Linked Life Data

15297809

Source:http://linkedlifedata.com/resource/pubmed/id/15297809

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2004-8-6
pubmed:abstractText
Recent studies have indicated that linkage disequilibrium (LD) between single nucleotide polymorphism (SNP) markers can be used to derive a reduced set of tagging SNPs (tSNPs) for genetic association studies. Previous strategies for identifying tSNPs have focused on LD measures or haplotype diversity, but the statistical power to detect disease-associated variants using tSNPs in genetic studies has not been fully characterized. We propose a new approach of selecting tSNPs based on determining the set of SNPs with the highest power to detect association. Two-locus genotype frequencies are used in the power calculations. To show utility, we applied this power method to a large number of SNPs that had been genotyped in Caucasian samples. We demonstrate that a significant reduction in genotyping efforts can be achieved although the reduction depends on genotypic relative risk, inheritance mode and the prevalence of disease in the human population. The tSNP sets identified by our method are remarkably robust to changes in the disease model when small relative risk and additive mode of inheritance are employed. We have also evaluated the ability of the method to detect unidentified SNPs. Our findings have important implications in applying tSNPs from different data sources in association studies.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0001-5652
pubmed:author
pubmed:copyrightInfo
Copyright 2004 S. Karger AG, Basel
pubmed:issnType
Print
pubmed:volume
57
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
156-70
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Selecting tagging SNPs for association studies using power calculations from genotype data.
pubmed:affiliation
Celera Diagnostics, Harbor Bay Pkwy, Alameda, CA 94502, USA. Xiaolan.Hu@celeradiagnostics.com
pubmed:publicationType
Journal Article, Comparative Study