Linked Life Data

15105583

Source:http://linkedlifedata.com/resource/pubmed/id/15105583

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2004-4-23
pubmed:abstractText
Genetic variation plays an important role in natural selection and population evolution. However, it also presents geneticists interested in aging research with problems in data analysis because of the large number of alleles and their various modes of action. Recently, a new statistical method based on survival analysis (the relative risk model or the RR model) has been introduced to assess gene-longevity associations [Yashin et al. (1999) Am J Hum Genet 65: 1178-1193] which outperforms the traditional gene frequency method. Here we extend the model to deal with polymorphic genes or gene markers. Assuming the Hardy-Weinberg equilibrium at birth, we first introduce an allele-based parameterization on gene frequency which helps to cut down the number of frequency parameters to be estimated. We then propose both the genotype and allele-based parameterizations on risk parameters to estimate genotype and allelic relative risks (the GRR and ARR models). While the GRR model allows us to investigate whether the alleles are recessive, dominant or codominant, the ARR model further minimizes the number of parameters to be estimated. As an example, we apply the methods to empirical data on Renin gene polymorphism and longevity. We show that our models can serve as useful tools in searching for important genetic variations implicated in human aging and longevity.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1389-5729
pubmed:author
pubmed:copyrightInfo
Copyright 2004 Kluwer Academic Publishers
pubmed:issnType
Print
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
89-97
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Assessing genetic association with human survival at multi-allelic loci.
pubmed:affiliation
Department of Clinical Biochemistry and Genetics, KKA, Odense University Hospital, Odense, Denmark. qihua.tan@ouh.fyns-amt.dk
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't