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

Authors

  • Chantel D. Sloan,

    1. Departments of Genetics and Community and Family Medicine, Computational Genetics Laboratory, Dartmouth Medical School, Lebanon, New Hampshire
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  • Li Shen,

    1. Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana
    2. Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana
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  • John D. West,

    1. Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana
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  • Heather A. Wishart,

    1. Department of Psychiatry, Brain Imaging Laboratory, Dartmouth Medical School, Lebanon, New Hampshire
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  • Laura A. Flashman,

    1. Department of Psychiatry, Brain Imaging Laboratory, Dartmouth Medical School, Lebanon, New Hampshire
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  • Laura A. Rabin,

    1. Department of Psychiatry, Brain Imaging Laboratory, Dartmouth Medical School, Lebanon, New Hampshire
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  • Robert B. Santulli,

    1. Department of Psychiatry, Brain Imaging Laboratory, Dartmouth Medical School, Lebanon, New Hampshire
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  • Stephen J. Guerin,

    1. Department of Psychiatry, Brain Imaging Laboratory, Dartmouth Medical School, Lebanon, New Hampshire
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  • C. Harker Rhodes,

    1. Department of Pathology and Laboratory Medicine, Dartmouth Medical School, Lebanon, New Hampshire
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  • Gregory J. Tsongalis,

    1. Department of Pathology and Laboratory Medicine, Dartmouth Medical School, Lebanon, New Hampshire
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  • Thomas W. McAllister,

    1. Department of Psychiatry, Brain Imaging Laboratory, Dartmouth Medical School, Lebanon, New Hampshire
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  • Tim A. Ahles,

    1. Department of Psychiatry and Behavioral Sciences, Memorial Sloan-Kettering Cancer Center, New York, New York
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  • Stephen L. Lee,

    1. Department of Medicine (Neurology), Dartmouth Medical School, Lebanon, New Hampshire
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  • Jason H. Moore,

    1. Departments of Genetics and Community and Family Medicine, Computational Genetics Laboratory, Dartmouth Medical School, Lebanon, New Hampshire
    2. Department of Computer Science, University of New Hampshire, Durham, New Hampshire
    3. Department of Computer Science, University of Vermont, Burlington, Vermont
    4. Translational Genomics Research Institute, Phoenix, Arizona
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  • Andrew J. Saykin

    Corresponding author
    1. Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana
    2. Department of Psychiatry, Brain Imaging Laboratory, Dartmouth Medical School, Lebanon, New Hampshire
    3. Departments of Medical and Molecular Genetics, Neurology and Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana
    • Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, 950 West Walnut St., R2, E124, Indianapolis, IN 46202.
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  • How to cite this article: Sloan CD, Shen L, West JD, Wishart HA, Flashman LA, Rabin LA, Santulli RB, Guerin SJ, Rhodes CH, Tsongalis GJ, McAllister TW, Ahles TA, Lee SL, Moore JH, Saykin AJ. 2010. Genetic Pathway-Based Hierarchical Clustering Analysis of Older Adults With Cognitive Complaints and Amnestic Mild Cognitive Impairment Using Clinical and Neuroimaging Phenotypes. Am J Med Genet Part B 153B:1060–1069.

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 genetic models 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. © 2010 Wiley-Liss, Inc.

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