Linked Life Data

21308767

Source:http://linkedlifedata.com/resource/pubmed/id/21308767

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2011-3-11
pubmed:abstractText
Many complex diseases are likely to be a result of the interplay of genes and environmental exposures. The standard analysis in a genome-wide association study (GWAS) scans for main effects and ignores the potentially useful information in the available exposure data. Two recently proposed methods that exploit environmental exposure information involve a two-step analysis aimed at prioritizing the large number of SNPs tested to highlight those most likely to be involved in a GE interaction. For example, Murcray et al. ([2009] Am J Epidemiol 169:219–226) proposed screening on a test that models the G-E association induced by an interaction in the combined case-control sample. Alternatively, Kooperberg and LeBlanc ([2008] Genet Epidemiol 32:255–263) suggested screening on genetic marginal effects. In both methods, SNPs that pass the respective screening step at a pre-specified significance threshold are followed up with a formal test of interaction in the second step. We propose a hybrid method that combines these two screening approaches by allocating a proportion of the overall genomewide significance level to each test. We show that the Murcray et al. approach is often the most efficient method, but that the hybrid approach is a powerful and robust method for nearly any underlying model. As an example, for a GWAS of 1 million markers including a single true disease SNP with minor allele frequency of 0.15, and a binary exposure with prevalence 0.3, the Murcray, Kooperberg and hybrid methods are 1.90, 1.27, and 1.87 times as efficient, respectively, as the traditional case-control analysis to detect an interaction effect size of 2.0.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1098-2272
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
35
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
201-10
pubmed:dateRevised
2011-9-26
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Sample size requirements to detect gene-environment interactions in genome-wide association studies.
pubmed:affiliation
Department of Preventive Medicine, University of Southern California, Los Angeles, California 90089-9010, USA. Murcray@usc.edu
pubmed:publicationType
Journal Article, Comparative Study, Comment, Evaluation Studies, Research Support, N.I.H., Extramural