Top

Published in:

01-12-2009 | METHODS

Empirical Bayes and semi-Bayes adjustments for a vast number of estimations

Author: Ulf Strömberg

Published in: European Journal of Epidemiology | Issue 12/2009

Abstract

Investigators in modern molecular/genetic epidemiology studies commonly analyze data on a vast number of candidate genetic markers. In such situations, rather than conventional estimation of effects (odds ratios), more accurate estimation methods are needed. The author proposes consideration of empirical Bayes and semi-Bayes methods, which yield ‘adjustments for multiple estimations’ by shrinking conventional effect estimates towards the overall average effect.

Available only for authorised users

Thomas DC, Clayton DG. Betting odds and genetic associations. J Natl Cancer Inst. 2004;96:421–3.PubMed

Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assesing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst. 2004;96:434–42.PubMedCrossRef

Moerkerke B, Goetghebeur E. Selecting “significant” differentially expressed genes from the combined perspective of the null and the alternative. J Comput Biol. 2006;13:1513–31.CrossRefPubMed

Strug LJ, Hodge SE. An alternative foundation for the planning and evaluation of linkage studies. Hum Hered. 2006;61:166–88.CrossRefPubMed

Wakefield JA. Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am J Hum Genet. 2007;81:208–27.CrossRefPubMed

The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661–78.CrossRef

Strömberg U, Björk J, Vineis P, Broberg K, Zeggini E. Ranking of genome-wide associations scan signals by different measures. Int J Epidemiol. 2009;38:1364–73.CrossRefPubMed

Garner C. Upward bias in odds ratio estimates from genome-wide association studies. Genet Epidemiol. 2007;31:288–95.CrossRefPubMed

Ioannidis JPA. Why most discovered true associations are inflated. Epidemiology. 2008;19:640–8.CrossRefPubMed

10.

Kraft P. Curses—winner’s and otherwise—in genetic epidemiology. Epidemiology. 2008;19:649–51.CrossRefPubMed

11.

Zollner S, Pritchard JK. Overcoming the winner’s curse: estimating penetrance parameters from case–control data. Am J Hum Genet. 2007;80:605–15.CrossRefPubMed

12.

Yu K, Chatterjee N, Wheeler W, Li Q, Wang S, Rothman N, et al. Flexible designs for following up positive findings. Am J Hum Genet. 2007;81:540–51.CrossRefPubMed

13.

Zhong H, Prentice RL. Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies. Biostatistics. 2008;9:621–34.CrossRefPubMed

14.

Xiao R, Boehnke M. Quantifying and correcting for the winner’s curse in genetic association studies. Genet Epidemiol. 2009;33:453–62CrossRefPubMed

15.

Sun L, Bull SB. Reduction of selection bias in genome-wide studies by resampling. Genet Epidemiol. 2005;28:352–67.CrossRefPubMed

16.

Gosh A, Zou F, Wright FA. Estimating odds ratios in genome scans: an approximate conditional likelihood approach. Am J Hum Genet. 2008;82:1064–74.CrossRef

17.

Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. 2008;4:e1000167.CrossRefPubMed

18.

Greenland S. Principles of multilevel modelling. Int J Epidemiol. 2000;29:158–67.CrossRefPubMed

19.

Morris CN. Parametric empirical Bayes inference: theory and applications. J Am Stat Assoc. 1983;78:47–55.CrossRef

20.

Greenland S, Poole C. Empirical-Bayes and semi-Bayes approaches to occupational and environmental hazard surveillence. Arch Environ Health. 1994;49:9–16.PubMed

21.

Steenland K, Bray I, Greenland S, Boffetta P. Empirical Bayes adjustments for multiple results in hypothesis-generating or surveillance studies. Cancer Epidemiol Biomarkers Prev. 2000;9:895–903.PubMed

22.

Hung RJ, Brennan P, Malaveille C, Porru S, Donato F, Boffetta P, et al. Using hierarchical modeling in genetic association studies mith mutiple markers: application to a case–control study of bladder cancer. Cancer Epidemiol Biomarkers Prev. 2004;13:1013–21.PubMed

23.

Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007;316:1336–41.CrossRefPubMed

24.

Strömberg U, Björk J, Broberg K, Mertens F, Vineis P. Selection of influential genetic markers among a large number of candidates based on effect estimation rather than hypothesis testing: an approach for genome-wide association studies. Epidemiology. 2008;19:302–8.CrossRefPubMed

25.

McCarthy MI, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009;9:164–71.CrossRefPubMed

Title: Empirical Bayes and semi-Bayes adjustments for a vast number of estimations
Author: Ulf Strömberg
Publication date: 01-12-2009
Publisher: Springer Netherlands
Published in: European Journal of Epidemiology / Issue 12/2009
Print ISSN: 0393-2990
Electronic ISSN: 1573-7284
DOI: https://doi.org/10.1007/s10654-009-9393-0

At a glance: The STEP trials

Springer Medicine

Empirical Bayes and semi-Bayes adjustments for a vast number of estimations

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 12/2009

Genetic determinants of serum lipid levels in Chinese subjects: a population-based study in Shanghai, China

Maternal overweight and smoking: prenatal risk factors for caries development in offspring during the teenage period

A note of thanks

Variable selection: current practice in epidemiological studies

Thinking big: large-scale collaborative research in observational epidemiology

Investigating spatio-temporal similarities in the epidemiology of childhood leukaemia and diabetes