Evidence map›Paper›PMID 25210737›Full record

ArticlePloS one2014

Quantitative trait loci for mercury accumulation in maize (Zea mays L.) identified using a RIL population.

Zhongjun Fu, Weihua Li, Qinbin Zhang, Long Wang, Xiaoxiang Zhang, Guiliang Song, Zhiyuan Fu, Dong Ding, Zonghua Liu, Jihua Tang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2014. 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
1.3field-weighted citation impact, top 22% of its field
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, 18 citations in OpenAlex.

  1. Article
  2. Article
  3. Genome-wide association analysis of lead accumulation in maize.Molecular genetics and genomics : MGG · 2018
    Article
  4. Article
  5. Article
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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

10 authors at 1 institution in 1 country.

Zhongjun FuNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China; Maize Research Institute, Chongqing Academy of Agricultural Sciences, Chongqing, China.
Weihua LiNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.
Qinbin ZhangNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.
Long WangNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.
Xiaoxiang ZhangNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.
Guiliang SongNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.
Zhiyuan FuNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.
Dong DingNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.
Zonghua LiuNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.
Jihua TangNational Key Laboratory of Wheat and Maize Crops Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.
Henan Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To investigate the genetic mechanism of mercury accumulation in maize (Zea mays L.), a population of 194 recombinant inbred lines derived from an elite hybrid Yuyu 22, was used to identify quantitative trait loci (QTLs) for mercury accumulation at two locations. The results showed that the average Hg concentration in the different tissues of maize followed the order: leaves > bracts > stems > axis > kernels. Twenty-three QTLs for mercury accumulation in five tissues were detected on chromosomes 1, 4, 7, 8, 9 and 10, which explained 6.44% to 26.60% of the phenotype variance. The QTLs included five QTLs for Hg concentration in kernels, three QTLs for Hg concentration in the axis, six QTLs for Hg concentration in stems, four QTLs for Hg concentration in bracts and five QTLs for Hg concentration in leaves. Interestingly, three QTLs, qKHC9a, qKHC9b, and qBHC9 were in linkage with two QTLs for drought tolerance. In addition, qLHC1 was in linkage with two QTLs for arsenic accumulation. The study demonstrated the concentration of Hg in Hg-contaminated paddy soil could be reduced, and maize production maintained simultaneously by selecting and breeding maize Hg pollution-safe cultivars (PSCs).

Indexed as

Genes, PlantChromosome MappingChromosomes, PlantEnvironmental PollutionMercuryQuantitative Trait LociSoil PollutantsZea maysMercurySoil Pollutants

Identifiers

PMID25210737
PMCPMC4161392
OpenAlexW2074004742

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.