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

ABSTRACT:

Background

Machine learning and artificial intelligence are increasingly being applied to identify phenotypically causal genetic variation. These data-driven methods require comprehensive training sets to deliver reliable results. However, large unbiased datasets for variant prioritization and effect predictions are rare as most of the available databases do not represent a broad ensemble of variant effects and are often biased towards the protein-coding genome, or even towards few well-studied genes.

Methods

To overcome these issues, we propose several alternative training sets derived from subsets of human standing variation. Specifically, we use variants identified from whole-genome sequences of 71,156 individuals contained in gnomAD v3.0 and approximate the benign set wi

SUBMITTER: Nazaretyan L 

PROVIDER: S-EPMC12323237 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature