Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals

(STable 1) Summary of publicly available datasets used to train the CNN models of human pancreatic islet epigenomic features. Where indicated, the original raw data was reprocessed with the default setting of either the ATAC-seq/DNase-seq pipeline (available from: https://github.com/kundajelab/atac_dnase_pipelines), or the AQUAS TF and histone ChIP-seq pipeline (available from: https://github.com/kundajelab/chipseq_pipeline), using the human genome GRCh37 as reference. (STable 2) Tested sets of CNN hyperparameters. Convolutional neural networks with each set of hyperparameters differing in numbers and sizes of convolutional filters were trained 100 times, for a total of 1000 CNNs trained. (STable3) Full list of transcription factor binding motifs with <5% FDR sequence match to motifs activating convolutional filters from the first layers of the 1000 CNN ensemble. No motif redundancy removal was applied here. (STable 4) CNN regulatory predictions at 28 T2D association signals fine-mapped to a single most likely causal variant with genetic PPA (gPPA) > = 0.80 or functional PPA (fPPA) > = 0.80. (STable 5) CNN regulatory predictions at signals with at least two variants with functional PPAs (fPPAs) > = 0.2. These are the signals where incorporating CNN predictions downstream of fine-mapping can yield the largest benefits. The table lists all the variants with fPPA > = 0.05 at these signals, together with their CNN q-value (lowest_Q), and the corresponding top scoring CNN feature and the mean predicted score difference across the 1000 trained models. (STable 6) Primer sequences used for cloning of the Prox1 enhancer. Prox1_enhancer_Forward (Reverse)_internal were designed for sequence validation. Restriction enzymes NheI and XhoI were used for all subsequent cloning. SDM = site directed mutagenesis.