varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction

Standing variation allows us to directly train state-of-the-art models for genome-wide variant prioritization or to augment evolutionary-derived variants in training. The proposed datasets have several advantages, like being substantially larger and potentially less biased. Datasets derived from standing variation represent natural allelic changes in the human genome and do not require extensive simulations and adaptations to annotations of evolutionary-derived sequence alterations used for CADD training. We provide datasets as well as trained models to the community for further development and application.