{"id":"W2277456962","doi":"10.1111/nph.13888","title":"Dissection of expression‐quantitative trait locus and allele specificity using a haploid/diploid plant system – insights into compensatory evolution of transcriptional regulation within populations","year":2016,"lang":"en","type":"article","venue":"New Phytologist","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; Genome Canada","keywords":"Expression quantitative trait loci; Biology; Genetics; Quantitative trait locus; Allele; Genetic architecture; Ploidy; Locus (genetics); Gene; Phenotype; Evolutionary biology; Genotype; Single-nucleotide polymorphism","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":[0.0003206741,0.0002611301,0.0002340863,0.0003187708,0.0001789805,0.0003189392,0.0002900851,0.0002988318,0.001052807],"category_scores_gemma":[0.0002183584,0.0002815005,0.0002491325,0.0002035337,0.0002832964,0.0002056718,0.0003574176,0.0006736513,0.0003139021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005438495,"about_ca_system_score_gemma":0.0002916296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001558682,"about_ca_topic_score_gemma":0.002819439,"domain_scores_codex":[0.9997717,0.00002889338,0.00003307654,0.0001004569,0.00004311041,0.00002274441],"domain_scores_gemma":[0.9997839,0.00008045487,0.0000465554,0.00004156977,0.00001492847,0.0000325706],"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":[0.00001953386,0.000005395136,0.0003731748,0.00001744034,0.00000370652,0.00002758057,0.00001946186,0.0001680041,0.9980637,0.0001955299,0.000009142405,0.001097332],"study_design_scores_gemma":[0.00005673263,0.0002180206,0.04444173,0.00001981604,0.00007117291,0.0007108598,0.0001053215,0.02085444,0.925648,0.0009701608,0.006847917,0.00005585195],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9619145,0.0003760014,0.03464628,0.00007719725,0.00001886435,0.0000714759,0.001197472,0.0002742545,0.001423921],"genre_scores_gemma":[0.9790177,0.0002569547,0.01723007,0.00008964062,0.000004836706,0.00006420529,0.001125565,0.0001292387,0.002081926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001558682,"threshold_uncertainty_score":0.003945887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04111272100117949,"score_gpt":0.2561385769748613,"score_spread":0.2150258559736818,"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."}}