Fig. 4

Unweighted PINES scores improve the prioritization of noncoding variants. a PINES improves the prioritization of variants residing in experimentally validated enhancer regions. The AUROC values (red) were computed by selecting 20,000 background variants as negative examples, and the variants residing in enhancer loci as positive examples. Based on AUROC values, the unweighted PINES approach performs at least as well as GWAVA, Eigen-PC, CADD, DANN, LINSIGHT, GenoCanyon, and FATHMM-MKL in its ability to pinpoint enhancer variants. b PINES delivers improved statistical power to identify functional noncoding variants detected by a massively parallel reporter assay. The AUROC values (red) were computed by selecting 20,000 background variants as negative examples, and the reported functional variants as positive examples. PINES achieves better classification accuracy than the other methods, outperforming GWAVA, Eigen-PC, CADD, DANN, LINSIGHT, GenoCanyon, and FATHMM-MKL in its ability to detect the functional variants