European Journal of Epidemiology

, Volume 24, Issue 12, pp 737–741

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

METHODS

DOI: 10.1007/s10654-009-9393-0

Cite this article as:
Strömberg, U. Eur J Epidemiol (2009) 24: 737. doi:10.1007/s10654-009-9393-0

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.

Keywords

Bayesian analysis Effect (odds ratio) Genome-wide association study Single nucleotide polymorphism Statistics 

Abbreviations

GWAS

Genome-wide association study

MAF

Minor allele frequency

OR

Odds ratio (here, per-allele odds ratio predicted towards the minor allele)

SNP

Single nucleotide polymorphism

T2D

Type 2 diabetes

Supplementary material

10654_2009_9393_MOESM1_ESM.pdf (79 kb)
Supplementary material 1 (PDF 79 kb)

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Department of Occupational and Environmental MedicineLund UniversityLundSweden
  2. 2.Department of Occupational and Environmental MedicineLund University HospitalLundSweden

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