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

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

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Article number17
JournalBioData Mining
Volume7
Issue number1
DOIs
StatePublished - Aug 22 2014

Fingerprint

Alzheimer's Disease
Alzheimer Disease
Genes
Gene
Functional Genomics
Genomics
Computational Biology
Bioinformatics
Endophenotypes
Gene-Environment Interaction
Gene-environment Interaction
Intergenic DNA
Heritability
Dementia
Multigene Family
Single Nucleotide Polymorphism
Interaction
DNA sequences
DNA Sequence
Confidence

ASJC Scopus subject areas

  • Genetics
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Zieselman, A. L., Fisher, J. M., Hu, T., Andrews, P. C., Greene, C. S., Shen, L., ... Moore, J. H. (2014). Computational genetics analysis of grey matter density in Alzheimer's disease. BioData Mining, 7(1), [17]. https://doi.org/10.1186/1756-0381-7-17

Computational genetics analysis of grey matter density in Alzheimer's disease. / Zieselman, Amanda L.; Fisher, Jonathan M.; Hu, Ting; Andrews, Peter C.; Greene, Casey S.; Shen, Li; Saykin, Andrew; Moore, Jason H.

In: BioData Mining, Vol. 7, No. 1, 17, 22.08.2014.

Research output: Contribution to journalArticle

Zieselman, AL, Fisher, JM, Hu, T, Andrews, PC, Greene, CS, Shen, L, Saykin, A & Moore, JH 2014, 'Computational genetics analysis of grey matter density in Alzheimer's disease', BioData Mining, vol. 7, no. 1, 17. https://doi.org/10.1186/1756-0381-7-17
Zieselman, Amanda L. ; Fisher, Jonathan M. ; Hu, Ting ; Andrews, Peter C. ; Greene, Casey S. ; Shen, Li ; Saykin, Andrew ; Moore, Jason H. / Computational genetics analysis of grey matter density in Alzheimer's disease. In: BioData Mining. 2014 ; Vol. 7, No. 1.
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