Identification of cancer risk-associated variants and genes using asymmetric data integration

Music City Center 

Cancer genomic research provides a significant opportunity to identify cancer risk-associated genes but often suffers from undesirably low statistical power due to limited sample sizes. Integrated analysis across different cancers has the potential to enhance statistical power for identifying pan-cancer risk genes. However, substantial heterogeneity among cancers makes this challenging. We developed a novel asymmetric integration method that addresses data heterogeneity and excludes uninformative datasets from the analysis. We applied this method to integrate genotype datasets with matched case and control individuals, using each cancer type as the primary dataset of interest and treating other cancers as auxiliary datasets. At the same FDR threshold, the integrated analysis identified more potential genetic variants and genes associated with cancer risk, highlighting the promise of this approach for integrating cancer datasets.

asymmetric data integration

cancer risk-associated genetic variants and genes