Evidence map›Paper›PMID 33771110›Full record

ArticleBMC plant biology2021

Development of genic KASP SNP markers from RNA-Seq data for map-based cloning and marker-assisted selection in maize.

Zhengjie Chen, Dengguo Tang, Jixing Ni, Peng Li, Le Wang, Jinhong Zhou, Chenyang Li, Hai Lan, Lujiang Li, Jian Liu

Open access · goldAbstract read
In one paragraph

Article in BMC plant biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
4.2field-weighted citation impact, top 6% 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.

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3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 42 citations in OpenAlex.

  1. Utilizing RNA-Seq-Derived SNPs for KASP Marker Development in Maize.Methods in molecular biology (Clifton, N.J.) · 2027
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  14. Analysis of Gene Regulatory Networks of Taro (International journal of genomics · 2025
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  20. Identification of Polymorphisms in theThe journal of poultry science · 2023
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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.

Zhengjie Chen *Maize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China.
Dengguo Tang *Maize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China.
Jixing NiMaize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China.
Peng LiMaize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China.
Le WangMaize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China.
Jinhong ZhouMaize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China.
Chenyang LiMaize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China.
Hai LanMaize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China.
Lujiang LiMaize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China.
Jian LiuMaize Research Institute, Sichuan Agricultural University, 211 Huiming Road, Wenjiang District, Chengdu City, 611000, Sichuan, China. liujian@sicau.edu.cn.
Sichuan Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMaize is one of the most important field crops in the world. Most of the key agronomic traits, including yield traits and plant architecture traits, are quantitative. Fine mapping of genes/ quantitative trait loci (QTL) influencing a key trait is essential for marker-assisted selection (MAS) in maize breeding. However, the SNP markers with high density and high polymorphism are lacking, especially kompetitive allele specific PCR (KASP) SNP markers that can be used for automatic genotyping. To date, a large volume of sequencing data has been produced by the next generation sequencing technology, which provides a good pool of SNP loci for development of SNP markers. In this study, we carried out a multi-step screening method to identify kompetitive allele specific PCR (KASP) SNP markers based on the RNA-Seq data sets of 368 maize inbred lines.

resultsA total of 2,948,985 SNPs were identified in the high-throughput RNA-Seq data sets with the average density of 1.4 SNP/kb. Of these, 71,311 KASP SNP markers (the average density of 34 KASP SNP/Mb) were developed based on the strict criteria: unique genomic region, bi-allelic, polymorphism information content (PIC) value ≥0.4, and conserved primer sequences, and were mapped on 16,161 genes. These 16,161 genes were annotated to 52 gene ontology (GO) terms, including most of primary and secondary metabolic pathways. Subsequently, the 50 KASP SNP markers with the PIC values ranging from 0.14 to 0.5 in 368 RNA-Seq data sets and with polymorphism between the maize inbred lines 1212 and B73 in in silico analysis were selected to experimentally validate the accuracy and polymorphism of SNPs, resulted in 46 SNPs (92.00%) showed polymorphism between the maize inbred lines 1212 and B73. Moreover, these 46 polymorphic SNPs were utilized to genotype the other 20 maize inbred lines, with all 46 SNPs showing polymorphism in the 20 maize inbred lines, and the PIC value of each SNP was 0.11 to 0.50 with an average of 0.35. The results suggested that the KASP SNP markers developed in this study were accurate and polymorphic.

conclusionsThese high-density polymorphic KASP SNP markers will be a valuable resource for map-based cloning of QTL/genes and marker-assisted selection in maize. Furthermore, the method used to develop SNP markers in maize can also be applied in other species.

Indexed as

Plant BreedingPolymorphism, Single NucleotideCloning, MolecularGenetic MarkersGenotyping TechniquesPolymerase Chain ReactionRNA-SeqSelection, GeneticZea maysGenetic MarkersHigh polymorphismKASPMaizeMolecular markerRNA-SeqSNPs

Identifiers

PMID33771110
PMCPMC8004444
OpenAlexW3137565981

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LicenceCC BY
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Registered trials

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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.