Linked Life Data

19922694

Source:http://linkedlifedata.com/resource/pubmed/id/19922694

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2009-11-20
pubmed:abstractText
Genomic selection describes a selection strategy based on genomic breeding values predicted from dense single nucleotide polymorphism (SNP) data. Multiple methods have been proposed but the critical issue is how to decide whether an SNP should be included in the predictive set to estimate breeding values. One major disadvantage of the traditional Bayes B approach is its high computational demands caused by the changing dimensionality of the models. The use of stochastic search variable selection (SSVS) retains the same assumptions about the distribution of SNP effects as Bayes B, while maintaining constant dimensionality. When Bayesian SSVS was used to predict genomic breeding values for real dairy data over a range of traits it produced accuracies higher or equivalent to other genomic selection methods with significantly decreased computational and time demands than Bayes B.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1469-5073
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
91
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
307-11
pubmed:dateRevised
2010-12-28
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle.
pubmed:affiliation
Biosciences Research Division, Department of Primary Industries Victoria, Bundoora, Australia. klara.verbyla@dpi.vic.gov.au
pubmed:publicationType
Journal Article, Evaluation Studies