Publicação
Predictability of genomic evolution of populations with contrasting initial history
| Resumo: | Predicting genomic evolution, which is influenced by selection, genetic drift, and population history, is a central question in evolutionary biology. This study investigates the genetic basis of adaptation in Drosophila subobscura populations that were initially collected from two contrasting European latitudes and years, and subsequently underwent independent lab adaptation. The goal was to test the predictability of evolution at the genomic level, complementing previous research done on phenotypic changes of those populations. First approach, I utilised the latest tools and reference genome to recreate existing analysis pipelines. I observed some evidence of convergent and parallel evolution, distinct from the major signal composed of drift and divergence. No common candidate SNPs were observed between populations indicating low repeatability of changes at such level. I also noted major genomic changes likely due to selection pressures acting on chromosomal inversion frequencies (CIF) across populations. After that analysis, I developed custom analysis tools, CAR (coverage adjusted rho) and CDD (convergence divergence detection), which analyse more nucleotide data points and differentiate between parallel, convergent, and divergent selection. This approach confirmed indication of changes in CIF and identified selection peaks across populations, even though the peak regions could not be functionally annotated. Most notably, I observed large genomic regions showing convergence - likely associated with inversions - while divergence was widespread across the genome, likely as a result of genetic drift. I also used these algorithms to successfully detect a component of parallel evolution, when comparing populations. An ontological functional analysis of candidate regions did not reveal similar motifs across populations, suggesting that predictability in evolution might be partially limited to larger structures like inversions. Future research with higher coverage data and more comprehensive genomic annotation may provide deeper insights into the underlying mechanisms of genomic evolution, thereby enhancing the predictability of such changes. |
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| Autores principais: | Barreira, Elias Miguel da Costa |
| Assunto: | Previsibilidade da Evolução Repetibilidade da Evolução Evolução Convergente Evolução Paralela Evolução Experimental Teses de mestrado - 2024 |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | Predicting genomic evolution, which is influenced by selection, genetic drift, and population history, is a central question in evolutionary biology. This study investigates the genetic basis of adaptation in Drosophila subobscura populations that were initially collected from two contrasting European latitudes and years, and subsequently underwent independent lab adaptation. The goal was to test the predictability of evolution at the genomic level, complementing previous research done on phenotypic changes of those populations. First approach, I utilised the latest tools and reference genome to recreate existing analysis pipelines. I observed some evidence of convergent and parallel evolution, distinct from the major signal composed of drift and divergence. No common candidate SNPs were observed between populations indicating low repeatability of changes at such level. I also noted major genomic changes likely due to selection pressures acting on chromosomal inversion frequencies (CIF) across populations. After that analysis, I developed custom analysis tools, CAR (coverage adjusted rho) and CDD (convergence divergence detection), which analyse more nucleotide data points and differentiate between parallel, convergent, and divergent selection. This approach confirmed indication of changes in CIF and identified selection peaks across populations, even though the peak regions could not be functionally annotated. Most notably, I observed large genomic regions showing convergence - likely associated with inversions - while divergence was widespread across the genome, likely as a result of genetic drift. I also used these algorithms to successfully detect a component of parallel evolution, when comparing populations. An ontological functional analysis of candidate regions did not reveal similar motifs across populations, suggesting that predictability in evolution might be partially limited to larger structures like inversions. Future research with higher coverage data and more comprehensive genomic annotation may provide deeper insights into the underlying mechanisms of genomic evolution, thereby enhancing the predictability of such changes. |
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