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Identifying genetic interactions associated with late-onset Alzheimer's disease

Floudas, CS and Um, N and Kamboh, MI and Barmada, MM and Visweswaran, S (2014) Identifying genetic interactions associated with late-onset Alzheimer's disease. BioData Mining, 7 (1).

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Abstract

© 2014 Floudas et al. Background: Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease. Results: We applied BCM to two late-onset Alzheimer's disease (LOAD) GWAS datasets to identify SNPs that interact with known Alzheimer associated SNPs. We also compared BCM with logistic regression that is implemented in PLINK. Gene Ontology analysis of genes from the top 200 dataset SNPs for both GWAS datasets showed overrepresentation of LOAD-related terms. Four genes were common to both datasets: APOE and APOC1, which have well established associations with LOAD, and CAMK1D and FBXL13, not previously linked to LOAD but having evidence of involvement in LOAD. Supporting evidence was also found for additional genes from the top 30 dataset SNPs. Conclusion: BCM performed well in identifying several SNPs having evidence of involvement in the pathogenesis of LOAD that would not have been identified by univariate analysis due to small main effect. These results provide support for applying BCM to identify potential genetic variants such as SNPs from high dimensional GWAS datasets.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Floudas, CS
Um, N
Kamboh, MIkamboh@pitt.eduKAMBOH
Barmada, MMbarmada@pitt.eduBARMADA
Visweswaran, Sshv3@pitt.eduSHV3
Date: 1 January 2014
Date Type: Publication
Journal or Publication Title: BioData Mining
Volume: 7
Number: 1
DOI or Unique Handle: 10.1186/s13040-014-0035-z
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Graduate School of Public Health > Human Genetics
School of Medicine > Biomedical Informatics
Refereed: Yes
Date Deposited: 22 Dec 2016 15:19
Last Modified: 02 Feb 2019 14:55
URI: http://d-scholarship.pitt.edu/id/eprint/29436

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