Summary
How can we accurately measure and predict genetic load? In SV-load, I aim to understand how an often overlooked, but high impact class of genomic variation contributes to genetic load. Structural variants (SVs) have been shown to have significant implications for individual and population fitness and impact more genomic content overall than traditional whole-genomic markers (i.e., single nucleotide polymorphisms, SNPs). However, whole-genome SVs are underrepresented in population studies as they have historically been difficult to characterise and genotype at the population-scale. As a result, the dynamics of SV diversity in the face of fundamental evolutionary processes (e.g., selection, recombination, and population size) is relatively unresolved and hinders the study of genetic load across disciplines. To address this gap, I will implement in-silico approaches to forecast and experimentally test how SVs respond to different demographic parameters in a non-model species (Coelopa frigida). During my PhD and first postdoc I honed the sequencing and bioinformatic skills necessary to study genome-wide SVs at the population-scale. I will bring this expertise to the host lab of Dr. Mérot and Dr. Glémin at the Université de Rennes (UR, France), experts in the field of evolutionary biology and ecology. Training with the host lab, and a secondment with A/Prof Bataillon at Aarhus University (AU, Denmark) will broaden my modelling and experimental skills. Through these collaborations, I will learn new multidisciplinary approaches in modelling, statistical genomics and experimentation. This project will not only complement my existing skills but enable me to gain a competitive advantage in establishing my European career in the exciting and dynamic field of evolutionary biology. Finally, this project will aid in advancing our understanding of genetic load, which remains a fundamental challenge for biologists in the agriculture, conservation, and evolution research sectors.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101151470 |
| Start date: | 01-05-2025 |
| End date: | 30-04-2027 |
| Total budget - Public funding: | - 195 914,00 Euro |
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Original description
How can we accurately measure and predict genetic load? In SV-load, I aim to understand how an often overlooked, but high impact class of genomic variation contributes to genetic load. Structural variants (SVs) have been shown to have significant implications for individual and population fitness and impact more genomic content overall than traditional whole-genomic markers (i.e., single nucleotide polymorphisms, SNPs). However, whole-genome SVs are underrepresented in population studies as they have historically been difficult to characterise and genotype at the population-scale. As a result, the dynamics of SV diversity in the face of fundamental evolutionary processes (e.g., selection, recombination, and population size) is relatively unresolved and hinders the study of genetic load across disciplines. To address this gap, I will implement in-silico approaches to forecast and experimentally test how SVs respond to different demographic parameters in a non-model species (Coelopa frigida). During my PhD and first postdoc I honed the sequencing and bioinformatic skills necessary to study genome-wide SVs at the population-scale. I will bring this expertise to the host lab of Dr. Mérot and Dr. Glémin at the Université de Rennes (UR, France), experts in the field of evolutionary biology and ecology. Training with the host lab, and a secondment with A/Prof Bataillon at Aarhus University (AU, Denmark) will broaden my modelling and experimental skills. Through these collaborations, I will learn new multidisciplinary approaches in modelling, statistical genomics and experimentation. This project will not only complement my existing skills but enable me to gain a competitive advantage in establishing my European career in the exciting and dynamic field of evolutionary biology. Finally, this project will aid in advancing our understanding of genetic load, which remains a fundamental challenge for biologists in the agriculture, conservation, and evolution research sectors.Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
17-11-2024
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