{"doi":"10.1111/pbr.12559","title":"<scp>QTL</scp> mapping for six ear leaf architecture traits under water‐stressed and well‐watered conditions in maize (<i>Zea mays</i> L.)","abstract":"<jats:title>Abstract</jats:title><jats:p>Morphological traits for ear leaf are determinant traits influencing plant architecture and drought tolerance in maize. However, the genetic controls of ear leaf architecture traits remain poorly understood under drought stress. Here, we identified 100 quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, leaf width, leaf size and leaf shape value of ear leaf across four populations under drought‐stressed and unstressed conditions, which explained 0.71%–20.62% of phenotypic variation in single watering condition. Forty‐five of the 100 <jats:styled-content style=\"fixed-case\">QTL</jats:styled-content>s were identified under water‐stressed conditions, and 29 stable <jats:styled-content style=\"fixed-case\">QTL</jats:styled-content>s (<jats:styled-content style=\"fixed-case\">sQTL</jats:styled-content>s) were identified under water‐stressed conditions, which could be useful for the genetic improvement of maize drought tolerance via <jats:styled-content style=\"fixed-case\">QTL</jats:styled-content> pyramiding. We further integrated 27 independent <jats:styled-content style=\"fixed-case\">QTL</jats:styled-content> studies in a meta‐analysis to identify 21 meta‐<jats:styled-content style=\"fixed-case\">QTL</jats:styled-content>s (<jats:styled-content style=\"fixed-case\">mQTL</jats:styled-content>s). Then, 24 candidate genes controlling leaf architecture traits coincided with 20 corresponding <jats:styled-content style=\"fixed-case\">mQTL</jats:styled-content>s. Thus, new/valuable information on quantitative traits has shed some light on the molecular mechanisms responsible for leaf architecture traits affected by watering conditions. Furthermore, alleles for leaf architecture traits provide useful targets for marker‐assisted selection to generate high‐yielding maize varieties.</jats:p>","journal":"Plant Breeding","year":2018,"id":46761,"datarank":1.0889038251053416,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"self_citation_contribution":0.519860385419959,"citation_network_contribution":0.5690434396853826,"self_endowment_contribution":0.519860385419959,"citer_contribution":0.5690434396853826,"corpus_percentile":null,"corpus_rank":null,"citation_count":31,"citer_count":25,"citers_with_citation_signal":21,"citers_with_endowment":21,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":216329,"name":"Peng Fang","orcid":null,"position":1,"is_corresponding":false},{"id":216330,"name":"Jinwen Zhang","orcid":null,"position":2,"is_corresponding":false},{"id":216331,"name":"Yunling Peng","orcid":"0000-0003-4442-0245","position":3,"is_corresponding":false},{"id":216328,"name":"Xiaoqiang Zhao","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"<scp>QTL</scp> mapping for six ear leaf architecture traits under water‐stressed and well‐watered conditions in maize (<i>Zea mays</i> L.)","abstract":"<jats:title>Abstract</jats:title><jats:p>Morphological traits for ear leaf are determinant traits influencing plant architecture and drought tolerance in maize. However, the genetic controls of ear leaf architecture traits remain poorly understood under drought stress. Here, we identified 100 quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, leaf width, leaf size and leaf shape value of ear leaf across four populations under drought‐stressed and unstressed conditions, which explained 0.71%–20.62% of phenotypic variation in single watering condition. Forty‐five of the 100 <jats:styled-content style=\"fixed-case\">QTL</jats:styled-content>s were identified under water‐stressed conditions, and 29 stable <jats:styled-content style=\"fixed-case\">QTL</jats:styled-content>s (<jats:styled-content style=\"fixed-case\">sQTL</jats:styled-content>s) were identified under water‐stressed conditions, which could be useful for the genetic improvement of maize drought tolerance via <jats:styled-content style=\"fixed-case\">QTL</jats:styled-content> pyramiding. We further integrated 27 independent <jats:styled-content style=\"fixed-case\">QTL</jats:styled-content> studies in a meta‐analysis to identify 21 meta‐<jats:styled-content style=\"fixed-case\">QTL</jats:styled-content>s (<jats:styled-content style=\"fixed-case\">mQTL</jats:styled-content>s). Then, 24 candidate genes controlling leaf architecture traits coincided with 20 corresponding <jats:styled-content style=\"fixed-case\">mQTL</jats:styled-content>s. Thus, new/valuable information on quantitative traits has shed some light on the molecular mechanisms responsible for leaf architecture traits affected by watering conditions. Furthermore, alleles for leaf architecture traits provide useful targets for marker‐assisted selection to generate high‐yielding maize varieties.</jats:p>","is_dataset_classified":null,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W2792070472","authors":[],"funders":[{"funder_name":"National Natural Science Foundation of China","grant_id":"31260330","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"31301333","title":null},{"funder_name":"China Agricultural University","grant_id":"GAUFX‐02Y09","title":null}],"total_grants":3,"fwci":1.971,"citation_percentile":0.86910374,"influential_citations":8,"citation_trend":[{"year":2018,"count":2},{"year":2019,"count":2},{"year":2020,"count":1},{"year":2021,"count":6},{"year":2022,"count":5},{"year":2023,"count":5},{"year":2024,"count":4},{"year":2025,"count":5},{"year":2026,"count":1}],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fpbr.12559","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1111/pbr.12559","host_type":"publisher"},{"url":"https://doi.org/10.1111/pbr.12559","host_type":"journal"}],"fields_of_study":["Genetic Mapping and Diversity in Plants and Animals","Genetics and Plant Breeding","Wheat and Barley Genetics and Pathology","Biology","Agricultural and Food Sciences"],"mesh_terms":[],"keywords":["Quantitative trait locus","Biology","Genetic architecture","Drought tolerance","Agronomy","Leaf spot","Allele","Marker-assisted selection","Zea mays","Inbred strain","Gene","Genetics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Clean water and sanitation"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-13T10:22:56.712760Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}