Genome interpretation using in silico predictors of variant impact

Abstract

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Fig. 1

Number of citations to the primary paper of variant prediction methods as a function of the year it was published. The number of citations were obtained by Google Scholar search on the 7th of March 2022. When methods could be matched to multiple primary papers or newer versions were introduced, the paper with the most citations was used here. Methods are classified as (i) analytical models not trained on available variant annotations (red color), (ii) machine learning approaches trained on variant annotations (blue color), (iii) ensemble models that integrate scores from available predictors (purple color), and (iv) models that combine scores from available predictors and additional features (black color)