Source:http://linkedlifedata.com/resource/pubmed/id/19220931
Switch to
| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
1
|
| pubmed:dateCreated |
2009-2-17
|
| pubmed:abstractText |
Dense marker genotypes allow the construction of the realized relationship matrix between individuals, with elements the realized proportion of the genome that is identical by descent (IBD) between pairs of individuals. In this paper, we demonstrate that by replacing the average relationship matrix derived from pedigree with the realized relationship matrix in best linear unbiased prediction (BLUP) of breeding values, the accuracy of the breeding values can be substantially increased, especially for individuals with no phenotype of their own. We further demonstrate that this method of predicting breeding values is exactly equivalent to the genomic selection methodology where the effects of quantitative trait loci (QTLs) contributing to variation in the trait are assumed to be normally distributed. The accuracy of breeding values predicted using the realized relationship matrix in the BLUP equations can be deterministically predicted for known family relationships, for example half sibs. The deterministic method uses the effective number of independently segregating loci controlling the phenotype that depends on the type of family relationship and the length of the genome. The accuracy of predicted breeding values depends on this number of effective loci, the family relationship and the number of phenotypic records. The deterministic prediction demonstrates that the accuracy of breeding values can approach unity if enough relatives are genotyped and phenotyped. For example, when 1000 full sibs per family were genotyped and phenotyped, and the heritability of the trait was 0.5, the reliability of predicted genomic breeding values (GEBVs) for individuals in the same full sib family without phenotypes was 0.82. These results were verified by simulation. A deterministic prediction was also derived for random mating populations, where the effective population size is the key parameter determining the effective number of independently segregating loci. If the effective population size is large, a very large number of individuals must be genotyped and phenotyped in order to accurately predict breeding values for unphenotyped individuals from the same population. If the heritability of the trait is 0.3, and N(e)=100, approximately 12474 individuals with genotypes and phenotypes are required in order to predict GEBVs of un-phenotyped individuals in the same population with an accuracy of 0.7 [corrected].
|
| pubmed:commentsCorrections | |
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:month |
Feb
|
| pubmed:issn |
1469-5073
|
| pubmed:author | |
| pubmed:issnType |
Electronic
|
| pubmed:volume |
91
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
47-60
|
| pubmed:dateRevised |
2010-12-28
|
| pubmed:meshHeading |
pubmed-meshheading:19220931-Algorithms,
pubmed-meshheading:19220931-Breeding,
pubmed-meshheading:19220931-Computer Simulation,
pubmed-meshheading:19220931-Genetic Variation,
pubmed-meshheading:19220931-Genotype,
pubmed-meshheading:19220931-Pedigree,
pubmed-meshheading:19220931-Phenotype,
pubmed-meshheading:19220931-Quantitative Trait Loci,
pubmed-meshheading:19220931-Selection, Genetic
|
| pubmed:year |
2009
|
| pubmed:articleTitle |
Increased accuracy of artificial selection by using the realized relationship matrix.
|
| pubmed:affiliation |
Biosciences Research Division, Department of Primary Industries Victoria, 1 Park Drive, Bundoora 3083, Australia. ben.hayes@dpi.vic.gov.au
|
| pubmed:publicationType |
Journal Article
|