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Underlying Genetic Models of Inheritance in Established Type 2 Diabetes Associations

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Author

  • Georgia Salanti
  • Lorraine Southam
  • David Altshuler
  • Kristin Ardlie
  • Ines Barroso
  • Michael Boehnke
  • Marilyn C. Cornelis
  • Timothy M. Frayling
  • Harald Grallert
  • Niels Grarup
  • Leif Groop
  • Torben Hansen
  • Andrew T. Hattersley
  • Frank B. Hu
  • Kristian Hveem
  • Thomas Illig
  • Johanna Kuusisto
  • Markku Laakso
  • Claudia Langenberg
  • Valeriya Lyssenko
  • Mark I. McCarthy
  • Andrew Morris
  • Andrew D. Morris
  • Colin N. A. Palmer
  • Felicity Payne
  • Carl G. P. Platou
  • Laura J. Scott
  • Benjamin F. Voight
  • Nicholas J. Wareham
  • Eleftheria Zeggini
  • John P. A. Ioannidis
Show all

Summary, in English

For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.

Publishing year

2009

Language

English

Pages

537-545

Publication/Series

American Journal of Epidemiology

Volume

170

Issue

5

Document type

Journal article review

Publisher

Oxford University Press

Topic

  • Public Health, Global Health, Social Medicine and Epidemiology

Keywords

  • Bayes theorem
  • type 2
  • meta-analysis
  • models
  • polymorphism
  • genetic
  • diabetes mellitus
  • population characteristics

Status

Published

Research group

  • Genomics, Diabetes and Endocrinology

ISBN/ISSN/Other

  • ISSN: 0002-9262