{"id":"W4415283170","doi":"10.1093/bioinformatics/btaf578","title":"Sparse polygenic risk score inference with the spike-and-slab LASSO","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill Genome Centre; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Inference; Lasso (programming language); R package; Polygenic risk score; Covariate; Elastic net regularization; Software","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002866242,0.0001188097,0.0001281363,0.00003204929,0.0001733596,0.00002752389,0.0001655325,0.0001212121,0.00000616863],"category_scores_gemma":[0.0002464024,0.00007278416,0.00003885705,0.0001152519,0.0001263403,0.000003656186,0.0001327406,0.0001063668,0.0000149356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009987149,"about_ca_system_score_gemma":0.0001114494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003673193,"about_ca_topic_score_gemma":0.0001622196,"domain_scores_codex":[0.9993537,0.00004720373,0.0002130201,0.0001135729,0.00006600532,0.0002064931],"domain_scores_gemma":[0.9993649,0.00005910887,0.0001493267,0.00032143,0.00006616708,0.00003906606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008652289,0.00005677492,0.9081201,0.00008156196,0.0004025308,8.091424e-7,0.0005874839,0.0007157983,0.001397929,0.002647643,0.04137816,0.04452466],"study_design_scores_gemma":[0.001403195,0.0005340308,0.8379151,0.00005144533,0.0002220672,0.00001782967,0.001406254,0.009772877,0.002096076,0.000739652,0.1453717,0.0004697737],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726621,0.0006336768,0.02080121,0.001029723,0.00008883476,0.000200451,0.0000285587,0.0000135692,0.004541907],"genre_scores_gemma":[0.9910755,0.0008342225,0.005760619,0.001086163,0.00004182875,0.00001762689,0.00003956717,0.000006028808,0.00113842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1039935,"threshold_uncertainty_score":0.2968052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01104433433225866,"score_gpt":0.2495141751595947,"score_spread":0.238469840827336,"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."}}