Linked Life Data

11246476

Source:http://linkedlifedata.com/resource/pubmed/id/11246476

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 4
pubmed:dateCreated
2001-3-14
pubmed:abstractText
In recent years variance components models have been developed for localising genes that contribute to human quantitative variation. In typical applications one assumes a multivariate normal model for phenotypes and estimates model parameters by maximum likelihood. For the joint analysis of several correlated phenotypes, however, finding the maximum likelihood estimates for an appropriate multivariate normal model can be a difficult computational task due to complex constraints among the model parameters. We propose an algorithm for computing maximum likelihood estimates in a multi-phenotype variance components linkage model that readily accommodates these parameter constraints. Data simulated for Genetic Analysis Workshop 10 are used to demonstrate the potential increase in power to detect linkage that can be obtained if correlated phenotypes are analysed jointly rather than individually.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0003-4800
pubmed:author
pubmed:issnType
Print
pubmed:volume
64
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
349-62
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
An EM algorithm for obtaining maximum likelihood estimates in the multi-phenotype variance components linkage model.
pubmed:affiliation
Department of Health Sciences Research, Mayo Clinic/Mayo Foundation, Rochester, MN 55905, USA. iturria@mayo.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.