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

Assessing the performance of a novel method for genomic selectio:rrBLUP-method6.

Journal of genetics - 1 Jan 2021

Ahmadi Zahra, Kesbi Farhad Ghafouri

Abstract excerpt

The aim of this study was to compare the predictive performance of ridge regression best linear unbiased prediction-method 6 (rrBLUPm6) with well-known genomic selection methods (rrBLUP, GBLUP and BayesA) in terms of accuracy of prediction, computing time and memory requirement. The impact of the genetic architecture and heritability on the accuracy of genomic evaluation was also studied. To this end, a genome...

Topics

Join the communities discussing this publication.

Chromosomes
Computational Biology
Genome
Genomics
Polymorphism, Single Nucleotide
Quantitative Trait, Heritable

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Quantitative Trait Loci

Selection, Genetic

Source

Identifiers

PMID: 34187971

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Assessing the performance of a novel method for genomic selectio:rrBLUP-method6.PMID 34187971Read ↗

Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.Semantic proximity Read ↗

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F2 populations by using genomic selection models.Semantic proximity Read ↗

The research progress of genomic selection in livestock.Semantic proximity Read ↗

Genomic selection: Essence, applications, and prospects.Semantic proximity Read ↗

Accuracy and bias of genomic prediction with different de-regression methods.Semantic proximity Read ↗

Genomic selection and its importance in animal breeding and genetic improvement revolution: A comprehensive reviewSemantic proximity Read ↗

Comparison of parametric, semiparametric and nonparametric methods in genomic evaluation.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 ↗

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

Comparisons of single-stage and two-stage approaches to genomic selection.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 ↗

Functional models in genome-wide selection.Semantic proximity Read ↗

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