Motivation Complex disorders arise from multiple genetic mechanisms, but most drug-prioritization methods treat each disorder as a single phenotype and therefore miss locus-specific therapeutic opportunities.

Results We present SIEVE, a framework that decomposes complex disorders into genetically localized subphenotypes and links GWAS summary statistics, reference expression, and perturbational transcriptional profiles to prioritize compounds that target locus-anchored disease mechanisms. SIEVE also constructs genetically calibrated mechanism vectors, projects away nonspecific expression programs using negative anchors, and aggregates evidence across cell lines, doses, and time points to produce robust drug rankings. Across simulations and analyses of real data, SIEVE improves compound prioritization relative to existing methods and shows that subphenotype-aware, genetics-guided modeling can sharpen therapeutic discovery in heterogeneous disorders.

The authors have declared no competing interest.

This study did not receive any funding.

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The source data were openly available before the initiation of the study and include: LINCS L1000 perturbation data: https://clue.io/data/CMap2020#LINCS2020 GTEx variant data: https://gtexportal.org/home/protectedDataAccess GTEx expression data: https://gtexportal.org/home/downloads/adult-gtex/bulk_tissue_expression Pan-UK Biobank summary statistics: https://pan.ukbb.broadinstitute.org/downloads 1000 Genomes Phase 3 data: https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/

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