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2024
Meta-quantitative trait loci analysis and candidate gene mining for drought tolerance-associated traits in maize (Zea mays L.)
Meta-quantitative trait loci analysis and candidate gene mining for drought tolerance-associated traits in maize (Zea mays L.)
Abstract (English)
Drought is one of the major abiotic stresses with a severe negative impact on maize production globally. Understanding the genetic architecture of drought tolerance in maize is a crucial step towards the breeding of drought-tolerant varieties and a targeted exploitation of genetic resources. In this study, 511 quantitative trait loci (QTL) related to grain yield components, flowering time, and plant morphology under drought conditions, as well as drought tolerance index were collected from 27 published studies and then projected on the IBM2 2008 Neighbors reference map for meta-analysis. In total, 83 meta-QTL (MQTL) associated with drought tolerance in maize were identified, of which 20 were determined as core MQTL. The average confidence interval of MQTL was strongly reduced compared to that of the previously published QTL. Nearly half of the MQTL were confirmed by co-localized marker-trait associations from genome-wide association studies. Based on the alignment of rice proteins related to drought tolerance, 63 orthologous genes were identified near the maize MQTL. Furthermore, 583 candidate genes were identified within the 20 core MQTL regions and maize–rice homologous genes. Based on KEGG analysis of candidate genes, plant hormone signaling pathways were found to be significantly enriched. The signaling pathways can have direct or indirect effects on drought tolerance and also interact with other pathways. In conclusion, this study provides novel insights into the genetic and molecular mechanisms of drought tolerance in maize towards a more targeted improvement of this important trait in breeding.
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International journal of molecular sciences, 25 (2024), 8, 4295.
https://doi.org/10.3390/ijms25084295.
ISSN: 1422-0067
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English
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630 Agriculture
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@article{Li2024,
url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16074},
doi = {10.3390/ijms25084295},
author = {Li, Ronglan and Wang, Yueli and Li, Dongdong et al.},
title = {Meta-quantitative trait loci analysis and candidate gene mining for drought tolerance-associated traits in maize (Zea mays L.)},
journal = {International journal of molecular sciences},
year = {2024},
volume = {25},
number = {8},
}