Genetic pathway-based hierarchical clustering analysis of older adults with cognitive complaints and amnestic mild cognitive impairment using clinical and neuroimaging phenotypes

Chantel D. Sloan, Li Shen, John D. West, Heather A. Wishart, Laura A. Flashman, Laura A. Rabin, Robert B. Santulli, Stephen J. Guerin, C. Harker Rhodes, Gregory J. Tsongalis, Thomas W. McAllister, Tim A. Ahles, Stephen L. Lee, Jason H. Moore, Andrew J. Saykin

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Hierarchical clustering is frequently used for grouping results in expression or haplotype analyses. These methods can elucidate patterns between measures that can then be applied to discerning their validity in discriminating between experimental conditions. Here a hierarchical clustering method is used to analyze the results of an imaging genetics study using multiple brain morphology and cognitive testing endpoints for older adults with amnestic mild cognitive impairment (MCI) or cognitive complaints (CC) compared to healthy controls (HC). The single nucleotide polymorphisms (SNPs) are a subset of those included on a larger array that are found in a reported Alzheimer's disease (AD) and neurodegeneration pathway. The results indicate that geneticmodels within the endpoints cluster together, while there are 4 distinct sets of SNPs that differentiate between the endpoints, with most significant results associated with morphology endpoints rather than cognitive testing of patients' reported symptoms. The genes found in at least one cluster are ABCB1, APBA1, BACE1, BACE2, BCL2, BCL2L1, CASP7, CHAT, CST3, DRD3, DRD5, IL6, LRP1, NAT1, and PSEN2. The greater associations with morphology endpoints suggests that changes in brain structure can be influenced by an individual's genetic background in the absence of dementia and in some cases (Cognitive Complaints group) even without those effects necessarily being detectable on commonly used clinical tests of cognition. The results are consistent with polygenic influences on early neurodegenerative changes and demonstrate the effectiveness of hierarchical clustering in identifying genetic associations among multiple related phenotypic endpoints.

Original languageEnglish (US)
Pages (from-to)1060-1069
Number of pages10
JournalAmerican Journal of Medical Genetics, Part B: Neuropsychiatric Genetics
Volume153
Issue number5
DOIs
StatePublished - Jul 2010

Keywords

  • Alzheimer's disease
  • Cognitive complaints
  • Genetics
  • Imaging
  • Mild cognitive impairment

ASJC Scopus subject areas

  • Genetics(clinical)
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience
  • Medicine(all)

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