Identification of pharmacogenetic markers in smoking cessation therapy

Daniel F. Heitjan, Mengye Guo, Riju Ray, E. Paul Wileyto, Leonard H. Epstein, Caryn Lerman

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

Pharmacogenetic clinical trials seek to identify genetic modifiers of treatment effects. When a trial has collected data on many potential genetic markers, a first step in analysis is to screen for evidence of pharmacogenetic effects by testing for treatment-by-marker interactions in a statistical model for the outcome of interest. This approach is potentially problematic because (i) individual significance tests can be overly sensitive, particularly when sample sizes are large; and (ii) standard significance tests fail to distinguish between markers that are likely, on biological grounds, to have an effect, and those that are not. One way to address these concerns is to perform Bayesian hypothesis tests [Berger (1985) Statistical decision theory and Bayesian analysis. New York: Springer; Kass and Raftery (1995) J Am Stat Assoc 90:773-795], which are typically more conservative than standard uncorrected frequentist tests, less conservative than multiplicity-corrected tests, and make explicit use of relevant biological information through specification of the prior distribution. In this article we use a Bayesian testing approach to screen a panel of genetic markers recorded in a randomized clinical trial of bupropion versus placebo for smoking cessation. From a panel of 59 single-nucleotide polymorphisms (SNPs) located on 11 candidate genes, we identify four SNPs (one each on CHRNA5 and CHRNA2 and two on CHAT) that appear to have pharmacogenetic relevance. Of these, the SNP on CHRNA5 is most robust to specification of the prior. An unadjusted frequentist test identifies seven SNPs, including these four, none of which remains significant upon correction for multiplicity. In a panel of 43 randomly selected control SNPs, none is significant by either the Bayesian or the corrected frequentist test.

Original languageEnglish (US)
Pages (from-to)712-719
Number of pages8
JournalAmerican Journal of Medical Genetics, Part B: Neuropsychiatric Genetics
Volume147
Issue number6
DOIs
StatePublished - Sep 5 2008

Fingerprint

Pharmacogenetics
Smoking Cessation
Single Nucleotide Polymorphism
Bayes Theorem
Genetic Markers
Decision Theory
Therapeutics
Bupropion
Decision Support Techniques
Statistical Models
Sample Size
Randomized Controlled Trials
Placebos
Clinical Trials
Genes

Keywords

  • Bayes factor
  • Bayesian hypothesis test
  • Bupropion
  • Importance sampling
  • Pharmacogenomics
  • Single-nucleotide polymorphism

ASJC Scopus subject areas

  • Genetics(clinical)
  • Neuropsychology and Physiological Psychology
  • Neuroscience(all)

Cite this

Identification of pharmacogenetic markers in smoking cessation therapy. / Heitjan, Daniel F.; Guo, Mengye; Ray, Riju; Wileyto, E. Paul; Epstein, Leonard H.; Lerman, Caryn.

In: American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics, Vol. 147, No. 6, 05.09.2008, p. 712-719.

Research output: Contribution to journalArticle

Heitjan, Daniel F. ; Guo, Mengye ; Ray, Riju ; Wileyto, E. Paul ; Epstein, Leonard H. ; Lerman, Caryn. / Identification of pharmacogenetic markers in smoking cessation therapy. In: American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics. 2008 ; Vol. 147, No. 6. pp. 712-719.
@article{e320a0fd2e3f478dae5e1c36ffd5114e,
title = "Identification of pharmacogenetic markers in smoking cessation therapy",
abstract = "Pharmacogenetic clinical trials seek to identify genetic modifiers of treatment effects. When a trial has collected data on many potential genetic markers, a first step in analysis is to screen for evidence of pharmacogenetic effects by testing for treatment-by-marker interactions in a statistical model for the outcome of interest. This approach is potentially problematic because (i) individual significance tests can be overly sensitive, particularly when sample sizes are large; and (ii) standard significance tests fail to distinguish between markers that are likely, on biological grounds, to have an effect, and those that are not. One way to address these concerns is to perform Bayesian hypothesis tests [Berger (1985) Statistical decision theory and Bayesian analysis. New York: Springer; Kass and Raftery (1995) J Am Stat Assoc 90:773-795], which are typically more conservative than standard uncorrected frequentist tests, less conservative than multiplicity-corrected tests, and make explicit use of relevant biological information through specification of the prior distribution. In this article we use a Bayesian testing approach to screen a panel of genetic markers recorded in a randomized clinical trial of bupropion versus placebo for smoking cessation. From a panel of 59 single-nucleotide polymorphisms (SNPs) located on 11 candidate genes, we identify four SNPs (one each on CHRNA5 and CHRNA2 and two on CHAT) that appear to have pharmacogenetic relevance. Of these, the SNP on CHRNA5 is most robust to specification of the prior. An unadjusted frequentist test identifies seven SNPs, including these four, none of which remains significant upon correction for multiplicity. In a panel of 43 randomly selected control SNPs, none is significant by either the Bayesian or the corrected frequentist test.",
keywords = "Bayes factor, Bayesian hypothesis test, Bupropion, Importance sampling, Pharmacogenomics, Single-nucleotide polymorphism",
author = "Heitjan, {Daniel F.} and Mengye Guo and Riju Ray and Wileyto, {E. Paul} and Epstein, {Leonard H.} and Caryn Lerman",
year = "2008",
month = "9",
day = "5",
doi = "10.1002/ajmg.b.30669",
language = "English (US)",
volume = "147",
pages = "712--719",
journal = "American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics",
issn = "1552-4841",
publisher = "Wiley-Liss Inc.",
number = "6",

