Evidence map›Paper›PMID 32569428›Full record

ArticlePlant biotechnology journal2021

Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high-throughput phenotypic analysis.

Ying Zhang, Jinglu Wang, Jianjun Du, Yanxin Zhao, Xianju Lu, Weiliang Wen, Shenghao Gu, Jiangchuan Fan, Chuanyu Wang, Sheng Wu and 4 more

Open access · goldAbstract read
In one paragraph

Article in Plant biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
4.9field-weighted citation impact, top 5% 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

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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

14 authors at 3 institutions in 2 countries.

Ying ZhangBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.ORCID 0000-0001-5275-0813
Jinglu WangBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Jianjun DuBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Yanxin ZhaoBeijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.ORCID 0000-0003-2044-6846
Xianju LuBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Weiliang WenBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Shenghao GuBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Jiangchuan FanBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Chuanyu WangBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Sheng WuBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Yongjian WangBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Shengjin LiaoBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Chunjiang ZhaoBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Xinyu GuoBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
National Engineering Research Center for Information Technology in Agriculture · CNZero to Three · USBeijing Academy of Agricultural and Forestry Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time-consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at high-throughput are urgently needed for large crop populations. In this study, we developed a standard process for stem micro-CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems including geometry-related, morphology-related and distribution-related traits. Next, we analysed the phenotypic variation of stem vascular bundles between natural population subgroup (480 inbred lines) based on 48 comprehensively phenotypic information. Also, the first database for stem micro-phenotypes, MaizeSPD, was established, storing 554 pieces of basic information of maize inbred lines, 523 pieces of experimental information, 1008 pieces of CT scanning images and processed images, and 24 192 pieces of phenotypic data. Combined with genome-wide association studies (GWASs), a total of 1562 significant single nucleotide polymorphism (SNPs) were identified for 30 stem micro-phenotypic traits, and 84 unique genes of 20 traits such as VBNum, VBAvArea and PZVBDensity were detected. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to plant signal transduction and stress response. The results presented here will advance our knowledge about phenotypic trait components of stem vascular bundles and provide useful information for understanding the genetic controls of vascular bundle formation and development.

Indexed as

Plant Vascular BundleZea maysGenetic Association StudiesPhenotypePlant BreedingPolymorphism, Single Nucleotidegenome-wide association studiesMaizeSPDmaize stemmicro-CTmicro-phenotypevascular bundle

Identifiers

PMID32569428
PMCPMC7769239
OpenAlexW3036906927

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.