L'effetto delle varianti genetiche sulle malattie complesse

We try to find associations between genetic variants and diseases through a method called transcriptome- wide association study (TWAS), which focuses on genetic variants that alter the expression of a gene. A basic ingredient of a transcriptome-wide association study is a regression model, typically trained on an external reference data set, used to predict gene expression. Then, in a second data set, the predicted expression is associated to the disease. We devise a regression model that improves the accuracy of the prediction of gene expression from the DNA sequence compared with existing models. The greater accuracy in the prediction of gene expression allows us to find new gene-disease association candidates. We also extend a method to perform the TWAS when not all the data, but just some summary statistics from genome-wide association studies are available. Finally, in an effort to further understand the effect of gene expression on the disease, as well as to model how the genes interact among themselves, we use the results from our predictive model to reconstruct a complex network of genes, and propose a new method to predict the effect of a genetic mutation on the steady-state expression levels.