Summary
The ability to identify damaging genetic variants is central to the diagnosis, treatment and prevention of human disease. Computational phenotype predictors are widely used for prioritising likely pathogenic mutations, but their utility is limited by their accuracy. Conversely, experimental characterisation of variants is powerful but time consuming and difficult to perform on a large scale, limiting applicability in routine variant prioritisation. In this project, we will improve our ability to identify pathogenic variants through a combination of computational and experimental approaches. Fundamental to our strategy will be our consideration of alternate molecular mechanisms by which mutations can cause disease, in contrast to the current overwhelming focus on loss-of-function.
First, we will use structural bioinformatics to investigate the different molecular mechanisms underlying pathogenic mutations, and learn how they are related to protein structure and phenotype. Next, we will perform deep mutational scanning (DMS) on at least 10 human disease genes, enabling us to measure fitness and elucidate molecular mechanisms for all possible single amino-acid substitutions. This will facilitate the direct identification of novel pathogenic variants, and allow us to evaluate the performance existing computational phenotype predictors. Finally, we will implement our own computational variant prioritisation pipeline and meta-predictor, using our new understanding of molecular mechanisms to integrate computational phenotype and stability predictors and DMS data with structural and other protein-level features. Crucially, we will demonstrate the utility of our approach in application to sequencing data from clinical and population cohort studies. Together, the knowledge we learn, the experimental data we measure, and the tools we develop will improve our ability to identify novel pathogenic variants, and thus diagnose human genetic disorders.
First, we will use structural bioinformatics to investigate the different molecular mechanisms underlying pathogenic mutations, and learn how they are related to protein structure and phenotype. Next, we will perform deep mutational scanning (DMS) on at least 10 human disease genes, enabling us to measure fitness and elucidate molecular mechanisms for all possible single amino-acid substitutions. This will facilitate the direct identification of novel pathogenic variants, and allow us to evaluate the performance existing computational phenotype predictors. Finally, we will implement our own computational variant prioritisation pipeline and meta-predictor, using our new understanding of molecular mechanisms to integrate computational phenotype and stability predictors and DMS data with structural and other protein-level features. Crucially, we will demonstrate the utility of our approach in application to sequencing data from clinical and population cohort studies. Together, the knowledge we learn, the experimental data we measure, and the tools we develop will improve our ability to identify novel pathogenic variants, and thus diagnose human genetic disorders.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101001169 |
| Start date: | 01-06-2021 |
| End date: | 31-05-2026 |
| Total budget - Public funding: | 2 000 000,00 Euro - 2 000 000,00 Euro |
Cordis data
Original description
The ability to identify damaging genetic variants is central to the diagnosis, treatment and prevention of human disease. Computational phenotype predictors are widely used for prioritising likely pathogenic mutations, but their utility is limited by their accuracy. Conversely, experimental characterisation of variants is powerful but time consuming and difficult to perform on a large scale, limiting applicability in routine variant prioritisation. In this project, we will improve our ability to identify pathogenic variants through a combination of computational and experimental approaches. Fundamental to our strategy will be our consideration of alternate molecular mechanisms by which mutations can cause disease, in contrast to the current overwhelming focus on loss-of-function.First, we will use structural bioinformatics to investigate the different molecular mechanisms underlying pathogenic mutations, and learn how they are related to protein structure and phenotype. Next, we will perform deep mutational scanning (DMS) on at least 10 human disease genes, enabling us to measure fitness and elucidate molecular mechanisms for all possible single amino-acid substitutions. This will facilitate the direct identification of novel pathogenic variants, and allow us to evaluate the performance existing computational phenotype predictors. Finally, we will implement our own computational variant prioritisation pipeline and meta-predictor, using our new understanding of molecular mechanisms to integrate computational phenotype and stability predictors and DMS data with structural and other protein-level features. Crucially, we will demonstrate the utility of our approach in application to sequencing data from clinical and population cohort studies. Together, the knowledge we learn, the experimental data we measure, and the tools we develop will improve our ability to identify novel pathogenic variants, and thus diagnose human genetic disorders.
Status
SIGNEDCall topic
ERC-2020-COGUpdate Date
27-04-2024
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