Affiliation:
1. Tata Consultancy Services
Abstract
Abstract
Variants of uncertain significance (VUS) represent variants that lack sufficient evidence to be confidently associated with a disease thus posing challenge in interpretation of genetic testing results. In this work, we present an improved gene specific approach to variant prediction that leverages a pre-trained protein language model for predicting VUS. Our deep learning model combines zero-shot log odd scores from evolutionary scale model (ESM-2) as a feature along with embeddings from ESM-2 as features for training a supervised model on variants associated with the gene. Our training set creation approach uses variant data from a gene family if the gene of interest has low or no functional data for training a gene specific predictor. We demonstrate the accuracy of our method by testing it on VUS of an enzyme NAGLU (Alpha-N-acetylglucosaminidase) whose deficiency due to mutations is known to cause a rare genetic disorder, Mucopolysaccharidosis IIIB or Sanfillipo B disease. Our model augmented with contextual information from the gene family improves prediction of VUS in the NAGLU gene and outperforms state-of-the-art pathogenicity predictors. Our results also indicate that genes that have sparse or no experimental variant impact data, the family variant data can serve as a proxy training data for making accurate predictions.
Publisher
Research Square Platform LLC