Evidence map›Paper›PMID 42808425›Full record

ArticleThe plant genome2026

A haplotype-based approach to explore genotype-by-environment interactions for barley yield.

Stephanie M Brunner, Zachary Aldiss, Samir Alahmad, Madhav Pandit, Silvina Baraibar, Dini Ganesalingam, David Moody, Lee Hickey, Kai P Voss-Fels, Hannah Robinson

Abstract read
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. Not yet cited in PubMed.

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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Stephanie M BrunnerQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia.ORCID https://orcid.org/0009-0008-3916-1238
Zachary AldissQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia.ORCID https://orcid.org/0000-0003-2468-1050
Samir AlahmadQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia.ORCID https://orcid.org/0000-0003-0136-1740
Madhav PanditQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia.ORCID https://orcid.org/0000-0002-0148-1569
Silvina BaraibarInterGrain Pty Ltd, Perth, Western Australia, Australia.ORCID https://orcid.org/0000-0002-0009-919X
Dini GanesalingamInterGrain Pty Ltd, Perth, Western Australia, Australia.ORCID https://orcid.org/0000-0002-6989-3252
David MoodyInterGrain Pty Ltd, Perth, Western Australia, Australia.ORCID https://orcid.org/0000-0001-8777-2238
Lee HickeyQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia.ORCID https://orcid.org/0000-0001-6909-7101
Kai P Voss-FelsQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia.ORCID https://orcid.org/0000-0003-0782-366X
Hannah RobinsonQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia.ORCID https://orcid.org/0000-0002-8303-8076

Funding

Australian Research Council IC230100016Australian Research Council LP200200927Deutsche Forschungsgemeinschaft (DFG) and University of Queensland IRTG 2843LOEWE Professorship for Plant Breeding at Hochschule Geisenheim University, supported by the Hessian Ministry of Science and Research, Art and Culture (HMWK, Hesse, Germany)
6 · The paper itself

Abstract

Climate change poses a significant threat to cereal production, making it increasingly important to understand the role of genotype-by-environment interactions (GEIs) in crops. Traditional breeding approaches consider GEIs at a whole genotype level missing valuable breeding potential that can be dissected by exploring this relationship at a chromosome segment level. A robust commercial dataset consisting of 961 spring barley (Hordeum vulgare L.) genotypes grown across 12 sites was used to explore these questions and to co-locate haplotypes under GEIs with key physiology genes to better understand the drivers of GEIs. A genomic best linear unbiased prediction model for barley yield was fitted to the data, where the GEI term had a factor analytic 4 (FA4) variance structure. Using the first three factor loadings from the FA4 model, the 12 sites were grouped into four environmental clusters. The barley single nucleotide polymorphisms were partitioned into 1741 haploblocks based on linkage disequilibrium and the local genomic estimated breeding values were calculated for each haplotype in each environmental cluster. The GEIs of each haploblock across the environmental clusters were explored, and those under large GEIs were identified across several chromosomes and found to vastly vary in size. Further analysis of a few key haploblocks revealed that for some blocks the best haplotypes were similar across clusters, but for others they were vastly different. This study provides a novel framework for investigating GEIs at a haplotype level, which can be utilized in a range of breeding applications across traits and crops.

Indexed as

Gene-Environment InteractionHaplotypesHordeumGenotypeLinkage DisequilibriumPlant BreedingPolymorphism, Single Nucleotide

Identifiers

PMID42808425
PMCPMC13621458

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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.