Evidence map›Paper›PMID 33313526›Full record

ArticlePlant phenomics (Washington, D.C.)2019

Precision Phenotyping Reveals Novel Loci for Quantitative Resistance to Septoria Tritici Blotch.

Steven Yates, Alexey Mikaberidze, Simon G Krattinger, Michael Abrouk, Andreas Hund, Kang Yu, Bruno Studer, Simone Fouche, Lukas Meile, Danilo Pereira and 2 more

Open access · goldAbstract read
In one paragraph

Article in Plant phenomics (Washington, D.C.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
9.9field-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

33 citing papers in PubMed, 75 citations in OpenAlex.

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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

12 authors at 2 institutions in 2 countries.

Steven YatesMolecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-5904-0072
Alexey MikaberidzePlant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
Simon G KrattingerBiological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Michael AbroukBiological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.ORCID https://orcid.org/0000-0001-9082-1432
Andreas HundCrop Science, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-2309-1625
Kang YuCrop Science, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-0686-6783
Bruno StuderMolecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland.
Simone FouchePlant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
Lukas MeilePlant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
Danilo PereiraPlant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-1247-242X
Petteri KaristoPlant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-4807-0190
Bruce A McDonaldPlant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-5332-2172
ETH Zurich · CHKing Abdullah University of Science and Technology · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate, high-throughput phenotyping for quantitative traits is a limiting factor for progress in plant breeding. We developed an automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance. 335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform the GWAS. We identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four independent resistance traits. Sixteen of the intervals overlapped with known STB resistance intervals, suggesting that our phenotyping approach can identify simultaneously (i.e., in a single experiment) many previously defined STB resistance intervals. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance, perhaps representing resistance to pathogen strains that had not been tested in earlier experiments. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes. Our experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance.

Identifiers

PMID33313526
PMCPMC7706307
OpenAlexW2976848213

What OpenQuestion holds

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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.