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. 2014 Aug 22:7:17.
doi: 10.1186/1756-0381-7-17. eCollection 2014.

Computational genetics analysis of grey matter density in Alzheimer's disease

Amanda L Zieselman  1 , Jonathan M Fisher  1 , Ting Hu  1 , Peter C Andrews  1 , Casey S Greene  1 , Li Shen  2 , Andrew J Saykin  2 , Jason H Moore  1
Affiliations

Computational genetics analysis of grey matter density in Alzheimer's disease

Amanda L Zieselman et al. BioData Min. .

Abstract

Background: Alzheimer's disease is the most common form of progressive dementia and there is currently no known cure. The cause of onset is not fully understood but genetic factors are expected to play a significant role. We present here a bioinformatics approach to the genetic analysis of grey matter density as an endophenotype for late onset Alzheimer's disease. Our approach combines machine learning analysis of gene-gene interactions with large-scale functional genomics data for assessing biological relationships.

Results: We found a statistically significant synergistic interaction among two SNPs located in the intergenic region of an olfactory gene cluster. This model did not replicate in an independent dataset. However, genes in this region have high-confidence biological relationships and are consistent with previous findings implicating sensory processes in Alzheimer's disease.

Conclusions: Previous genetic studies of Alzheimer's disease have revealed only a small portion of the overall variability due to DNA sequence differences. Some of this missing heritability is likely due to complex gene-gene and gene-environment interactions. We have introduced here a novel bioinformatics analysis pipeline that embraces the complexity of the genetic architecture of Alzheimer's disease while at the same time harnessing the power of functional genomics. These findings represent novel hypotheses about the genetic basis of this complex disease and provide open-access methods that others can use in their own studies.

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Figures

Figure 1
Figure 1
An overview of our bioinformatics analysis pipeline. In phase I we focus on identifying those genes with statistically significant pairs of SNPs that are associated with the phenotype. These genetic effects can be additive or non-additive for each genes. The goal of Phase II was to use bioinformatics analysis with functional genomics data to reduce the possibility of false-positive results. A final genetic model is constructed and interpreted.
See this image and copyright information in PMC

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