Linked Life Data

16862161

Source:http://linkedlifedata.com/resource/pubmed/id/16862161

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2006-7-28
pubmed:abstractText
Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1061-4036
pubmed:author
pubmed:issnType
Print
pubmed:volume
38
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
904-9
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Principal components analysis corrects for stratification in genome-wide association studies.
pubmed:affiliation
Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. aprice@broad.mit.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't