Neuroimaging genetic risk for Alzheimer’s disease in preclinical individuals: from candidate genes to polygenic approaches
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Better characterization of the preclinical phase of Alzheimer's disease (AD) is needed in order to develop effective interventions. Neuropathological changes in AD, including neuronal loss and the formation of proteinaceous deposits, begin up to 20 years before the onset of clinical symptoms. As such, the emergence of cognitive impairment should not be the sole basis used to diagnose AD nor to evaluate individuals for enrollment in clinical trials for preventative AD treatments. Instead, early preclinical biomarkers of disease and genetic risk should be used to determine most likely prognosis and enroll individuals in appropriate clinical trials. Neuroimaging-based biomarkers and genetic analysis together present a powerful system for classifying preclinical pathology in patients. Disease modifying interventions are more likely to produce positive outcomes when administered early in the course of AD. In this review, we examine the utility of the neuroimaging genetics field as it applies to AD and early detection during the preclinical phase. Neuroimaging studies focused on single genetic risk factors are summarized. However, we particularly focus on the recent increased interest in polygenic methods and discuss the benefits and disadvantages of these approaches. We discuss challenges in the neuroimaging genetics field, including limitations of statistical power arising from small effect sizes and the over-use of cross-sectional designs. Despite the limitations, neuroimaging genetics has already begun to influence clinical trial design and will play a major role in the prevention of AD.
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Increased genetic risk for LOAD as defined by a high PRS is associated with several imaging endophenotypes, including decreased cortical thickness in brain regions vulnerable to degeneration in AD, decreased total brain volume, and decreased hippocampal volume.
“increased genetic risk for LOAD as defined by a high PRS is associated with several imaging endophenotypes, including decreased cortical thickness in brain regions known to be vulnerable to degeneration in AD, decreased total brain volume, and decreased hippocampal volume [234]”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [234]
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Stevens, B. W., DiBattista, A. M., Rebeck, G. W., & Green, A. E. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.06.021 · Cited by 20 · see citing works
Westlye, E. T., Hodneland, E., Haász, J., Espeseth, T., Lundervold, A., & Lundervold, A. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.072 · Cited by 19 · see citing works
The utility of data-driven feature selection: Re: Chu et al. 2012
Kerr, W. T., Douglas, P. K., Anderson, A., & Cohen, M. S. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.050 · Cited by 18 · see citing works
How Well Do MCI Criteria Predict Progression to Severe Cognitive Impairment and Dementia?
Ganguli, M., Lee, C., Snitz, B. E., Hughes, T. F., McDade, E., & Chang, C. H. (2014) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000018 · Cited by 13 · see citing works
Hibar, D. P., Stein, J. L., Jahanshad, N., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40760-4_75 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [81]Bralten J Franke B Arias-Vásquez A Heister A Brunner HG Fernández G Rijpkema M CR1 genotype is associated with entorhinal cortex volume in young healthy adults. Neurobiol Aging 2011 32 2106.e7 11 21726919
Cited by
Sorted by citations
Genetic architecture of Alzheimer's disease
Neuner, S. M., TCW, J., & Goate, A. M. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104976 · Cited by 126 · see citing works
“brain regions known to be vulnerable to degeneration in AD, decreased total brain volume, and decreased hippocampal volume [234].”
Background
- Authors
- Harrison, T. M., & Bookheimer, S. Y.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Harrison, Theresa M.
- Venue
- Biological Psychiatry Cognitive Neuroscience and Neuroimaging
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 1
- Issue
- 1
- Pages
- 14-23
- ISSN
- 2451-9022 (ISSN-L)
- PMC
- PMC4743051
- OpenAlex
- W2218312646
- Cited by
- 12
- References
- 83
- Citations by year
- 2022: 1 · 2020: 4 · 2019: 1 · 2018: 2 · 2017: 2 · 2016: 2
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biological Psychiatry Cognitive Neuroscience and Neuroimaging (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute on Aging (5R01AG013308, 1F31AG047041)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
- Keywords
- Alzheimer's disease, preclinical Alzheimer's disease, neuroimaging genetics, genetic risk factors, polygenic risk score, candidate genes, neuroimaging biomarkers, early detection, clinical trial recruitment, disease-modifying interventions, statistical power, cross-section design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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