Evidence map›Paper›PMID 34961779›Full record

ArticleScientific reports2021

Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens.

Fabricio Almeida-Silva, Thiago M Venancio

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Review
  5. A Novel NeotropicalMicroorganisms · 2025
    Article
  6. Article
  7. Pathogenicity, Host Resistance, and Genetic Diversity ofJournal of fungi (Basel, Switzerland) · 2024
    Article
  8. Article
  9. Article
  10. 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

2 authors.

Fabricio Almeida-SilvaLaboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Lamego 2000, P5, sala 217, Campos dos Goytacazes, RJ, Brazil. fabricio_almeidasilva@hotmail.com.
Thiago M VenancioLaboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Lamego 2000, P5, sala 217, Campos dos Goytacazes, RJ, Brazil. thiago.venancio@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soybean is one of the most important legume crops worldwide. However, soybean yield is dramatically affected by fungal diseases, leading to economic losses of billions of dollars yearly. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. We identified 188, 56, 11, 8, and 3 high-confidence candidates for resistance to F. virguliforme, F. graminearum, C. gregata, M. phaseolina and P. pachyrhizi, respectively. The prioritized candidate genes are highly conserved in the pangenome of cultivated soybeans and are heavily biased towards fungal species-specific defense responses. The vast majority of the prioritized candidate resistance genes are related to plant immunity processes, such as recognition, signaling, oxidative stress, systemic acquired resistance, and physical defense. Based on the number of resistance alleles, we selected the five most resistant accessions against each fungal species in the soybean USDA germplasm. Interestingly, the most resistant accessions do not reach the maximum theoretical resistance potential. Hence, they can be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression network generated here is available in a user-friendly web application ( https://soyfungigcn.venanciogroup.uenf.br/ ) and an R/Shiny package ( https://github.com/almeidasilvaf/SoyFungiGCN ) that serve as a public resource to explore soybean-pathogenic fungi interactions at the transcriptional level.

Indexed as

Gene Expression Regulation, PlantFusariumGene Regulatory NetworksGenome-Wide Association StudyGlycine maxHost-Pathogen InteractionsPhakopsora pachyrhiziPlant DiseasesPlant ProteinsPlant Proteins

Identifiers

PMID34961779
PMCPMC8712514

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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.