Skip to main content

Multi-scale study of normal aging predicts novel late-onset Alzheimer's disease risk variants

Background

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by severe memory impairment and accumulation of neuropathological amyloid plaques and tau tangles. By contrast, ‘normal’ age-associated cognitive decline is less severe and generally occurs in the absence of neuropathology. Many believe aging- and AD-related memory impairments result from separate etiologies, but this distinction is not entirely consistent with emerging evidence that both are linked to hippocampal dysfunction. As aging is the most significant risk factor for late-onset AD (LOAD), we hypothesize that memory deficits in both ‘normal’ aging and AD are driven in part by some common underlying mechanisms, which are exacerbated in AD by disease-specific insults such as neurodegeneration, neuroinflammation, and neuropathologies. In addition, heritability estimates suggest a strong genetic component (50-80%) in both conditions[1, 2]. Thus, elucidating genetic correlates of memory decline in ‘normal’ aging may identify risk factors that influence susceptibility to LOAD.

Materials and methods

Given that genetically diverse mouse models have emerged as a powerful way to study complex human traits, we conducted a multi-scalar analysis of two independent mouse models of aging to test this hypothesis[3]. We combined memory tests with proteomic, transcriptomic, and genomic data to generate a list of the top 30 candidates that correlate with memory impairments. To evaluate the translational potential of these candidates, we performed secondary analysis using published hippocampal transcript data from LOAD patients relative to age-matched non-demented controls[4].

Results

Eighteen genes including TRPC3, GABRA3, GABRA5, GABRB1, GABRB2, WDFY3, and GRM1 were significantly differentially expressed relative to disease status, suggesting they may play a role in the human disease process. In addition, we wanted to identify whether any of our candidate genes contained single nucleotide polymorphisms (SNPs) that could be used to predict an individual's susceptibility to LOAD. To test the ability of our multi-scalar approach to detect known LOAD risk genes, we searched our full dataset for published risk genes. We identified APOE, SORL1, EPHA1, BIN1, and TREM2 as significantly differentially expressed relative to memory function in our aging models, validating our approach. We then tested our novel candidates against the freely available ADGC “TGEN2” dataset that is a clinical characterized and neuropathologically verified cohort[5, 6]. Our analysis yielded four nominally significant novel putative risk variants (GABRB1, GABRB2, GRM1, and WDFY3) using data generated by our study of aging models.

Conclusions

Future work will investigate functional significance of these SNPs and validate mechanistic relevance to cognitive decline during aging and AD. This work demonstrates the utility of studying ‘normal’ aging models to both better understand molecular mechanisms mediating memory function in diverse populations and to identify those candidates with the best potential to translate into effective treatments and predictive biomarkers for cognitive decline in elderly humans.

References

  1. Wilson RS, Barral S, Lee JH, Leurgans SE, Foroud TM, Sweet RA, et al: Heritability of different forms of memory in the Late Onset Alzheimer's Disease Family Study. J Alzheimers Dis. 2011, 23 (2): 249-255.

    PubMed Central  PubMed  Google Scholar 

  2. Deary IJ, Yang J, Davies G, Harris SE, Tenesa A, Liewald D, et al: Genetic contributions to stability and change in intelligence from childhood to old age. Nature. 2010, 482 (7384): 212-215.

    Google Scholar 

  3. Neuner SM, Wilmott LA, Hope KA, Hoffmann B, Chong JA, Abramowitz J, et al: TRPC3 channels critically regulate hippocampal excitability and contextual fear memory. Behav Brain Res. 2015, 281: 69-77.

    CAS  Article  PubMed  Google Scholar 

  4. Liang WS, Reiman EM, Valla J, Dunckley T, Beach TG, Grover A, et al: Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc Natl Acad Sci U S A. 2008, 105 (11): 4441-4466.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  5. Corneveaux JJ, Myers AJ, Allen AN, Pruzin JJ, Ramirez M, Engel A, et al: Association of CR1, CLU and PICALM with Alzheimer's disease in a cohort of clinically characterized and neuropathologically verified individuals. Hum Mol Genet. 2010, 19 (16): 3295-3301.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  6. Reiman EM, Webster JA, Myers AJ, Hardy J, Dunckley T, Zismann VL, et al: GAB2 alleles modify Alzheimer's risk in APOE epsilon4 carriers. Neuron. 2007, 54 (5): 713-720.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

Download references

Acknowledgments

Supported by NIH/NIA grant K99/R00 AG039511 (PI: Kaczorowski), NIH/NIA grant F31 AG050357 (PI: Neuner), and the American Federation for Aging grant RAG14141 (PI:Kaczorowski).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Catherine C Kaczorowski.

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Neuner, S.M., Wilmott, L., DeBoth, M. et al. Multi-scale study of normal aging predicts novel late-onset Alzheimer's disease risk variants. BMC Bioinformatics 16, P11 (2015). https://doi.org/10.1186/1471-2105-16-S15-P11

Download citation

  • Published:

  • DOI: https://doi.org/10.1186/1471-2105-16-S15-P11

Keywords

  • Cognitive Decline
  • Aging Model
  • Load Risk
  • Complex Human Trait
  • Human Disease Process