{"id":"W4361228097","doi":"10.1101/2023.03.28.534607","title":"<i>k</i> -mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Alliance de recherche numérique du Canada; Canadian Field Crop Research Alliance; Genome Canada; Syngenta Canada; Saskatchewan Pulse Growers; Fonds de recherche du Québec – Nature et technologies; Grain Farmers of Ontario; Génome Québec","keywords":"Genome-wide association study; Single-nucleotide polymorphism; Biology; Genetics; Candidate gene; Genetic association; Locus (genetics); Population; Indel; Computational biology; Gene; Genotype; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003828215,0.0005544781,0.001499022,0.001593928,0.0006328314,0.001294009,0.0006631877,0.0005604441,0.002729288],"category_scores_gemma":[0.005158877,0.0003096053,0.001586706,0.002200004,0.0003930675,0.0007399459,0.0009877989,0.0008159684,0.0008369584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000281515,"about_ca_system_score_gemma":0.0003271078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002207885,"about_ca_topic_score_gemma":0.006359079,"domain_scores_codex":[0.998346,0.0004565604,0.0001633886,0.0007632033,0.0001700136,0.0001007595],"domain_scores_gemma":[0.994108,0.00314501,0.001026037,0.00094431,0.0005216193,0.0002550789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003899063,0.0002451269,0.7595614,0.001510179,0.003637529,0.0009499529,0.0003301464,0.01203321,0.1298745,0.00126813,0.007071727,0.07961898],"study_design_scores_gemma":[0.0003070628,0.0004225291,0.8868088,0.0002402379,0.002655629,0.001732568,0.0002231472,0.06927319,0.01662045,0.004321395,0.01724458,0.0001503883],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9393123,0.003238228,0.03570578,0.0006920656,0.0001573928,0.00005723803,0.01546983,0.002123031,0.003244084],"genre_scores_gemma":[0.9513887,0.0004145922,0.03489504,0.0004224272,0.00009809468,0.00004272177,0.01173299,0.0003188675,0.0006865546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003828215,"threshold_uncertainty_score":0.02024579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02018276960607705,"score_gpt":0.2142743445808955,"score_spread":0.1940915749748185,"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."}}