Data-driven bioinformatic approaches for predicting genetic variant effects on gene expression and disease risks

This thesis studies how genetic variants affect epigenetic states and gene activity. It develops two computational methods to predict how changes in DNA influence gene function. These methods identify the potential effects of genetic variants, including rare variants, across different cell types and disease conditions. The approach avoids the need for time-consuming and costly laboratory experiments. This work improves our understanding of how genetic variation contributes to disease and supports more efficient assessment of disease risk in precision medicine.