Abstract
Next generation sequencing has become a common tool in the diagnosis of genetic diseases. However, for the vast majority of genetic variants that are discovered, a clinical interpretation is not available. Variant effect mapping allows the functional effects of many single amino acid variants to be characterized in parallel. Here, we combine multiplexed functional assays with machine learning to assess the effects of amino acid substitutions in the human intellectual disability-associated gene, GDI1. We show that the resulting variant effect map can be used to discriminate pathogenic from benign variants. Our variant effect map recovers known biochemical and structural features of GDI1 and reveals additional aspects of GDI1 function. We explore how our functional assays can aid in the interpretation of novel GDI1 variants as they are discovered, and to re-classify previously observed variants of unknown significance.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Updated data analysis and author list.
https://github.com/RachelSilverstein/tileseqMave/blob/master/R/legacy2.R