Use of Genetic Variation as Biomarkers for Alzheimer's Disease
Open accessAbstract
Late‐onset Alzheimer's disease (LOAD) is the most common cause of late‐onset dementia in western societies. Despite remarkable achievements in human genetics throughout the years, in particular technological advances in gene mapping and in statistical methods that relate genetic variants to disease, to date only a small proportion of the genetic contribution to LOAD can be explained leaving several remaining genetic risk factors to be identified. A possible explanation for the difficulty in gene identification is that LOAD is a multifactorial complex disorder with both genetic and environmental components. Multiple genes with small effects each (“quantitative trait loci”[QTLs]) are likely to contribute to the quantitative traits associated with the disease, such as memory performance, amyloid/tau pathology, or hippocampal atrophy. The motivation for identifying the genetics of LOAD is clear. Not only could it shed light on disease pathogenesis, but it may also provide potential targets for effective treatment, screening, and prevention. Here, we review the usefulness of genetic variation as diagnostic tools and biomarkers in LOAD and discuss the potentials and difficulties researchers face in designing appropriate studies for gene discovery.
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All high-penetrance AD genetic risk alleles (APOE ε4 allele, Down syndrome, APP mutations and duplications, PSEN1 and PSEN2 mutations) have been linked in various experimental systems with increased Aβ deposition and increased formation of putative toxic Aβ peptide species.
“All high-penetrance AD genetic risk alleles (i.e. APOE ε4 allele, Down syndrome, APP mutations and duplications, PSEN1 and PSEN2 mutations) have been linked in various experimental systems with increased Aβ deposition and increased formation of putative toxic Aβ peptide species (46, 55-57).”
From Correlation of Alzheimer Disease Neuropathologic Changes With Cognitive Status: A Review of the Literature (2012) · cites this paper as [57]
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Flagged first, then by citations
2 of the 123 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Nelson, P. T., Alafuzoff, I., Bigio, E. H., Bouras, C., Braak, H., Cairns, N. J., Castellani, R. J., Crain, B. J., Davies, P., Tredici, K. D., et al. (2012) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e31825018f7 · Cited by 2,050 · see citing works
“4 allele, Down syndrome, APP mutations and duplications, PSEN1 and PSEN2 mutations) have been linked in various experimental systems with increased A A deposition and increased formation of putative toxic A A peptide species (46, 55 Y 57).”
BackgroundThe genetics of Alzheimer's disease
Bagyinszky, E., Youn, Y. C., An, S. S. A., & Kim, S. (2014) · Clinical Interventions in Aging · doi:10.2147/cia.s51571 · Cited by 173 · see citing works
“The majority of recent studies in the genetics of AD have focused on the identification of novel risk-factor genes and mutations.(2,5,6)”
Genetics of age-related macular degeneration: current concepts, future directions
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“LOAD genetic variants may be best viewed as risk predictors for dementia [42,46].”
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“From a molecular perspective, AD is a multifactorial disorder with associations of genetic and environmental factors [1,2].”
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GSTM1 null genotype as risk factor for late-onset Alzheimer's disease in Italian patients
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“It is the outcome of multiple genetic and multiple environmental influences, operating additively and interactively [34].”
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Roed, L., Grave, G., Lindahl, T., Rian, E., Horndalsveen, P. O., Lannfelt, L., Nilsson, C. F., Swenson, F. J., Lönneborg, A., Sharma, P., et al. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-122404 · Cited by 20 · see citing works
“The results also support the concept of AD as a multifactorial sporadic disorder [54] with multiple genes and alterations in gene expression involved [55, 56].”
ResultTayran, H., Yılmaz, E., Bhattarai, P., Min, Y.-H., Wang, X., Ma, Y.-Y., Wang, N., Jeong, I., Nelson, N., Kassara, N., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100642 · Cited by 17 · see citing works
Self-citationDriscoll, I. F., Snively, B. M., Espeland, M. A., Shumaker, S. A., Rapp, S. R., Goveas, J. S., Casanova, R., Wactawski‐Wende, J., Manson, J. E., Rossom, R. C., et al. (2019) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5068 · Cited by 18 · see citing works
“Key points • A number of large‐scale GWAS investigations of AD have been conducted, and novel genes and their variants have been implicated in the pathogenesis of AD.”
BackgroundInfluentialEmergence of breath testing as a new non-invasive diagnostic modality for neurodegenerative diseases
Subramaniam, N. S., Bawden, C. S., Waldvogel, H. J., Faull, R. L. M., Howarth, G. S., & Snell, R. G. (2018) · Brain Research · doi:10.1016/j.brainres.2018.04.017 · Cited by 17 · see citing works
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“Specifically, ε4 polymorphisms have been found to be associated with increased risk and decreased age of onset of Alzheimer's disease in a gene dosage-dependent manner, accounting for 20–50% of risk for this disease (Mahley and Rall, 2000; Reitz and Mayeux, 2009; Sanan et al., 1994).”
BackgroundThe Genetic Architecture of Murine Glutathione Transferases
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Preliminary findings from an Internet‐based dementia risk assessment
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“However, APOE genotype and serum biomarkers are increasingly recognized as critically important in determining one’s risk for developing of Alzheimer’s disease [43,44].”
BackgroundVelez, J. I., Samper, L. A., Arcos-Holzinger, M., Espinosa, L. G., Isaza-Ruget, M. A., Lopera, F. J., & Arcos‐Burgos, M. (2021) · Diagnostics · doi:10.3390/diagnostics11050887 · Cited by 9 · see citing works
“We previously studied the association of common exonic functional variants (CEFVs) with ADAOO (Table 1) [35,36] using single-and multi-locus linear mixed-effects models [77] and recursive partitioning ML algorithms [36].”
BackgroundMethodResultInfluentialTayran, H., Yılmaz, E., Bhattarai, P., Min, Y., Wang, X., Ma, Y., Nelson, N. N., Kassara, N., Coşacak, M. İ., Dogru, R. M., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.089273 · Cited by 6 · see citing works
Self-citationSalivary biomarkers in Alzheimer's disease
Andrews, J. L., & Fernandez, F. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-815854-8.00015-x · Cited by 2 · see citing works
Association between physical activity and Alzheimer\'s disease
Stephen, R. (2014) · UEF eRepo (University of Eastern Finland)
- Authors
- Reitz, C., & Mayeux, R.
- Affiliations
- Columbia University Irving Medical Center (US)
- Columbia University (US)
- Corresponding author
- Mayeux, Richard
- Venue
- Annals of the New York Academy of Sciences
- Publisher
- Wiley
- Published
- 2009-10
- Volume
- 1180
- Issue
- 1
- Pages
- 75-96
- ISSN
- 0077-8923 (ISSN-L)
- 1749-6632
- PMC
- PMC2819087
- OpenAlex
- W2003137011
- Cited by
- 24
- References
- 123
- Citations by year
- 2024: 2 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 1 · 2016: 1 · 2014: 2 · 2013: 2 · 2012: 4
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annals of the New York Academy of Sciences (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Blanchette Hooker Rockefeller Fund
- National Institutes of Health (5r37ag015473-15)
- National Institute on Aging (R37AG15473, P01AG007232, K23AG034550, R37-AG015473, R01AG015473, P50AG008702, P01AG07232, P50AG08702)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- late-onset Alzheimer's disease, genetic biomarkers, genetic variation, genetic risk factors, quantitative trait loci, complex diseases, gene discovery, amyloid pathology, tau pathology, hippocampal atrophy, memory performance, dementia
- MeSH terms
- Alzheimer Disease, Humans, Phenotype, Genetic Variation, Biomarkers
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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.
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