Linked Life Data

16909391

Source:http://linkedlifedata.com/resource/pubmed/id/16909391

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2006-8-15
pubmed:abstractText
A significant proportion of the variation between individuals in gene expression levels is genetic, and it is likely that these differences correlate with phenotypic differences or with risk of disease. Cis-acting polymorphisms are important in determining interindividual differences in gene expression that lead to allelic expression imbalance, which is the unequal expression of homologous alleles in individuals heterozygous for such a polymorphism. This expression imbalance can be detected using a transcribed polymorphism, and, once it is established, the next step is to identify the polymorphisms that are responsible for or predictive of allelic expression levels. We present an expectation-maximization algorithm for such analyses, providing a formal statistical framework to test whether a candidate polymorphism is associated with allelic expression differences.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0002-9297
pubmed:author
pubmed:issnType
Print
pubmed:volume
79
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
539-43
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
An expectation-maximization algorithm for the analysis of allelic expression imbalance.
pubmed:affiliation
Division of Genomic Medicine, University of Sheffield, United Kingdom.
pubmed:publicationType
Journal Article