Dissecting the genetic architecture of yield-related traits by QTL mapping in maize
IntroductionMaize is a cornerstone of global agriculture, essential for ensuring food security, driving economic development, and meeting growing food demands. Yet, how to achieve optimal yield remains a multifaceted challenge influenced by biotic, environmental, and genetic factors whose comprehens...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1624954/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849405226379902976 |
|---|---|
| author | Hao Zhang Hao Zhang Ting Li Zhenyu Zhang Jie Wang Haoxiang Yang Jiachen Liu Wanchao Zhu Wanchao Zhu Jiquan Xue Jiquan Xue Shutu Xu Shutu Xu |
| author_facet | Hao Zhang Hao Zhang Ting Li Zhenyu Zhang Jie Wang Haoxiang Yang Jiachen Liu Wanchao Zhu Wanchao Zhu Jiquan Xue Jiquan Xue Shutu Xu Shutu Xu |
| author_sort | Hao Zhang |
| collection | DOAJ |
| description | IntroductionMaize is a cornerstone of global agriculture, essential for ensuring food security, driving economic development, and meeting growing food demands. Yet, how to achieve optimal yield remains a multifaceted challenge influenced by biotic, environmental, and genetic factors whose comprehensive understanding is still evolving.MethodsQTL mapping of eight essential yield traits was conducted across four environments — Sanya (SY) in 2021, and Yangling (YaL), Yulin (YuL), and Weinan (WN) in 2022 — using two types of populations: a KA105/KB024 recombinant inbred line (RIL) population and two immortalized backcross populations (IB1 and IB2) derived from the RILs by crossing with their respective parents. Key candidate genes were identified through the integration of RNA-seq data, gene-based association analysis and classic yield-related genes network dataset.ResultsGreater phenotypic variation was observed in RIL population than that in the IB1 and IB2 populations, while similar phenotype variations between IB1 and IB2 populations. A total of 121 QTLs were identified, including 10 QTLs that regulate multiple traits and 41 QTLs shared among these populations. Notably, 59.5% of the 42 QTLs identified in the IBL population (combined mapping using populations IB1, IB2, and RIL) exhibited an overdominance effect through the simultaneous calculation of additive and dominant effects. Through integrated transcriptome data and interaction networks, 20 genes located in these QTLs were investigated as candidate genes. Among them, Zm00001d005740 (ZmbHLH138) was significantly associated with ear diameter in the association mapping panel AM508.ConclusionThese findings illuminate the genetic mechanisms underpinning maize yield formation, providing a robust foundation for advancing high-yielding variety development through targeted field breeding strategies. |
| format | Article |
| id | doaj-art-e6d32745713c44e39af4e34e8e332eef |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-e6d32745713c44e39af4e34e8e332eef2025-08-20T03:36:44ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-08-011610.3389/fpls.2025.16249541624954Dissecting the genetic architecture of yield-related traits by QTL mapping in maizeHao Zhang0Hao Zhang1Ting Li2Zhenyu Zhang3Jie Wang4Haoxiang Yang5Jiachen Liu6Wanchao Zhu7Wanchao Zhu8Jiquan Xue9Jiquan Xue10Shutu Xu11Shutu Xu12Hainan Institute of Northwest A&F University, Sanya, Hainan, ChinaThe Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, ChinaThe Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, ChinaThe Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, ChinaThe Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, ChinaThe Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, ChinaThe Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, ChinaHainan Institute of Northwest A&F University, Sanya, Hainan, ChinaThe Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, ChinaHainan Institute of Northwest A&F University, Sanya, Hainan, ChinaThe Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, ChinaHainan Institute of Northwest A&F University, Sanya, Hainan, ChinaThe Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, ChinaIntroductionMaize is a cornerstone of global agriculture, essential for ensuring food security, driving economic development, and meeting growing food demands. Yet, how to achieve optimal yield remains a multifaceted challenge influenced by biotic, environmental, and genetic factors whose comprehensive understanding is still evolving.MethodsQTL mapping of eight essential yield traits was conducted across four environments — Sanya (SY) in 2021, and Yangling (YaL), Yulin (YuL), and Weinan (WN) in 2022 — using two types of populations: a KA105/KB024 recombinant inbred line (RIL) population and two immortalized backcross populations (IB1 and IB2) derived from the RILs by crossing with their respective parents. Key candidate genes were identified through the integration of RNA-seq data, gene-based association analysis and classic yield-related genes network dataset.ResultsGreater phenotypic variation was observed in RIL population than that in the IB1 and IB2 populations, while similar phenotype variations between IB1 and IB2 populations. A total of 121 QTLs were identified, including 10 QTLs that regulate multiple traits and 41 QTLs shared among these populations. Notably, 59.5% of the 42 QTLs identified in the IBL population (combined mapping using populations IB1, IB2, and RIL) exhibited an overdominance effect through the simultaneous calculation of additive and dominant effects. Through integrated transcriptome data and interaction networks, 20 genes located in these QTLs were investigated as candidate genes. Among them, Zm00001d005740 (ZmbHLH138) was significantly associated with ear diameter in the association mapping panel AM508.ConclusionThese findings illuminate the genetic mechanisms underpinning maize yield formation, providing a robust foundation for advancing high-yielding variety development through targeted field breeding strategies.https://www.frontiersin.org/articles/10.3389/fpls.2025.1624954/fullmaizeyieldQTL mappingrecombinant inbred line populationcandidate genes |
| spellingShingle | Hao Zhang Hao Zhang Ting Li Zhenyu Zhang Jie Wang Haoxiang Yang Jiachen Liu Wanchao Zhu Wanchao Zhu Jiquan Xue Jiquan Xue Shutu Xu Shutu Xu Dissecting the genetic architecture of yield-related traits by QTL mapping in maize Frontiers in Plant Science maize yield QTL mapping recombinant inbred line population candidate genes |
| title | Dissecting the genetic architecture of yield-related traits by QTL mapping in maize |
| title_full | Dissecting the genetic architecture of yield-related traits by QTL mapping in maize |
| title_fullStr | Dissecting the genetic architecture of yield-related traits by QTL mapping in maize |
| title_full_unstemmed | Dissecting the genetic architecture of yield-related traits by QTL mapping in maize |
| title_short | Dissecting the genetic architecture of yield-related traits by QTL mapping in maize |
| title_sort | dissecting the genetic architecture of yield related traits by qtl mapping in maize |
| topic | maize yield QTL mapping recombinant inbred line population candidate genes |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1624954/full |
| work_keys_str_mv | AT haozhang dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT haozhang dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT tingli dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT zhenyuzhang dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT jiewang dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT haoxiangyang dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT jiachenliu dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT wanchaozhu dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT wanchaozhu dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT jiquanxue dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT jiquanxue dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT shutuxu dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize AT shutuxu dissectingthegeneticarchitectureofyieldrelatedtraitsbyqtlmappinginmaize |