Machine Learning-Based Heading Date QTL Detection in Rice
Quantitative trait locus (QTL) analysis is a powerful approach for identifying variants associated with the phenotypic variation of complex traits. However, selecting optimal methods and pre-processing steps require considerable time and effort. In this study, we demonstrated applicability and repli...
Saved in:
| Published in | Plant breeding and biotechnology Vol. 13; pp. 108 - 118 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
한국육종학회
21.05.2025
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2287-9358 2287-9366 |
| DOI | 10.9787/PBB.2025.13.108 |
Cover
| Abstract | Quantitative trait locus (QTL) analysis is a powerful approach for identifying variants associated with the phenotypic variation of complex traits. However, selecting optimal methods and pre-processing steps require considerable time and effort. In this study, we demonstrated applicability and replicability of machine learning (ML) models in QTL analysis by evaluating their performance in comparison with conventional QTL analysis methods using 142 recombinant inbred lines derived from two japonica rice cultivars, Koshihikari and Baegilmi. Random forest and gradient boosting models showed the highest predictive accuracy, and consistently identified three QTLs associated with heading date: qDTH3, qDTH6, and qDTH7. Moreover, ML-based QTL analysis detected minor-effect qDTH10, where Koshihikari allele promoted heading date when combined with Koshihikari alleles of qDTH6 and qDTH7. These results demonstrate the applicability of ML models in QTL analysis on bi-parental mapping population in rice. KCI Citation Count: 0 |
|---|---|
| AbstractList | Quantitative trait locus (QTL) analysis is a powerful approach for identifying variants associated with the phenotypic variation of complex traits. However, selecting optimal methods and pre-processing steps require considerable time and effort. In this study, we demonstrated applicability and replicability of machine learning (ML) models in QTL analysis by evaluating their performance in comparison with conventional QTL analysis methods using 142 recombinant inbred lines derived from two japonica rice cultivars, Koshihikari and Baegilmi. Random forest and gradient boosting models showed the highest predictive accuracy, and consistently identified three QTLs associated with heading date: qDTH3, qDTH6, and qDTH7. Moreover, ML-based QTL analysis detected minor-effect qDTH10, where Koshihikari allele promoted heading date when combined with Koshihikari alleles of qDTH6 and qDTH7. These results demonstrate the applicability of ML models in QTL analysis on bi-parental mapping population in rice. KCI Citation Count: 0 |
| ArticleNumber | 9 |
| Author | Lee, Gileung Lee, Hyun-Sook Park, Jae-Ryoung Kang, Kyeongmin Suh, Jung-Pil Bak, Hyeok-Jin Jeung, Ji-Ung Mo, Youngjun Han, Jae-Hyuk Ha, Su-Kyung Lee, Seung Young Jin, Mina |
| Author_xml | – sequence: 1 givenname: Seung Young surname: Lee fullname: Lee, Seung Young organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea, Department of Crop Science and Biotechnology, Jeonbuk National University, Jeonju 54896, Republic of Korea – sequence: 2 givenname: Jae-Hyuk surname: Han fullname: Han, Jae-Hyuk organization: IRRI-KOREA Office, Wanju 55365, Republic of Korea – sequence: 3 givenname: Hyeok-Jin surname: Bak fullname: Bak, Hyeok-Jin organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea – sequence: 4 givenname: Su-Kyung surname: Ha fullname: Ha, Su-Kyung organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea – sequence: 5 givenname: Hyun-Sook surname: Lee fullname: Lee, Hyun-Sook organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea – sequence: 6 givenname: Gileung surname: Lee fullname: Lee, Gileung organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea – sequence: 7 givenname: