Evidence map›Paper›PMID 32594266›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2020

A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments.

Xiaoxiang Zhang, Zhongrong Guan, Zhaoling Li, Peng Liu, Langlang Ma, Yinchao Zhang, Lang Pan, Shijiang He, Yanling Zhang, Peng Li and 6 more

Abstract read
PubMed Publisher
In one paragraph

Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 pooled it
7.2field-weighted citation impact, top 2% 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

45 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.

  1. Pooled it
  2. Combined linkage mapping and association analysis reveal the genetic control of starch content in maize kernel.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  3. Article
  4. Genetic dissection of oil content in maize kernel using combined genome-wide association analysis and linkage mapping.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  5. Review
  6. Article
  7. Candidate genes for kernel ratio in maize identified via combined GWAS and QTL mapping across multiple environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Article
  8. Article
  9. Article
  10. GWAS and gene co-expression network analysis reveal the genetic control of seed germination under salt stress in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Quantitative trait locus analysis of gray leaf spot resistance in the maize IBM Syn10 DH population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024
    Article
  19. Article
  20. A combination of QTL mapping and genome-wide association study revealed the key gene for husk number in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024
    Article
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

16 authors at 3 institutions in 1 country.

Xiaoxiang Zhang *Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Zhongrong Guan *Chongqing Yudongnan Academy of Agricultural Sciences, Chongqing, 408000, China.
Zhaoling LiKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Peng LiuKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Langlang MaKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Yinchao ZhangKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Lang PanKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Shijiang HeKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Yanling ZhangKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Peng LiKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Fei GeKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Chaoying ZouKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Yongcong HeKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Shibin GaoKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Guangtang PanKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
Yaou ShenKey Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China. shenyaou@sicau.edu.cn.ORCID http://orcid.org/0000-0001-7603-0108
Sichuan Agricultural University · CNChongqing Academy of Agricultural Sciences · CNSouthwest Petroleum University · CN

Funding

This work was supported by the Major Project of China on New varieties of GMO Cultivation 2016ZX08003003
6 · The paper itself

Abstract

key messageUsing GWAS and QTL mapping identified 100 QTL and 138 SNPs, which control yield-related traits in maize. The candidate gene GRMZM2G098557 was further validated to regulate ear row number by using a segregation population. Understanding the genetic basis of yield-related traits contributes to the improvement of grain yield in maize. This study used an inter-mated B73 × Mo17 (IBM) Syn10 doubled-haploid (DH) population and an association panel to identify the genetic loci responsible for nine yield-related traits in maize. Using quantitative trait loci (QTL) mapping, 100 QTL influencing these traits were detected across different environments in the IBM Syn10 DH population, with 25 co-detected in multiple environments. Using a genome-wide association study (GWAS), 138 single-nucleotide polymorphisms (SNPs) were identified as correlated with these traits (P < 2.04E-06) in the association panel. Twenty-one pleiotropic QTL/SNPs were identified to control different traits in both populations. A combination of QTL mapping and GWAS uncovered eight significant SNPs (PZE-101097575, PZE-103169263, ZM011204-0763, PZE-104044017, PZE-104123110, SYN8062, PZE-108060911, and PZE-102043341) that were co-located within seven QTL confidence intervals. According to the eight co-localized SNPs by the two populations, 52 candidate genes were identified, among which the ear row number (ERN)-associated SNP SYN8062 was closely linked to SBP-transcription factor 7 (GRMZM2G098557). Several SBP-transcription factors were previously demonstrated to modulate maize ERN. We then validated the phenotypic effects of SYN8062 in the IBM Syn10 DH population, indicating that the ERN of the lines with the A-allele in SYN8062 was significantly (P < 0.05) larger than that of the lines with the G-allele in SYN8062 in each environment. These findings provide valuable information for understanding the genetic mechanisms of maize grain yield formation and for improving molecular marker-assisted selection for the high-yield breeding of maize.

Indexed as

Chromosome MappingGenetic Association StudiesEdible GrainGenes, PlantPhenotypePolymorphism, Single NucleotideQuantitative Trait LociSeedsZea mays

Identifiers

PMID32594266
OpenAlexW3038037001

What OpenQuestion holds

Textmetadata
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.