Evidence map›Paper›PMID 35451789›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.

Philippe Barre, Torben Asp, Stephen Byrne, Michael Casler, Marty Faville, Odd Arne Rognli, Isabel Roldan-Ruiz, Leif Skøt, Marc Ghesquière

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
6.5field-weighted citation impact, top 3% 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

7 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Estimation of ryegrass (Frontiers in plant science · 2025
    Article
  5. Article
  6. Article
  7. Review
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

9 authors at 9 institutions in 8 countries.

Philippe BarreINRAE, UR P3F, Lusignan, France. philippe.barre@inrae.fr.
Torben AspCenter for Quantitative Genetics and Genomics, Aarhus University, Slagelse, Denmark.
Stephen ByrneTeagasc, Crop Science Department, Oak Park, Carlow, Ireland.
Michael CaslerU.S. Dairy Forage Research Center, USDA-ARS, Madison, WI, USA.
Marty FavilleAgResearch Ltd , Grasslands Research Centre, Palmerston North, New Zealand.
Odd Arne RognliDepartment of Plant Sciences, Faculty of Biosciences, Norwegian, University of Life Sciences (NMBU), Ås, Norway.
Isabel Roldan-RuizFlanders Research Institute for Agriculture, Fisheries and Food (ILVO)-Plant Sciences Unit, Melle, Belgium.
Leif SkøtIBERS, Aberystwyth University, Ceredigion, UK.
Marc GhesquièreINRAE, UR P3F, Lusignan, France.
Aarhus University · DKAgResearch · NZInstitute of Biological, Environmental and Rural Sciences · GBInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement · FRNorwegian University of Life Sciences · NOTeagasc - The Irish Agriculture and Food Development Authority · IEUnité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères · FRU.S. Dairy Forage Research Center · USVlaams Instituut voor Biotechnologie · BE

Funding

Biotechnology and Biological Sciences Research Council BBS/E/G/00003012
6 · The paper itself

Abstract

The majority of forage grass species are obligate outbreeders. Their breeding classically consists of an initial selection on spaced plants for highly heritable traits such as disease resistances and heading date, followed by familial selection on swards for forage yield and quality traits. The high level of diversity and heterozygosity, and associated decay of linkage disequilibrium (LD) over very short genomic distances, has hampered the implementation of genomic selection (GS) in these species. However, next generation sequencing technologies in combination with the development of genomic resources have recently facilitated implementation of GS in forage grass species such as perennial ryegrass (Lolium perenne L.), switchgrass (Panicum virgatum L.), and timothy (Phleum pratense L.). Experimental work and simulations have shown that GS can increase significantly the genetic gain per unit of time for traits with different levels of heritability. The main reasons are (1) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (2) a reduction in the duration of selection cycles, and less importantly (3) an increase in the selection intensity associated with an increase in the genetic variance used for selection. Nevertheless, several factors should be taken into account for the successful implementation of GS in forage grasses. For example, it has been shown that the level of relatedness between the training and the selection population is particularly critical when working with highly structured meta-populations consisting of several genetic groups. A sufficient number of markers should be used to estimate properly the kinship between individuals and to reflect the variability of major QTLs. It is also important that the prediction models are trained for relevant environments when dealing with traits with high genotype × environment interaction (G × E). Finally, in these outbreeding species, measures to reduce inbreeding should be used to counterbalance the high selection intensity that can be achieved in GS.

Indexed as

LoliumPanicumGenomeGenomicsMultifactorial InheritancePhenotypePlant BreedingForage grassesGBSGenomic selectionOutbreedingPerennial ryegrassSwardSwitchgrassSyntheticsTimothy

Identifiers

PMID35451789
OpenAlexW4225711447

What OpenQuestion holds

Textmetadata
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.