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 large numbers of single amino acid variants to be characterized in parallel. Here, we employ a variant effect mapping framework, combining functional assays with machine learning, to assess the effects of 89% of all possible 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 at levels of precision higher than those achieved by current computational prediction tools. Our variant effect map recovers known biochemical and structural features of GDI1 and reveals new structural regions which may be important for GDI1 function. We explore how this variant effect map can be used to aid in the interpretation of novel GDI1 variants as they are discovered, and to re-classify previously observed variants of unknown significance.
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