Abstract
BackgroundIdentifying 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.ResultsWe 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.ConclusionBCM 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-014-0035-z) contains supplementary material, which is available to authorized users.
Highlights
Elucidating the genetic basis of common diseases will lead to an understanding of the biological mechanisms that underlie such diseases and can help in risk assessment, diagnosis, prognosis and development of new therapies
This section describes the results that were obtained from applying Bayesian Combinatorial Method (BCM) to the Alzheimer’s Disease Research Center (ADRC) late-onset Alzheimer’s disease (LOAD) dataset and from applying BCM to the ADRC and the Translational Genomics Research Institute (TGen) genome-wide association studies (GWASs) datasets
The known single nucleotide polymorphisms (SNPs) and the dataset SNP from the top scoring 200 SNP-Bayesian network (BN) models are given in Additional file 1: Table S1 and Additional file 1: Table S2 in the Supplemental Tables
Summary
Elucidating the genetic basis of common diseases will lead to an understanding of the biological mechanisms that underlie such diseases and can help in risk assessment, diagnosis, prognosis and development of new therapies. The development of high-throughput genotyping technologies has led to a flurry of genome-wide association studies (GWASs) with the aim of discovering SNPs that are associated with common diseases. One view is that SNPs may interact in subtle ways that lead to substantially greater effects than the effect due to any single SNP. Another view is that common diseases may be due to rare and usually deleterious SNPs that cause disease in individual patients and that in different individuals or subpopulations the disease is caused by different deleterious SNPs. Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease
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