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

Abstract

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.

Keywords: Alzheimer’s disease; Bayesian networks; Epistasis; Genome-wide association study.

Figure 1

A SNP Bayesian network model. In this model SNPs X ₂ and X ₃ have an effect on Z and the remaining SNPs do not have an effect on Z. The table gives counts for the states of Z conditioned on the joint states of X ₂ and X ₃.

Figure 2

Top 200 BCM model scores plot for both datasets tested. Plots of the distribution of BCM model scores for the top ranked 200 SNP-BN models for the two datasets, ADRC and TGen. The scores for the ADRC dataset (blue points) correspond to the left hand Y axis, while those for the TGen dataset correspond to the right hand Y axis. The dotted vertical line marks the top ranked 200 SNP-BN models.