GINOM: A statistical framework for assessing interval overlap of multiple genomic features

A common problem in genomics is to test for associations between two or more genomic features, typically represented as intervals interspersed across the genome. Existing methodologies can test for significant pairwise associations between two genomic intervals; however, they cannot test for associations involving multiple sets of intervals. This limits our ability to uncover more complex, yet biologically important associations between multiple sets of genomic features. We introduce GINOM (Genomic INterval Overlap Model), a new method that enables testing of significant associations between multiple genomic features. We demonstrate GINOM’s ability to identify higher-order associations with both simulated and real data. In particular, we used GINOM to explore L1 retrotransposable element insertion bias in lung cancer and found a significant pairwise association between L1 insertions and heterochromatic marks. Unlike other methods, GINOM also detected an association between L1 insertions and gene bodies marked by a facultative heterochromatic mark, which could explain the observed bias for L1 insertions towards cancer-associated genes.Author summary: The age of genomics has made a large number of datasets available for the wider scientific community. Many of these datasets come in the form of genomics tracks, represented as features associated with a collections of genomic intervals along chromosomes. A common talk in genomics is to identify putative associations between these features that can lead to new insights about genome organization and function. For example, activity of certain classes of genes might be influenced by the presence of specific combinations of chromatin modifications and binding of transcription factors at their promoters or enhancers. Here, we present a novel methodology, named GINOM (Genomic INterval Overlap Model), to test for the significance of these associations. We apply it to the problem of detecting biases of the locations along chromosomes where mobile genetic elements tend to insert themselves, and identify a potential preference for L1 elements towards gene bodies marked by a facultative heterochromatic mark.