Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

ABSTRACT: Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-learning framework, denoted 'Tissue Risk Assessment of Causality by Expression for variants' (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant

SUBMITTER: Dr Chanan, M Argov 

PROVIDER: S-SCDT-10_1038-S44320-024-00061-6 | biostudies-other |

REPOSITORIES: biostudies-other