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

Using Monte Carlo method to include polygenic effects in calculation of SNP-BLUP model reliability.

Journal of dairy science - 1 Jun 2020

Ben Zaabza H, Mäntysaari E A, Strandén I

Abstract excerpt

An SNP-BLUP model is computationally scalable even for large numbers of genotyped animals. When genetic variation cannot be completely captured by SNP markers, a more accurate model is obtained by fitting a residual polygenic effect (RPG) as well. However, inclusion of the RPG effect increases the size of the SNP-BLUP mixed model equations (MME) by the number of genotyped animals. Consequently, the calculation of...

Topics

Animals
Breeding
Genome
Genotype
Models, Genetic
Monte Carlo Method
Multifactorial Inheritance
Pedigree
Polymorphism, Single Nucleotide
Reproducibility of Results

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 32253036
DOI: 10.3168/jds.2019-17255

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

Using Monte Carlo method to include polygenic effects in calculation of SNP-BLUP model reliability.PMID 32253036Read ↗

A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction.Semantic proximity Read ↗

Single-step SNP-BLUP with on-the-fly imputed genotypes and residual polygenic effects.Semantic proximity Read ↗

Breeding value reliabilities for multiple-trait single-step genomic best linear unbiased predictor.Semantic proximity Read ↗

Genomic prediction when some animals are not genotyped.Semantic proximity Read ↗

Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM.Semantic proximity Read ↗

Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix.Semantic proximity Read ↗

Polygenic modeling with bayesian sparse linear mixed models.Semantic proximity Read ↗

Solving efficiently large single-step genomic best linear unbiased prediction models.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 ↗

Methods to approximate reliabilities in single-step genomic evaluation.Semantic proximity Read ↗

Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice.Semantic proximity Read ↗

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

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