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

Prediction of genomic breeding values for dairy traits in Italian Brown and Simmental bulls using a principal component approach.

Journal of dairy science - 1 Jun 2012

Pintus M A, Gaspa G, Nicolazzi E L, Vicario D, Rossoni A, Ajmone-Marsan P, Nardone A, Dimauro C, Macciotta N P P

Abstract excerpt

The large number of markers available compared with phenotypes represents one of the main issues in genomic selection. In this work, principal component analysis was used to reduce the number of predictors for calculating genomic breeding values (GEBV). Bulls of 2 cattle breeds farmed in Italy (634 Brown and 469 Simmental) were genotyped with the 54K Illumina beadchip (Illumina Inc., San Diego, CA). After data...

Read the complete abstract on PubMed

Topics

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

Share to Breeding
Share to Cattle

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Dairying

Share to Lactation

Share to Principal Component Analysis

Share to Quantitative Trait, Heritable

Source

Identifiers

PMID: 22612973
DOI: 10.3168/jds.2011-4274

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prediction of genomic breeding values for dairy traits in Italian Brown and Simmental bulls using a principal component approach.PMID 22612973Read ↗

Use of different statistical models to predict direct genomic values for productive and functional traits in Italian Holsteins.Semantic proximity Read ↗

Use of principal component approach to predict direct genomic breeding values for beef traits in Italian Simmental cattle.Semantic proximity Read ↗

Multiple-breed genomic evaluation by principal component analysis in small size populations.Semantic proximity Read ↗

Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle.Semantic proximity Read ↗

Genomic prediction in French Charolais beef cattle using high-density single nucleotide polymorphism markers.Semantic proximity Read ↗

Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances.Semantic proximity Read ↗

Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows.Semantic proximity Read ↗

Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population.Semantic proximity Read ↗

Genetic variances of SNP loci for milk yield in dairy cattle.Semantic proximity Read ↗

Optimizing genomic prediction for Australian Red dairy cattle.Semantic proximity Read ↗

Combined use of principal component analysis and random forests identify population-informative single nucleotide polymorphisms: application in cattle breeds.Semantic proximity Read ↗

A first step toward genomic selection in the multi-breed French dairy goat population.Semantic proximity Read ↗

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