Detecting Signals in Pharmacogenomic Genome-Wide Association Studies

In one common pharmacogenomic scenario, outcome measures are compared for treated and untreated subjects across genotype defined subgroups. The key question is whether treatment benefit (or harm) is particularly strong in certain subgroups, and therefore statistical analysis focuses on the interacti...

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Main Authors:	Wakefield, Jon, Skrivankova, Veronika, Hsu, Fang-Chi, Sale, Michele, Heagerty, Patrick
Format:	Online
Language:	English
Published:	2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4085158/

id	pubmed-4085158
recordtype	oai_dc
spelling	pubmed-40851582015-02-01 Detecting Signals in Pharmacogenomic Genome-Wide Association Studies Wakefield, Jon Skrivankova, Veronika Hsu, Fang-Chi Sale, Michele Heagerty, Patrick Article In one common pharmacogenomic scenario, outcome measures are compared for treated and untreated subjects across genotype defined subgroups. The key question is whether treatment benefit (or harm) is particularly strong in certain subgroups, and therefore statistical analysis focuses on the interaction between treatment and genotype. However, genome-wide analysis in such scenarios requires careful statistical thought since, in addition to the usual problems of multiple testing, the marker-defined sample sizes, and therefore power, vary across the individual genotypes being evaluated. The variability in power means the usual practice of using a common p-value threshold across tests has difficulties. The reason is that the use of a fixed threshold, with variable power, implies that the costs of type I and type II errors are varying across tests in a manner which is implicit rather than dictated by the analyst. In this paper we discuss this problem and describe an easily implementable solution based on Bayes factors. We pay particular attention to the specification of priors, which is not a straightforward task. The methods are illustrated using data from a randomized controlled clinical trial in which homocysteine levels are compared in individuals receiving low and high doses of folate supplements and across marker subgroups. The method we describe is implemented in the R computing environment with code available from http://faculty.washington.edu/jonno/cv.html. 2014-01-07 2014-08 /pmc/articles/PMC4085158/ /pubmed/24394200 http://dx.doi.org/10.1038/tpj.2013.44 Text en Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Wakefield, Jon Skrivankova, Veronika Hsu, Fang-Chi Sale, Michele Heagerty, Patrick
spellingShingle	Wakefield, Jon Skrivankova, Veronika Hsu, Fang-Chi Sale, Michele Heagerty, Patrick Detecting Signals in Pharmacogenomic Genome-Wide Association Studies
author_facet	Wakefield, Jon Skrivankova, Veronika Hsu, Fang-Chi Sale, Michele Heagerty, Patrick
author_sort	Wakefield, Jon
title	Detecting Signals in Pharmacogenomic Genome-Wide Association Studies
title_short	Detecting Signals in Pharmacogenomic Genome-Wide Association Studies
title_full	Detecting Signals in Pharmacogenomic Genome-Wide Association Studies
title_fullStr	Detecting Signals in Pharmacogenomic Genome-Wide Association Studies
title_full_unstemmed	Detecting Signals in Pharmacogenomic Genome-Wide Association Studies
title_sort	detecting signals in pharmacogenomic genome-wide association studies
description	In one common pharmacogenomic scenario, outcome measures are compared for treated and untreated subjects across genotype defined subgroups. The key question is whether treatment benefit (or harm) is particularly strong in certain subgroups, and therefore statistical analysis focuses on the interaction between treatment and genotype. However, genome-wide analysis in such scenarios requires careful statistical thought since, in addition to the usual problems of multiple testing, the marker-defined sample sizes, and therefore power, vary across the individual genotypes being evaluated. The variability in power means the usual practice of using a common p-value threshold across tests has difficulties. The reason is that the use of a fixed threshold, with variable power, implies that the costs of type I and type II errors are varying across tests in a manner which is implicit rather than dictated by the analyst. In this paper we discuss this problem and describe an easily implementable solution based on Bayes factors. We pay particular attention to the specification of priors, which is not a straightforward task. The methods are illustrated using data from a randomized controlled clinical trial in which homocysteine levels are compared in individuals receiving low and high doses of folate supplements and across marker subgroups. The method we describe is implemented in the R computing environment with code available from http://faculty.washington.edu/jonno/cv.html.
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4085158/
_version_	1613109359068315648

Detecting Signals in Pharmacogenomic Genome-Wide Association Studies

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