Application of principal component analysis to pharmacogenomic studies in Canada View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-12

AUTHORS

H Visscher, C J D Ross, M-P Dubé, A M K Brown, M S Phillips, B C Carleton, M R Hayden

ABSTRACT

Ethnicity can confound results in pharmacogenomic studies. Allele frequencies of loci that influence drug metabolism can vary substantially between different ethnicities and underlying ancestral genetic differences can lead to spurious findings in pharmacogenomic association studies. We evaluated the application of principal component analysis (PCA) in a pharmacogenomic study in Canada to detect and correct for genetic ancestry differences using genotype data from 2094 loci in 220 key drug biotransformation genes. Using 89 Coriell worldwide reference samples, we observed a strong correlation between principal component values and geographic origin. We further applied PCA to accurately infer the genetic ancestry in our ethnically diverse Canadian cohort of 524 patients from the GATC study of severe adverse drug reactions. We show that PCA can be successfully applied in pharmacogenomic studies using a limited set of markers to detect underlying differences in genetic ancestry thereby maximizing power and minimizing false-positive findings. More... »

PAGES

362

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/tpj.2009.36

DOI

http://dx.doi.org/10.1038/tpj.2009.36

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029963014

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19652663


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RDF/XML is a standard XML format for linked data.

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