Evidence map›Paper›PMID 35716230›Full record

ArticleRice (New York, N.Y.)2022

Assessment of Rice Sheath Blight Resistance Including Associations with Plant Architecture, as Revealed by Genome-Wide Association Studies.

Danting Li, Fantao Zhang, Shannon R M Pinson, Jeremy D Edwards, Aaron K Jackson, Xiuzhong Xia, Georgia C Eizenga

Open access · goldAbstract read
In one paragraph

Article in Rice (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.6field-weighted citation impact, top 6% 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

11 citing papers in PubMed, 22 citations in OpenAlex.

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  7. Ectopic Expression ofJournal of fungi (Basel, Switzerland) · 2023
    Article
  8. Article
  9. Progress in rice sheath blight resistance research.Frontiers in plant science · 2023
    Review
  10. Oxalactam A, a Novel Macrolactam with Potent Anti-Molecules (Basel, Switzerland) · 2022
    Article
  11. 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

7 authors at 3 institutions in 2 countries.

Danting LiGuangxi Key Laboratory of Rice Genetics and Breeding, Rice Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, Guangxi, China.
Fantao ZhangCollege of Life Sciences, Jiangxi Normal University, Nanchang, Jiangxi, China.
Shannon R M PinsonUSDA Dale Bumpers National Rice Research Center, 2890 Highway 130 East, Stuttgart, AR, 72160, USA. shannon.pinson@usda.gov.ORCID http://orcid.org/0000-0002-9925-1322
Jeremy D EdwardsUSDA Dale Bumpers National Rice Research Center, 2890 Highway 130 East, Stuttgart, AR, 72160, USA.
Aaron K JacksonUSDA Dale Bumpers National Rice Research Center, 2890 Highway 130 East, Stuttgart, AR, 72160, USA.
Xiuzhong XiaGuangxi Key Laboratory of Rice Genetics and Breeding, Rice Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, Guangxi, China.
Georgia C EizengaUSDA Dale Bumpers National Rice Research Center, 2890 Highway 130 East, Stuttgart, AR, 72160, USA. georgia.eizenga@usda.gov.ORCID http://orcid.org/0000-0003-0330-9225
Dale Bumpers National Rice Research Center · USRice Research Institute · CNJiangxi Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSheath blight (ShB) disease caused by Rhizoctonia solani Kühn, is one of the most economically damaging rice (Oryza sativa L.) diseases worldwide. There are no known major resistance genes, leaving only partial resistance from small-effect QTL to deploy for cultivar improvement. Many ShB-QTL are associated with plant architectural traits detrimental to yield, including tall plants, late maturity, or open canopy from few or procumbent tillers, which confound detection of physiological resistance.

resultsTo identify QTL for ShB resistance, 417 accessions from the Rice Diversity Panel 1 (RDP1), developed for association mapping studies, were evaluated for ShB resistance, plant height and days to heading in inoculated field plots in Arkansas, USA (AR) and Nanning, China (NC). Inoculated greenhouse-grown plants were used to evaluate ShB using a seedling-stage method to eliminate effects from height or maturity, and tiller (TN) and panicle number (PN) per plant. Potted plants were used to evaluate the RDP1 for TN and PN. Genome-wide association (GWA) mapping with over 3.4 million SNPs identified 21 targeted SNP markers associated with ShB which tagged 18 ShB-QTL not associated with undesirable plant architecture traits. Ten SNPs were associated with ShB among accessions of the Indica subspecies, ten among Japonica subspecies accessions, and one among all RDP1 accessions. Across the 18 ShB QTL, only qShB4-1 was not previously reported in biparental mapping studies and qShB9 was not reported in the GWA ShB studies. All 14 PN QTL overlapped with TN QTL, with 15 total TN QTL identified. Allele effects at the five TN QTL co-located with ShB QTL indicated that increased TN does not inevitably increase disease development; in fact, for four ShB QTL that overlapped TN QTL, the alleles increasing resistance were associated with increased TN and PN, suggesting a desirable coupling of alleles at linked genes.

conclusionsNineteen accessions identified as containing the most SNP alleles associated with ShB resistance for each subpopulation were resistant in both AR and NC field trials. Rice breeders can utilize these accessions and SNPs to develop cultivars with enhanced ShB resistance along with increased TN and PN for improved yield potential.

Indexed as

Genome-wide association mappingOryza sativaRhizoctonia solaniRiceSheath blight diseaseTillering

Identifiers

PMID35716230
PMCPMC9206596
OpenAlexW4283078650

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.