Functional interpretation of cancer-associated genetic variants

Genome-wide association studies have hitherto identified several common genetic variants that may significantly modulate cancer susceptibility. However, the precise molecular mechanisms behind these associations remain largely uncharacterized, creating barriers to understanding the biological processes behind oncogenesis. This thesis presents an integrated computational method for identifying functional regulatory variants associated with cancer and for revealing their precise gene-regulation role by combining analyses of heterogeneous high-throughput sequencing data. Application of the method to breast cancer susceptibility regions reveals functional variants and their perturbation on cis-regulatory elements that act on cancer-associated genes. It is also shown that a cancer-associated variant may interact with the tumor microenvironment. Overall, these computational methods built on multi-omics data have helped move toward the goal of understanding the genotype-phenotype association for personalized medicine