Jae-Ryoung surname: Park fullname: Park, Jae-Ryoung organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea – sequence: 8 givenname: Kyeongmin surname: Kang fullname: Kang, Kyeongmin organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea – sequence: 9 givenname: Jung-Pil surname: Suh fullname: Suh, Jung-Pil organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea – sequence: 10 givenname: Mina surname: Jin fullname: Jin, Mina organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea – sequence: 11 givenname: Ji-Ung surname: Jeung fullname: Jeung, Ji-Ung organization: National Institute of Crop and Food Science, Rural Development Administration, Wanju 55365, Republic of Korea – sequence: 12 givenname: Youngjun surname: Mo fullname: Mo, Youngjun organization: Department of Crop Science and Biotechnology, Jeonbuk National University, Jeonju 54896, Republic of Korea, Institute of Agricultural Science and Technology, Jeonbuk National University, Jeonju 54896, Republic of Korea |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003203529$$DAccess content in National Research Foundation of Korea (NRF) |
| BookMark | eNo9kL9PwzAQhS1UJErpzJoZKanPdhxn7A9KKwUBVZmtq30ppuCgpAv_PSlFTO_e6dMbvms2iE0kxm6BZ2VhisnzbJYJLvIMZAbcXLChEKZIS6n14P_OzRUbd9075xyA57xUQ1Y-onsLkZKKsI0h7tMZduSTFaHvW7LAIyUv2ypZ0JHcMTQxCTHZBEc37LLGj47Gfzlir8v77XyVVk8P6_m0Sh0YY1LQiKCw3BEIdFySAuk5qgIEdzmSqMEVNRkkrh3tpC9Urp33uVEOvdZyxO7Ou7Gt7cEF22D4zX1jD62dbrZrC7wAqZTs4ckZdm3TdS3V9qsNn9h-94g9mbK9KXsyZUH2PyN_AH2gWwk |
| Cites_doi | 10.18637/jss.v067.i01 10.1038/ng.143 10.1101/gad.1189604 10.1186/1471-2156-9-35 10.1038/s41598-020-60203-2 10.1093/plphys/kiab346 10.1007/s00122-014-2418-4 10.1104/pp.108.125542 10.1016/j.cj.2015.01.001 10.1016/j.cj.2018.01.002 10.1111/j.1365-3040.2009.02008.x 10.1007/978-3-658-43102-0 10.3390/genes11090957 10.1007/978-3-319-10247-4 10.1534/genetics.106.066811 10.1145/2939672.2939785 10.4249/scholarpedia.1883 10.1038/hdy.2016.87 10.1111/tpj.12268 10.1093/jxb/erm159 10.2135/cropsci2011.06.0297 10.1105/tpc.12.12.2473 10.1016/j.jhazmat.2022.129929 10.1016/S0168-9525(00)89157-X 10.1007/s00122-017-3011-4 10.1126/science.aaa8415 10.1023/A:1010933404324 10.1534/g3.115.021121 10.1111/nyas.12540 10.1016/j.cj.2018.03.001 10.3389/fpls.2021.777028 10.1093/g3journal/jkad300 10.1007/978-0-387-92125-9 10.1137/1.9781611972788.22 10.1371/journal.pgen.1004982 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION ACYCR |
| DOI | 10.9787/PBB.2025.13.108 |
| DatabaseName | CrossRef Korean Citation Index |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2287-9366 |
| EndPage | 118 |
| ExternalDocumentID | oai_kci_go_kr_ARTI_10713443 10_9787_PBB_2025_13_108 |
| GroupedDBID | AAYXX ALMA_UNASSIGNED_HOLDINGS CITATION DYU OK1 OZF P5Y ACYCR |
| ID | FETCH-LOGICAL-c1888-16aa14a9be12ac03e413d0a47120c5ae2f1c7fe8ae06ceb3d7456cdd584cad663 |
| ISSN | 2287-9358 |
| IngestDate | Sat Sep 06 06:29:12 EDT 2025 Thu Jul 03 08:44:29 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c1888-16aa14a9be12ac03e413d0a47120c5ae2f1c7fe8ae06ceb3d7456cdd584cad663 |
| Notes | https://doi.org/10.9787/PBB.2025.13.108 |
| OpenAccessLink | https://www.plantbreedbio.org/journal/download_pdf.php?doi=10.9787/PBB.2025.13.108 |
| PageCount | 11 |
| ParticipantIDs | nrf_kci_oai_kci_go_kr_ARTI_10713443 crossref_primary_10_9787_PBB_2025_13_108 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-05-21 |
| PublicationDateYYYYMMDD | 2025-05-21 |
| PublicationDate_xml | – month: 05 year: 2025 text: 2025-05-21 day: 21 |
| PublicationDecade | 2020 |
| PublicationTitle | Plant breeding and biotechnology |
| PublicationYear | 2025 |
| Publisher | 한국육종학회 |
| Publisher_xml | – name: 한국육종학회 |
| References | ref13 ref35 ref12 ref34 ref15 ref37 ref14 ref36 ref31 ref30 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref39 ref16 ref38 ref19 ref18 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 |
