Linked Life Data

12214310

Source:http://linkedlifedata.com/resource/pubmed/id/12214310

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2002-9-5
pubmed:abstractText
Association mapping for complex diseases using unrelated individuals can be more powerful than family-based analysis in many settings. In addition, this approach has major practical advantages, including greater efficiency in sample recruitment. Association mapping may lead to false-positive findings, however, if population stratification is not properly considered. In this paper, we propose a method that makes it possible to infer the number of subpopulations by a mixture model, using a set of independent genetic markers and then testing the association between a genetic marker and a trait. The proposed method can be effectively applied in the analysis of both qualitative and quantitative traits. Extensive simulations demonstrate that the method is valid in the presence of a population structure.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0741-0395
pubmed:author
pubmed:copyrightInfo
Copyright 2002 Wiley-Liss, Inc.
pubmed:issnType
Print
pubmed:volume
23
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
181-96
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Association mapping, using a mixture model for complex traits.
pubmed:affiliation
Department of Preventive Medicine and Epidemiology, Loyola University Medical Center, Maywood, Illinois 60153, USA. xzhul@lumc.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't