Computational analysis of protein covariation for the identification of disease-associated variants in coding regions

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Attaining Good Health and Well-Being for everyone are the main goals of very diverse initiatives. Local and global Institutions are targeting a reduction in mortality and an improvement of standard of care for the forthcoming years. A key element in achieving these goals is the development of better diagnostics tools; i.e., moreaccurate diagnoses may mean more precise treatments with fewer secondary effects.Because most of the human diseases have a genetic factor, it is essential that we improve the computational methods for precisely identifying the genome variants that cause congenital disorders (e.g. cystic fibrosis), or increase the risk of suffering multifactorial diseases such as type-2 diabetes.Current approaches for discovery of disease-causing variants and for genetic diagnostics focus in the identification of genome variants that 1) are present in patients, but non-existing or extremely rare in the healthy populations, and 2) are deemed detrimental based on their lack of evolutionary conservation. Both of these conditions are implicitly based on the assumption that single variants are responsible for the disease. Nevertheless, in many cases the analyses of data remain inconclusive in regards of the particular variants that cause the disorders.My hypothesis is that in many cases the disease is not caused by single variants, but by an unfavourable combination of genomic variants; that is, variants that are separately found in the human population, but that cause a harmful effect when found together in an individual. I will focus on variants within the coding genome (thepart of the genome that codifies for proteins), because proteins carry out the vast majority of biological processes within cells and tissues. First, I am going to identify which protein positions show evidence of covariation throughout evolution or withinthe human population; namely, those pairs of positions whereby changes have often occurred concurrently in order to maintain the fitness of the organism. Second, I am going to identify which combinations of amino acids are favoured within those covarying positions. Third, I am going to use this information for reanalysing genetic testing data from NHS patients suffering of cardiac or eye genetic disorders. My goal is to increase the rate of cases that can be genetically diagnosed. Finally, I will analyse sequencing data from patients suffering of Tetralogy of Fallot, which is atype of Congenital Heart Disease. This is a complex genetic disorder involving variants in various genes; however, the exact causes are unknown. I will identify cases where the disorder is caused by a harmful combination of variants. My research is going to produce a series of computational tools that will be made freely available to the academic and clinical communities. These tools will make adecisive contribution to the goal of achieving better genetic diagnoses.