}

TY - JOUR

T1 - Identification of pharmacogenetic markers in smoking cessation therapy

AU - Heitjan, Daniel F.

AU - Guo, Mengye

AU - Ray, Riju

AU - Wileyto, E. Paul

AU - Epstein, Leonard H.

AU - Lerman, Caryn

PY - 2008/9/5

Y1 - 2008/9/5

N2 - Pharmacogenetic clinical trials seek to identify genetic modifiers of treatment effects. When a trial has collected data on many potential genetic markers, a first step in analysis is to screen for evidence of pharmacogenetic effects by testing for treatment-by-marker interactions in a statistical model for the outcome of interest. This approach is potentially problematic because (i) individual significance tests can be overly sensitive, particularly when sample sizes are large; and (ii) standard significance tests fail to distinguish between markers that are likely, on biological grounds, to have an effect, and those that are not. One way to address these concerns is to perform Bayesian hypothesis tests [Berger (1985) Statistical decision theory and Bayesian analysis. New York: Springer; Kass and Raftery (1995) J Am Stat Assoc 90:773-795], which are typically more conservative than standard uncorrected frequentist tests, less conservative than multiplicity-corrected tests, and make explicit use of relevant biological information through specification of the prior distribution. In this article we use a Bayesian testing approach to screen a panel of genetic markers recorded in a randomized clinical trial of bupropion versus placebo for smoking cessation. From a panel of 59 single-nucleotide polymorphisms (SNPs) located on 11 candidate genes, we identify four SNPs (one each on CHRNA5 and CHRNA2 and two on CHAT) that appear to have pharmacogenetic relevance. Of these, the SNP on CHRNA5 is most robust to specification of the prior. An unadjusted frequentist test identifies seven SNPs, including these four, none of which remains significant upon correction for multiplicity. In a panel of 43 randomly selected control SNPs, none is significant by either the Bayesian or the corrected frequentist test.

AB - Pharmacogenetic clinical trials seek to identify genetic modifiers of treatment effects. When a trial has collected data on many potential genetic markers, a first step in analysis is to screen for evidence of pharmacogenetic effects by testing for treatment-by-marker interactions in a statistical model for the outcome of interest. This approach is potentially problematic because (i) individual significance tests can be overly sensitive, particularly when sample sizes are large; and (ii) standard significance tests fail to distinguish between markers that are likely, on biological grounds, to have an effect, and those that are not. One way to address these concerns is to perform Bayesian hypothesis tests [Berger (1985) Statistical decision theory and Bayesian analysis. New York: Springer; Kass and Raftery (1995) J Am Stat Assoc 90:773-795], which are typically more conservative than standard uncorrected frequentist tests, less conservative than multiplicity-corrected tests, and make explicit use of relevant biological information through specification of the prior distribution. In this article we use a Bayesian testing approach to screen a panel of genetic markers recorded in a randomized clinical trial of bupropion versus placebo for smoking cessation. From a panel of 59 single-nucleotide polymorphisms (SNPs) located on 11 candidate genes, we identify four SNPs (one each on CHRNA5 and CHRNA2 and two on CHAT) that appear to have pharmacogenetic relevance. Of these, the SNP on CHRNA5 is most robust to specification of the prior. An unadjusted frequentist test identifies seven SNPs, including these four, none of which remains significant upon correction for multiplicity. In a panel of 43 randomly selected control SNPs, none is significant by either the Bayesian or the corrected frequentist test.

KW - Bayes factor

KW - Bayesian hypothesis test

KW - Bupropion

KW - Importance sampling

KW - Pharmacogenomics

KW - Single-nucleotide polymorphism

UR - http://www.scopus.com/inward/record.url?scp=51449121809&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=51449121809&partnerID=8YFLogxK

U2 - 10.1002/ajmg.b.30669

DO - 10.1002/ajmg.b.30669

M3 - Article

C2 - 18165968

AN - SCOPUS:51449121809

VL - 147

SP - 712

EP - 719

JO - American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics

JF - American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics

SN - 1552-4841

IS - 6

ER -