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

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye.

BMC genomics - 4 Aug 2014

Bernal-Vasquez Angela-Maria, Möhring Jens, Schmidt Malthe, Schönleben Manfred, Schön Chris-Carolin, Piepho Hans-Peter

Abstract excerpt

BACKGROUND: Genomic prediction is becoming a daily tool for plant breeders. It makes use of genotypic information to make predictions used for selection decisions. The accuracy of the predictions depends on the number of genotypes used in the calibration; hence, there is a need of combining data across years. A proper phenotypic analysis is a crucial prerequisite for accurate calibration of genomic prediction...

Read the complete abstract on PubMed

Topics

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

Share to MET gene
Share to Genomics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Statistical

Share to Phenotype

Share to Reproducibility of Results

Share to Secale

Share to Statistics as Topic

Source

Open in PubMed Open full text

Identifiers

PMID: 25087599
DOI: 10.1186/1471-2164-15-646
PMCID: PMC4133075

Genes

MET

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye.PMID 25087599Read ↗

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.Semantic proximity Read ↗

Accuracies of univariate and multivariate genomic prediction models in African cassava.Semantic proximity Read ↗

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea.Semantic proximity Read ↗

Tailoring AI and ML models for genotype-by-environment prediction leveraging environmental covariates: A European rye example.Semantic proximity Read ↗

The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker-assisted selection and is equally augmented by multiple field evaluation locations and test years.Semantic proximity Read ↗

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.Semantic proximity Read ↗

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision.Semantic proximity Read ↗

Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks.Semantic proximity Read ↗

Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials.Semantic proximity Read ↗

A data-driven simulation platform to predict cultivars' performances under uncertain weather conditions.Semantic proximity Read ↗

Accounting for Correlation Between Traits in Genomic Prediction.Semantic proximity Read ↗

Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).Semantic proximity Read ↗

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