Evidence map›Paper›PMID 42101087›Full record

ArticleThe plant genome2026

Improving genomic prediction in wheat with random regression models with genotype-specific phenology-driven environmental covariates.

Rishap Dhakal, Guillermo Sniadower, Paula Silva, Bettina Lado, Pablo Sandro, Inés Rebollo, Martin Quincke, Julie C Dawson, Lucia Gutiérrez, Pablo González Barrios

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

10 authors.

Rishap DhakalDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Guillermo SniadowerFacultad de Agronomía, Universidad de la República, Montevideo, Uruguay.
Paula SilvaInstituto Nacional de Investigación Agropecuaria (INIA), Colonia, Uruguay.ORCID https://orcid.org/0000-0003-2655-2949
Bettina LadoFacultad de Agronomía, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0002-7054-1609
Pablo SandroDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID https://orcid.org/0009-0009-8997-0161
Inés RebolloDepartment of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, Minnesota, USA.ORCID https://orcid.org/0000-0002-9899-0801
Martin QuinckeDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Julie C DawsonDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Lucia GutiérrezDepartment of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Alnarp, Sweden.ORCID https://orcid.org/0000-0002-2957-3086
Pablo González BarriosFacultad de Agronomía, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0001-8258-158X

Funding

U.S. Department of Agriculture's National Institute of Food and AgricultureWheat Coordinated Agricultural Project (Wheat CAP) 2022-68013-36439
6 · The paper itself

Abstract

Wheat (Triticum aestivum L.), a crucial cereal crop for global food security, faces growing challenges from climate change. Future production requires varieties that are resilient to environmental extremes and fluctuations. The goal of this study was to assess strategies to increase selection response through genomic selection in wheat by integrating genotypic-specific phenology-derived environmental covariates (ECs) and random regression models (RRM) in multi-environment trials. We analyzed phenotypic and genomic data from 1683 genotypes from 2010 to 2020 across 71 environments using 45 ECs derived from vegetative, reproductive, and grain-filling phenological phases. Seven key ECs were selected via partial least squares regression to model genotype by environment interaction (GEI) and evaluate their integration in three different genomic prediction scenarios (CV0, CV1, and CV2). Genomic best linear unbiased prediction models (GBLUP), GBLUP models with GEI (GBLUP

Indexed as

Genome, PlantGenomicsTriticumEnvironmentGene-Environment InteractionGenotypeModels, GeneticPhenotypePrediction AlgorithmsRegression Analysis

Identifiers

PMID42101087
PMCPMC13155077

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.