Evidence map›Paper›PMID 42252414›Full record

ArticleGenetics, selection, evolution : GSE2026

Genetic parameters via predictivity in large populations under strong genomic selection.

Gopal Gowane, Jorge Hidalgo, Mary Kate Hollifield, Ignacy Misztal, Daniela Lourenco

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetics, selection, evolution : GSE, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

5 authors.

Gopal GowaneDepartment of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA. gopalgowane@gmail.com.ORCID http://orcid.org/0000-0001-6535-7818
Jorge HidalgoDepartment of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA.
Mary Kate HollifieldDepartment of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA.
Ignacy MisztalDepartment of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA.
Daniela LourencoDepartment of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA. danilino@uga.edu.

Funding

Agriculture and Food Research Initiative 2024-67015-42255
6 · The paper itself

Abstract

backgroundEstimation of accurate genetic parameters require using all data that were available upon selection. Existing methods for parameter estimation become impractical with large genomic data, and estimation becomes complex if parameters vary over time. This study aimed to test a new method called 'GPP' (Genetic parameters via predictivity), which combines within and across traits predictivity formulas with a deterministic formula to predict the accuracy of genomic breeding values. The GPP method can handle any data size and can estimate parameters across time slices.

resultsWe tested GPP using simulated datasets with negatively and positively correlated traits: primary production (h

conclusionsGenetic Parameters via predictivity is a fast, flexible, and accurate approach for estimating dynamic genetic parameters for positively or negatively correlated traits and also for weak and strong genetic correlation scenarios across time slices in large datasets, subject to genomic evaluation, where predictivity computation is feasible.

Indexed as

GenomicsModels, GeneticSelection, GeneticAnimalsBreedingComputer SimulationGenotypePhenotype

Identifiers

PMID42252414
PMCPMC13463401

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.