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

Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data.

Genetics - 1 Jul 2011

Ober Ulrike, Erbe Malena, Long Nanye, Porcu Emilio, Schlather Martin, Simianer Henner

Abstract excerpt

Genomic data provide a valuable source of information for modeling covariance structures, allowing a more accurate prediction of total genetic values (GVs). We apply the kriging concept, originally developed in the geostatistical context for predictions in the low-dimensional space, to the high-dimensional space spanned by genomic single nucleotide polymorphism (SNP) vectors and study its properties in different...

Read the complete abstract on PubMed

Topics

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

Share to Analysis of Variance
Share to Bayes Theorem
Share to Breeding

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Epistasis, Genetic

Share to Linear Models

Share to Plants

Share to Predictive Value of Tests

Share to Software

Source

Open in PubMed Open full text

Identifiers

PMID: 21515573
DOI: 10.1534/genetics.111.128694
PMCID: PMC3176539

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

Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data.PMID 21515573Read ↗

GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Semantic proximity Read ↗

Overview of Genomic Prediction Methods and the Associated Assumptions on the Variance of Marker Effect, and on the Architecture of the Target Trait.Semantic proximity Read ↗

Gaussian covariance graph models accounting for correlated marker effects in genome-wide prediction.Semantic proximity Read ↗

Genomic Prediction Using Multi-trait Weighted GBLUP Accounting for Heterogeneous Variances and Covariances Across the Genome.Semantic proximity Read ↗

Genomic Prediction of Complex Traits, Principles, Overview of Factors Affecting the Reliability of Genomic Prediction, and Algebra of the Reliability.Semantic proximity Read ↗

Efficient weighting methods for genomic best linear-unbiased prediction (BLUP) adapted to the genetic architectures of quantitative traits.Semantic proximity Read ↗

Multiple-trait genomic selection methods increase genetic value prediction accuracy.Semantic proximity Read ↗

A deterministic equation to predict the accuracy of multi-population genomic prediction with multiple genomic relationship matrices.Semantic proximity Read ↗

Predictive assessment of single-step BLUP with linear and non-linear similarity RKHS kernels: A case study in chickens.Semantic proximity Read ↗

Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition.Semantic proximity Read ↗

Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.Semantic proximity Read ↗

Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix.Semantic proximity Read ↗

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