A genome-wide benchmark for assessing the identifiability of gene assignment at kidney-function loci

This deposit contains the analysis code and derived tables for the manuscript"A genome-wide benchmark for assessing the identifiability of gene assignment atkidney-function loci" (Hu Y, submitted to the European Journal of Human Genetics, 2026). Contents• code/identifiability-0.1.0.zip — the released identifiability test: Python package, command-line interface, local web application, worked examples and unit tests. Input is a table of instruments (locus, gene, variant, cis-eQTL effect and standard error, outcome effect and standard error); output is a verdict per gene pair and per gene (assignable, unassignable, not evaluable), under an exact or a conservative variance.• code/*.py, code/export_genes.R — analysis scripts that produce every number in the manuscript and in Supplementary Tables S1–S10: locus preparation, rule-based nominate-and-audit, kidney shared-variant identifiability, GTEx gene-level comparison, simulation calibration and power, figures.• tables/S1–S10_*.csv — the ten supplementary tables, one row per record (93, 38, 39, 30, 20, 27, 140, 49, 982 and 390 rows).• tables/supporting/ — GTEx gene-level pairs and genes, simulation calibration and power, empirical z distribution, covariance sensitivity. DataAll inputs are public and were used under their original accessions: CKDGen eGFR summarystatistics (Stanzick et al. 2021), FinnGen release 12 (CKD endpoint N14), three kidneycis-eQTL references (N = 686, 356 and 303), GTEx v8, GWAS Catalog GCST90000025, and Nealelaboratory round 2 grip strength. No individual-level or identifiable data are included,and no source dataset is redistributed here. Reproducing the resultspip install identifiability-0.1.0.zipidentifiability run instruments.tsv -o out/The test has no dependency outside the Python standard library. Scripts run in the orderprep_loci.py → fetch_genes.py → baseline_audit_kidney.py → kidney_shared_snp.py →simulate_calibration.py → fig_s11.py. Licence and citationThis deposit is released under CC BY 4.0; the bundled software is additionally availableunder the MIT licence (see LICENSE). Cite as: Hu Y (2026). A genome-wide benchmark forassessing the identifiability of gene assignment at kidney-function loci: analysis codeand derived tables. Zenodo. https://doi.org/10.5281/zenodo.23955018