{"id":"W4410116415","doi":"10.1101/2025.05.05.651929","title":"CRISPRi perturbation screens and eQTLs provide complementary and distinct insights into GWAS target genes","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Uppsala Multidisciplinary Center for Advanced Computational Science; National Institutes of Health; Vetenskapsrådet; Knut och Alice Wallenbergs Stiftelse; European Commission","keywords":"Genome-wide association study; Computational biology; Biology; Gene; Genetics; Single-nucleotide polymorphism; Genotype","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002013374,0.0004981113,0.0004148969,0.0001345731,0.0002521679,0.0001596472,0.0002871448,0.0004464233,0.000008528727],"category_scores_gemma":[0.00008697803,0.0005130499,0.00009036399,0.0001159794,0.0001919041,0.0000134447,0.0005895143,0.0003114155,0.000001597282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005644499,"about_ca_system_score_gemma":0.0002910486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002076753,"about_ca_topic_score_gemma":0.00006762987,"domain_scores_codex":[0.9979074,0.0001217693,0.0004253911,0.001033964,0.0001822507,0.000329224],"domain_scores_gemma":[0.9986752,0.00002769469,0.000189647,0.0006537896,0.0002554491,0.0001982078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008870814,0.0001076293,0.03284223,0.0006207778,0.0001942074,0.000008762679,0.00003100523,0.00002019671,0.9655117,0.0002112973,0.000337486,0.00002595076],"study_design_scores_gemma":[0.001248162,0.0002732249,0.08524771,0.0003526841,0.00023637,4.580779e-8,0.00001382121,0.001313132,0.842338,0.0000371492,0.06771912,0.00122058],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826911,0.01063471,0.004858309,0.0002665447,0.0005115775,0.0006899776,0.0002610646,0.00006862546,0.0000180994],"genre_scores_gemma":[0.9885663,0.001408913,0.008981003,0.0004263175,0.0004329118,0.00008633403,0.00001709056,0.00005616542,0.00002500231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1231738,"threshold_uncertainty_score":0.9997321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01211950823361724,"score_gpt":0.2234131292024866,"score_spread":0.2112936209688693,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}