Dataset Information

Underlying genetic models of inheritance in established type 2 diabetes associations.

ABSTRACT: 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.

SUBMITTER: Salanti G

PROVIDER: S-EPMC2732984 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Underlying genetic models of inheritance in established type 2 diabetes associations.

Salanti Georgia G Southam Lorraine L Altshuler David D Ardlie Kristin K Barroso Inês I Boehnke Michael M Cornelis Marilyn C MC Frayling Timothy M TM Grallert Harald H Grarup Niels N Groop Leif L Hansen Torben T Hattersley Andrew T AT Hu Frank B FB Hveem Kristian K Illig Thomas T Kuusisto Johanna J Laakso Markku M Langenberg Claudia C Lyssenko Valeriya V McCarthy Mark I MI Morris Andrew A Morris Andrew D AD Palmer Colin N A CN Payne Felicity F Platou Carl G P CG Scott Laura J LJ Voight Benjamin F BF Wareham Nicholas J NJ Zeggini Eleftheria E Ioannidis John P A JP

American journal of epidemiology 20090714 5

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 w ...[more]

PMID: 19602701

Similar Datasets

Project description:Epidemiological evidence supports the observation that subjects with type 2 diabetes (T2D) are at higher risk to develop Alzheimer's disease (AD). However, whether and how these two conditions are causally linked is unknown. Possible mechanisms include shared genetic risk factors, which were investigated in this study based on recent genome wide association study (GWAS) findings. In order to achieve our goal, we retrieved single nucleotide polymorphisms (SNPs) associated with T2D and AD from large-scale GWAS meta-analysis consortia and tested for overlap among the T2D- and AD-associated SNPs at various p-value thresholds. We then explored the function of the shared T2D/AD GWAS SNPs by leveraging expressional quantitative trait loci, pathways, gene ontology data, and co-expression networks. We found 927 SNPs associated with both AD and T2D with p-value ≤0.01, an overlap significantly larger than random chance (overlapping p-value of 6.93E-28). Among these, 395 of the shared GWAS SNPs have the same risk allele for AD and T2D, suggesting common pathogenic mechanisms underlying the development of both AD and T2D. Genes influenced by shared T2D/AD SNPs with the same risk allele were first identified using a SNP annotation variation (ANNOVAR) software, followed by using Association Protein-Protein Link Evaluator (DAPPLE) software to identify additional proteins that are known to physically interact with the ANNOVAR gene annotations. We found that gene annotations from ANNOVAR and DAPPLE significantly enriched specific KEGG pathways pertaining to immune responses, cell signaling and neuronal plasticity, cellular processes in which abnormalities are known to contribute to both T2D and AD pathogenesis. Thus, our observation suggests that among T2D subjects with common genetic predispositions (e.g., SNPs with consistent risk alleles for T2D and AD), dysregulation of these pathogenic pathways could contribute to the elevated risks for AD in subjects. Interestingly, we found that 532 of the shared T2D/AD GWAS SNPs had divergent risk alleles in the two diseases. For individual shared T2D/AD SNPs with divergent alleles, one of the allelic forms may contribute to one of the diseases (e.g., T2D), but not necessarily to the other (e.g., AD), or vice versa. Collectively, our GWAS studies tentatively support the epidemiological observation of disease concordance between T2D and AD. Moreover, the studies provide the much needed information for the design of future novel therapeutic approaches, for a subpopulation of T2D subjects with genetic disposition to AD, that could benefit T2D and reduce the risk for subsequent development of AD.

Dataset Information

Underlying genetic models of inheritance in established type 2 diabetes associations.

Publications

Underlying genetic models of inheritance in established type 2 diabetes associations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets