Enhancer/gene relationships: need for more reliable genome-wide reference sets

Differences in cells’ functions arise from differential action of regulatory elements, in particular enhancers. Like promoters, enhancers are genomic regions bound by transcription factors (TF) that activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. As there is increasing evidence that variants associated with common diseases are located in enhancers active in cell types relevant to these diseases, knowing the set of enhancers and more importantly the sets of genes activated by each enhancer (the so-called enhancer/gene or E/G relationships) in a cell type, will certainly help understanding these diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: (1) genetic link methods or eQTL, (2) functional link methods based on 1D functional data such as open chromatin, histone mark and gene expression and (3) spatial link methods based on 3D data such as HiC. Since (1) and (3) are costly, there has been a focus on developing functional link methods and using data from (1) and (3) to evaluate them, however there is still no consensus on the best functional link method to date. For this reason we decided to start from the two latest benchmarks of the field, namely from the CRISPRi-FlowFISH ( CRiFF ) technique and from 3D and eQTL data in BENGI , and to evaluate the two methods claimed to be the best one on each of these benchmark studies, namely the ABC model and the Average-Rank method respectively, on the other method’s reference data. Not only did we manage to reproduce the results of the two benchmarks but we also saw that none of the two methods performed best on the two reference data. While CRiFF reference data are very reliable, it is not genome-wide and is mostly available on a cancer cell type. On the other hand BENGI is genome-wide but may contain many false positives. This study therefore calls for new reliable and genome-wide E/G reference data rather than new functional link E/G identification methods.