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

Topics Community

Article

Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

Journal of animal science - 1 May 2022

Junqueira Vinícius Silva, Lourenco Daniela, Masuda Yutaka, Cardoso Fernando Flores, Lopes Paulo Sávio, Silva Fabyano Fonseca E, Misztal Ignacy

Abstract excerpt

Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending...

Topics

Algorithms
Animals
Genome
Genomics
Genotype
Models, Genetic
Pedigree
Phenotype

Source

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Identifiers

PMID: 35289906
DOI: 10.1093/jas/skac082
PMCID: PMC9118993

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 31 Aug 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

Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?PMID 35289906Read ↗

A comprehensive study on size and definition of the core group in the proven and young algorithm for single-step GBLUP.Semantic proximity Read ↗

Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species.Semantic proximity Read ↗

The Dimensionality of Genomic Information and Its Effect on Genomic Prediction.Semantic proximity Read ↗

Technical note: Impact of pedigree depth on convergence of single-step genomic BLUP in a purebred swine population.Semantic proximity Read ↗

Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction.Semantic proximity Read ↗

Marker effect p-values for single-step GWAS with the algorithm for proven and young in large genotyped populations.Semantic proximity Read ↗

Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information.Semantic proximity Read ↗

Indirect predictions with a large number of genotyped animals using the algorithm for proven and young.Semantic proximity Read ↗

Extension of the reduced animal model to single-step methods.Semantic proximity Read ↗

Sparse single-step genomic BLUP in crossbreeding schemes.Semantic proximity Read ↗

Hot topic: Use of genomic recursions in single-step genomic best linear unbiased predictor (BLUP) with a large number of genotypes.Semantic proximity Read ↗

On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young.Semantic proximity Read ↗

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