Linked Life Data

10597515

Source:http://linkedlifedata.com/resource/pubmed/id/10597515

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2000-1-24
pubmed:abstractText
We apply a novel technique to detect significant covariates in linkage analysis using a logistic regression approach. An overall test of linkage is first performed to determine whether there is significant perturbation from the expected 50% sharing under the hypothesis of no linkage; if the overall test is significant, the importance of the individual covariate is assessed. In addition, association analyses were performed. These methods were applied to simulated data from multiple populations, and detected correct marker linkages and associations. No population heterogeneity was detected. These methods have the advantages of using all sib pairs and of providing a formal test for heterogeneity across populations.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0741-0395
pubmed:author
pubmed:issnType
Print
pubmed:volume
17 Suppl 1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
S691-5
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Covariates in linkage analysis.
pubmed:affiliation
Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.