Linked Life Data

10220908

Source:http://linkedlifedata.com/resource/pubmed/id/10220908

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1999-5-24
pubmed:abstractText
A typical problem in mapping quantitative trait loci (QTLs) comes from missing QTL genotype. A routine method for parameter estimation involving missing data is the mixture model maximum likelihood method. We developed an alternative QTL mapping method that describes a mixture of several distributions by a single model with a heterogeneous residual variance. The two methods produce similar results, but the heterogeneous residual variance method is computationally much faster than the mixture model approach. In addition, the new method can automatically generate sampling variances of the estimated parameters. We derive the new method in the context of QTL mapping for binary traits in a F2 population. Using the heterogeneous residual variance model, we identified a QTL on chromosome IV that controls Marek's disease susceptibility in chickens. The QTL alone explains 7.2% of the total disease variation.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0016-6707
pubmed:author
pubmed:issnType
Print
pubmed:volume
104
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
171-8
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Mapping quantitative trait loci for binary traits using a heterogeneous residual variance model: an application to Marek's disease susceptibility in chickens.
pubmed:affiliation
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA. xu@genetics.ucr.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S.