Opportunities and Challenges with Artificial Intelligence in Genomics

VUS pose a particular challenge to clinicians, and often the interpretation of these variants changes as new data are uncovered. As a classification question in a large and complex data set, this is an excellent application for AI-based approaches. To that end, many algorithms have been developed that predict the deleteriousness of variants based on biophysical properties, such as PolyPhen-2, SIFT, and PROVEAN. The categorization of VUS can be achieved via supervised or unsupervised methods. Using supervised methods of machine learning, labels come from manually curated mutations and/or experiments. Unsupervised methods, however, allow for the development of classification models from unlabeled data by learning some underlying structure within the data points or integrating external information. Several supervised methods using AI/ML have been developed to determine the likelihood of a variant being deleterious. DEOGEN2, for example, incorporates information about the molecular effects, involved domains, gene relevance, and gene interactions. This information is then “mapped” into a deleteriousness score for the variant. This tool is a predictor for missense SNVs for human proteins. It uses evolutionary-based features, prediction of early folding protein residues, features related to protein domains, interaction patches, and gene- and pathway-oriented features. This model achieved comparable performance with other published predictors in the Humsavar16 dataset. High-throughput experiments have been developed that can evaluate thousands of variants simultaneously, called multiplexed assays of variant effects. ^, These high-throughput sequencing assays interrogate the effects of various variant types and allow for massive scalability of functional assays. Historically, most functional assays have been reactive, wherein a variant will surface clinically, then a functional assay will be performed to explore the effects of the variant. However, given the vast number of uncharacterized variants that exist, and with current computing power that is available, it becomes attractive to take a more proactive experimental approach. Evolutionary model of variant effect (EVE) is an example of unsupervised learning in the classification of variants. EVE classifies human genetic variants solely on evolutionary sequences, looking at the distribution of sequence variation across different species to determine the likelihood of pathogenicity in humans. The model outperforms other state-of-the-art computational methods of variant classification. In fact, EVE has been found to be as accurate as high-throughput experiments in variant classification. The model was able to predict the pathogenicity of more than 36 million variants, including evidence for the classification of more than 256,000 VUS.