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

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
effect prediction tools. Second, the resulting models are interpretable, thereby illuminating
variants' mode-of-action. Application of TRACEvar to variants of 52 rare-disease patients
highlighted pathogenicity mechanisms and relevant disease processes. Lastly, interpretation
of large-scale models revealed that top-ranking determinants of pathogenicity included
attributes of disease-affected tissues, particularly cellular process activities. Hence, tissue
contexts and interpretable machine-learning models can greatly enhance the etiology of rare
diseases.

Article link: https://www.embopress.org/doi/full/10.1038/s44320-024-00061-6