A predictive model of diversification and convergence of colour patterns in East African cichlid fishes
How does morphological innovation arise and evolve to create the vast diversity of forms in the natural world? This is a fundamental topic in evolutionary biology, and yet long-standing questions remain, particularly regarding (1) the genomic and developmental mechanisms underlying trait evolution, and (2) how these mechanisms shape trait evolutionary trajectories. To solve these problems, our research uses the dramatic morphological diversity of cichlid fishes to study egg-spots – a sexually selected colour pattern that shows remarkable diversity within and between species. First, we will use a combination of powerful genetic mapping methods (GWAS and QTL) and functional genetics tools to identify and validate candidate genes underlying egg-spot variation across dozens of cichlid species (Aim 1). We will then leverage cutting-edge spatial and single-cell transcriptomics to reveal the underlying developmental programs (Aim 2). The application of spatial transcriptomics is particularly exciting, allowing us to precisely map all gene expression in the native tissue context, offering unprecedented insights into the molecular and developmental mechanisms behind colour pattern formation. Such detailed knowledge of genomic and developmental mechanisms will enable us to develop a data-driven mathematical model that goes beyond the traditional descriptive approaches in evo-devo, creating a predictive framework for understanding trait development and evolution (Aim 3). Importantly, our model will: (1) assess how genomic and developmental mechanisms constrain or promote certain evolutionary trajectories; and (2) predict the mechanisms underlying trait variation across East African Cichlid species, including those lacking empirical data. Taken together, our integrative approach will reveal how genomic and developmental systems shape trait evolution and provide a new conceptual framework for understanding and predicting the mechanisms underlying organismal diversity.
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Imported from OpenAIRE United Kingdom | Grant 101231577.
Open sourceOverview
Grant agreement 101231577 | PredictingSPOTS
Open sourceHow does morphological innovation arise and evolve to create the vast diversity of forms in the natural world? This is a fundamental topic in evolutionary biology, and yet long-standing questions remain, particularly regarding (1) the genomic and developmental mechanisms underlying trait evolution, and (2) how these mechanisms shape trait evolutionary trajectories. To solve these problems, our research uses the dramatic morphological diversity of cichlid fishes to study egg-spots – a sexually selected colour pattern that shows remarkable diversity within and between species. First, we will use a combination of powerful genetic mapping methods (GWAS and QTL) and functional genetics tools to identify and validate candidate genes underlying egg-spot variation across dozens of cichlid species (Aim 1). We will then leverage cutting-edge spatial and single-cell transcriptomics to reveal the underlying developmental programs (Aim 2). The application of spatial transcriptomics is particularly exciting, allowing us to precisely map all gene expression in the native tissue context, offering unprecedented insights into the molecular and developmental mechanisms behind colour pattern formation. Such detailed knowledge of genomic and developmental mechanisms will enable us to develop a data-driven mathematical model that goes beyond the traditional descriptive approaches in evo-devo, creating a predictive framework for understanding trait development and evolution (Aim 3). Importantly, our model will: (1) assess how genomic and developmental mechanisms constrain or promote certain evolutionary trajectories; and (2) predict the mechanisms underlying trait variation across East African Cichlid species, including those lacking empirical data. Taken together, our integrative approach will reveal how genomic and developmental systems shape trait evolution and provide a new conceptual framework for understanding and predicting the mechanisms underlying organismal diversity.