Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.

The plant genome - 1 Dec 2022

Atanda Sikiru Adeniyi, Steffes Jenna, Lan Yang, Al Bari Md Abdullah, Kim Jeong-Hwa, Morales Mario, Johnson Josephine P, Saludares Rica, Worral Hannah, Piche Lisa, Ross Andrew, Grusak Mike, Coyne Clarice, McGee Rebecca, Rao Jiajia, Bandillo Nonoy

Abstract excerpt

Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Genomics
Share to Minerals

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Share to Pisum sativum

Share to Plant Breeding

Source

Open in PubMed Open full text

Identifiers

PMID: 36193571
DOI: 10.1002/tpg2.20260
PMCID: PMC12807418

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.PMID 36193571Read ↗

Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop.Semantic proximity Read ↗

Multi-trait/environment sparse genomic prediction using the SFSI R-package.Semantic proximity Read ↗

A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library.Semantic proximity Read ↗

Enhancing predictive accuracy of yield traits in cassava through multi-trait genomic predictionSemantic proximity Read ↗

Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.Semantic proximity Read ↗

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.Semantic proximity Read ↗

Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes.Semantic proximity Read ↗

Utilization Strategies of Two Environment Phenotypes in Genomic Prediction.Semantic proximity Read ↗

PANOMICS meets germplasm.Semantic proximity Read ↗

Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates.Semantic proximity Read ↗

Genome-wide association studies and prediction of 17 traits related to phenology, biomass and cell wall composition in the energy grass Miscanthus sinensis.Semantic proximity Read ↗

Multitrait genomic prediction method in approximate genome-based kernel model.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.