Evidence map›Paper›PMID 38062375›Full record

ArticleBMC plant biology2023

Genetic dissection of maize (Zea maysL.) trace element traits using genome-wide association studies.

Hang Zhu, Ruiqiang Lai, Weiwei Chen, Chuanli Lu, Zaid Chachar, Siqi Lu, Huanzhang Lin, Lina Fan, Yuanqiang Hu, Yuxing An and 3 more

Abstract read
In one paragraph

Article in BMC plant biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Hang Zhu *Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China.
Ruiqiang Lai *Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China.
Weiwei Chen *Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China.
Chuanli Lu *Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China.
Zaid ChacharZhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China.
Siqi LuZhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China.
Huanzhang LinZhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China.
Lina FanZhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China.
Yuanqiang HuZhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China.
Yuxing AnInstitute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China.
Xuhui LiInstitute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China. ylws2201@163.com.
Xiangbo ZhangInstitute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, Guangdong, China. zhxiangbo@126.com.
Yongwen QiZhongkai University of Agriculture and Engineering, Guangzhou, 510225, Guangdong, China. yongwen2001@163.com.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2021A1515110745Guangdong Province special projects in key fields of ordinary colleges and universities, Guangdong Province key construction discipline research ability enhancement project 2022ZDJS023National Natural Science Foundation of China 32072027The project was supported by the Laboratory of Lingnan Modern Agriculture Project NZ2021014
6 · The paper itself

Abstract

Maize (Zea mays L.) is an important food and feed crop worldwide and serves as a a vital source of biological trace elements, which are important breeding targets. In this study, 170 maize materials were used to detect QTNs related to the content of Mn, Fe and Mo in maize grains through two GWAS models, namely MLM_Q + K and MLM_PCA + K. The results identified 87 (Mn), 205 (Fe), and 310 (Mo) QTNs using both methods in the three environments. Considering comprehensive factors such as co-location across multiple environments, strict significance threshold, and phenotypic value in multiple environments, 8 QTNs related to Mn, 10 QTNs related to Fe, and 26 QTNs related to Mo were used to identify 44 superior alleles. Consequently, three cross combinations with higher Mn element, two combinations with higher Fe element, six combinations with higher Mo element, and two combinations with multiple element (Mn/Fe/Mo) were predicted to yield offspring with higher numbers of superior alleles, thereby increasing the likelihood of enriching the corresponding elements. Additionally, the candidate genes identified 100 kb downstream and upstream the QTNs featured function and pathways related to maize elemental transport and accumulation. These results are expected to facilitate the screening and development of high-quality maize varieties enriched with trace elements, establish an important theoretical foundation for molecular marker assisted breeding and contribute to a better understanding of the regulatory network governing trace elements in maize.

Indexed as

Trace ElementsGenome-Wide Association StudyPhenotypePlant BreedingZea maysTrace ElementsCandidate geneCrosses combinationsGWASMaizeTrace element

Identifiers

PMID38062375
PMCPMC10704835

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.