| References_xml | – ident: ref1 doi: 10.18637/jss.v067.i01 – ident: ref36 doi: 10.1038/ng.143 – ident: ref9 doi: 10.1101/gad.1189604 – ident: ref2 doi: 10.1186/1471-2156-9-35 – ident: ref33 doi: 10.1038/s41598-020-60203-2 – ident: ref29 – ident: ref12 doi: 10.1093/plphys/kiab346 – ident: ref22 – ident: ref16 doi: 10.1007/s00122-014-2418-4 – ident: ref21 doi: 10.1104/pp.108.125542 – ident: ref24 doi: 10.1016/j.cj.2015.01.001 – ident: ref32 doi: 10.1016/j.cj.2018.01.002 – ident: ref27 – ident: ref30 doi: 10.1111/j.1365-3040.2009.02008.x – ident: ref11 doi: 10.1007/978-3-658-43102-0 – ident: ref25 doi: 10.3390/genes11090957 – ident: ref13 doi: 10.1007/978-3-319-10247-4 – ident: ref20 doi: 10.1534/genetics.106.066811 – ident: ref8 doi: 10.1145/2939672.2939785 – ident: ref28 doi: 10.4249/scholarpedia.1883 – ident: ref34 doi: 10.1038/hdy.2016.87 – ident: ref15 doi: 10.1111/tpj.12268 – ident: ref17 doi: 10.1093/jxb/erm159 – ident: ref14 doi: 10.2135/cropsci2011.06.0297 – ident: ref38 doi: 10.1105/tpc.12.12.2473 – ident: ref37 doi: 10.1016/j.jhazmat.2022.129929 – ident: ref23 doi: 10.1016/S0168-9525(00)89157-X – ident: ref3 doi: 10.1007/s00122-017-3011-4 – ident: ref18 doi: 10.1126/science.aaa8415 – ident: ref6 doi: 10.1023/A:1010933404324 – ident: ref4 doi: 10.1534/g3.115.021121 – ident: ref26 doi: 10.1111/nyas.12540 – ident: ref35 doi: 10.1016/j.cj.2018.03.001 – ident: ref39 doi: 10.3389/fpls.2021.777028 – ident: ref19 doi: 10.1093/g3journal/jkad300 – ident: ref7 doi: 10.1007/978-0-387-92125-9 – ident: ref10 – ident: ref5 doi: 10.1137/1.9781611972788.22 – ident: ref31 doi: 10.1371/journal.pgen.1004982 |
| SSID | ssj0001105094 |
| Score | 2.2915664 |
| Snippet | Quantitative trait locus (QTL) analysis is a powerful approach for identifying variants associated with the phenotypic variation of complex traits. However,... |
| SourceID | nrf crossref |
| SourceType | Open Website Index Database |
| StartPage | 108 |
| SubjectTerms | 농학 |
| Title | Machine Learning-Based Heading Date QTL Detection in Rice |
| URI | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003203529 |
| Volume | 13 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | Plant Breeding and Biotechnology, 2025, 13(0), , pp.108-118 |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1db9Mw0OrGCy8IBIjxpUhgCWlKiZ2ksR_zUTQQQ4BaaTxF_pyqSS2qspf9v_0vzk7SpAWhwUvkXq62c3fxffh8QeittpIZTVVIFbFhonUc8gh-WmmlMVJQ42O6519mZ8vk00V6MZncjrKWrhs5VTd_PFfyP1wFGPDVnZL9B87uOgUAtIG_cAUOw_VOPD73mZCmL5J6GRagk7Q7WuSPqlRgR55-W3yGRaUxqs9q_N5lu_UmqftsUXMKnrFXY34zQa42zW8hdzwvMa9wkbgGy3HBPYRhRgaUCvMU89LfmeO89JB9lAIXkcPq7zjcPMMsOeyF-YGYb8BwFBfVOEZBU7e9TocYxWh0QE9xXvlpQO_tGBSztMfhvpFjVg4rIQW3LnQbtq3SGsNm49WXRGykyEm7sB_qCHCb3S7116KYuqlOSTzd_W9cjftAS-7V475Sq_pyU19ta_A6PtbEn8hN4iN0j2ZZmy3wYzmE-ogrsuPyG3YP0haZclN5fzCRPfvoaL21I3Nn8RA96PyUIG-F7hGamPVjxDuBC_YFLugELnACF4DABTuBC1brwAncE7T8MF-UZ2H37Y1QEQavGZkJQRLBpSFUqCg2YOzoSIApQyOVCkMtUZk1TJhopoyMdQaWuNIa7FklNJixT9HxerM2z1AgUm0ps5pyGSWZzRhPY0GUJSyLFeiQE_Suf-L6Z1tiBShaO-LUQJzaEacmsStle4LeAEU8-f_Chud3wnqB7g-i-hIdN9tr8woMzEa-9uz7BbTSY_s |
| linkProvider | CAB International |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine+Learning-Based+Heading+Date+QTL+Detection+in+Rice&rft.jtitle=Plant+breeding+and+biotechnology&rft.au=%EC%9D%B4%EC%8A%B9%EC%98%81&rft.au=%ED%95%9C%EC%9E%AC%ED%98%81&rft.au=%EB%B0%95%ED%98%81%EC%A7%84&rft.au=%ED%95%98%EC%88%98%EA%B2%BD&rft.date=2025-05-21&rft.pub=%ED%95%9C%EA%B5%AD%EC%9C%A1%EC%A2%85%ED%95%99%ED%9A%8C&rft.issn=2287-9358&rft.eissn=2287-9366&rft.spage=108&rft.epage=118&rft_id=info:doi/10.9787%2FPBB.2025.13.108&rft.externalDBID=n%2Fa&rft.externalDocID=oai_kci_go_kr_ARTI_10713443 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2287-9358&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2287-9358&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2287-9358&client=summon |