Linked Life Data

19178434

Source:http://linkedlifedata.com/resource/pubmed/id/19178434

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2009-3-2
pubmed:abstractText
It has been shown that it is preferable to use a robust model that incorporated constraints on the genotype relative risk rather than rely on a model that assumes the disease operates in a recessive or dominant fashion. Previous methods are applicable to case-control studies, but not to family based studies of case children along with their parents (triads). We show here how to implement analogous constraints while analyzing triad data. The likelihood, conditional on the parents genotype, is maximized over the appropriately constrained parameter space. The asymptotic distribution for the maximized likelihood ratio statistic is found and used to estimate the null distribution of the test statistics. The properties of several methods of testing for association are compared by simulation. The constrained method provides higher power across a wide range of genetic models with little cost when compared to methods that restrict to a dominant, recessive, or multiplicative model, or make no modeling restriction. The methods are applied to two SNPs on the methylenetetrahydrofolate reductase (MTHFR) gene with neural tube defect (NTD) triads.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1469-1809
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
73
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
225-30
pubmed:dateRevised
2010-9-23
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Testing for genetic association with constrained models using triads.
pubmed:affiliation
Division of Epidemiology, Statistics, and Prevention Research, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH/DHHS, Bethesda, MD 20892, USA. jt3t@nih.gov
pubmed:publicationType
Journal Article, Research Support, N.I.H., Intramural