Linked Life Data

7945153

Source:http://linkedlifedata.com/resource/pubmed/id/7945153

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
1994-11-4
pubmed:abstractText
We extend the DeFries-Fulker regression model for the analysis of quantitative twin data to cover binary traits and genetic dominance. In the proposed logistic regression model, the cotwin's trait status, C, is the response variable, while the proband's trait status, P, is a predictor variable coded as k (affected) and 0 (unaffected). Additive genetic effects are modeled by the predictor variable PR, which equals P for monozygotic (MZ) and P/2 for dizygotic (DZ) twins; and dominant genetic effects, by PD, which equals P for MZ and P/4 for DZ twins. By setting an appropriate scale for P (i.e., the value of k), the regression coefficients of P, PR, and PD are estimates of the proportion of variance in liability due to common family environment, additive genetic effects, and dominant genetic effects, respectively, for a multifactorial liability-threshold model. This model was applied to data on lifetime depression from the Virginia Twin Registry and produced results similar to those from structural equation modeling.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0001-8244
pubmed:author
pubmed:issnType
Print
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
229-38
pubmed:dateRevised
2009-9-29
pubmed:meshHeading
pubmed:year
1994
pubmed:articleTitle
Logistic regression analysis of twin data: estimation of parameters of the multifactorial liability-threshold model.
pubmed:affiliation
Department of Psychiatry Medical College of Virginia, Richmond 23298.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't, Twin Study