Evidence map›Paper›PMID 39503804›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2024

Leveraging genomic prediction to surpass current yield gains in spring barley.

Johanna Åstrand, Firuz Odilbekov, Ramesh Vetukuri, Alf Ceplitis, Aakash Chawade

Abstract read
In one paragraph

Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Johanna ÅstrandDepartment of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.ORCID http://orcid.org/0000-0003-3602-4410
Firuz OdilbekovLantmännen Lantbruk, Svalöv, Sweden.ORCID http://orcid.org/0000-0003-0533-8230
Ramesh VetukuriDepartment of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.ORCID http://orcid.org/0000-0001-7129-5326
Alf CeplitisLantmännen Lantbruk, Svalöv, Sweden.ORCID http://orcid.org/0009-0004-2104-1976
Aakash ChawadeDepartment of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden. aakash.chawade@slu.se.ORCID http://orcid.org/0000-0002-6500-4139

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

key messageGenetic gain in Nordic spring barley varieties was estimated to 1.07% per year. Additionally, genomic predictive ability for yield was 0.61 in a population of breeding lines. Barley is one of the most important crops in Europe and meeting the growing demand for food and feed requires continuous increase in yield. Genomic prediction (GP) has the potential to be a cost-efficient tool in breeding for complex traits; however, the rate of yield improvement in current barley varieties is unknown. This study therefore investigated historical and current genetic gains in spring barley and how accounting for row-type population stratification in a breeding population influences GP results. The genetic gain in yield was estimated using historical data from field trials from 2014 to 2022, with 22-60 market varieties grown yearly. The genetic gain was estimated to 1.07% per year for all varieties, serving as a reference point for future breeding progress. To analyse the potential of using GP in spring barley a population of 375 breeding lines of two-row and six-row barley were tested in multi-environment trials in 2019-2022. The genetic diversity of the row-types was examined and used as a factor in the predictions, and the potential to predict untested locations using yield data from other locations was explored. This resulted in an overall predictive ability of 0.61 for yield (kg/ha), with 0.57 and 0.19 for the separate two-row and the six-row breeding lines, respectively. Together this displays the potential of implementing GP in breeding programs and the genetic gain in spring barley market varieties developed through GP will help in quantifying the benefit of GP over conventional breeding in the future.

Indexed as

HordeumPlant BreedingGenetic VariationGenome, PlantGenomicsGenotypePhenotypePolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID39503804
PMCPMC11541387

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Read underepoch 390

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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.