Differential expression analysis uses well-established tools (limma, edgeR, DESeq2) to compare conditions. However, it is unclear how these tools perform in difficult scenarios such as strong sample imbalance. Here, we study differences between cancer subtypes comparing genetic ancestries, where existing omics datasets are heavily biased towards Europeans. While the portability of genotype-phenotype relationships in genomics data has been analyzed in depth using polygenetic risk scores, similar analyses are lacking in transcriptomics or epigenomics.
We developed a differential comparison approach that accounts for ancestry-related sample imbalance. This approach uses meta-analysis of randomly drawn European subsets to obtain interaction effects between cancer subtypes and genetic ancestry. Based on simulated and permuted data, we show that this approach resulted in reliable FDR control under strong sample imbalance, while retaining sensitivity compared to standard approaches. Applied to TCGA data, our approach revealed robust ancestry-specific cancer pathways but to a smaller extent than expected from baseline differences between ancestries. Our approach further enabled us to assess portability of predictive models from Europeans to other ancestries. This revealed that, while model loss was increased in other ancestries, classification performance remained largely unchanged, suggesting that these models relied on features without ancestry-specific effects.
In summary, we provide a computational framework to robustly identify ancestry-specific cancer effects and to evaluate prediction performance across ancestries, applicable to various multi-omics data.

Co-authors: Natalia Nunes, Iuliia Trifonova, Arne Bathke, Georg Zimmermann, Nikolaus Fortelny