A framework for exhaustively mapping functional missense variants

• DOI: 10.15252/msb.20177908
• Publication Date: 2017-12-22T01:12:12Z
• Authors: Jochen Weile; Song Sun; Atina G Cote; Jennifer Knapp; Marta Verby; Joseph C Mellor; Yingzhou Wu; Carles Pons; Cassandra Wong; Natascha van Lies...; Fan Yang; Murat Tasan; Guihong Tan; Shan Yang; Douglas M Fowler; Robert Nussbaum; Jesse D Bloom; Marc Vidal; David E Hill; Patrick Aloy; Frederick P Roth
• Keywords: 0301 basic medicine; Medicine (General); variants of uncertain significance; QH301-705.5; DNA Mutational Analysis; SUMO-1 Protein; Mutation, Missense; Method; 610; Methods & Resources; 576; Machine Learning; 03 medical and health sciences; R5-920; deep mutational scanning; Calmodulin; genotype–phenotype; Methods; Humans; Disease; Biology (General); Phylogeny; Reproducibility of Results; Chromatin; 3. Good health; Genomics & Functional Genomics; Phenotype; complementation; Genome-Scale & Integrative Biology; Ubiquitin-Conjugating Enzymes; Epigenetics

Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin-like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.