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

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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.

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Fig. 1

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

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Acknowledgments

The author thanks associate professor Jonas Björk for work with the supplementary material. The Swedish Council for Working Life and Social Research and the Swedish Cancer Fund provided financial support.

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Authors and Affiliations

  1. Department of Occupational and Environmental Medicine, Lund University, Lund, Sweden

    Ulf Strömberg

  2. Department of Occupational and Environmental Medicine, Lund University Hospital, 221 85, Lund, Sweden

    Ulf Strömberg

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  1. Ulf Strömberg
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Correspondence to Ulf Strömberg.

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Strömberg, U. Empirical Bayes and semi-Bayes adjustments for a vast number of estimations. Eur J Epidemiol 24, 737–741 (2009). https://doi.org/10.1007/s10654-009-9393-0

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  • DOI: https://doi.org/10.1007/s10654-009-9393-0

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