Polygenic score prediction captures nearly all common genetic risk for Alzheimer's disease
Open accessAbstract
We estimate the maximum prediction accuracy for the risk of Alzheimer's disease based on disease prevalence and heritability of liability. We demonstrate that the recently reported AUC values for predicting of Alzheimer's disease using polygenic scores reach about 90% of the estimated maximum accuracy that can be achieved by predictors of genetic risk based on genomic profiles.
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AD is likely influenced by a large number of susceptibility genes, reflecting a complex set of biological pathways related to the disease.
“This supports the notion that AD is likely influenced by a large number of susceptibility genes, reflecting a complex set of biological pathways related to the disease [123].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [123]
Expanding PRS to include variants below the genome-wide statistical cutoff allows for a more complete view of the genetic contribution to disease risk and seems to provide an improved method for risk prediction over more selective PRS or standard predictors such as APOEε4 alleles, age, and sex.
“risk and seems to provide an improved method for risk prediction over more selective PRS or standard predictors such as APOEε4 alleles, age, and sex [118, 122, 123].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [123]
The most accurate prediction results from a combined consideration of the PRS as well as additional predictors (APOE genotype, age, and sex).
“However, evidence suggests the most accurate prediction results from a combined consideration of the PRS as well as these additional predictors (APOE genotype, age, and sex) [123].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [123]
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References 26 · Cited by 183
- Authors
- Escott-Price, V., Shoai, M., Pither, R., Williams, J., & Hardy, J.
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- Cardiff University (GB)
- Corresponding author
- Hardy, John
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 49
- Pages
- 214.e7-214.e11
- ISSN
- 0197-4580 (ISSN-L)
- OpenAlex
- W2485069266
- Cited by
- 183
- References
- 26
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 14 · 2022: 28 · 2021: 24 · 2020: 55 · 2019: 25 · 2018: 11 · 2017: 9 · 2016: 2
- FWCI
- 12.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Neurobiology of Aging (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Medical Research Council (MR/L023784/2, MR/L501517/1, MR/P005748/1, MR/L010305/1, MR/K013041/1, MR/L023784/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, polygenic risk score, genetic risk prediction, prediction accuracy, disease prevalence, genomic profiles
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Female, Forecasting, Humans, Male, Risk, ROC Curve, Prevalence, Area Under Curve, Genetic Predisposition to Disease, Multifactorial Inheritance, Genetic Association Studies
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