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衍生水稻群体中籽粒锌含量和产量的基因组预测与QTL分析。

Genomic prediction and QTL analysis for grain Zn content and yield in -derived rice populations.

作者信息

Hore Tapas Kumer, Balachiranjeevi C H, Inabangan-Asilo Mary Ann, Deepak C A, Palanog Alvin D, Hernandez Jose E, Gregorio Glenn B, Dalisay Teresita U, Diaz Maria Genaleen Q, Neto Roberto Fritsche, Kader Md Abdul, Biswas Partha Sarathi, Swamy B P Mallikarjuna

机构信息

International Rice Research Institute (IRRI), DAPO Box 4031, Los Banos, Laguna Philippines.

University of the Philippines Los Baños (UPLB), College, Los Banos, Laguna Philippines.

出版信息

J Plant Biochem Biotechnol. 2024;33(2):216-236. doi: 10.1007/s13562-024-00886-0. Epub 2024 May 9.

DOI:10.1007/s13562-024-00886-0
PMID:40308942
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12037680/
Abstract

UNLABELLED

Zinc (Zn) biofortification of rice can address Zn malnutrition in Asia. Identification and introgression of QTLs for grain Zn content and yield (YLD) can improve the efficiency of rice Zn biofortification. In four rice populations we detected 56 QTLs for seven traits by inclusive composite interval mapping (ICIM), and 16 QTLs for two traits (YLD and Zn) by association mapping. The phenotypic variance (PV) varied from 4.5% ( ) to 31.7% ( ). , , , , , , , , and were identified in both dry and wet seasons; , and were detected by both ICIM and association mapping. had the highest PV (17.8%) and additive effect (2.5 ppm). Epistasis and QTL co-locations were also observed for different traits. The multi-trait genomic prediction values were 0.24 and 0.16 for YLD and Zn respectively. was co-located with a gene () involved in Zn transport. These results are useful for Zn biofortificatiton of rice.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13562-024-00886-0.

摘要

未标记

水稻锌(Zn)生物强化可解决亚洲地区的锌营养不良问题。鉴定和导入控制籽粒锌含量和产量(YLD)的数量性状基因座(QTL)可提高水稻锌生物强化的效率。在四个水稻群体中,我们通过包容性复合区间作图(ICIM)检测到7个性状的56个QTL,通过关联作图检测到2个性状(YLD和Zn)的16个QTL。表型变异(PV)从4.5%( )到31.7%( )不等。 、 、 、 、 、 、 、 、 和 在旱季和雨季均被鉴定出来; 、 和 通过ICIM和关联作图均被检测到。 具有最高的PV(17.8%)和加性效应(2.5 ppm)。还观察到不同性状之间的上位性和QTL共定位。YLD和Zn的多性状基因组预测值分别为0.24和0.16。 与一个参与锌转运的基因( )共定位。这些结果对水稻锌生物强化具有重要意义。

补充信息

在线版本包含可在10.1007/s13562-024-00886-0获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/4983fb6530cb/13562_2024_886_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/c79ea4b259fc/13562_2024_886_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/510755b3bc75/13562_2024_886_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/f7c84bc59647/13562_2024_886_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/baa5491da7b0/13562_2024_886_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/340ebf5b8740/13562_2024_886_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/4d6df42486df/13562_2024_886_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/4983fb6530cb/13562_2024_886_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/c79ea4b259fc/13562_2024_886_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/510755b3bc75/13562_2024_886_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/f7c84bc59647/13562_2024_886_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/baa5491da7b0/13562_2024_886_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/340ebf5b8740/13562_2024_886_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/4d6df42486df/13562_2024_886_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/764d/12037680/4983fb6530cb/13562_2024_886_Fig7_HTML.jpg

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本文引用的文献

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Front Plant Sci. 2023 Mar 22;14:1157507. doi: 10.3389/fpls.2023.1157507. eCollection 2023.
2
Meta-QTL s and haplotypes for efficient zinc biofortification of rice.用于水稻高效锌生物强化的元数量性状位点和单倍型。
Plant Genome. 2023 Dec;16(4):e20315. doi: 10.1002/tpg2.20315. Epub 2023 Mar 10.
3
Identification of Genomic Regions Associated with High Grain Zn Content in Polished Rice Using Genotyping-by-Sequencing (GBS).
利用简化基因组测序(GBS)鉴定与精米中高锌含量相关的基因组区域
Plants (Basel). 2022 Dec 28;12(1):144. doi: 10.3390/plants12010144.
4
Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.多性状基因组预测模型提高了水稻籽粒微量元素的预测能力。
Front Genet. 2022 Jun 22;13:883853. doi: 10.3389/fgene.2022.883853. eCollection 2022.
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Improving Zinc and Iron Biofortification in Wheat through Genomics Approaches.通过基因组学方法提高小麦中的锌和铁的生物强化。
Mol Biol Rep. 2022 Aug;49(8):8007-8023. doi: 10.1007/s11033-022-07326-z. Epub 2022 Jun 3.
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