{"id":"W2554771545","doi":"10.1002/gepi.22024","title":"Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure","year":2016,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Sinai Health System; Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto","funders":"National Center for Research Resources; National Institutes of Health; National Research Foundation of Korea; Canadian Institutes of Health Research; National Research Foundation","keywords":"Linkage disequilibrium; Pairwise comparison; Type I and type II errors; Genetic association; Principal component analysis; Biology; Multiple comparisons problem; Regression; Genetics; Statistics; Computational biology; Mathematics; Haplotype; Gene; Single-nucleotide polymorphism; Allele","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001031232,0.0003528999,0.0006870921,0.0001068424,0.0001799346,0.00000619806,0.0004164688,0.0007016666,0.00004750865],"category_scores_gemma":[0.009220599,0.0002489393,0.0001926332,0.0001328386,0.0001301329,0.000005217487,0.0002573543,0.0001199033,0.00003735078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005358037,"about_ca_system_score_gemma":0.00009089836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003881551,"about_ca_topic_score_gemma":0.0001662811,"domain_scores_codex":[0.9964941,0.0008243219,0.0008755141,0.0009027366,0.00009488541,0.0008085],"domain_scores_gemma":[0.9969268,0.001336769,0.0004079061,0.0008014801,0.0002378603,0.0002891936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002820738,0.00009491216,0.4621342,0.00002699806,0.0001086166,0.000001887521,0.00003774199,0.0008001555,0.4898067,0.0003972867,0.02115466,0.02515479],"study_design_scores_gemma":[0.002750142,0.00161671,0.9276096,0.00006957977,0.00006572221,0.00003082883,0.00001648104,0.002982094,0.008656847,0.01114796,0.0444479,0.000606125],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8644152,0.0005979784,0.12681,0.006200782,0.0006769779,0.0008517605,0.0003449294,0.0000402701,0.00006218599],"genre_scores_gemma":[0.9292337,0.00016587,0.06635833,0.002186146,0.0006121752,0.0001546608,0.0004585959,0.00005507685,0.0007754077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4811499,"threshold_uncertainty_score":0.9999963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0342053439258588,"score_gpt":0.3251701648478508,"score_spread":0.290964820921992,"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."}}