Evidence map›Paper›PMID 42445683›Full record

ReviewFrontiers in plant science2026

Statistics and data science unlock the predictive power of quantitative genetics.

Osval A Montesinos-López, José Crossa, Srikanth Kumar Karaikal, Leonardo Ornella, Joel A Martínez-Regalado, Cinthia Leonora Murillo-Ávalos, Abelardo Montesinos-López, Iván Delgado-Enciso, Roberto de la Rosa Santa-María, Ezequiel Gamas-Alpuche and 6 more

Abstract readReview
In one paragraph

Review in Frontiers in plant science, 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
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Osval A Montesinos-LópezFacultad de Telemática, Universidad de Colima, Colima, Colima, Mexico.
José CrossaPost-Graduate College (COLPOS) Montecillos, México, Mexico.
Srikanth Kumar KaraikalPlant Breeding Department. School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States.
Leonardo OrnellaEvogenix Ltd, London, United Kingdom.
Joel A Martínez-RegaladoDepartamento de Estadística, Campus Miguel de Unamuno, Universidad de Salamanca, Salamanca, Spain.
Cinthia Leonora Murillo-ÁvalosCentro Universitario de Análisis Estadísticos y de Opinión Pública (CUOP), Universidad de Colima, Colima, Mexico.
Abelardo Montesinos-LópezCentro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Jalisco, Mexico.
Iván Delgado-EncisoSchool of Medicine, University of Colima, Colima, Mexico.
Roberto de la Rosa Santa-MaríaColegio de Postgraduados (CP), Campus Tabasco, Producción Agroalimentaria en el Trópico, H. Cárdenas, Tabasco, Mexico.
Ezequiel Gamas-AlpucheColegio de Postgraduados (CP), Campus Tabasco, Producción Agroalimentaria en el Trópico, H. Cárdenas, Tabasco, Mexico.
Verónica Miriam Guzmán-SandovalFacultad de Psicología, Universidad de Colima, Colima, Mexico.
Andrés Eduardo Solís-CovarrubiasFacultad de Telemática, Universidad de Colima, Colima, Colima, Mexico.
Nereyda C Pérez-GonzálezFacultad de Telemática, Universidad de Colima, Colima, Colima, Mexico.
Karol D Johnston NavarroFacultad de Telemática, Universidad de Colima, Colima, Colima, Mexico.
Mayte Muñoz-RosalesFacultad de Telemática, Universidad de Colima, Colima, Colima, Mexico.
Rodomiro OrtizDepartment of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Lomma, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plant breeding is undergoing a profound transition from an empirical and phenotype-centered discipline toward a predictive, inferential, and decision-oriented science. This transformation has been driven not only by the generation of large-scale genomic, phenotypic, environmental, pangenomic, and multi-omics datasets, but also by the statistical and data science methodologies required to convert such information into actionable breeding decisions. Quantitative genetics remains the theoretical foundation for understanding genetic variance, heritability, genotype-by-environment (

Indexed as

big datadata-driven methodsdata scienceoptimizationplant breedingquantitative geneticsstatistics

Identifiers

PMID42445683
PMCPMC13357433

What Socratic 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 Socratic graph.