| pubmed-article:21308767 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:21308767 | lifeskim:mentions | umls-concept:C0242618 | lld:lifeskim |
| pubmed-article:21308767 | lifeskim:mentions | umls-concept:C2350277 | lld:lifeskim |
| pubmed-article:21308767 | lifeskim:mentions | umls-concept:C0596609 | lld:lifeskim |
| pubmed-article:21308767 | lifeskim:mentions | umls-concept:C1514873 | lld:lifeskim |
| pubmed-article:21308767 | pubmed:issue | 3 | lld:pubmed |
| pubmed-article:21308767 | pubmed:dateCreated | 2011-3-11 | lld:pubmed |
| pubmed-article:21308767 | 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. | lld:pubmed |
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| pubmed-article:21308767 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:21308767 | pubmed:language | eng | lld:pubmed |
| pubmed-article:21308767 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:21308767 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:21308767 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:21308767 | pubmed:month | Apr | lld:pubmed |
| pubmed-article:21308767 | pubmed:issn | 1098-2272 | lld:pubmed |
| pubmed-article:21308767 | pubmed:author | pubmed-author:GaudermanW... | lld:pubmed |
| pubmed-article:21308767 | pubmed:author | pubmed-author:ThomasDuncan... | lld:pubmed |
| pubmed-article:21308767 | pubmed:author | pubmed-author:ContiDavid... | lld:pubmed |
| pubmed-article:21308767 | pubmed:author | pubmed-author:LewingerJuan... | lld:pubmed |
| pubmed-article:21308767 | pubmed:author | pubmed-author:MurcrayCassan... | lld:pubmed |
| pubmed-article:21308767 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:21308767 | pubmed:volume | 35 | lld:pubmed |
| pubmed-article:21308767 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:21308767 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:21308767 | pubmed:pagination | 201-10 | lld:pubmed |
| pubmed-article:21308767 | pubmed:dateRevised | 2011-9-26 | lld:pubmed |
| pubmed-article:21308767 | pubmed:meshHeading | pubmed-meshheading:21308767... | lld:pubmed |
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| pubmed-article:21308767 | pubmed:meshHeading | pubmed-meshheading:21308767... | lld:pubmed |
| pubmed-article:21308767 | pubmed:meshHeading | pubmed-meshheading:21308767... | lld:pubmed |
| pubmed-article:21308767 | pubmed:meshHeading | pubmed-meshheading:21308767... | lld:pubmed |
| pubmed-article:21308767 | pubmed:meshHeading | pubmed-meshheading:21308767... | lld:pubmed |
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| pubmed-article:21308767 | pubmed:meshHeading | pubmed-meshheading:21308767... | lld:pubmed |
| pubmed-article:21308767 | pubmed:year | 2011 | lld:pubmed |
| pubmed-article:21308767 | pubmed:articleTitle | Sample size requirements to detect gene-environment interactions in genome-wide association studies. | lld:pubmed |
| pubmed-article:21308767 | pubmed:affiliation | Department of Preventive Medicine, University of Southern California, Los Angeles, California 90089-9010, USA. Murcray@usc.edu | lld:pubmed |
| pubmed-article:21308767 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:21308767 | pubmed:publicationType | Comparative Study | lld:pubmed |
| pubmed-article:21308767 | pubmed:publicationType | Comment | lld:pubmed |
| pubmed-article:21308767 | pubmed:publicationType | Evaluation Studies | lld:pubmed |
| pubmed-article:21308767 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:21308767 | lld:pubmed |
