{"id":"W2279116951","doi":"10.1002/sta4.102","title":"A genome‐wide association study of multiple longitudinal traits with related subjects","year":2016,"lang":"en","type":"article","venue":"Stat","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Framingham Heart Study; Pleiotropy; Trait; Type I and type II errors; Genetic association; Biology; Random effects model; Genome-wide association study; Quantitative trait locus; Statistical power; Single-nucleotide polymorphism; Genetics; Phenotype; Computational biology; Statistics; Gene; Computer science; Meta-analysis; Mathematics; Framingham Risk Score; Medicine; Genotype; Disease; Internal 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":[0.01088817,0.0004877495,0.0008739758,0.001214436,0.0009060623,0.0008171459,0.001013894,0.001361345,0.002086882],"category_scores_gemma":[0.01396013,0.0003828846,0.001580599,0.001704906,0.0008653467,0.0004007677,0.001108092,0.001072225,0.0001763889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003270476,"about_ca_system_score_gemma":0.001244389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002552372,"about_ca_topic_score_gemma":0.004156096,"domain_scores_codex":[0.9958887,0.002647089,0.0001441071,0.0009739113,0.0002303845,0.0001157843],"domain_scores_gemma":[0.9881606,0.009165729,0.001063191,0.001208906,0.0001871881,0.0002143338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001534053,0.0004250581,0.829828,0.0003363644,0.003499593,0.001971991,0.0009758474,0.01450897,0.02380816,0.01443006,0.001222815,0.1074591],"study_design_scores_gemma":[0.0003879853,0.002231336,0.7594737,0.0001403873,0.002751948,0.003261386,0.0005250087,0.1801328,0.005242839,0.03944065,0.00628396,0.0001279795],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6741192,0.0008741016,0.3214753,0.001035604,0.00009008116,0.0001889639,0.001016508,0.0002351408,0.0009652259],"genre_scores_gemma":[0.8965679,0.0003076693,0.1007665,0.0001801497,0.00006158195,0.000324067,0.0006604351,0.00001969314,0.001111925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01088817,"threshold_uncertainty_score":0.0575828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01127690114381227,"score_gpt":0.2104456684246835,"score_spread":0.1991687672808712,"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."}}