Linked Life Data

17096677

Source:http://linkedlifedata.com/resource/pubmed/id/17096677

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2007-2-21
pubmed:abstractText
The questions addressed in this paper are: What single nucleotide polymorphism (SNP) genotyping errors are most costly, in terms of minimum sample size necessary (MSSN) to maintain constant asymptotic power and significance level, when performing case-control studies of genetic association applying the Cochran-Armitage trend test? And which trend test or chi2 test is more powerful under standard genetic models with genotyping errors? Our strategy is to expand the non-centrality parameter of the asymptotic distribution of the trend test to approximate the MSSN using a Taylor series linear in the genotyping error rates. We apply our strategy to example scenarios that assume recessive, dominant, additive, or over-dominant disease models. The most costly errors are recording the more common homozygote as the less common homozygote, and the more common homozygote as the heterozygote, with MSSN that become indefinitely large as the minor SNP allele frequency approaches zero. Misclassifying the heterozygote as the less common homozygote is costly when using the recessive trend test on data from a recessive model. The chi2 test has power close to, but less than, the optimal trend test and is never dominated over all genetic models studied by any specific trend test.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0003-4800
pubmed:author
pubmed:issnType
Print
pubmed:volume
71
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
249-61
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
The effects of SNP genotyping errors on the power of the Cochran-Armitage linear trend test for case/control association studies.
pubmed:affiliation
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural