Assessing concordance among human, in silico predictions and functional assays on genetic variant classification.

Motivation: A variety of in silico tools have been developed and frequently used to aid high-throughput rapid variant classification, but their performances vary, and their ability to classify variants of uncertain significance were not systemically assessed previously due to lack of validation data. This has been changed recently by advances of functional assays, where functional impact of genetic changes can be measured in single-nucleotide resolution using saturation genome editing (SGE) assay. Results: We demonstrated the neural network model AIVAR (Artificial Intelligent VARiant classifier) was highly comparable to human experts on multiple verified datasets. Although highly accurate on known variants, AIVAR together with CADD and PhyloP showed non-significant concordance with SGE function scores. Moreover, our results indicated that neural network model trained from functional assay data may not produce accurate prediction on known variants.