Linked Life Data

1418924

Source:http://linkedlifedata.com/resource/pubmed/id/1418924

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
1992-12-22
pubmed:abstractText
Standard regression models for disease incidence data can be used to test for associations between a disease and measured genetic and environmental factors and their interactions. Complications arise when the gene is not observed, requiring segregation and linkage analysis approaches, or when the candidate gene(s) are found to be highly polymorphic, as in the HLA region. We propose a Bayesian approach to the latter problem, in which the log relative risks for all alleles at a given locus are taken to be independent and exchangeable, assuming there is no preferential zygotic assortment and negligible recombination. Multi-locus problems can be addressed either by adding exchangeable interaction terms or by adopting a multivariate prior for haplotype effects. Some simulations based on our current work on family studies of IDDM are discussed.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0785-3890
pubmed:author
pubmed:issnType
Print
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
387-92
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1992
pubmed:articleTitle
Empirical Bayes methods for testing associations with large numbers of candidate genes in the presence of environmental risk factors, with applications to HLA associations in IDDM.
pubmed:affiliation
Department of Preventive Medicine, University of Southern California, Los Angeles 90033-9987.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't