impact of Research:
This project will elucidate the genetic basis of species’ rapid adaptation to novel environments. Therefore, our findings will complement ongoing investigations of genetic resilience and adaptability in threatened species. All computational tools developed will be implemented as open-source software packages and publicly shared with manuals and sample datasets.
