NPIF Innovation fellowship

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The recent sequencing of large numbers of individuals is giving us an unprecedented view of human genetic diversity and has kindled the hope that, by combining data from patient and control cohorts, we will be able to accurately predict which genetic variants contribute to disease in any given patient. However, although increasing volumes of genetic data have highlighted more and more statistical associations, analysis of genetic variants by themselves does not provide an understanding of mechanism, which is key to identifying variants that are potentially amenable to pharmacological intervention. In addition, the genetic architecture of disease can be complex and depend on interactions between specific variants. A variant that is harmless in one individual, might be pathogenic when combined with a second variant in another individual. Conversely, even variants normally considered as very harmful might be present in some individuals (recently described in the press as “mutant superheroes”) without obvious effects because they are masked by compensating mutations elsewhere in the genome. Understanding this combinatorial complexity and predicting variant impact not only at the population level but also for specific individuals is a key challenge for personalized medicine. At the same time, understanding variant interactions provides an opportunity as it may highlight pathways for intervention away from the site of a focal mutation. Here, we propose to combine large-scale human variation data with structural bioinformatics to move us closer to an understanding of the molecular determinants of pathogenicity and how it is influenced by interactions between genetic variants, with the explicit aim to reveal novel opportunities for pharmacological intervention. Building on a successful collaboration between the Warnecke and Marsh labs, in which we used a force field approach to reveal the differential impact of mutations on polymer formation in sickle cell anaemia, we will carry out molecular dynamic simulations to characterize the joint structural impact of variants in haemoglobin proteins, including known disease and compensatory variants and all variant combinations found in current human sequencing data (>>100,000 individuals). Globin disorders are an ideal model system to develop a structural understanding of joint variant impact because we have several high- resolution structures of haemoglobin in both oxygenated and deoxygenated states that can be used as templates for modelling and the relationship between structure and function has been characterized in exquisite detail to allow functional interpretation of a given mutation. Making use of high performance computing resources at Imperial College London that allow comprehensive exploration, this strategy will enable identification of variants that are predicted to yield pathogenic effects in one genetic context but not in another and those that are present in the human population despite being predicted to cause disease, seeding the discovery of novel and potentially actionable compensatory mutations elsewhere in the genome, which we will pursue by integrating protein-protein and co-expression with structural data. In summary, we propose to develop an integrated structural bioinformatics approach to assess the combined impact of genetic variants, which will not only allow personalized predictions of pathogenicity but be critical for informed pursuit of novel targets for pharmacological intervention.