Genomics of diversity and adaptation in great tits (Parus major)

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This evolutionary genetics project will use state-of-the-art genomics tools to study signatures of natural selection in an ecological model organism, the great tit (Parus major). The study will focus on a complied dataset, where more than 500,000 single nucleotide polymorphism (SNP) markers have been typed in >2000 birds, from more than 20 different great tit populations across Europe and Asia. The PhD project has several objectives: First, the genetic structure of great tit populations will be described, to gain insight into the evolutionary history of contemporary populations. Second, genomic regions of high divergence will be identified, to infer how local adaptation has occurred. Finally, the causes of local adaptation will be investigated by identifying which phenotypic traits are affected by regions of the genome that have undergone adaptive evolution by natural selection. The research project combines state-of-the-art genomics technology with painstaking ecological studies of wild birds, to address fundamental topics in evolutionary genetics - the genetic architecture of quantitative traits, the maintenance of genetic variation in the face of selection, and the genetic basis to local adaptation. In many ways the project can be thought of as an ecological equivalent of the famous human HapMap genome projects, with the aim to produce a great tit HapMap, to first describe a wild vertebrate. This bioinformatics research will make use of a collaboration network of ecologists, geneticists and genome biologists studying great tit populations across the species entire distribution. The primary supervisor Jon Slate collaborates on great tit population genetics with Kai Zeng (Sheffield), Ben Sheldon (Oxford), Marcel Visser (Netherlands Institute of Ecology) and Martien Groenen (Wageningen University). The research groups regularly exchange resources, datasets and ideas. In addition, the co-supervisor, Kanchon Dasmahaptara has considerable expertise in using next-generation genomics datasets to understand gene flow, adaptation, speciation and adaptive introgression in lepidoptera, especially in Heliconius butterflies.