Multi-ISM maps 5,000 genes with 45-fold fewer model evaluations and improves rare-variant expression prediction
The work introduces Multi-ISM, a framework that reformulates in silico saturation mutagenesis (ISM) as a sparse recovery problem, generating mutational maps with 45-fold fewer model evaluations than exhaustive single-variant ISM while matching or exceeding its accuracy on variant-effect benchmarks, and applies it to 5,000 protein-coding genes (including 3,317 OMIM disease genes) to produce base-pair-resolution, tissue-resolved attribution maps across 500-kb windows for enhancer-gene prioritization and cell-type-specific regulatory element identification, with gene-level summaries showing more constrained genes have smaller predicted mutational effects and with aggregation into gene-level rare-variant burdens improving personalized expression prediction over a common-variant elastic net, wi