Identification of three-dimensional (3D) interactions between regulatory 
elements across the genome is crucial to unravel the complex regulatory 
machinery that orchestrates proliferation and differentiation of cells. ChIA-PET 
is a novel method to identify such interactions, where physical contacts between 
regions bound by a specific protein are quantified using next-generation 
sequencing. However, determining the significance of the observed interaction 
frequencies in such datasets is challenging, and few methods have been proposed. 
Despite the fact that regions that are close in linear genomic distance have a 
much higher tendency to interact by chance, no methods to date are capable of 
taking such dependency into account. Here, we propose a statistical model taking 
into account the genomic distance relationship, as well as the general 
propensity of anchors to be involved in contacts overall. Using both real and 
simulated data, we show that the previously proposed statistical test, based on 
Fisher's exact test, leads to invalid results when data are dependent on genomic 
distance. We also evaluate our method on previously validated cell-line specific 
and constitutive 3D interactions, and show that relevant interactions are 
significant, while avoiding over-estimating the significance of short nearby 
interactions.