↓ Skip to main content

Integration of sudden death syndrome resistance loci in the soybean genome

Overview of attention for article published in Theoretical and Applied Genetics, February 2018
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
40 Mendeley
Title
Integration of sudden death syndrome resistance loci in the soybean genome
Published in
Theoretical and Applied Genetics, February 2018
DOI 10.1007/s00122-018-3063-0
Pubmed ID
Authors

Hao-Xun Chang, Mitchell G. Roth, Dechun Wang, Silvia R. Cianzio, David A. Lightfoot, Glen L. Hartman, Martin I. Chilvers

Abstract

Complexity and inconsistencies in resistance mapping publications of soybean sudden death syndrome (SDS) result in interpretation difficulty. This review integrates SDS mapping literature and proposes a new nomenclature system for reproducible SDS resistance loci. Soybean resistance to sudden death syndrome (SDS) is composed of foliar resistance to phytotoxins and root resistance to pathogen invasion. There are more than 80 quantitative trait loci (QTL) and dozens of single nucleotide polymorphisms (SNPs) associated with soybean resistance to SDS. The validity of these QTL and SNPs is questionable because of the complexity in phenotyping methodologies, the disease synergism between SDS and soybean cyst nematode (SCN), the variability from the interactions between soybean genotypes and environments, and the inconsistencies in the QTL nomenclature. This review organizes SDS mapping results and proposes the Rfv (resistance to Fusarium virguliforme) nomenclature based on supporting criteria described in the text. Among ten reproducible loci receiving our Rfv nomenclature, Rfv18-01 is mostly supported by field studies and it co-localizes to the SCN resistance locus rhg1. The possibility that Rfv18-01 is a pleiotropic resistance locus and the concern about Rfv18-01 being confounded with Rhg1 is discussed. On the other hand, Rfv06-01, Rfv06-02, Rfv09-01, Rfv13-01, and Rfv16-01 were identified both by screening soybean leaves against phytotoxic culture filtrates and by evaluating SDS severity in fields. Future phenotyping using leaf- and root-specific resistance screening methodologies may improve the precision of SDS resistance, and advanced genetic studies may further clarify the interactions among soybean genotypes, F. virguliforme, SCN, and environments. The review provides a summary of the SDS resistance literature and proposes a framework for communicating SDS resistance loci for future research considering molecular interactions and genetic breeding for soybean SDS resistance.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 30%
Student > Bachelor 3 8%
Student > Doctoral Student 3 8%
Student > Postgraduate 2 5%
Researcher 2 5%
Other 5 13%
Unknown 13 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 45%
Biochemistry, Genetics and Molecular Biology 3 8%
Arts and Humanities 1 3%
Social Sciences 1 3%
Engineering 1 3%
Other 0 0%
Unknown 16 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 April 2018.
All research outputs
#14,050,687
of 23,794,258 outputs
Outputs from Theoretical and Applied Genetics
#2,651
of 3,565 outputs
Outputs of similar age
#228,012
of 448,776 outputs
Outputs of similar age from Theoretical and Applied Genetics
#26
of 48 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,565 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 448,776 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.