SUPR
Host Plant Driven Speciation in Tephritis conura
Dnr:

NAISS 2024/6-4

Type:

NAISS Medium Storage

Principal Investigator:

Anna Runemark

Affiliation:

Lunds universitet

Start Date:

2024-01-26

End Date:

2025-02-01

Primary Classification:

10615: Evolutionary Biology

Secondary Classification:

10609: Genetics (medical to be 30107 and agricultural to be 40402)

Allocation

Abstract

One major challenge in evolutionary biology is to understand the genomic basis of ecological divergence and speciation. We can now map species divergence across the genome, but we lack knowledge on the importance of coding divergence vs. regulation of expression, and of interactions between genes, for speciation. C ombining cutting-edge sequencing techniques with experimental approaches I will address two central questions in speciation: 1/how do associations between ecological and reproductive isolation traits arise and 2/how does gene expression interact with coding divergence during ecological speciation? My study species, the peacock fly Tephritis conura has both host plant specialist- and oligophagous generalist populations which enables detailed studies across different time points along the speciation continuum. I will address how correlational selection creates genetic coupling between traits by comparing specialists and generalists. Genomic approaches will be combined with crossing experiments (F2) to study how trait associations are built up and maintained. I will also address how regulation of gene expression contributes to host adaptation. C ombining coding divergence with gene expression profiles of specialists and generalists on native-and non-native host plants will reveal if the same genes that are differentially expressed also accumulate coding sequence divergence. These different lines of inuquiry will reveal how genetic architecture evolves during speciation. This project is funded by a Swedish Research Council Establishment grant. I am an associate senior lecturer and docent at Lund University. We are currently using 85% of our 30TB and expect two data sets that are equal in size to these we already work with, and will hence apply to increase storage.