Evidence map›Paper›PMID 42864710›Full record

ArticleThe plant genome2026

Enhanced disease resistance in chickpea and canola via MAGPIE: Major-Effect Allele and Genomic Polygenic Integration for Enhancement.

Zibei Lin, Irene van den Berg, Shahin Yazdifar, Shimna Sudheesh, Adnan Riaz, Denise Barbulescu, Yongjun Li, Kristy Hobson, Muhammad Ahsan Asif, Matthew Hayden and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The plant genome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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

1 citing paper in PubMed.

  1. 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

11 authors.

Zibei LinAgriculture Science and Technology, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.ORCID https://orcid.org/0009-0001-8325-4326
Irene van den BergAgriculture Science and Technology, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.
Shahin YazdifarAgriculture Science and Technology, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.
Shimna SudheeshAgriculture Science and Technology, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.
Adnan RiazAgriculture Science and Technology, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.
Denise BarbulescuAgriculture Science and Technology, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.
Yongjun LiAgriculture Science and Technology, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.
Kristy HobsonNSW Department of Primary Industries and Regional Development, Tamworth, New South Wales, Australia.
Muhammad Ahsan AsifNSW Department of Primary Industries and Regional Development, Tamworth, New South Wales, Australia.
Matthew HaydenAgriculture Science and Technology, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.
Sukhjiwan KaurAgriculture Science and Technology, Department of Energy, Environment and Climate Action, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.

Funding

Agriculture VictoriaGrains Research and Development Corporation
6 · The paper itself

Abstract

The global agricultural industry suffers major economic losses due to fungal diseases. Though selective breeding and introgression of resistance genes with major effects have helped to reduce the impact of these diseases, resistance breakdown occurs within a few generations. Quantitative resistance that relies on multiple small effect genes has proven to be more durable than relying on major effect genes. In this study, we present MAGPIE (Major-Effect Allele and Genomic Polygenic Integration for Enhancement), a computational framework that integrates genomic data, predictive breeding approaches, and cross selection to accumulate favorable major-effect alleles and polygenic resistance. MAGPIE was implemented to improve fungal resistance in chickpea and canola. Seven cycles of selection resulted in 76% genetic gain for resistance to Ascochyta blight in chickpea, and 15% genetic gain for blackleg resistance in canola. For both diseases, more than 90% of genetic gain could be attributed to polygenic resistance rather than major effect genes. Progenies from both breeding programs were field validated, with stem breakage being reduced by 9.7% in chickpea, and survival percentage improved by 10% compared to the founder parents in canola. These results highlight the power of integrating genomic data and predictive breeding approaches to deliver durable, polygenic resistance. This work demonstrates a scalable framework for accelerating resistance improvement in crops vulnerable to rapidly evolving fungal pathogens.

Indexed as

Brassica napusCicerDisease ResistancePlant DiseasesAllelesAscomycotaGenome, PlantGenomicsMultifactorial InheritancePlant Breeding

Identifiers

PMID42864710
PMCPMC13658840

What OpenQuestion holds

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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.