Germline genetic contribution to metabolic pathways in cancer

Abstract

Cancer research is essential in improving cancer prevention, detection, and treatment. The analysis of cancer genomes helps uncover gene abnormalities that cause the emergence and spread of many types of cancer. While many studies have investigated various landscapes of cancer, the role of inherited genetic mutations is primarily unexplored. In this work, we studied the genetic variations affecting metabolic pathways in cancer from the SNP-level, gene-level, and pathway-level aspects. First, we identified the significant SNPs and genes associated with metabolic traits. Then we introduced A-LAVA to perform gene set analysis and detect the most significant gene sets associated with the target traits. A-LAVA is a competitive gene set analysis approach that resolves the bias resulting from overlapping gene sets, as a potential confounding effect, in addition to other standard corrections performed in current methods. We also showed that accounting for the shared genes present in the gene sets is essential for any gene set analysis approach when there is an overlap between gene sets, as it remarkably affects the results.