GRAM: A GeneRAlized Model to predict the molecular effect of a non-coding variant in a cell-type specific manner.There has been much effort to prioritize genomic variants with respect to their impact on "function".  Here, we coupled multiple genomic predictors to build GRAM, a GeneRAlized Model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant on its associated gene, in a transferable, cell-specific manner.  Second, we implemented GRAM integrating only SELEX features and expression profiles; thus, the program combines a universal regulatory score with an easily obtainable modifier reflecting the particular cell type.  We benchmarked GRAM on large-scale MPRA datasets, achieving AUROC scores of 0.72 in GM12878 and 0.66 in a multi-cell line dataset.  We then evaluated the performance of GRAM on targeted regions using luciferase assays in the MCF7 and K562 cell lines.  Finally, we illustrated the utility of GRAM in fine-mapping causal variants and developed a practical software pipeline to carry this out. 
GRAM