{"id":"W2947212901","doi":"10.3389/fgene.2019.00510","title":"Optimizing Selection of the Reference Population for Genotype Imputation From Array to Sequence Variants","year":2019,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Guelph","funders":"Ontario Ministry of Research and Innovation; U.S. Department of Agriculture; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs; Agricultural Research Service; Genome Canada; Ontario Ministry of Research, Innovation and Science; Ontario Genomics; Ministry of Agriculture, Food and Rural Affairs; University of Guelph; Aarhus Universitet; Genome Alberta","keywords":"Imputation (statistics); Selection (genetic algorithm); Genotype; Population; Biology; Genetics; Sequence (biology); Computer science; Computational biology; Statistics; Artificial intelligence; Gene; Mathematics; Machine learning; Missing data; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008783809,0.0000978307,0.0001141761,0.00003515126,0.00003492497,0.000008770505,0.0001957901,0.0001278222,0.000006979253],"category_scores_gemma":[0.00003865461,0.00008897276,0.00003663986,0.0001205838,0.00001877116,0.000002878413,0.0000365792,0.00005649545,0.000001535048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002619466,"about_ca_system_score_gemma":0.0000565772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004950244,"about_ca_topic_score_gemma":0.0000434088,"domain_scores_codex":[0.9992391,0.00004478342,0.0002040196,0.0002643799,0.00009882679,0.0001489129],"domain_scores_gemma":[0.999554,0.000009613666,0.00009657023,0.0002353417,0.00007388501,0.00003058137],"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":[0.0001707472,0.00003368534,0.2147125,0.00002590372,0.00003623336,1.606818e-8,0.0003569076,0.1466956,0.6281748,0.0002553267,0.0005548178,0.008983401],"study_design_scores_gemma":[0.0006054552,0.0004415146,0.8364017,0.00003761879,0.00002987559,7.914612e-7,0.0001307807,0.002833585,0.1510543,0.006704555,0.001528821,0.0002310252],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7020103,0.0001670644,0.2966069,0.00002288777,0.0006371401,0.0004045159,0.00003601281,0.000003009982,0.0001121633],"genre_scores_gemma":[0.6394897,0.00001134087,0.3602105,0.00005626203,0.00005847977,0.00001266233,0.00006131652,0.00001021689,0.00008954205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6216892,"threshold_uncertainty_score":0.3628204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415989867514787,"score_gpt":0.2482962243755907,"score_spread":0.2341363257004428,"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."}}