Evidence map›Paper›PMID 26940065›Full record

ArticleBMC genomics2016

Genetic dissection of maize plant architecture with an ultra-high density bin map based on recombinant inbred lines.

Zhiqiang Zhou, Chaoshu Zhang, Yu Zhou, Zhuanfang Hao, Zhenhua Wang, Xing Zeng, Hong Di, Mingshun Li, Degui Zhang, Hongjun Yong and 3 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed
11.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

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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

64 citing papers in PubMed, 135 citations in OpenAlex.

  1. Fine mapping and candidate gene analysis of a major QTL qHSW_11 for seed weight in soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  2. International journal of molecular sciences · 2026
    Article
  3. Article
  4. Genome-Wide Association Analysis of Fresh Maize.International journal of molecular sciences · 2025
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4 more citing papers are in PubMed but not listed here.

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 at 3 institutions in 1 country.

Zhiqiang ZhouInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian District, Beijing, 100081, China. zzq880211@163.com.
Chaoshu ZhangCollege of Agronomy, Northeast Agricultural University, Mucai Street, XiangFang District, Harbin, Heilongjiang, 150030, China. zhangchaoshu@126.com.
Yu ZhouCollege of Agronomy, Northeast Agricultural University, Mucai Street, XiangFang District, Harbin, Heilongjiang, 150030, China. Zhouyu0924@126.com.
Zhuanfang HaoInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian District, Beijing, 100081, China. haozhuanfang@caas.cn.
Zhenhua WangCollege of Agronomy, Northeast Agricultural University, Mucai Street, XiangFang District, Harbin, Heilongjiang, 150030, China. zhenhuawang_2006@163.com.
Xing ZengCollege of Agronomy, Northeast Agricultural University, Mucai Street, XiangFang District, Harbin, Heilongjiang, 150030, China. zengxing980@hotmail.com.
Hong DiCollege of Agronomy, Northeast Agricultural University, Mucai Street, XiangFang District, Harbin, Heilongjiang, 150030, China. dihongdh@163.com.
Mingshun LiInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian District, Beijing, 100081, China. limingshun@caas.cn.
Degui ZhangInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian District, Beijing, 100081, China. zhangdegui@caas.cn.
Hongjun YongInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian District, Beijing, 100081, China. yonghongjun@caas.cn.
Shihuang ZhangInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian District, Beijing, 100081, China. zhangshihuang@caas.cn.
Jianfeng WengInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian District, Beijing, 100081, China. wengjianfeng@caas.cn.
Xinhai LiInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Zhongguancun South Street, Haidian District, Beijing, 100081, China. lixinhai@caas.cn.
Institute of Crop Sciences · CNNortheast Agricultural University · CNChinese Academy of Agricultural Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPlant architecture attributes, such as plant height, ear height, and internode number, have played an important role in the historical increases in grain yield, lodging resistance, and biomass in maize (Zea mays L.). Analyzing the genetic basis of variation in plant architecture using high density QTL mapping will be of benefit for the breeding of maize for many traits. However, the low density of molecular markers in existing genetic maps has limited the efficiency and accuracy of QTL mapping. Genotyping by sequencing (GBS) is an improved strategy for addressing a complex genome via next-generation sequencing technology. GBS has been a powerful tool for SNP discovery and high-density genetic map construction. The creation of ultra-high density genetic maps using large populations of advanced recombinant inbred lines (RILs) is an efficient way to identify QTL for complex agronomic traits.

resultsA set of 314 RILs derived from inbreds Ye478 and Qi319 were generated and subjected to GBS. A total of 137,699,000 reads with an average of 357,376 reads per individual RIL were generated, which is equivalent to approximately 0.07-fold coverage of the maize B73 RefGen_V3 genome for each individual RIL. A high-density genetic map was constructed using 4183 bin markers (100-Kb intervals with no recombination events). The total genetic distance covered by the linkage map was 1545.65 cM and the average distance between adjacent markers was 0.37 cM with a physical distance of about 0.51 Mb. Our results demonstrated a relatively high degree of collinearity between the genetic map and the B73 reference genome. The quality and accuracy of the bin map for QTL detection was verified by the mapping of a known gene, pericarp color 1 (P1), which controls the color of the cob, with a high LOD value of 80.78 on chromosome 1. Using this high-density bin map, 35 QTL affecting plant architecture, including 14 for plant height, 14 for ear height, and seven for internode number were detected across three environments. Interestingly, pQTL10, which influences all three of these traits, was stably detected in three environments on chromosome 10 within an interval of 14.6 Mb. Two MYB transcription factor genes, GRMZM2G325907 and GRMZM2G108892, which might regulate plant cell wall metabolism are the candidate genes for qPH10.

conclusionsHere, an ultra-high density accurate linkage map for a set of maize RILs was constructed using a GBS strategy. This map will facilitate identification of genes and exploration of QTL for plant architecture in maize. It will also be helpful for further research into the mechanisms that control plant architecture while also providing a basis for marker-assisted selection.

Indexed as

Genetic MarkersQuantitative Trait LociChromosome MappingDNA, PlantGenetic LinkageGenotyping TechniquesHigh-Throughput Nucleotide SequencingPhenotypePlant BreedingPolymorphism, Single NucleotideZea maysDNA, PlantGenetic Markers

Identifiers

PMID26940065
PMCPMC4778306
OpenAlexW2289812953

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LicenceCC BY
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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.